Notebook Metadata¶
- Notebook Name: 02_model.ipynb
- Title: MoneyLion DS Assessment
- Author: Khoon Ching Wong
- Created: 2024-09-24
- Last Modified: 2025-09-25
- Description:
Continuation of01_eda.ipynb.
This notebook focuses on model training, where Optuna is applied for hyperparameter optimization, model selection and performance reporting, with the goal of minimizing institutional financial losses. - Inputs:
- temp/Loan-level/clean_df.parquet
- Outputs:
- temp/Loan-level/best_trials_ClassifierX.json
- temp/Loan-level/all_metrics_ClassifierX.csv
- temp/Loan-level/cm_final_ClassifierX.csv
- temp/Loan-level/df_metrics_ClassifierX.csv
- temp/Loan-level/exec_time_ClassifierX.csv
- temp/Loan-level/lloss_auc_test_ClassifierX.csv
- temp/Loan-level/lloss_auc_train_ClassifierX.csv
- temp/Loan-level/cm_ClassifierX_all.pkl
- temp/Loan-level/cm_labels_ClassifierX_all.pkl
- Repository/Project Link: https://github.com/wongkhoon/DS-Assessment/tree/main/MoneyLion/notebooks
Import libraries¶
In [1]:
import gc
import os
import platform
from IPython.display import display, Markdown, HTML
import json
import pickle
import pandas as pd
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
from tqdm import tqdm
import time
import logging
import optuna
import optunahub
from optuna.visualization import plot_pareto_front
import lightgbm as lgb
import sklearn.datasets
from sklearn.model_selection import GroupShuffleSplit, StratifiedGroupKFold
from sklearn.utils.class_weight import compute_sample_weight
from catboost import Pool
from catboost import CatBoostClassifier
import xgboost as xgb
from xgboost import XGBClassifier
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.metrics import log_loss, average_precision_score, roc_curve, roc_auc_score, precision_recall_curve
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import session_info
Display settings configuration¶
- Configure display settings for enhanced output in Jupyter notebook
In [2]:
# Display full output in output cell, not only last result
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# Maximum rows and columns of Pandas DataFrame
# pd.options.display.max_rows,
pd.options.display.max_columns
# Print all the contents of a Pandas DataFrame
#pd.set_option('display.max_rows', None) # Print unlimited number of rows by setting to None, default is 10
pd.set_option("display.max_columns", None) # Do not truncate cols to display all of them by setting to None
pd.set_option("display.width", None) # Auto-detect the width of df to display all columns in single line by setting to None
pd.set_option("display.max_colwidth", None) # Auto detect the max size of col and print contents of that col without truncation
Create Temporary Directory for Intermediate Files¶
- Create a
tempdirectory to store intermediate files (e.g. model metrics, Optuna trials). - These files will be reused for generating plots and tables, as well as for reloading intermediate data i.e.
clean_df.parquet.
In [3]:
# Create the directory path if it doesn't exist and raise no errors if already exist
os.makedirs("temp/Loan-level", exist_ok = True)
temp_dir = "temp/Loan-level"
Function¶
In [4]:
def save_results(clf_name, best_trials,
exec_time, lloss_auc_train, lloss_auc_test,
df_metrics, cm_final, cm_all, cm_labels):
"""
Save results for a given classifier.
Parameters:
clf_name (str): Name identifier for the algorithm.
exec_time (list of dict): Execution time data.
lloss_auc_train (list of dict): Training log loss/AUC metrics.
lloss_auc_test (list of dict): Test log loss/AUC metrics.
df_metrics (pd.DataFrame): Additional metrics in Pandas DataFrame form.
cm_final (array-like): The final confusion matrix.
cm_all (list): List of confusion matrices for trials.
cm_labels (list): Corresponding labels for the confusion matrices.
"""
# Save tabular data as CSV
pd.DataFrame(exec_time).to_csv(f'{temp_dir}/exec_time_{clf_name}.csv', index = False)
pd.DataFrame(lloss_auc_train).to_csv(f'{temp_dir}/lloss_auc_train_{clf_name}.csv', index = False)
pd.DataFrame(lloss_auc_test).to_csv(f'{temp_dir}/lloss_auc_test_{clf_name}.csv', index = False)
pd.DataFrame(all_metrics).to_csv(f'{temp_dir}/all_metrics_{clf_name}.csv', index = False)
df_metrics.to_csv(f'{temp_dir}/df_metrics_{clf_name}.csv', index = False)
pd.DataFrame(cm_final).to_csv(f'{temp_dir}/cm_final_{clf_name}.csv', index = False)
# Saves all the parameters of the best trial(s) into a JSON file
with open(f'{temp_dir}/best_trials_{clf_name}.json', "w") as f:
json.dump(best_trials, f, indent = 4)
# Pickle complex objects (like lists, matrices, etc)
with open(f'{temp_dir}/cm_{clf_name}_all.pkl', "wb") as f:
pickle.dump(cm_all, f)
with open(f'{temp_dir}/cm_labels_{clf_name}_all.pkl', "wb") as f:
pickle.dump(cm_labels, f)
Load Parquet Data File¶
- Load the processed Parquet data file generated in
01_eda.ipynb - Drop columns not of interest
In [5]:
# Load aggregated loan level parquet data file
clean_df = pd.read_parquet(f'{temp_dir}/clean_df.parquet', engine = "pyarrow")
#clean_df.columns.tolist()
# Drop columns not of interest
cols_to_drop = ["underwritingid", "loanId", "clarityFraudId",
"applicationDate", "originatedDate",
"loanStatus", "fpymtDate", "fpymtAmt", "fpymtStatus", "yr_mth", "mth",
"principal_tot", "fees_tot", "paymentAmount_tot"]
clean_df.drop(columns = cols_to_drop, inplace = True)
clean_df.info(verbose = "all")
<class 'pandas.core.frame.DataFrame'> RangeIndex: 32312 entries, 0 to 32311 Data columns (total 270 columns): # Column Dtype --- ------ ----- 0 cfinq.thirtydaysago Int32 1 cfinq.twentyfourhoursago Int32 2 cfinq.oneminuteago Int32 3 cfinq.onehourago Int32 4 cfinq.ninetydaysago Int32 5 cfinq.sevendaysago Int32 6 cfinq.tenminutesago Int32 7 cfinq.fifteendaysago Int32 8 cfinq.threesixtyfivedaysago Int32 9 cfind.inquiryonfilecurrentaddressconflict boolean 10 cfind.totalnumberoffraudindicators Int32 11 cfind.telephonenumberinconsistentwithaddress boolean 12 cfind.inquiryageyoungerthanssnissuedate boolean 13 cfind.onfileaddresscautious boolean 14 cfind.inquiryaddressnonresidential boolean 15 cfind.onfileaddresshighrisk boolean 16 cfind.ssnreportedmorefrequentlyforanother boolean 17 cfind.currentaddressreportedbytradeopenlt90days boolean 18 cfind.inputssninvalid boolean 19 cfind.inputssnissuedatecannotbeverified boolean 20 cfind.inquiryaddresscautious boolean 21 cfind.morethan3inquiriesinthelast30days boolean 22 cfind.onfileaddressnonresidential boolean 23 cfind.creditestablishedpriortossnissuedate boolean 24 cfind.driverlicenseformatinvalid boolean 25 cfind.inputssnrecordedasdeceased boolean 26 cfind.inquiryaddresshighrisk boolean 27 cfind.inquirycurrentaddressnotonfile boolean 28 cfind.bestonfilessnissuedatecannotbeverified boolean 29 cfind.highprobabilityssnbelongstoanother boolean 30 cfind.maxnumberofssnswithanybankaccount Int32 31 cfind.bestonfilessnrecordedasdeceased boolean 32 cfind.currentaddressreportedbynewtradeonly boolean 33 cfind.creditestablishedbeforeage18 boolean 34 cfind.telephonenumberinconsistentwithstate boolean 35 cfind.driverlicenseinconsistentwithonfile boolean 36 cfind.workphonepreviouslylistedascellphone boolean 37 cfind.workphonepreviouslylistedashomephone boolean 38 cfindvrfy.ssnnamematch category 39 cfindvrfy.nameaddressmatch category 40 cfindvrfy.phonematchtype category 41 cfindvrfy.phonematchresult category 42 cfindvrfy.overallmatchresult category 43 cfindvrfy.phonetype category 44 cfindvrfy.ssndobreasoncode category 45 cfindvrfy.ssnnamereasoncode category 46 cfindvrfy.nameaddressreasoncode category 47 cfindvrfy.ssndobmatch category 48 cfindvrfy.overallmatchreasoncode float64 49 clearfraudscore float64 50 anon_ssn object 51 payFrequency category 52 apr float64 53 originated boolean 54 nPaidOff Int32 55 approved boolean 56 isFunded boolean 57 loanAmount float64 58 originallyScheduledPaymentAmount float64 59 state category 60 leadType category 61 leadCost float64 62 fpStatus category 63 hasCF boolean 64 sum_days_btw_pymts float64 65 mean_days_btw_pymts float64 66 med_days_btw_pymts float64 67 std_days_btw_pymts float64 68 cnt_days_btw_pymts Int32 69 min_days_btw_pymts float64 70 max_days_btw_pymts float64 71 sum_fees_Cancelled float64 72 sum_fees_Checked float64 73 sum_fees_Complete float64 74 sum_fees_None float64 75 sum_fees_Pending float64 76 sum_fees_Rejected float64 77 sum_fees_Rejected Awaiting Retry float64 78 sum_fees_Returned float64 79 sum_fees_Skipped float64 80 sum_principal_Cancelled float64 81 sum_principal_Checked float64 82 sum_principal_Complete float64 83 sum_principal_None float64 84 sum_principal_Pending float64 85 sum_principal_Rejected float64 86 sum_principal_Rejected Awaiting Retry float64 87 sum_principal_Returned float64 88 sum_principal_Skipped float64 89 sum_pymtAmt_Cancelled float64 90 sum_pymtAmt_Checked float64 91 sum_pymtAmt_Complete float64 92 sum_pymtAmt_None float64 93 sum_pymtAmt_Pending float64 94 sum_pymtAmt_Rejected float64 95 sum_pymtAmt_Rejected Awaiting Retry float64 96 sum_pymtAmt_Returned float64 97 sum_pymtAmt_Skipped float64 98 mean_fees_Cancelled float64 99 mean_fees_Checked float64 100 mean_fees_Complete float64 101 mean_fees_None float64 102 mean_fees_Pending float64 103 mean_fees_Rejected float64 104 mean_fees_Rejected Awaiting Retry float64 105 mean_fees_Returned float64 106 mean_fees_Skipped float64 107 mean_principal_Cancelled float64 108 mean_principal_Checked float64 109 mean_principal_Complete float64 110 mean_principal_None float64 111 mean_principal_Pending float64 112 mean_principal_Rejected float64 113 mean_principal_Rejected Awaiting Retry float64 114 mean_principal_Returned float64 115 mean_principal_Skipped float64 116 mean_pymtAmt_Cancelled float64 117 mean_pymtAmt_Checked float64 118 mean_pymtAmt_Complete float64 119 mean_pymtAmt_None float64 120 mean_pymtAmt_Pending float64 121 mean_pymtAmt_Rejected float64 122 mean_pymtAmt_Rejected Awaiting Retry float64 123 mean_pymtAmt_Returned float64 124 mean_pymtAmt_Skipped float64 125 med_fees_Cancelled float64 126 med_fees_Checked float64 127 med_fees_Complete float64 128 med_fees_None float64 129 med_fees_Pending float64 130 med_fees_Rejected float64 131 med_fees_Rejected Awaiting Retry float64 132 med_fees_Returned float64 133 med_fees_Skipped float64 134 med_principal_Cancelled float64 135 med_principal_Checked float64 136 med_principal_Complete float64 137 med_principal_None float64 138 med_principal_Pending float64 139 med_principal_Rejected float64 140 med_principal_Rejected Awaiting Retry float64 141 med_principal_Returned float64 142 med_principal_Skipped float64 143 med_pymtAmt_Cancelled float64 144 med_pymtAmt_Checked float64 145 med_pymtAmt_Complete float64 146 med_pymtAmt_None float64 147 med_pymtAmt_Pending float64 148 med_pymtAmt_Rejected float64 149 med_pymtAmt_Rejected Awaiting Retry float64 150 med_pymtAmt_Returned float64 151 med_pymtAmt_Skipped float64 152 std_fees_Cancelled float64 153 std_fees_Checked float64 154 std_fees_None float64 155 std_fees_Pending float64 156 std_fees_Rejected float64 157 std_fees_Rejected Awaiting Retry float64 158 std_fees_Skipped float64 159 std_principal_Cancelled float64 160 std_principal_Checked float64 161 std_principal_None float64 162 std_principal_Pending float64 163 std_principal_Rejected float64 164 std_principal_Rejected Awaiting Retry float64 165 std_principal_Skipped float64 166 std_pymtAmt_Cancelled float64 167 std_pymtAmt_Checked float64 168 std_pymtAmt_None float64 169 std_pymtAmt_Pending float64 170 std_pymtAmt_Rejected float64 171 std_pymtAmt_Rejected Awaiting Retry float64 172 std_pymtAmt_Skipped float64 173 min_fees_Cancelled float64 174 min_fees_Checked float64 175 min_fees_Complete float64 176 min_fees_None float64 177 min_fees_Pending float64 178 min_fees_Rejected float64 179 min_fees_Rejected Awaiting Retry float64 180 min_fees_Returned float64 181 min_fees_Skipped float64 182 min_principal_Cancelled float64 183 min_principal_Checked float64 184 min_principal_Complete float64 185 min_principal_None float64 186 min_principal_Pending float64 187 min_principal_Rejected float64 188 min_principal_Rejected Awaiting Retry float64 189 min_principal_Returned float64 190 min_principal_Skipped float64 191 min_pymtAmt_Cancelled float64 192 min_pymtAmt_Checked float64 193 min_pymtAmt_Complete float64 194 min_pymtAmt_None float64 195 min_pymtAmt_Pending float64 196 min_pymtAmt_Rejected float64 197 min_pymtAmt_Rejected Awaiting Retry float64 198 min_pymtAmt_Returned float64 199 min_pymtAmt_Skipped float64 200 max_fees_Cancelled float64 201 max_fees_Checked float64 202 max_fees_Complete float64 203 max_fees_None float64 204 max_fees_Pending float64 205 max_fees_Rejected float64 206 max_fees_Rejected Awaiting Retry float64 207 max_fees_Returned float64 208 max_fees_Skipped float64 209 max_principal_Cancelled float64 210 max_principal_Checked float64 211 max_principal_Complete float64 212 max_principal_None float64 213 max_principal_Pending float64 214 max_principal_Rejected float64 215 max_principal_Rejected Awaiting Retry float64 216 max_principal_Returned float64 217 max_principal_Skipped float64 218 max_pymtAmt_Cancelled float64 219 max_pymtAmt_Checked float64 220 max_pymtAmt_Complete float64 221 max_pymtAmt_None float64 222 max_pymtAmt_Pending float64 223 max_pymtAmt_Rejected float64 224 max_pymtAmt_Rejected Awaiting Retry float64 225 max_pymtAmt_Returned float64 226 max_pymtAmt_Skipped float64 227 cnt_custom Int32 228 cnt_non custom Int32 229 cnt_pymtStatus_Cancelled Int32 230 cnt_pymtStatus_Checked Int32 231 cnt_pymtStatus_Complete Int32 232 cnt_pymtStatus_None Int32 233 cnt_pymtStatus_Pending Int32 234 cnt_pymtStatus_Rejected Int32 235 cnt_pymtStatus_Rejected Awaiting Retry Int32 236 cnt_pymtStatus_Returned Int32 237 cnt_pymtStatus_Skipped Int32 238 cnt_pymtRCode_C01 Int32 239 cnt_pymtRCode_C02 Int32 240 cnt_pymtRCode_C03 Int32 241 cnt_pymtRCode_C05 Int32 242 cnt_pymtRCode_C07 Int32 243 cnt_pymtRCode_LPP01 Int32 244 cnt_pymtRCode_MISSED Int32 245 cnt_pymtRCode_R01 Int32 246 cnt_pymtRCode_R02 Int32 247 cnt_pymtRCode_R03 Int32 248 cnt_pymtRCode_R04 Int32 249 cnt_pymtRCode_R06 Int32 250 cnt_pymtRCode_R07 Int32 251 cnt_pymtRCode_R08 Int32 252 cnt_pymtRCode_R09 Int32 253 cnt_pymtRCode_R10 Int32 254 cnt_pymtRCode_R13 Int32 255 cnt_pymtRCode_R15 Int32 256 cnt_pymtRCode_R16 Int32 257 cnt_pymtRCode_R19 Int32 258 cnt_pymtRCode_R20 Int32 259 cnt_pymtRCode_R29 Int32 260 cnt_pymtRCode_R99 Int32 261 cnt_pymtRCode_RAF Int32 262 cnt_pymtRCode_RBW Int32 263 cnt_pymtRCode_RFG Int32 264 cnt_pymtRCode_RIR Int32 265 cnt_pymtRCode_RUP Int32 266 cnt_pymtRCode_RWC Int32 267 cnt_pymtRCode_RXL Int32 268 cnt_pymtRCode_RXS Int32 269 target Int8 dtypes: Int32(55), Int8(1), boolean(31), category(14), float64(168), object(1) memory usage: 52.5+ MB
In [6]:
"""
# Print counts of unique values for all columns
for col in clean_df.columns:
print(f'Value counts for column: {col}')
print(clean_df[col].value_counts(dropna = False)) # Count occurrences of each unique value
print("-" * 40)
""";
Models¶
Identity and hashing assumption
- Assume a consistent, deterministic hashing algorithm that yields a unique hash for every loan applicant (anon_ssn)Data splitting
- Train–test (80/20) withGroupShuffleSplitso that noanon_ssnappears in both train and testCross-validation on the training split
-StratifiedGroupKFold(5 folds) to:- Keep all samples from the same
anon_ssnin the same fold (prevent leakage) - Preserve class balance
- Use shuffling and a fixed
random_statefor robustness and reproducibility
- Keep all samples from the same
Models (tree-based gradient boosting)
- LightGBM: Grows trees by expanding the single branch that improves accuracy the most
- HistGradientBoostingClassifier: Grows trees step by step, testing options in groups (buckets) for speed
- XGBoost: Grows trees evenly level by level, keeping the structure balanced
- CatBoostClassifier: Grows trees in a perfectly symmetrical way, splitting all branches the same way at each step
Advantages of these classifiers:
- Require minimal preprocessing (e.g. no need for extensive encoding or scaling)
- Less sensitive to outliers in the data
- Less sensitive to multicollinearity
- Capable of capturing non-linear relationships effectively
- Support categorical features natively
Hyperparameter optimization
- Optuna multi-objective hyperparameter optimization with stratified 5-fold CV over 100 sequential trials:- Calibration: Log-loss (minimize) — evaluates how closely the predicted probabilities match the true outcomes. Lower values indicate better probability calibration.
- Discrimination: PR-AUC and ROC-AUC (maximize)
- ROC-AUC: Measures how well the model separates positives from negatives across all decision thresholds.
- PR-AUC: Focuses on precision–recall trade-offs, particularly valuable for imbalanced datasets where one class is much rarer.
Evaluation and reporting
- Report objective metrics above (on CV and hold-out test)
- Confusion matrix and classification report
- Elapsed time
In [7]:
# Runs the objective() function 100 times, each with a different set of hyperparameters suggested by Optuna
n_trials = 100
In [8]:
# Unsure which sampler to use so load the AutoSampler plugin from OptunaHub to automatically select the most suitable built-in sampler for the search space during Optuna optimization
module = optunahub.load_module(package = "samplers/auto_sampler")
In [9]:
seed = 42
Optuna with LightGBM¶
In [10]:
# Aggregated loan level data
X_df = clean_df.drop(columns = ["target", "anon_ssn"])
cat_cols = X_df.select_dtypes(include = "category").columns
cat_indices = [X_df.columns.get_loc(col) for col in cat_cols]
X_df = X_df.apply(lambda x: x.map(lambda z: np.nan if pd.isna(z) else z))
feature_name = list(X_df.columns)
y_df = clean_df["target"]
# Extract the grouping variable, anon_ssn
anon_ssn = clean_df["anon_ssn"];
In [11]:
# Convert categorical columns to numerical codes for Optuna with LightGBM, with NaN as -1
for col in X_df.columns:
if isinstance(X_df[col].dtype, pd.CategoricalDtype):
#print(col+": ordered?", {X_df[col].cat.ordered})
X_df[col] = X_df[col].cat.codes
In [12]:
X = X_df.to_numpy()
y = y_df.to_numpy()
In [13]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring no data leakage due anon_ssn (grouped by anon_ssn) appear in both sets
# and use a fixed random seed for reproducibility
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = seed)
# Generate the train and test indices from X, y, while ensuring all samples from the same anon_ssn stay together in train or test sets
train_idx, test_idx = next(gss.split(X, y, groups = anon_ssn))
# Use the indices to slice out the training and testing subsets of our feature matrix
X_train, X_test = X[train_idx], X[test_idx]
# Likewise, slice out the corresponding labels for training and testing
y_train, y_test = y[train_idx], y[test_idx]
# Keep track of anon_ssn for cross-validation
anon_ssn_train = anon_ssn[train_idx]
In [14]:
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Overview of training and test sets:</span>"))
print(f'Training set:\n- {X_train.shape[0]} rows and {X_train.shape[1]} features')
print(f'- Target proportion:\n{pd.Series(y_train).value_counts(normalize = True)}\n')
print(f'Test set:\n- {X_test.shape[0]} rows and {X_test.shape[1]} features')
print(f'- Target proportion:\n{pd.Series(y_test).value_counts(normalize = True)}\n')
print(f'Training set for anon_ssn: {anon_ssn_train.shape[0]} row entries with {anon_ssn_train.nunique()} unique anon_ssn values')
Overview of training and test sets:
Training set: - 25838 rows and 268 features - Target proportion: 0 0.507973 1 0.492027 Name: proportion, dtype: float64 Test set: - 6474 rows and 268 features - Target proportion: 0 0.505715 1 0.494285 Name: proportion, dtype: float64 Training set for anon_ssn: 25838 row entries with 24008 unique anon_ssn values
The target distribution between safe and risky loans does not suffer from severe class imbalance and is well balanced across both the training and test sets.
In [15]:
del X_df, y_df, X, y, gss, train_idx, test_idx;
In [16]:
# Define the Optuna objective function
def objective(trial):
# https://lightgbm.readthedocs.io/en/latest/Parameters.html
# https://lightgbm.readthedocs.io/en/stable/Parameters.html
# https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html
# Define parameter search space
param = {"objective": "binary",
"metric": ["binary_logloss", "auc", "average_precision"],
"device_type": "cpu",
#"device_type": "gpu", # Uses OpenCL backend on Windows
#"gpu_platform_id": 0,
#"gpu_device_id": 0,
"verbosity": 2,
"boosting_type": trial.suggest_categorical("boosting_type", ["gbdt", "rf", "dart"]),
"lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 1e1, log = True),
"lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 1e1, log = True),
"num_leaves": trial.suggest_int("num_leaves", 2, 256),
"feature_fraction": trial.suggest_float("feature_fraction", 1e-1, 1e0),
"bagging_fraction": trial.suggest_float("bagging_fraction", 1e-1, 1e0),
"bagging_freq": trial.suggest_int("bagging_freq", 1, 10),
"min_child_samples": trial.suggest_int("min_child_samples", 5, 200),
"learning_rate": trial.suggest_float("learning_rate", 1e-2, 1e-1, log = True),
"random_state": seed,
"deterministic": True,
"bagging_seed": seed,
"feature_fraction_seed": seed,
"drop_seed": seed,
"force_col_wise": True,
"num_threads": -1,
"is_unbalance": trial.suggest_categorical("is_unbalance", [True, False]),
"max_depth": -1, # <= 0, No tree depth limit
"max_bin": trial.suggest_int("max_bin", 40, 255),
"min_sum_hessian_in_leaf": trial.suggest_float("min_sum_hessian_in_leaf", 1e-10, 1e1, log = True),
"max_delta_step": trial.suggest_float("max_delta_step", 1, 100),
"feature_fraction_bynode": trial.suggest_float("feature_fraction_bynode", 1e-1, 1e0),
}
# Define how to split using StratifiedGroupKFold (5 folds, stratified, anon_ssn-safe)
sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
# Lists to store the performance metrics from each fold
lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
# Iterate over each fold of the stratified, anon_ssn-aware split, numbering folds starting from 1
for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):
# Split into training and validation sets for this fold
X_train_fold, X_valid_fold = X_train[train_index], X_train[valid_index]
y_train_fold, y_valid_fold = y_train[train_index], y_train[valid_index]
# Summarize the composition of classes in the train and validation sets
train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])
print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where 0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')
# Create LightGBM datasets for efficient training
dtrain_fold = lgb.Dataset(X_train_fold, label = y_train_fold, categorical_feature = cat_indices)
dvalid_fold = lgb.Dataset(X_valid_fold, label = y_valid_fold)
# Suggest the number of boosting rounds (trees) for the LightGBM model
# This value is being tuned using Optuna's suggest_int method, which picks an integer between 50 and 500
# The number of boosting rounds defines how many trees are built during training
num_round = trial.suggest_int("num_boost_round", 5, 100)
# https://lightgbm.readthedocs.io/en/stable/pythonapi/lightgbm.train.html
# Train the LightGBM model using the provided parameters and training data
# The num_boost_round parameter is specified separately rather than included in the param dictionary
# This is because num_boost_round is not a model hyperparameter that affects the tree-building process,
# but rather controls the number of boosting iterations during training.
# Including it as a separate argument in lgb.train allows for more flexible and clear tuning using Optuna
start_fold = time.perf_counter()
clf = lgb.train(params = param, train_set = dtrain_fold,
num_boost_round = num_round, # Get num_rounds from Optuna
valid_sets = [dvalid_fold]
)
end_fold = time.perf_counter()
# Predict probabilities and convert to binary labels at 0.5 threshold
y_prob_fold = clf.predict(X_valid_fold)
y_pred_fold = np.rint(y_prob_fold) # Set y_pred_fold = 1 if y_prob_fold => 0.5 and 0 if y_prob_fold < 0.5
print(f'Trial {trial.number}, Fold {fold_idx}: '
f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
f'Elapsed Time = {end_fold - start_fold} seconds')
# Calculate and store the evaluation metrics for this fold
lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))
del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, dtrain_fold, dvalid_fold, clf, start_fold, end_fold
gc.collect()
# Calculate average metrics across all folds for Optuna to optimize
mean_lloss = np.mean(lloss_scores)
mean_pr_auc = np.mean(pr_auc_scores)
mean_roc_auc = np.mean(roc_auc_scores)
del lloss_scores, pr_auc_scores, roc_auc_scores
gc.collect()
# Return the metrics to Optuna for optimization
return mean_lloss, mean_pr_auc, mean_roc_auc
In [17]:
# Initialize a progress bar for visual feedback during optimization
# https://tqdm.github.io/docs/tqdm/
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
ascii = True, # Plain text mode
dynamic_ncols = True # Auto-fit width
)
# Callback for Optuna to update the progress bar after each trial
def update_progress(study_lgbm, trial):
trial_progress.update(1)
# Disable Optuna's stdout handler so notebook isn’t spammed
optuna.logging.disable_default_handler()
# Enable propagation to Python’s logging
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)
# Configure Python logging
logging.basicConfig(filename = "optuna_debug_LGBM.log", filemode = "w", level = logging.DEBUG, format = "%(asctime)s %(levelname)s %(message)s")
study_lgbm = optuna.create_study(study_name = "Optuna for LGBM",
directions = ["minimize", "maximize", "maximize"],
sampler = module.AutoSampler(seed = seed)
)
start_optuna = time.perf_counter()
study_lgbm.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')
# Create a Pareto-front plot (3D scatter plot) to show trade-off between log-loss, PR_AUC and ROC-AUC for each trial
fig = plot_pareto_front(study_lgbm, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)
fig.show()
trial_progress.close()
# Plot optimization history plot for each objective
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
optuna.visualization.plot_optimization_history(study_lgbm,
target = lambda t: t.values[i], # Correctly target each objective
target_name = obj).show()
best_trials = study_lgbm.best_trials
best_trials_lgbm = {}
# Lists to store the performance metrics from best trial(s)
exec_time_lgbm, lloss_auc_train_lgbm, lloss_auc_test_lgbm, all_metrics = [], [], [], []
# List to store confusion matrices and their labels
cm_lgbm_all, cm_labels_lgbm_all = [], []
for i, trial in enumerate(best_trials):
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Training with Best Trial {trial.number} </span>"))
best_params = trial.params
# Non-optimized and best Optuna optimized parameters
full_params = {"objective": "binary",
"metric": ["binary_logloss", "auc", "average_precision"],
"device_type": "cpu",
"verbosity": 2,
"random_state": seed,
"deterministic": True,
"bagging_seed": seed,
"feature_fraction_seed": seed,
"drop_seed": seed,
"force_col_wise": True,
"num_threads": -1,
"max_depth": -1, # <= 0, No tree depth limit
**best_params
}
# Prepare the data - 80% training set and 20% test set
dtrain_all = lgb.Dataset(X_train, label = y_train, categorical_feature = cat_indices, feature_name = feature_name)
dtest_all = lgb.Dataset(X_test, label = y_test, categorical_feature = cat_indices,
feature_name = feature_name)
#display(HTML(str(trial.params)))
display(HTML(full_params.__repr__()))
# To be able to store num_boost_round as well
best_trials_lgbm[trial.number] = full_params.copy()
# Extract `num_boost_round` separately (default to 100 if not found)
num_boost_round = full_params.pop("num_boost_round", 100) # Remove from dictionary and set default
start_train = time.perf_counter()
# https://lightgbm.readthedocs.io/en/stable/pythonapi/lightgbm.train.html
final_lgbm = lgb.train(params = full_params,
train_set = dtrain_all,
num_boost_round = num_boost_round # Pass explicitly
)
end_train = time.perf_counter()
print(f'Training Elapsed Time: {end_train - start_train} seconds')
# Save trained final_lgbm from the best trial
final_lgbm.save_model(f'{temp_dir}/booster_LGBM_best_trial_{trial.number}.txt')
y_prob_all = final_lgbm.predict(X_test)
y_pred_all = np.rint(y_prob_all)
print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
print(f'PR-AUC: (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
print(f'ROC-AUC: (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')
exec_time_lgbm.append({"Classifier": "LGBM",
"Best Trial": trial.number,
"Optimization Elapsed Time (s)": end_optuna - start_optuna,
"Training Elapsed Time (s)": end_train - start_train})
lloss_auc_train_lgbm.append({"Classifier": "LGBM",
"Best Trial": trial.number,
"Set": "Training",
"Log loss": trial.values[0],
"PR-AUC": trial.values[1],
"ROC-AUC": trial.values[2]})
lloss_auc_test_lgbm.append({"Classifier": "LGBM",
"Best Trial": trial.number,
"Set": "Test",
"Log loss": log_loss(y_test, y_prob_all),
"PR-AUC": average_precision_score(y_test, y_prob_all),
"ROC-AUC": roc_auc_score(y_test, y_prob_all)})
report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
all_metrics.append({"Classifier": "LGBM",
"Trial": trial.number,
"Accuracy": accuracy_score(y_test, y_pred_all),
"Precision (Safe)": report["Safe"]["precision"],
"Recall (Safe)": report["Safe"]["recall"],
"F1-score (Safe)": report["Safe"]["f1-score"],
"Precision (Risky)": report["Risky"]["precision"],
"Recall (Risky)": report["Risky"]["recall"],
"F1-score (Risky)": report["Risky"]["f1-score"],
"Precision (Macro avg)": report["macro avg"]["precision"],
"Recall (Macro avg)": report["macro avg"]["recall"],
"F1-score (Macro avg)": report["macro avg"]["f1-score"],
"Precision (Weighted avg)": report["weighted avg"]["precision"],
"Recall (Weighted avg)": report["weighted avg"]["recall"],
"F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})
# Store confusion matrix
cm_final_lgbm = confusion_matrix(y_test, y_pred_all)
cm_lgbm_all.append(cm_final_lgbm)
cm_labels_lgbm_all.append(f'LGBM Confusion Matrix for Best Trial {trial.number}') # Store label for subplots
df_metrics_lgbm = pd.DataFrame(all_metrics)
gc.collect();
Optimization Progress: 0%| | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 0, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 11600 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5180 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5031 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5244 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5130 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5020 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5047 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5095 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5097 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5087 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5076 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5106 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5129 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5092 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5088 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5075 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5038 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5091 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5010 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 0, Fold 1: Log loss = 0.32964674084575407, Average precision = 0.9635670908320297, ROC-AUC = 0.9569285821826472, Elapsed Time = 1.5621259999998074 seconds Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 0, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 11614 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5044 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5139 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5043 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5046 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5091 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5108 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5249 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5106 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5224 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5086 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5179 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5136 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5105 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5102 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5182 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5071 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5167 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5054 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5098 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5023 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 Trial 0, Fold 2: Log loss = 0.32852376972252656, Average precision = 0.9609631282739565, ROC-AUC = 0.9569558301650926, Elapsed Time = 1.8901175999999396 seconds Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 0, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 11619 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5036 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5055 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5082 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5104 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5238 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5096 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5084 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5127 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5101 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5092 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5224 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5154 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5179 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5068 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5040 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5091 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5023 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 Trial 0, Fold 3: Log loss = 0.32251907251604217, Average precision = 0.9651567134011153, ROC-AUC = 0.9593725320472691, Elapsed Time = 1.500842399999783 seconds Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 0, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 11600 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5033 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5132 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5149 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5024 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5040 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5094 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5166 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5096 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5075 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5116 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5140 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5097 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5219 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5140 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5060 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5048 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5015 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 0, Fold 4: Log loss = 0.3300627228082963, Average precision = 0.9630914115390379, ROC-AUC = 0.9564496496109836, Elapsed Time = 1.4581862999993973 seconds Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 0, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 11602 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5028 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5136 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5023 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5035 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5083 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5233 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5166 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5082 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5077 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5152 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5093 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5085 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5061 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5162 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5038 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5082 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5020 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 Trial 0, Fold 5: Log loss = 0.3261443116609128, Average precision = 0.9628277217413703, ROC-AUC = 0.956578860338517, Elapsed Time = 1.429886599999918 seconds
Optimization Progress: 1%|1 | 1/100 [00:15<25:26, 15.42s/it]
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 1, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 21036 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 15798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15944 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15852 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15754 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15868 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15771 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15706 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15895 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15954 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 Trial 1, Fold 1: Log loss = 0.3089323124480566, Average precision = 0.9724404048219716, ROC-AUC = 0.9668692316332334, Elapsed Time = 1.6683110999993005 seconds Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 1, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 21050 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 15831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15971 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15881 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15787 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15827 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15827 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15895 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15886 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 15792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15886 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15962 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 Trial 1, Fold 2: Log loss = 0.3117475916409317, Average precision = 0.970029552862639, ROC-AUC = 0.9663060734623168, Elapsed Time = 2.4145504999996774 seconds Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 1, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 21049 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 15814 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15958 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15865 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15810 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15713 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15873 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15779 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15865 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 Trial 1, Fold 3: Log loss = 0.31072578688090474, Average precision = 0.972312781336129, ROC-AUC = 0.9677071394862806, Elapsed Time = 2.2389018999992913 seconds Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 1, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 21033 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 15793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15752 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15777 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15777 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15779 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 15892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15839 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15747 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15839 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15952 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 Trial 1, Fold 4: Log loss = 0.31275412815430426, Average precision = 0.9708694402491778, ROC-AUC = 0.9654651029707362, Elapsed Time = 2.2350790000000416 seconds Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 1, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 21038 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 15787 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15935 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15744 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15852 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15689 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Debug] Re-bagging, using 15889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15809 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 Trial 1, Fold 5: Log loss = 0.3118220763524803, Average precision = 0.9697970675961709, ROC-AUC = 0.9645898593538078, Elapsed Time = 2.185679899999741 seconds
Optimization Progress: 2%|2 | 2/100 [00:33<27:54, 17.08s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 2, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 10006 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 15148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15286 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15065 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15136 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15053 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15061 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15118 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15213 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15094 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15149 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 Trial 2, Fold 1: Log loss = 0.31630301819399675, Average precision = 0.9665739919306886, ROC-AUC = 0.9600088780870172, Elapsed Time = 1.318768000000091 seconds Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 2, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 10022 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 15180 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15082 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15068 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15228 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15336 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15089 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15358 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15154 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15293 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 Trial 2, Fold 2: Log loss = 0.31566260066916224, Average precision = 0.9642587196322059, ROC-AUC = 0.9608000568143709, Elapsed Time = 1.4685500999994474 seconds Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 2, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 10024 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 15163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15300 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15219 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15267 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15074 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15053 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15222 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15080 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15276 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15115 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 Trial 2, Fold 3: Log loss = 0.31411749801159977, Average precision = 0.9676660962346362, ROC-AUC = 0.9623256481686586, Elapsed Time = 1.5098760999999286 seconds Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 2, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 10003 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 15143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15124 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15109 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15055 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15040 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15284 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15058 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15321 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15103 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15128 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 Trial 2, Fold 4: Log loss = 0.315470578568722, Average precision = 0.9666878301659132, ROC-AUC = 0.9606021737471715, Elapsed Time = 1.6039383999996062 seconds Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 2, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 10007 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 15138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15118 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15244 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15152 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15046 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15154 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15318 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15058 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15101 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Debug] Re-bagging, using 15090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 Trial 2, Fold 5: Log loss = 0.31903414895241994, Average precision = 0.964618303671335, ROC-AUC = 0.9587533210709176, Elapsed Time = 1.4919348000003083 seconds
Optimization Progress: 3%|3 | 3/100 [00:49<26:41, 16.51s/it]
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 3, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817 [LightGBM] [Info] Total Bins 24071 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5514 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5491 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5495 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 Trial 3, Fold 1: Log loss = 0.5597832453424669, Average precision = 0.9667125671758896, ROC-AUC = 0.9600614535686803, Elapsed Time = 0.5946309999999357 seconds Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 3, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987 [LightGBM] [Info] Total Bins 24079 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5524 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5503 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5501 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 Trial 3, Fold 2: Log loss = 0.5610194828577617, Average precision = 0.9630685411421096, ROC-AUC = 0.9596014769308462, Elapsed Time = 0.6716820000001462 seconds Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 3, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893 [LightGBM] [Info] Total Bins 24073 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5519 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5494 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5499 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 Trial 3, Fold 3: Log loss = 0.5581836889306623, Average precision = 0.9675225486549472, ROC-AUC = 0.9629256315551232, Elapsed Time = 0.6689807000002475 seconds Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 3, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155 [LightGBM] [Info] Total Bins 24060 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5511 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5489 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5492 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 Trial 3, Fold 4: Log loss = 0.5640538981012556, Average precision = 0.9650158233172024, ROC-AUC = 0.9589714609994016, Elapsed Time = 0.713144000000284 seconds Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 3, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356 [LightGBM] [Info] Total Bins 24068 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5509 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5489 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5492 data to train [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 Trial 3, Fold 5: Log loss = 0.5609692072882091, Average precision = 0.962104120896841, ROC-AUC = 0.956462776136596, Elapsed Time = 0.6953654999997525 seconds
Optimization Progress: 4%|4 | 4/100 [01:00<22:48, 14.26s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 4, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 8523 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 4037 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 Trial 4, Fold 1: Log loss = 0.3602231587822408, Average precision = 0.9516449899508185, ROC-AUC = 0.9442643136748826, Elapsed Time = 0.46940489999997226 seconds Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 4, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 8527 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 4045 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 Trial 4, Fold 2: Log loss = 0.3391353717235677, Average precision = 0.9522235072890253, ROC-AUC = 0.9490507569027792, Elapsed Time = 0.4991823000000295 seconds Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 4, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 8536 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 4040 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 Trial 4, Fold 3: Log loss = 0.3550606159355602, Average precision = 0.956825668397955, ROC-AUC = 0.9500223018655315, Elapsed Time = 0.5020086000004085 seconds Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 4, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 8509 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 4036 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 Trial 4, Fold 4: Log loss = 0.34068448193557793, Average precision = 0.9555064606052168, ROC-AUC = 0.9494628073802251, Elapsed Time = 0.5262147999992521 seconds Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 4, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 8515 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 4034 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 Trial 4, Fold 5: Log loss = 0.3415364396590086, Average precision = 0.9558100300661114, ROC-AUC = 0.947333147539156, Elapsed Time = 0.507986699999492 seconds
Optimization Progress: 5%|5 | 5/100 [01:10<20:19, 12.84s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 5, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 27687 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 8104 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 8171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 Trial 5, Fold 1: Log loss = 0.5627463031660861, Average precision = 0.961618792767833, ROC-AUC = 0.9552116641095865, Elapsed Time = 0.49536629999965953 seconds Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 5, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 27695 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 8121 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 8186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 Trial 5, Fold 2: Log loss = 0.5602560012330817, Average precision = 0.9623815050366534, ROC-AUC = 0.958124667482665, Elapsed Time = 0.5083058999998684 seconds Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 5, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 27691 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 8112 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 8176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 Trial 5, Fold 3: Log loss = 0.5581777532188745, Average precision = 0.9660936011271237, ROC-AUC = 0.9598167635388275, Elapsed Time = 0.525013399999807 seconds Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 5, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 27682 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 8100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 8169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 Trial 5, Fold 4: Log loss = 0.5550038444062219, Average precision = 0.9652116478276259, ROC-AUC = 0.9590724258508171, Elapsed Time = 0.5312815999996019 seconds Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 5, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 27687 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 8098 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 8168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 Trial 5, Fold 5: Log loss = 0.5596013303068881, Average precision = 0.9631739580524441, ROC-AUC = 0.9570591011277707, Elapsed Time = 0.5385897999994995 seconds
Optimization Progress: 6%|6 | 6/100 [01:21<19:00, 12.13s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 6, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792997 [LightGBM] [Info] Total Bins 20218 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 12618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12703 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12488 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12502 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12650 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17 Trial 6, Fold 1: Log loss = 0.5724751092579775, Average precision = 0.9547962934851068, ROC-AUC = 0.9497921326766179, Elapsed Time = 0.5775649000006524 seconds Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 6, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956 [LightGBM] [Info] Total Bins 20183 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 12643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12729 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12666 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12681 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12672 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 19 Trial 6, Fold 2: Log loss = 0.5658112154684879, Average precision = 0.960626113189713, ROC-AUC = 0.9565368545294133, Elapsed Time = 0.6053950999994413 seconds Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 6, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792768 [LightGBM] [Info] Total Bins 20183 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 12629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12664 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12656 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12502 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12611 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12667 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12653 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17 Trial 6, Fold 3: Log loss = 0.5633294475258322, Average precision = 0.9644919097673798, ROC-AUC = 0.9585736075931082, Elapsed Time = 0.6343689000004815 seconds Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 6, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 20218 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 12614 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12651 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12500 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17 Trial 6, Fold 4: Log loss = 0.5648543846673204, Average precision = 0.9643431122624986, ROC-AUC = 0.9583624678402732, Elapsed Time = 0.7327912000000651 seconds Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 6, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364 [LightGBM] [Info] Total Bins 20213 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 12611 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12647 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 20 [LightGBM] [Debug] Re-bagging, using 12698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12635 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19 [LightGBM] [Debug] Re-bagging, using 12599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12636 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 Trial 6, Fold 5: Log loss = 0.5652957851302245, Average precision = 0.9619926890222837, ROC-AUC = 0.956429333184698, Elapsed Time = 0.6392338000005111 seconds
Optimization Progress: 7%|7 | 7/100 [01:31<17:55, 11.57s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 7, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9953 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9970 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9910 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9935 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9660 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 Trial 7, Fold 1: Log loss = 0.34242023144480144, Average precision = 0.9722076973965884, ROC-AUC = 0.966624128322412, Elapsed Time = 1.238731899999948 seconds Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 7, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 11158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9932 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9997 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9918 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9683 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 Trial 7, Fold 2: Log loss = 0.3405479248799782, Average precision = 0.9708784536841033, ROC-AUC = 0.9671138269243669, Elapsed Time = 1.569465699999455 seconds Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 7, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 11164 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9897 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9917 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9941 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9682 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 Trial 7, Fold 3: Log loss = 0.3415467230855122, Average precision = 0.9724346573249535, ROC-AUC = 0.9676446882433956, Elapsed Time = 1.8036743000002389 seconds Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 7, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 11145 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9920 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9932 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 Trial 7, Fold 4: Log loss = 0.3424819866368217, Average precision = 0.9721901674147935, ROC-AUC = 0.9669495470813634, Elapsed Time = 1.786351100000502 seconds Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 7, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9961 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9826 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9914 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9930 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9660 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 Trial 7, Fold 5: Log loss = 0.3435956901704336, Average precision = 0.9697871855943496, ROC-AUC = 0.9651655240324767, Elapsed Time = 1.7220666000002893 seconds
Optimization Progress: 8%|8 | 8/100 [01:47<19:41, 12.85s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 8, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7358 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9466 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9408 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9414 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9365 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 Trial 8, Fold 1: Log loss = 0.31846836571832476, Average precision = 0.9630925930307719, ROC-AUC = 0.9555496706863013, Elapsed Time = 1.1809659999998985 seconds Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 8, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7374 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9433 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9412 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 Trial 8, Fold 2: Log loss = 0.3197585532648229, Average precision = 0.960242214402566, ROC-AUC = 0.9550711226695712, Elapsed Time = 1.3920828999998776 seconds Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 8, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045 [LightGBM] [Info] Total Bins 7369 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9428 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9408 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9545 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9267 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9365 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9410 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 Trial 8, Fold 3: Log loss = 0.31408900076580654, Average precision = 0.9649005536183861, ROC-AUC = 0.9582289971203205, Elapsed Time = 1.5456070000000182 seconds Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 8, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 7355 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9513 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9464 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9539 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9364 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 Trial 8, Fold 4: Log loss = 0.3132359088595296, Average precision = 0.9633989236643054, ROC-AUC = 0.9558619671142203, Elapsed Time = 1.369531599999391 seconds Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 8, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7363 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9464 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9399 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9358 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 Trial 8, Fold 5: Log loss = 0.3175104373823987, Average precision = 0.9616862841710087, ROC-AUC = 0.954297456477714, Elapsed Time = 1.3472390000006271 seconds
Optimization Progress: 9%|9 | 9/100 [02:02<20:23, 13.45s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 9, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 17568 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 3223 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3121 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Debug] Re-bagging, using 3107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3121 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 Trial 9, Fold 1: Log loss = 0.5713470071668255, Average precision = 0.9584267499871053, ROC-AUC = 0.9509777695329864, Elapsed Time = 0.5892558999994435 seconds Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 9, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 17582 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 3229 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3127 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 Trial 9, Fold 2: Log loss = 0.5698247483669396, Average precision = 0.9531280055612432, ROC-AUC = 0.9486416448731555, Elapsed Time = 0.7269937999999456 seconds Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 9, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 17585 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 3225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3220 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3122 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3129 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 Trial 9, Fold 3: Log loss = 0.5710888801937607, Average precision = 0.9603293675144042, ROC-AUC = 0.9536839010025906, Elapsed Time = 0.6494276999992508 seconds Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 9, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 17572 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 3223 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3117 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3109 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3122 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 Trial 9, Fold 4: Log loss = 0.5738447768195227, Average precision = 0.958603345988887, ROC-AUC = 0.9520692653156602, Elapsed Time = 0.6557638000003863 seconds Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 9, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 17567 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 3222 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3117 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3108 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 Trial 9, Fold 5: Log loss = 0.5738391657501708, Average precision = 0.9533676394410786, ROC-AUC = 0.9467562937777529, Elapsed Time = 0.6461589999998978 seconds
Optimization Progress: 10%|# | 10/100 [02:13<19:19, 12.88s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 10, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 9564 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6149 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6075 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6258 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5987 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 Trial 10, Fold 1: Log loss = 0.30683541454586777, Average precision = 0.9681071884451107, ROC-AUC = 0.962354461507578, Elapsed Time = 1.1848104000000603 seconds Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 10, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949 [LightGBM] [Info] Total Bins 9556 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6093 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6117 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6136 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6011 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 Trial 10, Fold 2: Log loss = 0.3178055408882424, Average precision = 0.9640378864927284, ROC-AUC = 0.9600655975327024, Elapsed Time = 1.279888199999732 seconds Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 10, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784 [LightGBM] [Info] Total Bins 9563 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6086 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6009 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 Trial 10, Fold 3: Log loss = 0.31589710878430993, Average precision = 0.9666835495238092, ROC-AUC = 0.9617884169949245, Elapsed Time = 1.5711670000000595 seconds Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 10, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 9562 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6077 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6109 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5990 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 Trial 10, Fold 4: Log loss = 0.3124373915207655, Average precision = 0.967054943875468, ROC-AUC = 0.9605874947244933, Elapsed Time = 1.385622499999954 seconds Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 10, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340 [LightGBM] [Info] Total Bins 9544 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6179 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6137 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6071 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6116 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5989 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 Trial 10, Fold 5: Log loss = 0.31701231614868497, Average precision = 0.9634314803611199, ROC-AUC = 0.9594070936216859, Elapsed Time = 1.3575648000005458 seconds
Optimization Progress: 11%|#1 | 11/100 [02:27<19:42, 13.29s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 11, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 19014 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10167 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10127 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10000 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 Trial 11, Fold 1: Log loss = 0.27839808071739647, Average precision = 0.9699116494901384, ROC-AUC = 0.9638715330054535, Elapsed Time = 0.9067294999995283 seconds Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 11, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 19026 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10128 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10026 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 11, Fold 2: Log loss = 0.2773252453732645, Average precision = 0.9679454703507686, ROC-AUC = 0.9644505774154984, Elapsed Time = 0.9466196999992462 seconds Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 11, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 19029 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10134 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10021 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10037 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 Trial 11, Fold 3: Log loss = 0.2747655322336973, Average precision = 0.9708889291563291, ROC-AUC = 0.9660925867515482, Elapsed Time = 0.9953179000003729 seconds Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 11, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 19010 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10123 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9999 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 Trial 11, Fold 4: Log loss = 0.27565143365103895, Average precision = 0.9698695678849122, ROC-AUC = 0.9640579286393842, Elapsed Time = 1.3377421000004688 seconds Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 11, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 19008 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9992 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10032 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 Trial 11, Fold 5: Log loss = 0.28328383290878856, Average precision = 0.9680872451642151, ROC-AUC = 0.9623242851569032, Elapsed Time = 1.3138933999998699 seconds
Optimization Progress: 12%|#2 | 12/100 [02:40<19:21, 13.20s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 12, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 8297 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6273 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6300 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6080 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6116 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6253 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6274 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6255 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 Trial 12, Fold 1: Log loss = 0.2795864886375474, Average precision = 0.9725727838068232, ROC-AUC = 0.967581689154634, Elapsed Time = 0.7844225999997434 seconds Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 12, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 8313 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6102 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6273 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6322 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6228 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6370 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 Trial 12, Fold 2: Log loss = 0.2794407935119923, Average precision = 0.9694707487668018, ROC-AUC = 0.9668603322487413, Elapsed Time = 0.7739711000003808 seconds Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 12, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 8313 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6278 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6343 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6118 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6220 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6266 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6217 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6383 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17 Trial 12, Fold 3: Log loss = 0.2764566261179014, Average precision = 0.9718120509134004, ROC-AUC = 0.9675455939218537, Elapsed Time = 0.7659664000002522 seconds Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 12, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 8295 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6270 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6343 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6083 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6105 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6270 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6274 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6213 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 Trial 12, Fold 4: Log loss = 0.2785140783863654, Average precision = 0.9718978332825114, ROC-AUC = 0.9670789013116639, Elapsed Time = 0.7898058999999193 seconds Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 12, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 8302 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6293 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6336 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6083 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6293 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6261 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 Trial 12, Fold 5: Log loss = 0.281669785331137, Average precision = 0.9701642707511918, ROC-AUC = 0.9660935287888076, Elapsed Time = 0.8207968000006076 seconds
Optimization Progress: 13%|#3 | 13/100 [02:52<18:29, 12.76s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 13, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804600 [LightGBM] [Info] Total Bins 29002 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 19497 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19452 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19514 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14 [LightGBM] [Debug] Re-bagging, using 19469 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19477 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19483 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 33 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19435 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19467 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19496 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19436 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19473 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19432 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19449 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Re-bagging, using 19491 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19458 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19471 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19429 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19529 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19504 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19529 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19402 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19436 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19464 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19493 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19521 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 Trial 13, Fold 1: Log loss = 0.3439567228683248, Average precision = 0.9642014557062354, ROC-AUC = 0.9580364013516679, Elapsed Time = 1.7321501999995235 seconds Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 13, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804566 [LightGBM] [Info] Total Bins 29006 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 19534 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19485 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19553 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19503 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19517 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19506 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19475 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19514 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19532 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19470 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19505 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19468 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19526 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19493 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19506 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19569 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19540 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19560 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19439 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19469 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19504 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19535 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19554 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 Trial 13, Fold 2: Log loss = 0.3371119253550399, Average precision = 0.9651042928311725, ROC-AUC = 0.9614018885315396, Elapsed Time = 2.2189558999998553 seconds Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 13, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804456 [LightGBM] [Info] Total Bins 29001 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 19516 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19468 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19533 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19490 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19491 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19497 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Re-bagging, using 19453 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Re-bagging, using 19492 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Re-bagging, using 19513 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19457 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19451 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19466 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19507 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19480 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Re-bagging, using 19483 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19449 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19550 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19521 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19549 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19419 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19447 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19485 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19513 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19543 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 Trial 13, Fold 3: Log loss = 0.3337444841372523, Average precision = 0.9682855889749199, ROC-AUC = 0.963323212720671, Elapsed Time = 2.7470613999994384 seconds Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 13, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155 [LightGBM] [Info] Total Bins 28992 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 19490 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19445 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19509 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19468 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19474 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19433 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 13 [LightGBM] [Debug] Re-bagging, using 19462 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19428 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19426 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19444 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19483 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19451 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19463 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19422 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19523 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19498 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19521 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19399 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19422 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19464 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19488 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19512 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 Trial 13, Fold 4: Log loss = 0.3345920525712018, Average precision = 0.9672589026802049, ROC-AUC = 0.961008492075414, Elapsed Time = 2.6501938000001246 seconds Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 13, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804939 [LightGBM] [Info] Total Bins 28998 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 19484 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19440 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19503 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19460 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19463 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19433 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19454 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19478 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19423 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19459 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19422 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19437 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19477 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19449 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19453 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19419 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19518 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19489 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19518 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19393 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19419 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 36 [LightGBM] [Debug] Re-bagging, using 19452 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19507 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 Trial 13, Fold 5: Log loss = 0.34868921272817555, Average precision = 0.9628221406956591, ROC-AUC = 0.9572423685041711, Elapsed Time = 2.0537867000002734 seconds
Optimization Progress: 14%|#4 | 14/100 [03:11<21:02, 14.68s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 14, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 12063 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 18369 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 Trial 14, Fold 1: Log loss = 0.4712521550359508, Average precision = 0.9693039444042607, ROC-AUC = 0.9631592995272987, Elapsed Time = 0.7223698000007062 seconds Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 14, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 12084 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 18405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 Trial 14, Fold 2: Log loss = 0.46625701603010106, Average precision = 0.9680161782461034, ROC-AUC = 0.9641126350972598, Elapsed Time = 0.6809782000000268 seconds Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 14, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 12088 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 18388 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18438 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18374 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 Trial 14, Fold 3: Log loss = 0.4677295498601943, Average precision = 0.970775523157016, ROC-AUC = 0.9657632504984061, Elapsed Time = 0.7300248999999894 seconds Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 14, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 12066 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 18362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 Trial 14, Fold 4: Log loss = 0.4673188628179886, Average precision = 0.969554405860509, ROC-AUC = 0.9634035571817232, Elapsed Time = 0.6793779999998151 seconds Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 14, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 12068 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 18356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18382 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 14, Fold 5: Log loss = 0.46827598185049213, Average precision = 0.9680601591436406, ROC-AUC = 0.963749029225424, Elapsed Time = 0.6934823000001415 seconds
Optimization Progress: 15%|#5 | 15/100 [03:23<19:19, 13.65s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 15, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 19890 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10809 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10614 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10650 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 Trial 15, Fold 1: Log loss = 0.3307535494949883, Average precision = 0.9624177756608816, ROC-AUC = 0.9556137470545782, Elapsed Time = 0.7917609000005541 seconds Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 15, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 19902 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10830 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10810 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10840 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10742 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 Trial 15, Fold 2: Log loss = 0.32437769588339677, Average precision = 0.9611297020298436, ROC-AUC = 0.9558363108777972, Elapsed Time = 0.8626070999998774 seconds Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 15, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 19855 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10818 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10816 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10635 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10822 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 10786 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10730 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 Trial 15, Fold 3: Log loss = 0.32260896629795294, Average precision = 0.9643834699614529, ROC-AUC = 0.958475641908486, Elapsed Time = 0.8613505000002988 seconds Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 15, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 19885 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10612 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 10719 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 Trial 15, Fold 4: Log loss = 0.3249451621825596, Average precision = 0.963468797794335, ROC-AUC = 0.9561431039292688, Elapsed Time = 0.9371393000001262 seconds Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 15, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 19883 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10787 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10713 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 Trial 15, Fold 5: Log loss = 0.3293120261072558, Average precision = 0.96140222102461, ROC-AUC = 0.9544851085966967, Elapsed Time = 0.9248790000001463 seconds
Optimization Progress: 16%|#6 | 16/100 [03:34<18:13, 13.02s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 16, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 24804 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2840 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2873 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2977 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2990 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2890 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2874 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2898 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2913 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2894 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 15 [LightGBM] [Debug] Re-bagging, using 2917 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2964 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 Trial 16, Fold 1: Log loss = 0.20747082145114132, Average precision = 0.972974517916921, ROC-AUC = 0.9679727192995033, Elapsed Time = 1.2898568999999043 seconds Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 16, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 24811 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2952 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2957 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2838 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2883 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2847 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2996 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2907 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2887 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2771 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 16 [LightGBM] [Debug] Re-bagging, using 2857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2916 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2904 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 15 Trial 16, Fold 2: Log loss = 0.2027997548495244, Average precision = 0.9719972349009797, ROC-AUC = 0.9685757650895721, Elapsed Time = 1.3284340999998676 seconds Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 16, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 24804 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2951 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2841 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2847 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2901 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2904 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2861 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 15 [LightGBM] [Debug] Re-bagging, using 2907 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2920 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2971 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 Trial 16, Fold 3: Log loss = 0.20250456993786586, Average precision = 0.9731164233792264, ROC-AUC = 0.9689353974246613, Elapsed Time = 1.448365900000681 seconds Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 16, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 24794 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2976 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2844 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2888 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2873 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2894 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2858 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 19 [LightGBM] [Debug] Re-bagging, using 2911 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 18 [LightGBM] [Debug] Re-bagging, using 2899 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 17 [LightGBM] [Debug] Re-bagging, using 2913 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15 [LightGBM] [Debug] Re-bagging, using 2960 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 Trial 16, Fold 4: Log loss = 0.2061300286833578, Average precision = 0.9725030883078034, ROC-AUC = 0.9671212245851722, Elapsed Time = 1.462485399999423 seconds Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 16, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 24798 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2946 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2874 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2922 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2981 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2890 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2768 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2849 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2909 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2909 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2902 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2916 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2786 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 Trial 16, Fold 5: Log loss = 0.20904784892066716, Average precision = 0.9722563489089914, ROC-AUC = 0.9676975270795013, Elapsed Time = 1.4544285000001764 seconds
Optimization Progress: 17%|#7 | 17/100 [03:49<18:40, 13.51s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 17, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794337 [LightGBM] [Info] Total Bins 10352 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 3248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3027 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 Trial 17, Fold 1: Log loss = 0.30963063875852925, Average precision = 0.9650738928081896, ROC-AUC = 0.958796878808735, Elapsed Time = 1.334805999999844 seconds Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 17, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956 [LightGBM] [Info] Total Bins 10364 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 3254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3132 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3180 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3028 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Debug] Re-bagging, using 3169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3104 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 Trial 17, Fold 2: Log loss = 0.31070298288297354, Average precision = 0.9617001013894317, ROC-AUC = 0.9575550821699083, Elapsed Time = 1.725110099999256 seconds Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 17, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792768 [LightGBM] [Info] Total Bins 10368 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 3250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3244 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3136 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3153 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3258 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3105 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3180 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 Trial 17, Fold 3: Log loss = 0.3052696325563928, Average precision = 0.9660969774617566, ROC-AUC = 0.9609569877974786, Elapsed Time = 1.5482718000002933 seconds Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 17, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 10347 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 3248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3238 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3131 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3121 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3028 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3116 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3174 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3092 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 Trial 17, Fold 4: Log loss = 0.30706864130283307, Average precision = 0.9650277959863939, ROC-AUC = 0.9583006222117307, Elapsed Time = 1.5326796999997896 seconds Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 17, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364 [LightGBM] [Info] Total Bins 10351 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 3247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3238 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3131 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3025 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3103 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3249 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3081 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 Trial 17, Fold 5: Log loss = 0.31262231004306085, Average precision = 0.9626557154725396, ROC-AUC = 0.9569277074856475, Elapsed Time = 1.2901283999999578 seconds
Optimization Progress: 18%|#8 | 18/100 [04:05<19:29, 14.26s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 18, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7940 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6760 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6778 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6679 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6628 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6700 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6818 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6697 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6717 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6703 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6784 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6612 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6719 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6867 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6756 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 Trial 18, Fold 1: Log loss = 0.20574575048184507, Average precision = 0.9739334509548979, ROC-AUC = 0.9692472266433423, Elapsed Time = 0.9175384999998641 seconds Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 18, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7956 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6835 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6744 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6713 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6826 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6566 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6729 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6697 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6626 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6734 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6764 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 Trial 18, Fold 2: Log loss = 0.2013883232521865, Average precision = 0.9735480108160133, ROC-AUC = 0.9702207110840527, Elapsed Time = 1.02073879999989 seconds Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 18, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7963 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6766 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6681 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6641 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6710 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6820 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6627 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6673 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6767 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6888 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6758 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 Trial 18, Fold 3: Log loss = 0.20044029078217634, Average precision = 0.9740716095881156, ROC-AUC = 0.9701093303059779, Elapsed Time = 1.2052254000000175 seconds Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 18, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 7949 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6757 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6752 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6812 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6694 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6569 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6683 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6791 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6611 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6670 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6770 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6752 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 Trial 18, Fold 4: Log loss = 0.20460782943851485, Average precision = 0.9734286703235141, ROC-AUC = 0.9687859896850433, Elapsed Time = 1.236685199999556 seconds Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 18, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7944 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6692 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6551 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6726 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6697 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6669 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6784 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6619 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6764 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6726 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6863 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6749 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 Trial 18, Fold 5: Log loss = 0.209003953144028, Average precision = 0.971670356080389, ROC-AUC = 0.9682428701484496, Elapsed Time = 1.1931323999997403 seconds
Optimization Progress: 19%|#9 | 19/100 [04:18<18:56, 14.03s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 19, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 21915 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 8731 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8796 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8664 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8704 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8720 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8788 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8763 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8545 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Re-bagging, using 8540 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8563 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 34 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8874 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 Trial 19, Fold 1: Log loss = 0.19924243631778799, Average precision = 0.9757228974576991, ROC-AUC = 0.9711200788154266, Elapsed Time = 2.895565999999235 seconds Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 19, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949 [LightGBM] [Info] Total Bins 21927 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 8749 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8813 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8729 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8685 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8699 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8730 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8789 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Re-bagging, using 8564 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8547 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8676 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8687 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8906 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 Trial 19, Fold 2: Log loss = 0.18703719630522814, Average precision = 0.9756550682922964, ROC-AUC = 0.9730873784993419, Elapsed Time = 3.661527400000523 seconds Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 19, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784 [LightGBM] [Info] Total Bins 21870 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 8740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8803 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8720 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8682 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8789 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8727 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8796 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 31 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8542 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 31 [LightGBM] [Debug] Re-bagging, using 8665 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8676 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 28 [LightGBM] [Debug] Re-bagging, using 8886 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 Trial 19, Fold 3: Log loss = 0.1889680747041436, Average precision = 0.9750371189788504, ROC-AUC = 0.9728410831062014, Elapsed Time = 3.0296307999997225 seconds Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 19, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 21910 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 8727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8711 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8702 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8718 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8787 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8761 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8542 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8536 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8561 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 29 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Re-bagging, using 8662 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 34 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8669 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8872 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 Trial 19, Fold 4: Log loss = 0.19223260795860275, Average precision = 0.9760122026562066, ROC-AUC = 0.9717124056071781, Elapsed Time = 3.7891062999997303 seconds Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 19, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340 [LightGBM] [Info] Total Bins 21919 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 8725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8710 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8656 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8694 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8725 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8781 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8758 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8544 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8526 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8554 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8664 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 31 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 29 [LightGBM] [Debug] Re-bagging, using 8678 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24 [LightGBM] [Debug] Re-bagging, using 8870 data to train [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 36 Trial 19, Fold 5: Log loss = 0.2012878534321624, Average precision = 0.9731398997533733, ROC-AUC = 0.9698096352860301, Elapsed Time = 3.564209299999675 seconds
Optimization Progress: 20%|## | 20/100 [04:44<23:14, 17.43s/it]
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 20, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 14105 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9482 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9558 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 20, Fold 1: Log loss = 0.3012211491529579, Average precision = 0.9681789405533022, ROC-AUC = 0.9619023422377081, Elapsed Time = 1.2858525999999983 seconds Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 20, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 14121 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9558 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9497 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9445 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 20, Fold 2: Log loss = 0.3005685009027957, Average precision = 0.9655611009212376, ROC-AUC = 0.9629260432887872, Elapsed Time = 1.4934327999999368 seconds Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 20, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 14124 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9486 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9440 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9571 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9417 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 Trial 20, Fold 3: Log loss = 0.2985064549274648, Average precision = 0.96914730997676, ROC-AUC = 0.9638280143935819, Elapsed Time = 1.4035541000002922 seconds Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 20, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 14106 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9480 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9418 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9551 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 Trial 20, Fold 4: Log loss = 0.29806484641027425, Average precision = 0.9684156480487836, ROC-AUC = 0.962071715296704, Elapsed Time = 1.396743599999354 seconds Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 20, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 14104 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9480 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9414 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9514 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 Trial 20, Fold 5: Log loss = 0.3018067238368054, Average precision = 0.9668616390071544, ROC-AUC = 0.9616186388718577, Elapsed Time = 1.3381429000000935 seconds
Optimization Progress: 21%|##1 | 21/100 [04:59<22:04, 16.77s/it]
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 21, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 15336 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 Trial 21, Fold 1: Log loss = 0.4294722857380578, Average precision = 0.9663014875585593, ROC-AUC = 0.9613171412798784, Elapsed Time = 0.7071726000003764 seconds Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 21, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 15352 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10578 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10551 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17 Trial 21, Fold 2: Log loss = 0.4269684819092223, Average precision = 0.9661205485665564, ROC-AUC = 0.9627950090760351, Elapsed Time = 0.7049581999999646 seconds Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 21, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 15353 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10566 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10539 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17 Trial 21, Fold 3: Log loss = 0.4242086322595482, Average precision = 0.9697600374481333, ROC-AUC = 0.9651152624216275, Elapsed Time = 0.7312525000006644 seconds Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 21, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 15336 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10552 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 Trial 21, Fold 4: Log loss = 0.42668125159574394, Average precision = 0.9685943640991859, ROC-AUC = 0.9628020898159473, Elapsed Time = 0.7318037000004551 seconds Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 21, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 15335 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 Trial 21, Fold 5: Log loss = 0.4274642890160897, Average precision = 0.9688491111364995, ROC-AUC = 0.9643741151552311, Elapsed Time = 0.7462249999998676 seconds
Optimization Progress: 22%|##2 | 22/100 [05:10<19:37, 15.10s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 22, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295 [LightGBM] [Info] Total Bins 22226 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Debug] Re-bagging, using 14767 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Debug] Re-bagging, using 14796 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14659 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 14652 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 14702 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 Trial 22, Fold 1: Log loss = 0.5228777373379064, Average precision = 0.9647546789177879, ROC-AUC = 0.9574565027506537, Elapsed Time = 0.7402208000003156 seconds Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 22, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 22241 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 14827 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14688 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 14750 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 14664 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14723 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 Trial 22, Fold 2: Log loss = 0.5212665422613845, Average precision = 0.9636074131965384, ROC-AUC = 0.9593215022666567, Elapsed Time = 0.7811039999996865 seconds Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 22, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045 [LightGBM] [Info] Total Bins 22123 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14660 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14780 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 14813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14679 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Debug] Re-bagging, using 14731 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 Trial 22, Fold 3: Log loss = 0.5236276273082059, Average precision = 0.9654209196951052, ROC-AUC = 0.9595892303358342, Elapsed Time = 0.7161302000004071 seconds Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 22, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 22222 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Debug] Re-bagging, using 14760 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14791 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Debug] Re-bagging, using 14658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 14714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 14696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9 Trial 22, Fold 4: Log loss = 0.5223469997818746, Average precision = 0.9648356841262216, ROC-AUC = 0.9584982301718444, Elapsed Time = 0.688500400000521 seconds Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 22, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 22235 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14757 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Debug] Re-bagging, using 14789 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 14652 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 14709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 14639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Debug] Re-bagging, using 14700 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 Trial 22, Fold 5: Log loss = 0.5231191120911644, Average precision = 0.963700468976446, ROC-AUC = 0.9570614792932389, Elapsed Time = 0.7112767999997232 seconds
Optimization Progress: 23%|##3 | 23/100 [05:22<18:10, 14.16s/it]
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 23, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 16789 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14650 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 Trial 23, Fold 1: Log loss = 0.5225291289612243, Average precision = 0.9675563224495939, ROC-AUC = 0.9608456049287123, Elapsed Time = 0.8706756999999925 seconds Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 23, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 16800 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14554 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14675 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14708 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 Trial 23, Fold 2: Log loss = 0.5232042123614699, Average precision = 0.9658566296699482, ROC-AUC = 0.9612999140197047, Elapsed Time = 0.9562309000002642 seconds Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 23, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 16807 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14695 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14558 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14607 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 Trial 23, Fold 3: Log loss = 0.5238859532622018, Average precision = 0.9673452313722839, ROC-AUC = 0.9617849558417042, Elapsed Time = 0.9833689000006416 seconds Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 23, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 16788 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14671 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 Trial 23, Fold 4: Log loss = 0.5247934093888289, Average precision = 0.966912358126536, ROC-AUC = 0.9606433048157923, Elapsed Time = 1.0001123000001826 seconds Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 23, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 16787 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14641 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14669 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 Trial 23, Fold 5: Log loss = 0.5265657072592534, Average precision = 0.9651006232741035, ROC-AUC = 0.9590224253571893, Elapsed Time = 0.924916399999347 seconds
Optimization Progress: 24%|##4 | 24/100 [05:34<17:10, 13.55s/it]
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 24, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 27471 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2796 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2739 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2824 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 Trial 24, Fold 1: Log loss = 0.3751901903993324, Average precision = 0.9602343850255711, ROC-AUC = 0.9531562913733195, Elapsed Time = 0.8463048000003255 seconds Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 24, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 27480 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2750 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2763 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 Trial 24, Fold 2: Log loss = 0.3735114996032227, Average precision = 0.9565893234197076, ROC-AUC = 0.9526023379477813, Elapsed Time = 0.9060810000000856 seconds Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 24, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 27477 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2742 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2768 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 Trial 24, Fold 3: Log loss = 0.36761244261956777, Average precision = 0.9631109753164551, ROC-AUC = 0.9575659605512803, Elapsed Time = 0.9513429000007818 seconds Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 24, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 27467 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2824 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2767 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 Trial 24, Fold 4: Log loss = 0.37014187852829117, Average precision = 0.961520701652486, ROC-AUC = 0.9546189444933249, Elapsed Time = 0.9763474999999744 seconds Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 24, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 27472 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2739 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2820 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2765 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2720 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 Trial 24, Fold 5: Log loss = 0.37331798813142764, Average precision = 0.9577758761061415, ROC-AUC = 0.9510315664307081, Elapsed Time = 0.9897025999998732 seconds
Optimization Progress: 25%|##5 | 25/100 [05:46<16:26, 13.15s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 25, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24366 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10397 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10280 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10417 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10370 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10378 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10342 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10482 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10335 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10582 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10276 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 Trial 25, Fold 1: Log loss = 0.3522762790360541, Average precision = 0.967843534468308, ROC-AUC = 0.9614237858648427, Elapsed Time = 1.9450667999999496 seconds Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 25, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24373 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10313 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10439 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10436 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10404 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10382 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10387 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10443 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10317 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10486 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10306 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 Trial 25, Fold 2: Log loss = 0.34525464550036206, Average precision = 0.9670754836092235, ROC-AUC = 0.962763066167194, Elapsed Time = 2.3343526999997266 seconds Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 25, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24368 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10506 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10403 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10280 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10340 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10491 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10422 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10400 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10434 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10397 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10515 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10412 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10355 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10437 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10501 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10344 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 Trial 25, Fold 3: Log loss = 0.3457557444663702, Average precision = 0.9693219298702005, ROC-AUC = 0.9640517102310485, Elapsed Time = 1.9869880999995075 seconds Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 25, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24356 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10303 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10396 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10283 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10402 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10417 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10377 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10358 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10369 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10355 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10327 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10578 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10382 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10326 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10289 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 Trial 25, Fold 4: Log loss = 0.3457546230696314, Average precision = 0.9686800320577474, ROC-AUC = 0.962634808567661, Elapsed Time = 1.9513052000002062 seconds Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 25, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24360 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10430 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10448 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10466 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10479 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10377 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10506 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10374 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10365 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10323 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 Trial 25, Fold 5: Log loss = 0.3514596909372222, Average precision = 0.9655090119026054, ROC-AUC = 0.9597230923582856, Elapsed Time = 1.796817899999951 seconds
Optimization Progress: 26%|##6 | 26/100 [06:06<18:29, 14.99s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 26, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 29191 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 16006 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16052 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15995 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15998 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15998 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16006 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16060 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 Trial 26, Fold 1: Log loss = 0.39860973853534337, Average precision = 0.9658180373732671, ROC-AUC = 0.9595059756814499, Elapsed Time = 1.4416620000001785 seconds Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 26, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 29195 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 16039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Debug] Re-bagging, using 16169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16081 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15983 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Debug] Re-bagging, using 16021 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16021 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16020 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 16052 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16087 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 Trial 26, Fold 2: Log loss = 0.3992144562722906, Average precision = 0.9625837281038332, ROC-AUC = 0.9588862137440397, Elapsed Time = 1.9067242000000988 seconds Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 26, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 29190 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 16022 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16065 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15969 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Debug] Re-bagging, using 16005 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16010 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16007 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16032 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16068 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 Trial 26, Fold 3: Log loss = 0.3947824437055664, Average precision = 0.9669910235944826, ROC-AUC = 0.9615008403078079, Elapsed Time = 1.8139153000001897 seconds Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 26, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 29181 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 16001 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16134 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16048 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15988 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15991 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15993 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16005 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16048 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 Trial 26, Fold 4: Log loss = 0.3947772666877869, Average precision = 0.9661043514929211, ROC-AUC = 0.9597404331518798, Elapsed Time = 1.62656380000044 seconds Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 26, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 29189 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 15995 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16045 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15939 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15995 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15996 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 16041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 Trial 26, Fold 5: Log loss = 0.4027633484140435, Average precision = 0.9629441663634337, ROC-AUC = 0.9568721178678261, Elapsed Time = 1.5187667000000147 seconds
Optimization Progress: 27%|##7 | 27/100 [06:21<18:31, 15.22s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 27, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 11253 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7981 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7859 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7810 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 Trial 27, Fold 1: Log loss = 0.3770457259073332, Average precision = 0.9679646461912859, ROC-AUC = 0.9618513350587649, Elapsed Time = 0.6671121999997922 seconds Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 27, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 11266 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7928 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7996 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7825 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 Trial 27, Fold 2: Log loss = 0.3768335963671184, Average precision = 0.9652852768593705, ROC-AUC = 0.9619789853596772, Elapsed Time = 0.766985599999316 seconds Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 27, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 11269 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7987 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7825 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 Trial 27, Fold 3: Log loss = 0.3753375017459059, Average precision = 0.9697573184424777, ROC-AUC = 0.9644684029817684, Elapsed Time = 0.8073904000002585 seconds Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 27, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 11250 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7979 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7855 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7810 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 Trial 27, Fold 4: Log loss = 0.37479868622985846, Average precision = 0.9683889358539486, ROC-AUC = 0.9620702995534002, Elapsed Time = 0.7829996000000392 seconds Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 27, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 11253 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7978 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7853 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 Trial 27, Fold 5: Log loss = 0.3770404917952498, Average precision = 0.9671860255854553, ROC-AUC = 0.9620701930401501, Elapsed Time = 0.7725092999999106 seconds
Optimization Progress: 28%|##8 | 28/100 [06:33<17:04, 14.23s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 28, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 28226 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 3312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3310 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 Trial 28, Fold 1: Log loss = 0.36599374427258163, Average precision = 0.9603317174096473, ROC-AUC = 0.9534624091280596, Elapsed Time = 0.48313599999983126 seconds Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 28, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 28232 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 3319 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3319 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 Trial 28, Fold 2: Log loss = 0.3654086901441953, Average precision = 0.9566215972223977, ROC-AUC = 0.9521490673125862, Elapsed Time = 0.4869757999995272 seconds Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 28, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 28228 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 3314 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3314 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 Trial 28, Fold 3: Log loss = 0.36509507073811576, Average precision = 0.9613520695863139, ROC-AUC = 0.9549487267771667, Elapsed Time = 0.5294050000002244 seconds Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 28, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 28219 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 3312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 Trial 28, Fold 4: Log loss = 0.3631950589967323, Average precision = 0.9604381240848454, ROC-AUC = 0.9526484533525994, Elapsed Time = 0.5496585000000778 seconds Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 28, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 28223 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 3311 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3307 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3174 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 Trial 28, Fold 5: Log loss = 0.3715326470936202, Average precision = 0.9579557801661939, ROC-AUC = 0.9517082288241085, Elapsed Time = 0.5568551000005755 seconds
Optimization Progress: 29%|##9 | 29/100 [06:43<15:19, 12.95s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 29, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 23584 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 13595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13670 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13744 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13669 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 Trial 29, Fold 1: Log loss = 0.29683447983407174, Average precision = 0.9730766746375938, ROC-AUC = 0.9680085662188191, Elapsed Time = 1.040899100000388 seconds Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 29, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956 [LightGBM] [Info] Total Bins 23515 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 13622 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13691 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13694 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 13655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13686 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 Trial 29, Fold 2: Log loss = 0.2948225769523191, Average precision = 0.9701316593407077, ROC-AUC = 0.9676732451354972, Elapsed Time = 1.1777079000003141 seconds Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 29, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 23508 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 13606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13760 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13566 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13672 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 Trial 29, Fold 3: Log loss = 0.29240503741367013, Average precision = 0.9738813449367276, ROC-AUC = 0.9697626130442739, Elapsed Time = 1.215357199999744 seconds Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 29, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 23522 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 13590 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13665 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13741 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 17 [LightGBM] [Debug] Re-bagging, using 13524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 17 [LightGBM] [Debug] Re-bagging, using 13559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13651 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 Trial 29, Fold 4: Log loss = 0.2952701846010938, Average precision = 0.9724463513089439, ROC-AUC = 0.9672528141996373, Elapsed Time = 1.2341573000003336 seconds Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 29, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364 [LightGBM] [Info] Total Bins 23533 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 13587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13656 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13635 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 Trial 29, Fold 5: Log loss = 0.2996227692538168, Average precision = 0.9708768054126033, ROC-AUC = 0.9662428329896142, Elapsed Time = 1.2330916000000798 seconds
Optimization Progress: 30%|### | 30/100 [06:57<15:15, 13.08s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 30, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 8631 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7722 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7689 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7843 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7783 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7855 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 Trial 30, Fold 1: Log loss = 0.3174825538284151, Average precision = 0.9605865459743074, ROC-AUC = 0.9548121203213796, Elapsed Time = 0.9692813999999998 seconds Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 30, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 8643 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7705 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 Trial 30, Fold 2: Log loss = 0.31623480166398493, Average precision = 0.9587342409317563, ROC-AUC = 0.9555490523199048, Elapsed Time = 1.113342200000261 seconds Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 30, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 8652 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7783 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7728 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7704 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7859 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 Trial 30, Fold 3: Log loss = 0.3125646760074285, Average precision = 0.9619599029006753, ROC-AUC = 0.9580770073484797, Elapsed Time = 1.2622389000007388 seconds Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 30, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 8625 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7843 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7690 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7850 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 Trial 30, Fold 4: Log loss = 0.3106067431192909, Average precision = 0.9648280060412078, ROC-AUC = 0.9583181327210168, Elapsed Time = 1.1553637999995772 seconds Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 30, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 8630 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7770 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7717 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7684 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7835 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 7580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 7638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 Trial 30, Fold 5: Log loss = 0.3168421825858889, Average precision = 0.9579488880533888, ROC-AUC = 0.9524936922876838, Elapsed Time = 1.0095891000000847 seconds
Optimization Progress: 31%|###1 | 31/100 [07:10<14:57, 13.01s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 31, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 9440 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16552 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16487 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17 Trial 31, Fold 1: Log loss = 0.25510860932062523, Average precision = 0.9695418810095376, ROC-AUC = 0.9637145533712832, Elapsed Time = 1.2519100999998045 seconds Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 31, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 9431 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16621 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16557 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16657 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16665 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 Trial 31, Fold 2: Log loss = 0.2563946312758171, Average precision = 0.9671244359838015, ROC-AUC = 0.9639688540705282, Elapsed Time = 1.475114300000314 seconds Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 31, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 9438 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16617 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16592 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 Trial 31, Fold 3: Log loss = 0.24453248742690947, Average precision = 0.9713019505932408, ROC-AUC = 0.9668311667517409, Elapsed Time = 1.4896720999995523 seconds Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 31, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 9441 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16593 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16623 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16619 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16608 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16624 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 12 Trial 31, Fold 4: Log loss = 0.25129239627683203, Average precision = 0.9696580915958111, ROC-AUC = 0.9637685954157338, Elapsed Time = 1.4521058000000266 seconds Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 31, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 9420 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16500 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16480 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16513 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 Trial 31, Fold 5: Log loss = 0.25288434539497634, Average precision = 0.9680446458583952, ROC-AUC = 0.96308552105977, Elapsed Time = 1.429005899999538 seconds
Optimization Progress: 32%|###2 | 32/100 [07:24<15:15, 13.47s/it]
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 32, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24150 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 Trial 32, Fold 1: Log loss = 0.21780981303912247, Average precision = 0.9729802326851044, ROC-AUC = 0.9680215607270712, Elapsed Time = 1.631260299999667 seconds Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 32, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7418 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 32, Fold 2: Log loss = 0.2169047217816407, Average precision = 0.9718437056956949, ROC-AUC = 0.9688737977402326, Elapsed Time = 1.995632499999374 seconds Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 32, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24152 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 Trial 32, Fold 3: Log loss = 0.21615489727185902, Average precision = 0.9730476703738112, ROC-AUC = 0.9688875432192698, Elapsed Time = 1.7993200999999317 seconds Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 32, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24142 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 Trial 32, Fold 4: Log loss = 0.21562484342864127, Average precision = 0.9726778588701402, ROC-AUC = 0.9675838000815469, Elapsed Time = 1.8426258999998026 seconds Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 32, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24146 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 Trial 32, Fold 5: Log loss = 0.22144917844599368, Average precision = 0.9705417710797805, ROC-AUC = 0.9670558683090873, Elapsed Time = 1.8122117000002618 seconds
Optimization Progress: 33%|###3 | 33/100 [07:41<16:15, 14.56s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 33, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 12184 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5686 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 Trial 33, Fold 1: Log loss = 0.314628043679088, Average precision = 0.9658566787223346, ROC-AUC = 0.9591776776453832, Elapsed Time = 0.796326699999554 seconds Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 33, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 12205 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5812 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5809 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5704 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5896 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5735 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 Trial 33, Fold 2: Log loss = 0.3131834756667966, Average precision = 0.9634034482236395, ROC-AUC = 0.959467635384063, Elapsed Time = 0.8349615999995876 seconds Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 33, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 12209 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5839 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5888 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5837 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 Trial 33, Fold 3: Log loss = 0.31472151021701855, Average precision = 0.9662963901788351, ROC-AUC = 0.960705377006867, Elapsed Time = 0.8537070000002132 seconds Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 33, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 12187 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5688 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 Trial 33, Fold 4: Log loss = 0.31352811928269, Average precision = 0.9659387169422231, ROC-AUC = 0.9593025213941166, Elapsed Time = 1.0476693999999043 seconds Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 33, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 12189 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5681 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5871 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5838 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5733 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 Trial 33, Fold 5: Log loss = 0.3174828222684896, Average precision = 0.9638476806395812, ROC-AUC = 0.957613362317225, Elapsed Time = 1.1022882999996 seconds
Optimization Progress: 34%|###4 | 34/100 [07:54<15:23, 13.99s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 34, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 15448 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7590 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7479 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 Trial 34, Fold 1: Log loss = 0.2139692345325275, Average precision = 0.9721244320632372, ROC-AUC = 0.9677689893080589, Elapsed Time = 0.9208619000000908 seconds Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 34, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 15463 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7545 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7607 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7419 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7659 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7571 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7602 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 Trial 34, Fold 2: Log loss = 0.2093861632111387, Average precision = 0.971479734768855, ROC-AUC = 0.9680098975975593, Elapsed Time = 0.9724216999993587 seconds Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 34, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 15465 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7419 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7659 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7404 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 Trial 34, Fold 3: Log loss = 0.20776298384101446, Average precision = 0.9728767115684074, ROC-AUC = 0.9690121447351948, Elapsed Time = 0.9004064999999173 seconds Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 34, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 15448 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7462 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7533 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7600 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7501 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7392 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 Trial 34, Fold 4: Log loss = 0.21491292913763768, Average precision = 0.9721148252396932, ROC-AUC = 0.9669598298485185, Elapsed Time = 0.8922624000006181 seconds Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 34, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 15447 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7594 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7533 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7334 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7410 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7583 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7402 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13 Trial 34, Fold 5: Log loss = 0.213919976562517, Average precision = 0.9716793669447494, ROC-AUC = 0.9675710384036564, Elapsed Time = 0.8812234000006356 seconds
Optimization Progress: 35%|###5 | 35/100 [08:07<14:52, 13.72s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 35, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7358 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2130 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2139 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 Trial 35, Fold 1: Log loss = 0.4108622812425437, Average precision = 0.9573498878587887, ROC-AUC = 0.9509579790462808, Elapsed Time = 0.8165712999998505 seconds Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 35, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7374 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2251 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 Trial 35, Fold 2: Log loss = 0.41126862578961465, Average precision = 0.9547552990346806, ROC-AUC = 0.950380902116305, Elapsed Time = 0.8838040999999066 seconds Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 35, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7372 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2115 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2162 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2074 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2153 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2137 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 Trial 35, Fold 3: Log loss = 0.40891116404980826, Average precision = 0.9584163722175101, ROC-AUC = 0.9530043863345244, Elapsed Time = 0.9321852000002764 seconds Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 35, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 7355 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2132 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2069 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2144 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 Trial 35, Fold 4: Log loss = 0.41120225290133705, Average precision = 0.955421450053424, ROC-AUC = 0.9491549949808173, Elapsed Time = 0.9162712999996074 seconds Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 35, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7363 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2131 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2064 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2167 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 Trial 35, Fold 5: Log loss = 0.413217515676604, Average precision = 0.954621797764589, ROC-AUC = 0.9490674247069096, Elapsed Time = 0.9273424000002706 seconds
Optimization Progress: 36%|###6 | 36/100 [08:19<14:00, 13.13s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 36, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 11033 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 11682 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11729 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11741 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11502 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11592 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Debug] Re-bagging, using 11705 data to train [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 Trial 36, Fold 1: Log loss = 0.5434716286160565, Average precision = 0.9693808772361909, ROC-AUC = 0.9630761794831353, Elapsed Time = 1.2474445999996533 seconds Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 36, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 11045 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 11707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11746 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11764 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11720 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11 Trial 36, Fold 2: Log loss = 0.5449643510258457, Average precision = 0.9666909822952459, ROC-AUC = 0.9626037309924519, Elapsed Time = 1.2437085000001389 seconds Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 36, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 11051 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 11693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11752 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11593 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11715 data to train [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 Trial 36, Fold 3: Log loss = 0.5417627858781487, Average precision = 0.9701888548272047, ROC-AUC = 0.964835811920332, Elapsed Time = 1.0679775999997219 seconds Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 36, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 11032 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 11678 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Debug] Re-bagging, using 11499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11681 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 11589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11702 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 Trial 36, Fold 4: Log loss = 0.5420935465836577, Average precision = 0.9693905511387977, ROC-AUC = 0.963465030246238, Elapsed Time = 1.0401756999999634 seconds Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 36, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 11035 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 11675 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Re-bagging, using 11493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11675 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Debug] Re-bagging, using 11585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 11707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 Trial 36, Fold 5: Log loss = 0.5418271649599954, Average precision = 0.9683225385236497, ROC-AUC = 0.9626778143172994, Elapsed Time = 1.0533833999998024 seconds
Optimization Progress: 37%|###7 | 37/100 [08:32<13:49, 13.16s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 37, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 23907 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 17461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17476 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17479 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Debug] Re-bagging, using 17408 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 17358 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 Trial 37, Fold 1: Log loss = 0.45498393800634146, Average precision = 0.9655085446117008, ROC-AUC = 0.9593522820148838, Elapsed Time = 0.8728379999993194 seconds Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 37, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 23835 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 17495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Debug] Re-bagging, using 17506 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17453 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17444 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 17392 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 Trial 37, Fold 2: Log loss = 0.4584689056532689, Average precision = 0.9614054215271424, ROC-AUC = 0.9579318719212271, Elapsed Time = 0.9410419999994701 seconds Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 37, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 23830 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 17478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17494 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17437 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17414 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 Trial 37, Fold 3: Log loss = 0.45767112706573265, Average precision = 0.964278662255139, ROC-AUC = 0.9595124077828394, Elapsed Time = 0.9496364000005997 seconds Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 37, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 23819 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 17455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 10 [LightGBM] [Debug] Re-bagging, using 17475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17401 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17354 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 Trial 37, Fold 4: Log loss = 0.45301620955825905, Average precision = 0.9650677575600484, ROC-AUC = 0.9586500127565922, Elapsed Time = 1.0068817000001218 seconds Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 37, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 23826 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 17449 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Debug] Re-bagging, using 17467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Debug] Re-bagging, using 17405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Debug] Re-bagging, using 17398 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 17395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 17356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 Trial 37, Fold 5: Log loss = 0.4544535693194063, Average precision = 0.9638041944638919, ROC-AUC = 0.9578615833379782, Elapsed Time = 0.9900883999998769 seconds
Optimization Progress: 38%|###8 | 38/100 [08:44<13:16, 12.84s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 38, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798290 [LightGBM] [Info] Total Bins 17915 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14894 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15018 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15035 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 Trial 38, Fold 1: Log loss = 0.28412838776738836, Average precision = 0.9727748937905127, ROC-AUC = 0.9688282657738395, Elapsed Time = 0.6541177000008247 seconds Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 38, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798259 [LightGBM] [Info] Total Bins 17930 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14922 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15044 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15065 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 10 Trial 38, Fold 2: Log loss = 0.2830484800282979, Average precision = 0.9732954845031008, ROC-AUC = 0.9701862643367514, Elapsed Time = 0.719259399999828 seconds Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 38, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798104 [LightGBM] [Info] Total Bins 17934 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15032 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15051 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 Trial 38, Fold 3: Log loss = 0.2825482226615492, Average precision = 0.9733412577409921, ROC-AUC = 0.9699044450838382, Elapsed Time = 0.7281477999995332 seconds Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 38, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797795 [LightGBM] [Info] Total Bins 17917 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15011 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15030 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 Trial 38, Fold 4: Log loss = 0.28471134543479953, Average precision = 0.9735303764738737, ROC-AUC = 0.9686139396172069, Elapsed Time = 0.7371172000002844 seconds Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 38, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646 [LightGBM] [Info] Total Bins 17916 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14884 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15009 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15028 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 Trial 38, Fold 5: Log loss = 0.2870246817623127, Average precision = 0.9718168184861367, ROC-AUC = 0.9678533712353454, Elapsed Time = 0.7391860000007 seconds
Optimization Progress: 39%|###9 | 39/100 [08:55<12:27, 12.25s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 39, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 20107 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6705 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6569 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6766 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6651 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6767 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6667 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6647 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6735 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6733 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6620 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6812 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6689 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6614 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6691 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6635 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6723 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 Trial 39, Fold 1: Log loss = 0.4118523317409845, Average precision = 0.9640110933053028, ROC-AUC = 0.9570288041945675, Elapsed Time = 1.676568099999713 seconds Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 39, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 20073 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6666 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6515 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6678 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6821 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6660 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6751 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6742 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6671 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6820 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6713 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6622 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6669 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6734 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6690 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6636 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6612 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 Trial 39, Fold 2: Log loss = 0.4136268071513568, Average precision = 0.9608336604594377, ROC-AUC = 0.9558583901805829, Elapsed Time = 1.9231171000001268 seconds Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 39, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 20072 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6711 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6705 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6636 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6582 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6768 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6689 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6632 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6751 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6614 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6719 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6659 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6719 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6647 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6690 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6688 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6810 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6620 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6641 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6592 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 Trial 39, Fold 3: Log loss = 0.4062084001497198, Average precision = 0.9655958077729394, ROC-AUC = 0.9596640213423737, Elapsed Time = 1.8035933000001023 seconds Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 39, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 20105 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6702 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6695 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6571 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6761 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6706 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6652 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6762 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6635 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6497 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6596 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6664 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6628 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6726 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6557 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6611 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6660 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6703 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6672 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6623 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6691 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6650 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6666 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6614 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 Trial 39, Fold 4: Log loss = 0.4129772817691646, Average precision = 0.9637340592536143, ROC-AUC = 0.9571041700942555, Elapsed Time = 1.6821828000001915 seconds Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 39, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 20102 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6700 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6695 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6566 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6760 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6660 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6571 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6678 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6614 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6728 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6729 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6565 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6602 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6664 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6700 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6667 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6710 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6671 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6665 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6720 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6596 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 Trial 39, Fold 5: Log loss = 0.4146718211969893, Average precision = 0.9620377715252689, ROC-AUC = 0.9551693513925275, Elapsed Time = 1.58937909999986 seconds
Optimization Progress: 40%|#### | 40/100 [09:11<13:26, 13.44s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 40, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295 [LightGBM] [Info] Total Bins 27148 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 18199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 18129 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18129 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 Trial 40, Fold 1: Log loss = 0.31388538222959717, Average precision = 0.9656748092626154, ROC-AUC = 0.9587817185491079, Elapsed Time = 1.5820659000000887 seconds Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 40, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 27157 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 18235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18276 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 18191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18166 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 Trial 40, Fold 2: Log loss = 0.31499227675900127, Average precision = 0.9634456488372517, ROC-AUC = 0.9585552518239325, Elapsed Time = 1.7733633999996528 seconds Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 40, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045 [LightGBM] [Info] Total Bins 27151 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 18218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 18158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 Trial 40, Fold 3: Log loss = 0.311584660495534, Average precision = 0.9669552582564993, ROC-AUC = 0.9613503553851933, Elapsed Time = 1.7890182999999524 seconds Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 40, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 27145 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 18192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 18238 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18125 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 Trial 40, Fold 4: Log loss = 0.3075476142307136, Average precision = 0.9664963265197322, ROC-AUC = 0.9599498141352582, Elapsed Time = 1.762109899999814 seconds Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 40, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 27151 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 18186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 18203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18233 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18117 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 Trial 40, Fold 5: Log loss = 0.3169454622000901, Average precision = 0.9636389789724216, ROC-AUC = 0.9571915352172864, Elapsed Time = 1.74589079999987 seconds
Optimization Progress: 41%|####1 | 41/100 [09:28<14:13, 14.47s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 41, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 25876 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5098 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5301 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5062 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5222 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5266 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5121 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5228 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5258 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5223 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5303 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5137 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5300 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5217 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5213 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5097 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 Trial 41, Fold 1: Log loss = 0.27293726745619906, Average precision = 0.9709746855113583, ROC-AUC = 0.9652606011289389, Elapsed Time = 1.5060511000001497 seconds Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 41, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 25878 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5112 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5322 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5311 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5085 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5251 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5307 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5251 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5286 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5300 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5131 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5217 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5301 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5233 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5249 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5213 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5310 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 Trial 41, Fold 2: Log loss = 0.2702378761409877, Average precision = 0.9704559815636802, ROC-AUC = 0.96690608420606, Elapsed Time = 1.9325926999999865 seconds Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 41, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 25877 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5266 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5108 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5313 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5301 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5078 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5152 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5273 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5129 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5223 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5261 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5233 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5131 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5220 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5098 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 Trial 41, Fold 3: Log loss = 0.2688744270662882, Average precision = 0.9722850054993755, ROC-AUC = 0.967731668528667, Elapsed Time = 1.771677599999748 seconds Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 41, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 25866 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5291 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5092 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5238 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5180 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5284 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5219 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5258 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5267 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5122 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 Trial 41, Fold 4: Log loss = 0.2683222397204066, Average precision = 0.9710829752471616, ROC-AUC = 0.9657162111251791, Elapsed Time = 1.8824927999994543 seconds Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 41, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 25873 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5289 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5095 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5087 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5140 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5261 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5289 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5122 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5222 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5140 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5124 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5096 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 Trial 41, Fold 5: Log loss = 0.276624281389057, Average precision = 0.9678688271291056, ROC-AUC = 0.9640171673819742, Elapsed Time = 1.8289052000000083 seconds
Optimization Progress: 42%|####2 | 42/100 [09:45<14:43, 15.23s/it]
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 42, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817 [LightGBM] [Info] Total Bins 16928 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5608 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5567 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5575 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5431 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Re-bagging, using 5645 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5586 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5492 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5644 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 Trial 42, Fold 1: Log loss = 0.22486720866549162, Average precision = 0.9744573034457623, ROC-AUC = 0.9691979371292831, Elapsed Time = 1.8659290999994482 seconds Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 42, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987 [LightGBM] [Info] Total Bins 16941 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5618 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5448 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5664 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5499 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5655 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 Trial 42, Fold 2: Log loss = 0.22524210099664396, Average precision = 0.9739922954990727, ROC-AUC = 0.9709663238279531, Elapsed Time = 2.416602799999964 seconds Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 42, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893 [LightGBM] [Info] Total Bins 16947 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5613 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5571 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5578 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5442 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5655 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5501 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5646 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 Trial 42, Fold 3: Log loss = 0.22862562686622087, Average precision = 0.9740826150240826, ROC-AUC = 0.9698103920072041, Elapsed Time = 2.305730500000209 seconds Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 42, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805384 [LightGBM] [Info] Total Bins 16929 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5605 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5565 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5572 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Re-bagging, using 5432 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5642 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5583 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5492 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 29 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5642 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 Trial 42, Fold 4: Log loss = 0.2264667894282577, Average precision = 0.9738043014455474, ROC-AUC = 0.9688653458228716, Elapsed Time = 2.3237183999999615 seconds Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 42, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356 [LightGBM] [Info] Total Bins 16928 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5603 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5565 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5572 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5427 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5640 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22 [LightGBM] [Debug] Re-bagging, using 5579 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5497 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5637 data to train [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 Trial 42, Fold 5: Log loss = 0.22583290083240043, Average precision = 0.9723513066476789, ROC-AUC = 0.9693319956152575, Elapsed Time = 1.9339842999997927 seconds
Optimization Progress: 43%|####3 | 43/100 [10:03<15:17, 16.10s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 43, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295 [LightGBM] [Info] Total Bins 20104 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15858 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Re-bagging, using 15771 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15898 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 15815 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 Trial 43, Fold 1: Log loss = 0.5212313297273229, Average precision = 0.9627836292091663, ROC-AUC = 0.9549571509824445, Elapsed Time = 0.6328744000002189 seconds Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 43, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 20073 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 15833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15887 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15847 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15899 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15815 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 Trial 43, Fold 2: Log loss = 0.5209099033618703, Average precision = 0.9621351418743129, ROC-AUC = 0.9574910446044405, Elapsed Time = 0.6737821000006079 seconds Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 43, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045 [LightGBM] [Info] Total Bins 20069 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 15816 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15959 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15871 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15814 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 5 [LightGBM] [Debug] Re-bagging, using 15909 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15830 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 Trial 43, Fold 3: Log loss = 0.5124468728151358, Average precision = 0.9650361000889902, ROC-AUC = 0.9592594426279242, Elapsed Time = 0.6866634000007252 seconds Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 43, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 20105 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 15795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15937 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15854 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15753 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15796 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15861 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15895 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 Trial 43, Fold 4: Log loss = 0.521958522934716, Average precision = 0.9634605752827095, ROC-AUC = 0.9565819843535032, Elapsed Time = 0.690412699999797 seconds Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 43, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 20102 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 15789 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15777 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15791 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15856 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15777 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15690 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15778 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 Trial 43, Fold 5: Log loss = 0.5191817461829943, Average precision = 0.9611701754237488, ROC-AUC = 0.9542330230570574, Elapsed Time = 0.7347669999999198 seconds
Optimization Progress: 44%|####4 | 44/100 [10:14<13:28, 14.45s/it]
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 44, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 12968 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 3425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3435 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3412 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3398 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3367 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3388 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 Trial 44, Fold 1: Log loss = 0.48721062203998294, Average precision = 0.9535683851934116, ROC-AUC = 0.9451276269817372, Elapsed Time = 0.7687642999999298 seconds Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 44, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 12982 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 3432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3444 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3318 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3394 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3377 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3365 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3224 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3370 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3383 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3385 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 Trial 44, Fold 2: Log loss = 0.4897891343912608, Average precision = 0.9531068724675854, ROC-AUC = 0.9482266905537914, Elapsed Time = 0.8361282999994728 seconds Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 44, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 12988 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 3427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3439 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3417 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3396 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3375 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3367 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3389 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 Trial 44, Fold 3: Log loss = 0.48084034590108415, Average precision = 0.961348036882991, ROC-AUC = 0.9536307798249077, Elapsed Time = 0.8631216000003405 seconds Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 44, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 12968 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 3425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3335 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3399 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3222 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3385 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3378 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3354 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 Trial 44, Fold 4: Log loss = 0.4804119373286586, Average precision = 0.9578869844868307, ROC-AUC = 0.9496273316547088, Elapsed Time = 0.8647602999999435 seconds Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 44, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 12970 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 3423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3336 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3307 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3404 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3397 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3354 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Debug] Re-bagging, using 3219 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3284 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3384 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 Trial 44, Fold 5: Log loss = 0.48515789446581037, Average precision = 0.9568248681429414, ROC-AUC = 0.9481767274213626, Elapsed Time = 0.8975406999998086 seconds
Optimization Progress: 45%|####5 | 45/100 [10:26<12:38, 13.78s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 45, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 14564 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9576 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9620 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 Trial 45, Fold 1: Log loss = 0.3353843979777529, Average precision = 0.9716630024206406, ROC-AUC = 0.9657538696749401, Elapsed Time = 0.9208911999994598 seconds Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 45, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 14576 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9621 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9596 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9647 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6 [LightGBM] [Debug] Re-bagging, using 9496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 45, Fold 2: Log loss = 0.3353204673392179, Average precision = 0.9697656759149734, ROC-AUC = 0.9668807423258869, Elapsed Time = 0.9826597999999649 seconds Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 45, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 14580 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9612 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9545 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 Trial 45, Fold 3: Log loss = 0.3302802993933815, Average precision = 0.9730979632333543, ROC-AUC = 0.9685356342277355, Elapsed Time = 1.0227054999995744 seconds Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 45, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 14561 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9484 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 Trial 45, Fold 4: Log loss = 0.33213599249562337, Average precision = 0.9722569957339893, ROC-AUC = 0.9670891840788189, Elapsed Time = 1.1327862000007372 seconds Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 45, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340 [LightGBM] [Info] Total Bins 14563 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9569 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9611 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 Trial 45, Fold 5: Log loss = 0.3349875746116728, Average precision = 0.9701929619761038, ROC-AUC = 0.9653170577634097, Elapsed Time = 1.2616589999997814 seconds
Optimization Progress: 46%|####6 | 46/100 [10:39<12:05, 13.44s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 46, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 15571 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 17830 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20 [LightGBM] [Debug] Re-bagging, using 17848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 164 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 18 Trial 46, Fold 1: Log loss = 0.5045576873189126, Average precision = 0.9703136294974727, ROC-AUC = 0.9661611056145836, Elapsed Time = 0.6460338999995656 seconds Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 46, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949 [LightGBM] [Info] Total Bins 15586 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 17866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 157 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 175 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 155 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 20 [LightGBM] [Debug] Re-bagging, using 17876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19 Trial 46, Fold 2: Log loss = 0.501208742468372, Average precision = 0.9713255478845191, ROC-AUC = 0.9676740797483173, Elapsed Time = 0.6990193999999974 seconds Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 46, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784 [LightGBM] [Info] Total Bins 15588 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 17849 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 146 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 150 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 170 and depth = 20 [LightGBM] [Debug] Re-bagging, using 17860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 169 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 20 Trial 46, Fold 3: Log loss = 0.4985493422929144, Average precision = 0.9706569359296852, ROC-AUC = 0.9679967477198524, Elapsed Time = 0.7081786000007924 seconds Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 46, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 15571 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 17823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 18 [LightGBM] [Debug] Re-bagging, using 17842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 162 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19 Trial 46, Fold 4: Log loss = 0.5015279726399929, Average precision = 0.9721892005210573, ROC-AUC = 0.9666833128274094, Elapsed Time = 0.725632300000143 seconds Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 46, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646 [LightGBM] [Info] Total Bins 15573 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 17817 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 152 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 156 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 18 [LightGBM] [Debug] Re-bagging, using 17839 data to train [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 18 Trial 46, Fold 5: Log loss = 0.5064286734003713, Average precision = 0.9674327831857938, ROC-AUC = 0.9628119577132452, Elapsed Time = 0.7206710000000385 seconds
Optimization Progress: 47%|####6 | 47/100 [10:50<11:18, 12.80s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 47, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794337 [LightGBM] [Info] Total Bins 22776 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10223 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10261 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10317 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 Trial 47, Fold 1: Log loss = 0.3166572862889005, Average precision = 0.9661894137719051, ROC-AUC = 0.959624942644929, Elapsed Time = 1.3378720000000612 seconds Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 47, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 22787 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10449 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10336 data to train [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 Trial 47, Fold 2: Log loss = 0.3114679041777061, Average precision = 0.9638589230571969, ROC-AUC = 0.9598386587195339, Elapsed Time = 1.3030173999995895 seconds Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 47, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 22678 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10437 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10379 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10244 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10443 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10258 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10323 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 Trial 47, Fold 3: Log loss = 0.307874197146473, Average precision = 0.9678081239560853, ROC-AUC = 0.9624470895012085, Elapsed Time = 1.2034134999994421 seconds Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 47, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 22770 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10361 data to train [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10223 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10316 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 9 Trial 47, Fold 4: Log loss = 0.31159670408281126, Average precision = 0.9665855502745488, ROC-AUC = 0.9600392295018252, Elapsed Time = 1.1818234000002121 seconds Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 47, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794703 [LightGBM] [Info] Total Bins 22726 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10365 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10311 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 Trial 47, Fold 5: Log loss = 0.3116467143025362, Average precision = 0.9643136124715688, ROC-AUC = 0.9583216097207514, Elapsed Time = 1.2777191000004677 seconds
Optimization Progress: 48%|####8 | 48/100 [11:04<11:26, 13.20s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 48, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 25983 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5347 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5294 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5327 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5355 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 Trial 48, Fold 1: Log loss = 0.26771342615351423, Average precision = 0.9693245107568664, ROC-AUC = 0.9633420441347271, Elapsed Time = 0.9700815000005605 seconds Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 48, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 25985 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5357 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5305 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5333 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5174 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5376 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 Trial 48, Fold 2: Log loss = 0.26531075075348565, Average precision = 0.966748909372579, ROC-AUC = 0.9628439477368487, Elapsed Time = 1.041997000000265 seconds Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 48, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 25984 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5352 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5170 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5366 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5325 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 Trial 48, Fold 3: Log loss = 0.2653291047390906, Average precision = 0.9690770258886212, ROC-AUC = 0.9640110793019425, Elapsed Time = 0.9497467000001052 seconds Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 48, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 25973 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5344 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5162 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5351 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5328 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 Trial 48, Fold 4: Log loss = 0.2724567192845021, Average precision = 0.9676774602201403, ROC-AUC = 0.9611384424081586, Elapsed Time = 0.9449987999996665 seconds Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 48, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 25979 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5342 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5349 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 Trial 48, Fold 5: Log loss = 0.28253181094529456, Average precision = 0.9636006730319187, ROC-AUC = 0.9580317708043029, Elapsed Time = 0.9368782000001374 seconds
Optimization Progress: 49%|####9 | 49/100 [11:17<11:16, 13.26s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 49, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24796 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 12453 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12317 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 Trial 49, Fold 1: Log loss = 0.3063735045663137, Average precision = 0.9679374832296199, ROC-AUC = 0.961229092284309, Elapsed Time = 0.7717340999997759 seconds Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 49, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24803 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 12564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 Trial 49, Fold 2: Log loss = 0.307872180921878, Average precision = 0.9646038770080863, ROC-AUC = 0.9606868529664113, Elapsed Time = 0.8407884999996895 seconds Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 49, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24796 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 12464 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 Trial 49, Fold 3: Log loss = 0.29787310753822754, Average precision = 0.9689564130818498, ROC-AUC = 0.9635157581791565, Elapsed Time = 0.9720113000003039 seconds Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 49, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24786 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 12449 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Debug] Re-bagging, using 12314 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 Trial 49, Fold 4: Log loss = 0.31610381071939087, Average precision = 0.9654936821814473, ROC-AUC = 0.9590535741110323, Elapsed Time = 0.9608648000003086 seconds Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 49, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24790 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 12446 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 Trial 49, Fold 5: Log loss = 0.31440501143902616, Average precision = 0.9644052616094297, ROC-AUC = 0.958472028686621, Elapsed Time = 0.9044985000000452 seconds
Optimization Progress: 50%|##### | 50/100 [11:32<11:18, 13.58s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 50, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 15120 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 Trial 50, Fold 1: Log loss = 0.2982694639889566, Average precision = 0.9688332355090242, ROC-AUC = 0.964010439817802, Elapsed Time = 0.5601753000000826 seconds Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 50, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 15133 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 Trial 50, Fold 2: Log loss = 0.3062531891195165, Average precision = 0.9664051336574846, ROC-AUC = 0.9624566873883326, Elapsed Time = 0.5621974999994563 seconds Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 50, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 15138 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 Trial 50, Fold 3: Log loss = 0.3072467004888748, Average precision = 0.9687241074849424, ROC-AUC = 0.9633100452899424, Elapsed Time = 0.5728681999999026 seconds Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 50, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 15120 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14375 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14514 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 Trial 50, Fold 4: Log loss = 0.3113197561966934, Average precision = 0.9667452770254485, ROC-AUC = 0.9607278023371986, Elapsed Time = 0.5734934000001886 seconds Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 50, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 15119 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14512 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 Trial 50, Fold 5: Log loss = 0.32099173466049086, Average precision = 0.9641892476702298, ROC-AUC = 0.9583259201456626, Elapsed Time = 0.5571890000001076 seconds
Optimization Progress: 51%|#####1 | 51/100 [11:43<10:28, 12.83s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 51, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24796 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 12453 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12317 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12486 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12428 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12410 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 Trial 51, Fold 1: Log loss = 0.40943127585042277, Average precision = 0.9668415201170047, ROC-AUC = 0.96053000267657, Elapsed Time = 1.668200600000091 seconds Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 51, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24803 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12443 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12497 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 Trial 51, Fold 2: Log loss = 0.40561261665555776, Average precision = 0.9647727106963009, ROC-AUC = 0.9609372368124345, Elapsed Time = 1.935546699999577 seconds Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 51, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24796 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 12464 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12438 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12476 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12367 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 Trial 51, Fold 3: Log loss = 0.40089889042129667, Average precision = 0.9691804405867582, ROC-AUC = 0.9640356083443289, Elapsed Time = 1.9567003000001932 seconds Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 51, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24786 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 12449 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12314 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12479 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Debug] Re-bagging, using 12459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 12410 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 Trial 51, Fold 4: Log loss = 0.4101429593770919, Average precision = 0.9659798685954358, ROC-AUC = 0.9598668068699614, Elapsed Time = 1.9267745999995896 seconds Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 51, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24790 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 12446 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Debug] Re-bagging, using 12426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Debug] Re-bagging, using 12402 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 Trial 51, Fold 5: Log loss = 0.4074947757547993, Average precision = 0.9650135359660748, ROC-AUC = 0.958990617394051, Elapsed Time = 1.938223399999515 seconds
Optimization Progress: 52%|#####2 | 52/100 [12:00<11:15, 14.07s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 52, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 25876 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5347 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5294 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5327 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5355 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5370 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5261 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5126 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5207 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5205 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5307 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5363 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5283 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5288 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5364 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5265 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5286 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5350 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 Trial 52, Fold 1: Log loss = 0.21649941651454954, Average precision = 0.972431254470847, ROC-AUC = 0.966919716044603, Elapsed Time = 0.8327139999992141 seconds Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 52, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 25878 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5357 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5305 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5333 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5174 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5376 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5379 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5277 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5149 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5315 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5368 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5300 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5300 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5226 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5353 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5367 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5197 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5271 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5298 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5247 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5356 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 Trial 52, Fold 2: Log loss = 0.21555834957559505, Average precision = 0.9703827467001086, ROC-AUC = 0.9668916681664406, Elapsed Time = 0.844282199999725 seconds Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 52, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 25877 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5352 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5170 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5366 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5325 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5243 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5369 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5265 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5142 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5168 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5195 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5316 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5356 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5296 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5337 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5381 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5196 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5256 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15 [LightGBM] [Debug] Re-bagging, using 5280 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5254 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5363 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15 Trial 52, Fold 3: Log loss = 0.21758157949179519, Average precision = 0.9725401558795804, ROC-AUC = 0.9687837086226657, Elapsed Time = 0.8842640000002575 seconds Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 52, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 25866 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5344 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5162 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5351 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5328 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5235 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5366 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5130 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5153 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5305 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5357 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5287 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5282 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5320 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5365 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5185 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5284 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5245 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5339 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15 Trial 52, Fold 4: Log loss = 0.2224244821480468, Average precision = 0.9709196358797433, ROC-AUC = 0.9655868568948784, Elapsed Time = 1.0510160999992877 seconds Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 52, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 25873 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5342 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5349 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5240 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5361 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5130 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5148 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5195 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5214 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5311 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5351 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5282 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5200 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5325 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5356 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5187 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5250 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5278 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5251 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5342 data to train [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 Trial 52, Fold 5: Log loss = 0.22496569853134174, Average precision = 0.9695400144057988, ROC-AUC = 0.9648006242684355, Elapsed Time = 1.025408299999981 seconds
Optimization Progress: 53%|#####3 | 53/100 [12:12<10:34, 13.51s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 53, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817 [LightGBM] [Info] Total Bins 16928 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5608 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 145 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 153 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 168 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 168 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 173 and depth = 22 [LightGBM] [Debug] Re-bagging, using 5567 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5575 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 28 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 29 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 27 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 23 [LightGBM] [Debug] Re-bagging, using 5645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 210 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 26 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 23 [LightGBM] [Debug] Re-bagging, using 5492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 175 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 16 [LightGBM] [Debug] Re-bagging, using 5644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 17 Trial 53, Fold 1: Log loss = 0.3327160762671582, Average precision = 0.9716258069958176, ROC-AUC = 0.9672362891508104, Elapsed Time = 2.8530601000002207 seconds Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 53, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987 [LightGBM] [Info] Total Bins 16941 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 140 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 144 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 164 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 157 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 170 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 167 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 21 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 21 [LightGBM] [Debug] Re-bagging, using 5448 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 180 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21 [LightGBM] [Debug] Re-bagging, using 5664 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 23 [LightGBM] [Debug] Re-bagging, using 5499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 21 [LightGBM] [Debug] Re-bagging, using 5655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 18 Trial 53, Fold 2: Log loss = 0.32865200527720656, Average precision = 0.9737266722440454, ROC-AUC = 0.9695671333720391, Elapsed Time = 3.666922400000658 seconds Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 53, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893 [LightGBM] [Info] Total Bins 16947 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 147 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 146 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 166 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 169 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 23 [LightGBM] [Debug] Re-bagging, using 5571 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5578 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5442 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 180 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5501 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 19 Trial 53, Fold 3: Log loss = 0.32894213973091035, Average precision = 0.9721211712269816, ROC-AUC = 0.9693382455625006, Elapsed Time = 3.4834272999996756 seconds Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 53, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805384 [LightGBM] [Info] Total Bins 16929 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 144 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 144 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 152 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 163 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 171 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 166 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5565 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 25 [LightGBM] [Debug] Re-bagging, using 5572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 17 [LightGBM] [Debug] Re-bagging, using 5432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 205 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21 [LightGBM] [Debug] Re-bagging, using 5642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 205 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 207 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 207 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 205 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5583 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 175 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 171 and depth = 16 Trial 53, Fold 4: Log loss = 0.32957811689794386, Average precision = 0.9740590844027639, ROC-AUC = 0.9695006420023321, Elapsed Time = 4.050504700000602 seconds Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 53, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356 [LightGBM] [Info] Total Bins 16928 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 141 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 150 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 156 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 156 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 162 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5565 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 26 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 20 [LightGBM] [Debug] Re-bagging, using 5579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 207 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19 [LightGBM] [Debug] Re-bagging, using 5497 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 169 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 18 [LightGBM] [Debug] Re-bagging, using 5637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 180 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 170 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 171 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 23 Trial 53, Fold 5: Log loss = 0.3317355849042862, Average precision = 0.9704764770963913, ROC-AUC = 0.9674112554112553, Elapsed Time = 3.419964799999434 seconds
Optimization Progress: 54%|#####4 | 54/100 [12:38<13:16, 17.32s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 54, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7940 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5327 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5355 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 Trial 54, Fold 1: Log loss = 0.2752874065988546, Average precision = 0.9677937124264382, ROC-AUC = 0.9611638956998035, Elapsed Time = 0.7653597999997146 seconds Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 54, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7956 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5174 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 Trial 54, Fold 2: Log loss = 0.27866620105733475, Average precision = 0.9643420683093281, ROC-AUC = 0.9599818327551237, Elapsed Time = 0.8094517999998061 seconds Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 54, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7963 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11 Trial 54, Fold 3: Log loss = 0.2700365962158037, Average precision = 0.9682625609104769, ROC-AUC = 0.9626263170440428, Elapsed Time = 0.8366236999991088 seconds Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 54, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 7949 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5344 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5162 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 54, Fold 4: Log loss = 0.27234685971359907, Average precision = 0.9677491230315155, ROC-AUC = 0.961371816514899, Elapsed Time = 0.8441063999998732 seconds Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 54, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7944 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5342 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 Trial 54, Fold 5: Log loss = 0.27751199575788493, Average precision = 0.9634017326562361, ROC-AUC = 0.9582249967486018, Elapsed Time = 0.8501927999996042 seconds
Optimization Progress: 55%|#####5 | 55/100 [12:50<11:43, 15.64s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 55, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 15120 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 Trial 55, Fold 1: Log loss = 0.319710227830236, Average precision = 0.9637692004130884, ROC-AUC = 0.9577537334566466, Elapsed Time = 0.46974260000024515 seconds Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 55, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 15136 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 5812 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5809 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 Trial 55, Fold 2: Log loss = 0.31212313747011217, Average precision = 0.9633393334570692, ROC-AUC = 0.958773920382793, Elapsed Time = 0.5101971000003687 seconds Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 55, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 15138 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5839 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 5807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 Trial 55, Fold 3: Log loss = 0.31374128541596613, Average precision = 0.9651192620136533, ROC-AUC = 0.9601149494129884, Elapsed Time = 0.5250800000003437 seconds Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 55, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 15120 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 Trial 55, Fold 4: Log loss = 0.3170786943123466, Average precision = 0.9644478627146373, ROC-AUC = 0.9579533180254225, Elapsed Time = 0.5307753999995839 seconds Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 55, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 15119 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 Trial 55, Fold 5: Log loss = 0.31820035782452255, Average precision = 0.9625577655712161, ROC-AUC = 0.9560946807127064, Elapsed Time = 0.5251232000000527 seconds
Optimization Progress: 56%|#####6 | 56/100 [13:00<10:12, 13.92s/it]
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 56, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7940 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7590 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7479 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 Trial 56, Fold 1: Log loss = 0.20832250070227745, Average precision = 0.9728527881784961, ROC-AUC = 0.9678535282927785, Elapsed Time = 0.7831826000001456 seconds Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 56, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7956 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7545 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7607 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7419 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7659 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7571 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7602 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 56, Fold 2: Log loss = 0.20397070967147965, Average precision = 0.9727620209508783, ROC-AUC = 0.9690584747951481, Elapsed Time = 0.8534793999997419 seconds Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 56, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7963 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Debug] Re-bagging, using 7597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7419 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7659 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7404 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 Trial 56, Fold 3: Log loss = 0.2031745484476355, Average precision = 0.9729414594836683, ROC-AUC = 0.9691502898941549, Elapsed Time = 0.8719132999995054 seconds Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 56, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 7946 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7462 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7533 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7600 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7535 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7501 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7392 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 Trial 56, Fold 4: Log loss = 0.2059442149093668, Average precision = 0.9734037759849372, ROC-AUC = 0.9688341249573786, Elapsed Time = 0.9462315000000672 seconds Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 56, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7944 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7594 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7533 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7334 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7410 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7583 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7402 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 Trial 56, Fold 5: Log loss = 0.21099260244594162, Average precision = 0.9715680379175089, ROC-AUC = 0.9680349293053157, Elapsed Time = 0.9077146999998149 seconds
Optimization Progress: 57%|#####6 | 57/100 [13:12<09:31, 13.30s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 57, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7358 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2130 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2139 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2249 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 Trial 57, Fold 1: Log loss = 0.37892899961134713, Average precision = 0.9575913146504536, ROC-AUC = 0.9511021882154448, Elapsed Time = 0.9399035000005824 seconds Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 57, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7374 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2251 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2253 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2115 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2226 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2229 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 Trial 57, Fold 2: Log loss = 0.3760319081535994, Average precision = 0.9562582062183459, ROC-AUC = 0.9522469446342134, Elapsed Time = 1.015178399999968 seconds Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 57, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7372 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2115 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2197 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2162 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2074 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2153 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2137 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2253 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2109 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 Trial 57, Fold 3: Log loss = 0.3728635305151177, Average precision = 0.9598185775067866, ROC-AUC = 0.9544234591547803, Elapsed Time = 1.0134969999999157 seconds Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 57, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 7355 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2132 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2069 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2144 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2108 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 Trial 57, Fold 4: Log loss = 0.3768339494526506, Average precision = 0.9570589877268126, ROC-AUC = 0.9501171788378864, Elapsed Time = 1.086921899999652 seconds Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 57, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7363 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2241 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2131 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2064 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2167 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 Trial 57, Fold 5: Log loss = 0.37952485796871727, Average precision = 0.9561166398751471, ROC-AUC = 0.9498930940304331, Elapsed Time = 1.0671511999998984 seconds
Optimization Progress: 58%|#####8 | 58/100 [13:24<09:08, 13.05s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 58, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5098 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5301 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5062 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 Trial 58, Fold 1: Log loss = 0.4291124910075804, Average precision = 0.9672170501464812, ROC-AUC = 0.9607728655549341, Elapsed Time = 1.0771875000000364 seconds Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 58, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 11158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5112 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5322 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5311 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5085 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 Trial 58, Fold 2: Log loss = 0.43217372256804676, Average precision = 0.9640188943857604, ROC-AUC = 0.9597874438419383, Elapsed Time = 1.1487709999992148 seconds Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 58, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 11164 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5266 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5108 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5313 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5301 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5078 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 Trial 58, Fold 3: Log loss = 0.4258938337035939, Average precision = 0.9683770395765154, ROC-AUC = 0.9628013310090435, Elapsed Time = 1.3509405000004335 seconds Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 58, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 11145 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5291 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 Trial 58, Fold 4: Log loss = 0.4284234880137364, Average precision = 0.96641092870218, ROC-AUC = 0.9596838034197204, Elapsed Time = 1.3038344999995388 seconds Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 58, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5289 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5095 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5066 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 Trial 58, Fold 5: Log loss = 0.4294260241321941, Average precision = 0.9645658906559297, ROC-AUC = 0.9585099306987719, Elapsed Time = 1.2730691999995543 seconds
Optimization Progress: 59%|#####8 | 59/100 [13:38<09:03, 13.26s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 59, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7940 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6760 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6778 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6679 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6628 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6700 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6818 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6697 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6717 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6703 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6784 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6612 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6719 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6867 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6756 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 Trial 59, Fold 1: Log loss = 0.22675598811748662, Average precision = 0.9723914444623574, ROC-AUC = 0.9673419628817099, Elapsed Time = 0.8552437999996982 seconds Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 59, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7956 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6835 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6744 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6713 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6826 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6566 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6729 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6697 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6626 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6734 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6764 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 Trial 59, Fold 2: Log loss = 0.2252146869930471, Average precision = 0.9707824030321003, ROC-AUC = 0.9679105027980773, Elapsed Time = 0.9205776999997397 seconds Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 59, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7963 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6766 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6681 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6641 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6710 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6820 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6640 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6627 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6673 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6767 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6888 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6758 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 59, Fold 3: Log loss = 0.2235571018816946, Average precision = 0.972709413979505, ROC-AUC = 0.9680875653706502, Elapsed Time = 0.9554589000008491 seconds Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 59, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 7949 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6757 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6752 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6812 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6694 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6569 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6683 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6791 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6611 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6670 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6770 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6721 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6752 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 Trial 59, Fold 4: Log loss = 0.22644173638755147, Average precision = 0.9724764906199391, ROC-AUC = 0.9675457985507556, Elapsed Time = 0.9750905000000785 seconds Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 59, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7944 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6692 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6551 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6726 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6697 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6669 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6784 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6619 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6764 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6726 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6863 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6749 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 Trial 59, Fold 5: Log loss = 0.2278463569606474, Average precision = 0.9716918799256338, ROC-AUC = 0.9674079854337365, Elapsed Time = 1.0714367000000493 seconds
Optimization Progress: 60%|###### | 60/100 [13:50<08:36, 12.91s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 60, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 24804 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10304 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10526 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10397 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10215 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 Trial 60, Fold 1: Log loss = 0.27400577030241996, Average precision = 0.9722423793246776, ROC-AUC = 0.966849291784364, Elapsed Time = 0.6557243000006565 seconds Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 60, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 24811 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10518 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10481 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10475 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10330 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10555 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10346 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10483 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10237 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10313 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 Trial 60, Fold 2: Log loss = 0.2655808748637191, Average precision = 0.9727684389468915, ROC-AUC = 0.9691335140750623, Elapsed Time = 0.7152415000000474 seconds Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 60, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 24804 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10506 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10470 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10465 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10537 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10347 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10403 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10236 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10305 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 Trial 60, Fold 3: Log loss = 0.270243769829823, Average precision = 0.9713635394785448, ROC-AUC = 0.9683922220964838, Elapsed Time = 0.7405485999997836 seconds Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 60, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 24794 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10492 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10454 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10303 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10520 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10345 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10396 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10451 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10214 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 Trial 60, Fold 4: Log loss = 0.27232331528172177, Average precision = 0.9720421759528667, ROC-AUC = 0.9666652062156794, Elapsed Time = 0.7559318000003259 seconds Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 60, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 24798 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10456 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10296 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10516 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10430 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10448 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10216 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10285 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 Trial 60, Fold 5: Log loss = 0.2777135945673179, Average precision = 0.969383736130569, ROC-AUC = 0.9651062928487821, Elapsed Time = 0.7577618000004804 seconds
Optimization Progress: 61%|######1 | 61/100 [14:01<08:01, 12.34s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 61, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 12968 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 3425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3435 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 6 Trial 61, Fold 1: Log loss = 0.48690144509883243, Average precision = 0.9569593258807947, ROC-AUC = 0.9484052303045075, Elapsed Time = 0.49105599999984406 seconds Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 61, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 12982 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 3432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3444 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 Trial 61, Fold 2: Log loss = 0.4883409069805142, Average precision = 0.9495550603192829, ROC-AUC = 0.943807567419265, Elapsed Time = 0.4926711999996769 seconds Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 61, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 12988 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 3427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3439 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Debug] Re-bagging, using 3341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6 Trial 61, Fold 3: Log loss = 0.48699146362382356, Average precision = 0.9595972136306565, ROC-AUC = 0.9528731634820043, Elapsed Time = 0.5241814000000886 seconds Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 61, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 12968 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 3425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3335 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 Trial 61, Fold 4: Log loss = 0.48865133948204637, Average precision = 0.9537333610631538, ROC-AUC = 0.94517988583446, Elapsed Time = 0.545862599999964 seconds Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 61, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 12970 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 3423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4 [LightGBM] [Debug] Re-bagging, using 3336 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4 Trial 61, Fold 5: Log loss = 0.480761489092147, Average precision = 0.9529118812446569, ROC-AUC = 0.9442202032588298, Elapsed Time = 0.5731816999996227 seconds
Optimization Progress: 62%|######2 | 62/100 [14:11<07:22, 11.66s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 62, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 23584 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 13595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13670 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13744 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13669 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Debug] Re-bagging, using 13639 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 Trial 62, Fold 1: Log loss = 0.2946442850084093, Average precision = 0.9735029491011513, ROC-AUC = 0.9683388059630155, Elapsed Time = 1.019398600000386 seconds Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 62, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956 [LightGBM] [Info] Total Bins 23515 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 13622 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13691 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13694 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13572 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 13686 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 Trial 62, Fold 2: Log loss = 0.2933031130971272, Average precision = 0.972143220909115, ROC-AUC = 0.9683382798053137, Elapsed Time = 1.161121699999967 seconds Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 62, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 23508 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 13606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Debug] Re-bagging, using 13760 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13566 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13672 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 Trial 62, Fold 3: Log loss = 0.29255958868463744, Average precision = 0.9741501584594792, ROC-AUC = 0.9695752593156188, Elapsed Time = 1.2140692000002673 seconds Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 62, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 23522 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 13590 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 13665 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13741 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Debug] Re-bagging, using 13524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13651 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 Trial 62, Fold 4: Log loss = 0.2928080487627608, Average precision = 0.9730172557150623, ROC-AUC = 0.968042724450453, Elapsed Time = 1.2142837999999756 seconds Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 62, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364 [LightGBM] [Info] Total Bins 23533 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 13587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13656 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Debug] Re-bagging, using 13553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13635 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 Trial 62, Fold 5: Log loss = 0.29784747770938497, Average precision = 0.9711117089106877, ROC-AUC = 0.9667426936439811, Elapsed Time = 1.2246425000003 seconds
Optimization Progress: 63%|######3 | 63/100 [14:24<07:29, 12.14s/it]
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 63, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 12063 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 18369 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 Trial 63, Fold 1: Log loss = 0.3871478465053016, Average precision = 0.9701212026243069, ROC-AUC = 0.964097219234979, Elapsed Time = 0.6317716000003202 seconds Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 63, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 12084 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 18405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 18390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 Trial 63, Fold 2: Log loss = 0.3901727806816126, Average precision = 0.9682952301589332, ROC-AUC = 0.9648423901975787, Elapsed Time = 0.8101253000004363 seconds Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 63, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 12088 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 18388 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18438 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 18374 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 Trial 63, Fold 3: Log loss = 0.38395415345069356, Average precision = 0.9714498437181125, ROC-AUC = 0.967068707202088, Elapsed Time = 0.7840771999999561 seconds Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 63, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 12066 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 18362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 18386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 Trial 63, Fold 4: Log loss = 0.3813460075386185, Average precision = 0.9717720706462006, ROC-AUC = 0.9665523193153882, Elapsed Time = 0.7806607000002259 seconds Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 63, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 12068 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 18356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 18382 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 18411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 18345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 Trial 63, Fold 5: Log loss = 0.39025751567850453, Average precision = 0.9689818062249139, ROC-AUC = 0.9639260539174703, Elapsed Time = 0.7991799999999785 seconds
Optimization Progress: 64%|######4 | 64/100 [14:36<07:06, 11.85s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 64, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 10006 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 15148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15286 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15065 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15136 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15053 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15191 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15061 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15175 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15118 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15213 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15094 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15149 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 Trial 64, Fold 1: Log loss = 0.31630301819399675, Average precision = 0.9665739919306886, ROC-AUC = 0.9600088780870172, Elapsed Time = 1.321477500000583 seconds Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 64, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 10022 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 15180 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15312 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15192 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15194 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15187 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15082 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15068 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15228 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15336 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15089 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15186 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15358 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15154 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15293 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15172 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15218 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 Trial 64, Fold 2: Log loss = 0.31566260066916224, Average precision = 0.9642587196322059, ROC-AUC = 0.9608000568143709, Elapsed Time = 1.3881407999997464 seconds Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 64, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 10024 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 15163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15300 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15219 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15183 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15267 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15133 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15074 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15053 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15222 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15080 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15276 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15115 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15164 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 Trial 64, Fold 3: Log loss = 0.31411749801159977, Average precision = 0.9676660962346362, ROC-AUC = 0.9623256481686586, Elapsed Time = 1.5844845999999961 seconds Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 64, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 10003 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 15143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15247 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15124 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15185 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15248 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15109 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15055 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15040 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15284 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15201 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15058 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15161 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15321 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15103 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15128 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 Trial 64, Fold 4: Log loss = 0.315470578568722, Average precision = 0.9666878301659132, ROC-AUC = 0.9606021737471715, Elapsed Time = 1.5104277000000366 seconds Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 64, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 10007 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 15138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15245 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15118 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15176 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15244 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15152 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15046 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15154 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15173 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15318 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15058 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15135 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15200 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15101 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Debug] Re-bagging, using 15090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15145 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 Trial 64, Fold 5: Log loss = 0.31903414895241994, Average precision = 0.964618303671335, ROC-AUC = 0.9587533210709176, Elapsed Time = 1.6158471999997346 seconds
Optimization Progress: 65%|######5 | 65/100 [14:51<07:30, 12.88s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 65, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24150 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 Trial 65, Fold 1: Log loss = 0.387947778516032, Average precision = 0.9633361701294318, ROC-AUC = 0.9562298659803177, Elapsed Time = 1.4673411999992823 seconds Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 65, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7418 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 Trial 65, Fold 2: Log loss = 0.38556459991614683, Average precision = 0.9615695436743681, ROC-AUC = 0.9566611359657123, Elapsed Time = 1.68528249999963 seconds Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 65, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24152 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 Trial 65, Fold 3: Log loss = 0.3833619629188997, Average precision = 0.9651936736622466, ROC-AUC = 0.9589117472142232, Elapsed Time = 1.7250509000004968 seconds Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 65, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24142 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 Trial 65, Fold 4: Log loss = 0.38938473344963875, Average precision = 0.9621895753988847, ROC-AUC = 0.9551340515175576, Elapsed Time = 1.6923073999996632 seconds Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 65, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24146 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 Trial 65, Fold 5: Log loss = 0.39631969132039463, Average precision = 0.9600423777986307, ROC-AUC = 0.9534142652769263, Elapsed Time = 1.6810988000006546 seconds
Optimization Progress: 66%|######6 | 66/100 [15:07<07:48, 13.77s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 66, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14436 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 Trial 66, Fold 1: Log loss = 0.3087517333061513, Average precision = 0.9687092433911032, ROC-AUC = 0.9625075577254891, Elapsed Time = 1.0490066000002116 seconds Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 66, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 11158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14375 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14458 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14392 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 Trial 66, Fold 2: Log loss = 0.30928391734393224, Average precision = 0.966970769585805, ROC-AUC = 0.9628082870072635, Elapsed Time = 1.1502757999996902 seconds Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 66, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 11164 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14440 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9 [LightGBM] [Debug] Re-bagging, using 14395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14448 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14447 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14515 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 Trial 66, Fold 3: Log loss = 0.3057466631568812, Average precision = 0.9694848143497176, ROC-AUC = 0.9645859317063306, Elapsed Time = 1.2300295000004553 seconds Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 66, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 11145 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14375 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14514 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14343 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14387 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14430 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 Trial 66, Fold 4: Log loss = 0.3149989869422477, Average precision = 0.9668836789137704, ROC-AUC = 0.9608261592404225, Elapsed Time = 1.2065956000005826 seconds Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 66, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14512 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14433 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 19 [LightGBM] [Debug] Re-bagging, using 14493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 Trial 66, Fold 5: Log loss = 0.3105819669547541, Average precision = 0.9669574604331346, ROC-AUC = 0.9615033721643164, Elapsed Time = 1.2555413000000044 seconds
Optimization Progress: 67%|######7 | 67/100 [15:20<07:29, 13.63s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 67, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 9448 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16552 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 117 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13 Trial 67, Fold 1: Log loss = 0.28741825917483693, Average precision = 0.9739333585759847, ROC-AUC = 0.9687488051026895, Elapsed Time = 1.8393102 seconds Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 67, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 9436 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16621 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16557 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 Trial 67, Fold 2: Log loss = 0.28359683000303204, Average precision = 0.9717995056127583, ROC-AUC = 0.9694156890821415, Elapsed Time = 2.248905299999933 seconds Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 67, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 9446 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16617 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17 Trial 67, Fold 3: Log loss = 0.2814484218320297, Average precision = 0.9743597628338133, ROC-AUC = 0.9699769035740771, Elapsed Time = 2.2512329000001046 seconds Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 67, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 9446 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16593 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16623 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 116 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 Trial 67, Fold 4: Log loss = 0.28250526105517854, Average precision = 0.973973155793459, ROC-AUC = 0.9691755426320545, Elapsed Time = 2.24522160000015 seconds Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 67, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 9428 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16500 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 Trial 67, Fold 5: Log loss = 0.2847991314597355, Average precision = 0.9727540451579517, ROC-AUC = 0.9683353213310295, Elapsed Time = 2.201694000000316 seconds
Optimization Progress: 68%|######8 | 68/100 [15:38<08:00, 15.03s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 68, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798290 [LightGBM] [Info] Total Bins 21918 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14894 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 15018 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15035 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14902 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14960 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Re-bagging, using 14907 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14960 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 14950 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 15011 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Re-bagging, using 14870 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14919 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 30 [LightGBM] [Debug] Re-bagging, using 14818 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 14902 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 14795 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 15006 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28 Trial 68, Fold 1: Log loss = 0.19255620850863048, Average precision = 0.9771368172255197, ROC-AUC = 0.9729349784679504, Elapsed Time = 2.999589400000332 seconds Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 68, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798259 [LightGBM] [Info] Total Bins 21930 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14922 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15044 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15065 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14932 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 14990 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14924 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Re-bagging, using 14980 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 14987 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 15039 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14902 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14939 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14837 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Re-bagging, using 14927 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 14812 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15038 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 37 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 39 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 Trial 68, Fold 2: Log loss = 0.1810799720171419, Average precision = 0.9768380951427735, ROC-AUC = 0.9741089445911172, Elapsed Time = 3.4721553999997923 seconds Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 68, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798104 [LightGBM] [Info] Total Bins 21873 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14905 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 15032 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Re-bagging, using 15051 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14922 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14971 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 14913 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Re-bagging, using 14971 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 14972 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15016 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 29 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 14894 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 14931 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Re-bagging, using 14826 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26 [LightGBM] [Debug] Re-bagging, using 14917 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 14798 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 31 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 15021 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 Trial 68, Fold 3: Log loss = 0.18652658405551348, Average precision = 0.9771003102923845, ROC-AUC = 0.9738919193208194, Elapsed Time = 3.2772579000002224 seconds Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 68, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797795 [LightGBM] [Info] Total Bins 21913 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14889 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15011 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 15030 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14900 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 14954 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14902 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14953 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 14953 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Re-bagging, using 15001 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 14867 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 14913 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 14810 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 14904 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 36 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Re-bagging, using 14781 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 15008 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 Trial 68, Fold 4: Log loss = 0.18663521781285533, Average precision = 0.9769212559836613, ROC-AUC = 0.9733175604626709, Elapsed Time = 3.38944790000005 seconds Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 68, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646 [LightGBM] [Info] Total Bins 21922 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14884 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 15009 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15028 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 14894 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 14949 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14893 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14956 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 14944 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14997 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 14866 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 14907 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Re-bagging, using 14801 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 14900 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 14784 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 31 [LightGBM] [Debug] Re-bagging, using 15003 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 Trial 68, Fold 5: Log loss = 0.1877665116315995, Average precision = 0.9769218532521622, ROC-AUC = 0.9735586645114542, Elapsed Time = 3.328153100000236 seconds
Optimization Progress: 69%|######9 | 69/100 [16:02<09:10, 17.77s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 69, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 7940 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7722 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7689 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7843 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7783 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7855 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7683 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7935 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7762 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7741 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7821 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7913 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 18 [LightGBM] [Debug] Re-bagging, using 7722 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7841 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7765 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 Trial 69, Fold 1: Log loss = 0.2022036418702006, Average precision = 0.9745308643163686, ROC-AUC = 0.970485438981374, Elapsed Time = 0.9094229000002088 seconds Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 69, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 7956 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7705 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7644 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7959 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7830 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7761 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7667 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7733 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7914 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7783 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 16 Trial 69, Fold 2: Log loss = 0.1989488805167814, Average precision = 0.9738621553061394, ROC-AUC = 0.9705636610792121, Elapsed Time = 0.955718700000034 seconds Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 69, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 7963 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7783 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7728 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7704 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7859 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7679 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7942 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7820 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7756 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7822 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7724 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7844 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7835 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7926 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 Trial 69, Fold 3: Log loss = 0.1985734875654708, Average precision = 0.9746141114328595, ROC-AUC = 0.9702187328447189, Elapsed Time = 1.015855300000112 seconds Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 69, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 7949 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7843 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7690 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7775 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7850 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7584 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7678 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7929 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7759 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7822 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7765 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 Trial 69, Fold 4: Log loss = 0.20015244545998195, Average precision = 0.9746741557231141, ROC-AUC = 0.9703810107989919, Elapsed Time = 1.0393052999997963 seconds Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 69, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 7944 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7770 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7717 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7684 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7835 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7682 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7734 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7928 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7753 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7728 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7815 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7900 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16 [LightGBM] [Debug] Re-bagging, using 7661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7838 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7827 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7900 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7765 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 Trial 69, Fold 5: Log loss = 0.20355979520113743, Average precision = 0.9736788859168499, ROC-AUC = 0.9702772420712333, Elapsed Time = 1.0688019000008353 seconds
Optimization Progress: 70%|####### | 70/100 [16:15<08:03, 16.13s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 70, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295 [LightGBM] [Info] Total Bins 20104 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15858 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15755 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Re-bagging, using 15771 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15898 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 15815 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 Trial 70, Fold 1: Log loss = 0.5212313297273229, Average precision = 0.9627836292091663, ROC-AUC = 0.9549571509824445, Elapsed Time = 0.6239304999999149 seconds Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 70, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 20073 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 15833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15887 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15788 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15847 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15899 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15815 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 Trial 70, Fold 2: Log loss = 0.5209099033618703, Average precision = 0.9621351418743129, ROC-AUC = 0.9574910446044405, Elapsed Time = 0.6758497000000716 seconds Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 70, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045 [LightGBM] [Info] Total Bins 20069 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 15816 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15959 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15871 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15774 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15814 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 5 [LightGBM] [Debug] Re-bagging, using 15909 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15830 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 Trial 70, Fold 3: Log loss = 0.5124468728151358, Average precision = 0.9650361000889902, ROC-AUC = 0.9592594426279242, Elapsed Time = 0.7345519999998942 seconds Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 70, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 20105 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 15795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15937 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15854 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15753 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15796 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15861 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15772 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15785 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15698 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15895 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 Trial 70, Fold 4: Log loss = 0.521958522934716, Average precision = 0.9634605752827095, ROC-AUC = 0.9565819843535032, Elapsed Time = 0.705152999999882 seconds Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 70, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 20102 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 15789 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15851 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15790 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15777 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15791 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15856 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15777 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15690 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15778 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Debug] Re-bagging, using 15701 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Debug] Re-bagging, using 15892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 15811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 Trial 70, Fold 5: Log loss = 0.5191817461829943, Average precision = 0.9611701754237488, ROC-AUC = 0.9542330230570574, Elapsed Time = 0.7133186000000933 seconds
Optimization Progress: 71%|#######1 | 71/100 [16:26<07:01, 14.54s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 71, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9953 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9970 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9910 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9935 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 Trial 71, Fold 1: Log loss = 0.33959284495604364, Average precision = 0.9724411600723212, ROC-AUC = 0.9668345796112281, Elapsed Time = 1.8166848999999274 seconds Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 71, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 11158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9932 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9997 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9918 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 Trial 71, Fold 2: Log loss = 0.34029115995704734, Average precision = 0.9706272485357673, ROC-AUC = 0.9671575302865818, Elapsed Time = 2.0769625000002634 seconds Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 71, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 11164 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9897 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9917 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9941 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 Trial 71, Fold 3: Log loss = 0.34119889364049394, Average precision = 0.9716527135534025, ROC-AUC = 0.9667977590989204, Elapsed Time = 2.086481299999832 seconds Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 71, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 11145 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9920 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9932 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 Trial 71, Fold 4: Log loss = 0.33957611513155844, Average precision = 0.9720091270541313, ROC-AUC = 0.9665419620354276, Elapsed Time = 2.2675779999999577 seconds Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 71, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 11147 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9961 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9826 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9914 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9930 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 Trial 71, Fold 5: Log loss = 0.34555205055908644, Average precision = 0.969191136142906, ROC-AUC = 0.964253497575386, Elapsed Time = 1.7241317999996681 seconds
Optimization Progress: 72%|#######2 | 72/100 [16:43<07:12, 15.43s/it]
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 72, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 19014 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10167 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10127 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10000 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 Trial 72, Fold 1: Log loss = 0.27839808071739647, Average precision = 0.9699116494901384, ROC-AUC = 0.9638715330054535, Elapsed Time = 0.9093064999997296 seconds Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 72, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 19026 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10128 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10026 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 72, Fold 2: Log loss = 0.2773252453732645, Average precision = 0.9679454703507686, ROC-AUC = 0.9644505774154984, Elapsed Time = 0.9629867000003287 seconds Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 72, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 19029 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10134 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10021 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10037 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 Trial 72, Fold 3: Log loss = 0.2747655322336973, Average precision = 0.9708889291563291, ROC-AUC = 0.9660925867515482, Elapsed Time = 1.03429369999958 seconds Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 72, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 19010 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10123 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9999 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 Trial 72, Fold 4: Log loss = 0.27565143365103895, Average precision = 0.9698695678849122, ROC-AUC = 0.9640579286393842, Elapsed Time = 1.0720799000000625 seconds Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 72, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 19008 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9992 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10032 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 Trial 72, Fold 5: Log loss = 0.28328383290878856, Average precision = 0.9680872451642151, ROC-AUC = 0.9623242851569032, Elapsed Time = 1.0523961999997482 seconds
Optimization Progress: 73%|#######3 | 73/100 [16:55<06:32, 14.54s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 73, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 21915 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 8731 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8796 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8715 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8664 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8776 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8704 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8720 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8788 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8763 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8545 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8874 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8724 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8682 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8682 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8762 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8877 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8552 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8737 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8789 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8827 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8742 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8695 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8649 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 27 [LightGBM] [Debug] Re-bagging, using 8741 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8673 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Re-bagging, using 8750 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Re-bagging, using 8691 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 27 [LightGBM] [Debug] Re-bagging, using 8792 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8708 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8678 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8785 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8675 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8925 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8668 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8689 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8586 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Re-bagging, using 8618 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 Trial 73, Fold 1: Log loss = 0.19207670774348418, Average precision = 0.9764999102293743, ROC-AUC = 0.972403846613422, Elapsed Time = 2.5400171000001137 seconds Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 73, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949 [LightGBM] [Info] Total Bins 21927 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 8749 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8813 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8729 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8685 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8730 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8789 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8564 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8676 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8687 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8906 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8756 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8699 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8701 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8883 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8574 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8682 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8763 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8835 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8757 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8715 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8813 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8660 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Re-bagging, using 8759 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8696 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8763 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8689 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8818 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8681 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Re-bagging, using 8715 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 31 [LightGBM] [Debug] Re-bagging, using 8686 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8804 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8671 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8944 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8684 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Re-bagging, using 8720 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8615 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8653 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 Trial 73, Fold 2: Log loss = 0.18436194570607786, Average precision = 0.9759969547269485, ROC-AUC = 0.9731626454009343, Elapsed Time = 3.1395682000002125 seconds Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 73, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784 [LightGBM] [Info] Total Bins 21870 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 8740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8803 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8720 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8682 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8789 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8796 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8542 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8665 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8676 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8886 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8740 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8697 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8699 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8759 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8886 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8565 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8667 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8744 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8800 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8845 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Re-bagging, using 8751 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8688 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8797 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8667 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8766 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8682 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8751 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Re-bagging, using 8686 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8800 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8683 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8717 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8690 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8790 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8667 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8930 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8670 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8697 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8597 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8623 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 Trial 73, Fold 3: Log loss = 0.19152865274959918, Average precision = 0.97518386567535, ROC-AUC = 0.9725112201558301, Elapsed Time = 3.040055299999949 seconds Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 73, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 21910 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 8727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8711 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8702 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8787 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8761 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8542 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8536 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8669 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8872 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8714 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8681 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8684 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8747 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8879 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8559 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8656 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8742 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8782 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8823 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8743 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8679 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8801 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8659 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 35 [LightGBM] [Debug] Re-bagging, using 8744 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8667 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Re-bagging, using 8740 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8680 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28 [LightGBM] [Debug] Re-bagging, using 8801 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8671 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8706 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Re-bagging, using 8665 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Re-bagging, using 8789 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8663 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8924 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8662 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8695 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8600 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8609 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 Trial 73, Fold 4: Log loss = 0.19165113364949743, Average precision = 0.9757806427128366, ROC-AUC = 0.9716902007911471, Elapsed Time = 3.2279449000006935 seconds Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 73, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340 [LightGBM] [Info] Total Bins 21919 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 8725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 8792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8710 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Debug] Re-bagging, using 8656 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8694 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8781 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8758 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Debug] Re-bagging, using 8544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8554 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8664 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8678 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8870 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8715 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8673 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8665 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8751 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8867 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8567 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8655 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8731 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8790 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8818 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 8739 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8681 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8787 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8659 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8749 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Re-bagging, using 8671 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8735 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8672 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28 [LightGBM] [Debug] Re-bagging, using 8785 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 8680 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 8706 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8667 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8784 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 8657 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 8924 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 8659 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Re-bagging, using 8695 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 27 [LightGBM] [Debug] Re-bagging, using 8584 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Re-bagging, using 8625 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 Trial 73, Fold 5: Log loss = 0.19210909317188127, Average precision = 0.9761174581789205, ROC-AUC = 0.9729034799249392, Elapsed Time = 3.01584129999992 seconds
Optimization Progress: 74%|#######4 | 74/100 [17:18<07:21, 16.97s/it]
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 74, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 19898 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2840 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2873 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2977 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2990 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2890 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2874 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2898 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2913 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2894 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2917 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 18 [LightGBM] [Debug] Re-bagging, using 2964 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 Trial 74, Fold 1: Log loss = 0.20789237807057712, Average precision = 0.9728302046346347, ROC-AUC = 0.9679757065427798, Elapsed Time = 1.160290700000587 seconds Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 74, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 19910 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2952 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2957 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2838 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2883 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2847 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2996 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2907 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2887 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2771 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 15 [LightGBM] [Debug] Re-bagging, using 2916 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2904 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 17 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 16 [LightGBM] [Debug] Re-bagging, using 2807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 Trial 74, Fold 2: Log loss = 0.20502511626084324, Average precision = 0.9716575349201336, ROC-AUC = 0.9684218928351069, Elapsed Time = 1.2803405000004204 seconds Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 74, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 19863 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2951 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2841 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2847 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2901 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 17 [LightGBM] [Debug] Re-bagging, using 2879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2904 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2861 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2907 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2920 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2795 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Debug] Re-bagging, using 2971 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 Trial 74, Fold 3: Log loss = 0.20485285144190643, Average precision = 0.9725765501366499, ROC-AUC = 0.9683117879053462, Elapsed Time = 1.4039001000001008 seconds Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 74, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 19893 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2976 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2844 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2986 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2888 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2873 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2773 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2894 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2858 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2911 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2899 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2913 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2960 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 Trial 74, Fold 4: Log loss = 0.20620693439015805, Average precision = 0.9719124400290444, ROC-AUC = 0.9663950227830355, Elapsed Time = 1.486533999999665 seconds Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 74, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 19891 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2946 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2945 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2874 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2922 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Debug] Re-bagging, using 2981 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2890 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2768 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2849 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 2909 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2857 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12 [LightGBM] [Debug] Re-bagging, using 2909 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2902 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14 [LightGBM] [Debug] Re-bagging, using 2916 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 2786 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16 [LightGBM] [Debug] Re-bagging, using 2963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14 Trial 74, Fold 5: Log loss = 0.21038025074049305, Average precision = 0.9709620645493768, ROC-AUC = 0.9671163628931869, Elapsed Time = 1.355631500000527 seconds
Optimization Progress: 75%|#######5 | 75/100 [17:32<06:43, 16.15s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 75, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24366 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10397 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10280 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10252 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10417 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10370 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10378 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10342 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10482 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10335 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10582 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10276 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10190 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 Trial 75, Fold 1: Log loss = 0.3386479276944911, Average precision = 0.9700048427156842, ROC-AUC = 0.9639866912337552, Elapsed Time = 1.6730855999994674 seconds Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 75, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24373 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10313 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10439 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10436 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10404 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10382 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10387 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10443 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10317 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10486 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10306 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 Trial 75, Fold 2: Log loss = 0.33831803482587663, Average precision = 0.968553079645746, ROC-AUC = 0.9648312367353467, Elapsed Time = 2.0940998000005493 seconds Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 75, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24368 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10506 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10403 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10280 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10340 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10491 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10422 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10400 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10434 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10397 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10515 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10412 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10355 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10437 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10501 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10344 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 Trial 75, Fold 3: Log loss = 0.3374498500721996, Average precision = 0.9709400920837394, ROC-AUC = 0.9658439856593888, Elapsed Time = 2.0018864000003305 seconds Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 75, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24356 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10303 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10396 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10283 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10402 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10259 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10417 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10377 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10358 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10373 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10369 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10355 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10409 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10372 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10327 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10578 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10382 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10326 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10289 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 Trial 75, Fold 4: Log loss = 0.33745229817959493, Average precision = 0.9701460653297643, ROC-AUC = 0.9644183470794558, Elapsed Time = 2.0010793999999805 seconds Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 75, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24360 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10430 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10448 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10466 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10423 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10479 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10377 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10506 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10363 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10374 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10365 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10323 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 10380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 Trial 75, Fold 5: Log loss = 0.3406738747682316, Average precision = 0.9684612209351231, ROC-AUC = 0.96299306987719, Elapsed Time = 1.9582474000008006 seconds
Optimization Progress: 76%|#######6 | 76/100 [17:50<06:34, 16.45s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 76, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 15448 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 12453 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12317 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12486 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 Trial 76, Fold 1: Log loss = 0.3155715255143001, Average precision = 0.9662255319221411, ROC-AUC = 0.9594275605454466, Elapsed Time = 0.8203442000003633 seconds Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 76, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 15463 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 12495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 Trial 76, Fold 2: Log loss = 0.3124389825131124, Average precision = 0.9638516831597628, ROC-AUC = 0.960129938593741, Elapsed Time = 0.881771999999728 seconds Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 76, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 15465 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 12464 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Debug] Re-bagging, using 12550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 12496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 Trial 76, Fold 3: Log loss = 0.3054877716963616, Average precision = 0.9673556159887645, ROC-AUC = 0.9617291259354142, Elapsed Time = 0.9247225999997681 seconds Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 76, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 15448 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 12449 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12473 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12314 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12478 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 12350 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 Trial 76, Fold 4: Log loss = 0.3103363416716246, Average precision = 0.9664082453702845, ROC-AUC = 0.9597652459161022, Elapsed Time = 0.9628636999996161 seconds Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 76, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 15447 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 12446 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Debug] Re-bagging, using 12469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Debug] Re-bagging, using 12308 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 12346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 Trial 76, Fold 5: Log loss = 0.3148505789874095, Average precision = 0.9643632385544947, ROC-AUC = 0.9577471341248165, Elapsed Time = 0.9563079999998081 seconds
Optimization Progress: 77%|#######7 | 77/100 [18:01<05:47, 15.10s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 77, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 8297 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6273 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6329 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6300 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6080 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6116 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6253 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6193 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6269 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6274 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18 Trial 77, Fold 1: Log loss = 0.22585042950322304, Average precision = 0.9734482911791483, ROC-AUC = 0.9681996004263393, Elapsed Time = 1.0046127000005072 seconds Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 77, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 8313 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6231 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6102 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6113 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6159 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6273 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6221 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6322 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6228 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6370 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6282 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 Trial 77, Fold 2: Log loss = 0.22227296826877135, Average precision = 0.9713868294735187, ROC-AUC = 0.968683506017255, Elapsed Time = 1.0871165999997174 seconds Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 77, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 8313 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6278 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6343 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6118 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6264 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6368 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6220 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6315 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6210 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6266 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6217 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6383 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6279 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 Trial 77, Fold 3: Log loss = 0.22081356715814243, Average precision = 0.9731649895583823, ROC-AUC = 0.9689281741483757, Elapsed Time = 1.1825113999993846 seconds Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 77, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 8295 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6270 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6199 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 21 [LightGBM] [Debug] Re-bagging, using 6343 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6083 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6105 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6250 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6256 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6198 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6298 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6208 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6270 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6353 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6274 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 20 Trial 77, Fold 4: Log loss = 0.22251997590152925, Average precision = 0.9732956379599811, ROC-AUC = 0.9683390618778182, Elapsed Time = 1.1666599000000133 seconds Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 77, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 8302 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6293 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6336 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 21 [LightGBM] [Debug] Re-bagging, using 6206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6083 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6151 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 21 [LightGBM] [Debug] Re-bagging, using 6163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 21 [LightGBM] [Debug] Re-bagging, using 6260 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6257 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 21 [LightGBM] [Debug] Re-bagging, using 6205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6293 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6147 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6204 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6261 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6212 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 20 Trial 77, Fold 5: Log loss = 0.2264348846158163, Average precision = 0.9721371360826228, ROC-AUC = 0.9677990450179292, Elapsed Time = 1.1781983000000764 seconds
Optimization Progress: 78%|#######8 | 78/100 [18:15<05:19, 14.51s/it]
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 78, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 24158 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7403 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7458 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7636 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7515 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7642 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7387 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7428 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7476 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17 [LightGBM] [Debug] Re-bagging, using 7705 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7488 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7411 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 19 [LightGBM] [Debug] Re-bagging, using 7577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18 [LightGBM] [Debug] Re-bagging, using 7486 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 Trial 78, Fold 1: Log loss = 0.20672061417487297, Average precision = 0.9748272179255015, ROC-AUC = 0.9703260695525826, Elapsed Time = 1.8581021999998484 seconds Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 78, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 24163 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7418 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7415 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7488 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7581 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7661 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7567 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7539 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7434 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7580 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7487 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18 [LightGBM] [Debug] Re-bagging, using 7570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7460 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7512 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7436 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 Trial 78, Fold 2: Log loss = 0.20328855586184658, Average precision = 0.9741812854286478, ROC-AUC = 0.9719177065689493, Elapsed Time = 2.229027600000336 seconds Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 78, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 24160 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7401 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7401 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7675 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7500 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7484 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7462 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7713 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7477 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7608 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Debug] Re-bagging, using 7497 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 Trial 78, Fold 3: Log loss = 0.20468469213154777, Average precision = 0.9756805906632632, ROC-AUC = 0.971607257225684, Elapsed Time = 2.315972100000181 seconds Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 78, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 24150 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7401 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7514 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7501 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7576 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7389 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7558 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7651 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7494 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7558 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7447 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7430 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Debug] Re-bagging, using 7509 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7450 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7705 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 7404 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 16 [LightGBM] [Debug] Re-bagging, using 7578 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 7487 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 Trial 78, Fold 4: Log loss = 0.20804533960468277, Average precision = 0.9746462747165743, ROC-AUC = 0.9700041250289111, Elapsed Time = 2.1942557000002125 seconds Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 78, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 24154 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7628 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7485 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7582 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7399 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7495 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7419 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 7533 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7558 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7449 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7703 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7369 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Debug] Re-bagging, using 7413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 7481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 Trial 78, Fold 5: Log loss = 0.20993519748703102, Average precision = 0.9730666504093994, ROC-AUC = 0.9692958029095369, Elapsed Time = 2.297114899999542 seconds
Optimization Progress: 79%|#######9 | 79/100 [18:33<05:30, 15.71s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 79, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 9448 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 Trial 79, Fold 1: Log loss = 0.35574942751144145, Average precision = 0.953768924563304, ROC-AUC = 0.9488460727310095, Elapsed Time = 0.5450129999999263 seconds Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 79, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 9436 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 Trial 79, Fold 2: Log loss = 0.36948599381691194, Average precision = 0.947260346271916, ROC-AUC = 0.9453780052510802, Elapsed Time = 0.5532783000007839 seconds Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 79, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 9446 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16617 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 Trial 79, Fold 3: Log loss = 0.36693852889771394, Average precision = 0.9495115214204645, ROC-AUC = 0.9450564499041711, Elapsed Time = 0.5432987000003777 seconds Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 79, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 9446 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 Trial 79, Fold 4: Log loss = 0.35530964163414613, Average precision = 0.9599567042135402, ROC-AUC = 0.9534670510335225, Elapsed Time = 0.6347985000002154 seconds Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 79, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 9428 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 Trial 79, Fold 5: Log loss = 0.35161469182991933, Average precision = 0.9587201253380273, ROC-AUC = 0.9516898723594003, Elapsed Time = 0.5850366999993639 seconds
Optimization Progress: 80%|######## | 80/100 [18:45<04:48, 14.44s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 80, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 23592 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9953 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9970 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9910 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 Trial 80, Fold 1: Log loss = 0.29717970856942566, Average precision = 0.9729448820054003, ROC-AUC = 0.9678465082710793, Elapsed Time = 0.96355179999955 seconds Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 80, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 23517 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9932 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9997 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9918 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 Trial 80, Fold 2: Log loss = 0.2975817993313782, Average precision = 0.971209615433894, ROC-AUC = 0.9677456288291657, Elapsed Time = 1.021224500000244 seconds Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 80, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 23516 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9897 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 9823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9917 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 Trial 80, Fold 3: Log loss = 0.29619044182555126, Average precision = 0.9728419392402384, ROC-AUC = 0.9684816853829782, Elapsed Time = 1.0942439000000377 seconds Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 80, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 23527 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9920 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 Trial 80, Fold 4: Log loss = 0.29494973302308586, Average precision = 0.9727109825081937, ROC-AUC = 0.9675677053155648, Elapsed Time = 1.1352965000005497 seconds Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 80, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 23538 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9961 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9826 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 9914 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 Trial 80, Fold 5: Log loss = 0.29787818514134823, Average precision = 0.9707340038556295, ROC-AUC = 0.9663314196533079, Elapsed Time = 1.196688500000164 seconds
Optimization Progress: 81%|########1 | 81/100 [18:58<04:27, 14.09s/it]
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 81, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 20226 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 13083 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Re-bagging, using 13151 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Re-bagging, using 13216 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Re-bagging, using 12985 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13153 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Re-bagging, using 13052 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 Trial 81, Fold 1: Log loss = 0.32300591972987625, Average precision = 0.9734513638765099, ROC-AUC = 0.9695915064309373, Elapsed Time = 1.536184399999911 seconds Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 81, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 20188 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 13110 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13171 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13241 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Re-bagging, using 13018 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Re-bagging, using 13176 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13058 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 Trial 81, Fold 2: Log loss = 0.32332806823816257, Average precision = 0.9720980957530481, ROC-AUC = 0.9681450290005192, Elapsed Time = 1.8439387999997052 seconds Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 81, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 20188 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 13094 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13161 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13228 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Re-bagging, using 13008 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13161 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13053 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 Trial 81, Fold 3: Log loss = 0.3216995749037648, Average precision = 0.9744215142566041, ROC-AUC = 0.9700077529832132, Elapsed Time = 1.765900899999906 seconds Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 81, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 20226 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 13078 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Re-bagging, using 13147 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13212 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Re-bagging, using 12981 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13145 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Re-bagging, using 13050 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 Trial 81, Fold 4: Log loss = 0.32243371529582, Average precision = 0.9739904678441192, ROC-AUC = 0.9691439492025341, Elapsed Time = 1.831553900000472 seconds Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 81, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340 [LightGBM] [Info] Total Bins 20221 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 13075 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13143 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 13212 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 12974 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 13140 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Debug] Re-bagging, using 13044 data to train [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 Trial 81, Fold 5: Log loss = 0.32431580591947057, Average precision = 0.9719594880006669, ROC-AUC = 0.9685382828902143, Elapsed Time = 1.7745714000002408 seconds
Optimization Progress: 82%|########2 | 82/100 [19:14<04:24, 14.70s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 82, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 24158 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5686 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5875 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5869 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5623 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5645 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5738 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 Trial 82, Fold 1: Log loss = 0.30882726610776134, Average precision = 0.9673871031756777, ROC-AUC = 0.961117444066857, Elapsed Time = 1.5058353999993415 seconds Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 82, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629 [LightGBM] [Info] Total Bins 24166 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5812 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5809 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5704 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5896 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5735 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5880 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5764 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5649 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5641 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5736 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 Trial 82, Fold 2: Log loss = 0.3052057853384527, Average precision = 0.9662910981216197, ROC-AUC = 0.9624116941699411, Elapsed Time = 1.8040215000000899 seconds Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 82, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 24160 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5839 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5807 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5696 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5888 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5837 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5869 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5749 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5651 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 Trial 82, Fold 3: Log loss = 0.3009154014630842, Average precision = 0.9692448578588674, ROC-AUC = 0.9639620212171702, Elapsed Time = 1.7570170999997572 seconds Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 82, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 24150 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5831 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5688 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5842 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5727 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5867 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5738 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5626 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5737 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 Trial 82, Fold 4: Log loss = 0.30238588560447804, Average precision = 0.9679750797460415, ROC-AUC = 0.9617191952140127, Elapsed Time = 1.7509574999994584 seconds Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 82, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 24154 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5801 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13 [LightGBM] [Debug] Re-bagging, using 5681 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5871 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5838 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5733 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5860 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5739 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5626 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5728 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 Trial 82, Fold 5: Log loss = 0.30639183738309717, Average precision = 0.9655618446251829, ROC-AUC = 0.9598895639410661, Elapsed Time = 1.7526851000002353 seconds
Optimization Progress: 83%|########2 | 83/100 [19:30<04:16, 15.08s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 83, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 28977 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2928 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2933 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2958 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2907 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2827 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2965 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2868 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2854 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2747 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2848 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2868 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2896 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2779 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2943 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2803 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2841 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2995 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 Trial 83, Fold 1: Log loss = 0.3518844215145265, Average precision = 0.9577284032215908, ROC-AUC = 0.9496131221232846, Elapsed Time = 1.1002770000004602 seconds Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 83, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 28981 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2933 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2943 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2821 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2967 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2903 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2971 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2885 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2867 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2745 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2884 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2844 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2877 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2792 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2951 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2911 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2928 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2931 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2855 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2998 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 Trial 83, Fold 2: Log loss = 0.3468327660580311, Average precision = 0.9540629330455027, ROC-AUC = 0.9482948253094668, Elapsed Time = 1.2421316999998453 seconds Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 83, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 28976 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2930 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2937 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2824 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2865 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2907 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2961 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2859 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2756 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2824 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Debug] Re-bagging, using 2882 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2849 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2883 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2900 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2779 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2951 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2797 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2923 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2862 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2998 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 Trial 83, Fold 3: Log loss = 0.33874433004893345, Average precision = 0.9606818061407815, ROC-AUC = 0.9534419212470264, Elapsed Time = 1.305384199999935 seconds Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 83, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 28967 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2928 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2931 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2819 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2864 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2957 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2961 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2866 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2853 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2747 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2836 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2872 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2887 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2873 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2893 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2782 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2940 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2911 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2918 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2924 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2846 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2983 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 Trial 83, Fold 4: Log loss = 0.34823218671688666, Average precision = 0.9580260734276069, ROC-AUC = 0.9501910210281099, Elapsed Time = 1.2381753999998182 seconds Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 83, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 28973 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2931 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2819 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2863 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2953 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2904 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2956 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2868 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2852 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2742 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2825 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2887 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2845 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2885 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2896 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2770 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2943 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2799 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2910 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2925 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5 [LightGBM] [Debug] Re-bagging, using 2855 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2981 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4 Trial 83, Fold 5: Log loss = 0.35669165442933026, Average precision = 0.9562607558042574, ROC-AUC = 0.9485020901844936, Elapsed Time = 1.1870617000004131 seconds
Optimization Progress: 84%|########4 | 84/100 [19:44<03:55, 14.74s/it]
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 84, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295 [LightGBM] [Info] Total Bins 11136 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 9953 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9892 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9802 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9970 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9834 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 9910 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9921 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 Trial 84, Fold 1: Log loss = 0.33400758482481047, Average precision = 0.9622280943411928, ROC-AUC = 0.9545879277134923, Elapsed Time = 1.0007489999998143 seconds Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 84, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 11153 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 9972 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9932 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9828 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9997 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9918 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9936 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 Trial 84, Fold 2: Log loss = 0.332232484757396, Average precision = 0.9601227420006524, ROC-AUC = 0.9546932706837543, Elapsed Time = 1.0486750999998549 seconds Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 84, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045 [LightGBM] [Info] Total Bins 11153 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9897 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Debug] Re-bagging, using 9823 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9917 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 Trial 84, Fold 3: Log loss = 0.32143217731769164, Average precision = 0.9645005379665792, ROC-AUC = 0.957877012886325, Elapsed Time = 1.1556067000001349 seconds Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 84, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 11137 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 9949 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9800 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9963 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9832 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9920 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 Trial 84, Fold 4: Log loss = 0.33049159274169043, Average precision = 0.9629257029960119, ROC-AUC = 0.9557678574409083, Elapsed Time = 1.2255662999996275 seconds Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 84, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 11139 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 9947 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9793 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9961 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 9826 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 9914 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 Trial 84, Fold 5: Log loss = 0.33872359839674515, Average precision = 0.9608101868097333, ROC-AUC = 0.953562603348011, Elapsed Time = 1.1282185999998546 seconds
Optimization Progress: 85%|########5 | 85/100 [19:57<03:32, 14.19s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 85, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 25876 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 2806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2796 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2739 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2824 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2769 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2714 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 Trial 85, Fold 1: Log loss = 0.4490798698237985, Average precision = 0.9661487854658025, ROC-AUC = 0.9595509336927583, Elapsed Time = 0.9809285999999702 seconds Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 85, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 25878 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 2811 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2804 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Debug] Re-bagging, using 2676 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2750 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2833 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2763 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 Trial 85, Fold 2: Log loss = 0.4472839815272487, Average precision = 0.9633424876446511, ROC-AUC = 0.9583642013620273, Elapsed Time = 1.0804873999995834 seconds Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 85, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 25877 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 2808 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2798 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2680 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2742 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2829 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2768 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 Trial 85, Fold 3: Log loss = 0.4468608871653139, Average precision = 0.9668104377238605, ROC-AUC = 0.961073387885121, Elapsed Time = 1.1630433999998786 seconds Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 85, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 25866 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 2806 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2740 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2824 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2767 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2716 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 Trial 85, Fold 4: Log loss = 0.44792467876332814, Average precision = 0.9659312944127066, ROC-AUC = 0.959395066298514, Elapsed Time = 1.1747064999999566 seconds Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 85, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 25873 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 2805 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2794 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2739 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2820 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 2765 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 2720 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 Trial 85, Fold 5: Log loss = 0.44603286692157473, Average precision = 0.9641034079380562, ROC-AUC = 0.957529309031455, Elapsed Time = 1.116951500000141 seconds
Optimization Progress: 86%|########6 | 86/100 [20:10<03:13, 13.83s/it]
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 86, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 22771 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16552 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16487 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 Trial 86, Fold 1: Log loss = 0.305143960093892, Average precision = 0.9682342423734314, ROC-AUC = 0.961671428332449, Elapsed Time = 2.3427920000003724 seconds Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 86, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 22782 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16621 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16557 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16657 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16665 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 Trial 86, Fold 2: Log loss = 0.3031811940840515, Average precision = 0.9666190300590372, ROC-AUC = 0.9625416661481951, Elapsed Time = 2.989899200000764 seconds Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 86, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 22670 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16617 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16592 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 Trial 86, Fold 3: Log loss = 0.30205762492099164, Average precision = 0.9691763190075562, ROC-AUC = 0.9639460698153729, Elapsed Time = 2.8637853000000177 seconds Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 86, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 22765 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16593 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16623 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16619 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 10 [LightGBM] [Debug] Re-bagging, using 16608 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16624 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11 Trial 86, Fold 4: Log loss = 0.30270349940476843, Average precision = 0.9686390023398933, ROC-AUC = 0.9624188704574013, Elapsed Time = 2.87171219999982 seconds Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 86, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 22721 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16500 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16480 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16513 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 Trial 86, Fold 5: Log loss = 0.3074469106939738, Average precision = 0.9656815610375317, ROC-AUC = 0.9594476710699886, Elapsed Time = 2.8436714999998003 seconds
Optimization Progress: 87%|########7 | 87/100 [20:31<03:30, 16.17s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 87, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798290 [LightGBM] [Info] Total Bins 17915 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14894 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15018 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Re-bagging, using 15035 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 Trial 87, Fold 1: Log loss = 0.3128623381671113, Average precision = 0.9699769960360858, ROC-AUC = 0.9640660025427417, Elapsed Time = 0.6332750999999917 seconds Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 87, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798259 [LightGBM] [Info] Total Bins 17930 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14922 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Debug] Re-bagging, using 15044 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 15065 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 Trial 87, Fold 2: Log loss = 0.31324368228512456, Average precision = 0.9690407017541076, ROC-AUC = 0.9654967266485197, Elapsed Time = 0.7145328000005975 seconds Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 87, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798104 [LightGBM] [Info] Total Bins 17934 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14905 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Debug] Re-bagging, using 15032 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15051 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 Trial 87, Fold 3: Log loss = 0.301574581235144, Average precision = 0.9727812892251233, ROC-AUC = 0.9680483640483093, Elapsed Time = 0.7058385999998791 seconds Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 87, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797795 [LightGBM] [Info] Total Bins 17917 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14889 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10 [LightGBM] [Debug] Re-bagging, using 15011 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Re-bagging, using 15030 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 Trial 87, Fold 4: Log loss = 0.31242890490674685, Average precision = 0.9695993487992027, ROC-AUC = 0.9642784120307781, Elapsed Time = 0.6826785000002928 seconds Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 87, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646 [LightGBM] [Info] Total Bins 17916 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14884 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15009 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Re-bagging, using 15028 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 Trial 87, Fold 5: Log loss = 0.3150488481613542, Average precision = 0.9685165788771627, ROC-AUC = 0.9634251528157108, Elapsed Time = 0.7011179999999513 seconds
Optimization Progress: 88%|########8 | 88/100 [20:43<02:57, 14.83s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 88, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 22663 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6273 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6246 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6158 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 Trial 88, Fold 1: Log loss = 0.3061306220134525, Average precision = 0.9662695164729078, ROC-AUC = 0.9598435341716732, Elapsed Time = 0.7078392000003078 seconds Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 88, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 22674 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6262 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6232 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 Trial 88, Fold 2: Log loss = 0.3087993840397771, Average precision = 0.9633442503351907, ROC-AUC = 0.9590662624914983, Elapsed Time = 0.7561420999991242 seconds Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 88, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 22561 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6278 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6230 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 Trial 88, Fold 3: Log loss = 0.30574315365828897, Average precision = 0.9666086984224752, ROC-AUC = 0.9610126672188462, Elapsed Time = 0.7820138999995834 seconds Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 88, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 22657 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6270 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6227 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 Trial 88, Fold 4: Log loss = 0.30470793975541044, Average precision = 0.9663651109636795, ROC-AUC = 0.9594859719211906, Elapsed Time = 0.8285013000004255 seconds Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 88, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 22613 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6268 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 6243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 6225 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Debug] Re-bagging, using 6155 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 Trial 88, Fold 5: Log loss = 0.3070984501419724, Average precision = 0.9657053793675368, ROC-AUC = 0.9597007970570203, Elapsed Time = 0.8024282999995194 seconds
Optimization Progress: 89%|########9 | 89/100 [20:55<02:32, 13.84s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 89, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981 [LightGBM] [Info] Total Bins 9564 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6149 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6075 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6239 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6209 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 Trial 89, Fold 1: Log loss = 0.33416366488173527, Average precision = 0.9717220616218375, ROC-AUC = 0.9662786536375065, Elapsed Time = 1.0397594999994908 seconds Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 89, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949 [LightGBM] [Info] Total Bins 9556 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6196 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6165 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6143 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6093 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6263 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 Trial 89, Fold 2: Log loss = 0.33738411543798374, Average precision = 0.9680395626741372, ROC-AUC = 0.9650552164666985, Elapsed Time = 1.1089407999988907 seconds Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 89, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784 [LightGBM] [Info] Total Bins 9563 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6141 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6086 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6254 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 Trial 89, Fold 3: Log loss = 0.332700635870908, Average precision = 0.9727254923682255, ROC-AUC = 0.9683099820862748, Elapsed Time = 1.2187215000012657 seconds Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 89, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474 [LightGBM] [Info] Total Bins 9562 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6181 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6138 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 6077 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 Trial 89, Fold 4: Log loss = 0.3365947795505942, Average precision = 0.9718392168322076, ROC-AUC = 0.9664804144581072, Elapsed Time = 1.2304527000014787 seconds Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 89, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340 [LightGBM] [Info] Total Bins 9544 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6179 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6137 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11 [LightGBM] [Debug] Re-bagging, using 6071 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 6202 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11 Trial 89, Fold 5: Log loss = 0.338789382130623, Average precision = 0.9691199604516071, ROC-AUC = 0.9640932686769597, Elapsed Time = 1.192119699999239 seconds
Optimization Progress: 90%|######### | 90/100 [21:08<02:17, 13.73s/it]
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 90, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 12968 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 4096 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4091 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4061 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3955 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4115 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Debug] Re-bagging, using 4078 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4013 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4058 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3916 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3992 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4001 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4059 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4043 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4028 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3968 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4090 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 Trial 90, Fold 1: Log loss = 0.331844758191932, Average precision = 0.9606197678651246, ROC-AUC = 0.9531092422917175, Elapsed Time = 1.0667802999996638 seconds Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 90, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 12982 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 4104 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4100 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4065 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3970 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4128 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4067 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Debug] Re-bagging, using 4020 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4078 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4005 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3914 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3983 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Debug] Re-bagging, using 4007 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4059 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4051 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4042 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4093 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 Trial 90, Fold 2: Log loss = 0.33514325857746285, Average precision = 0.9569969912288875, ROC-AUC = 0.9518625674936214, Elapsed Time = 1.074133999998594 seconds Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 90, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 12988 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 4099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4095 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4063 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3964 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Debug] Re-bagging, using 4124 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4070 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4021 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4156 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4068 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3994 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3927 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3978 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4003 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4063 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4035 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 5 [LightGBM] [Debug] Re-bagging, using 4043 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4108 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3968 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4117 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4104 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 Trial 90, Fold 3: Log loss = 0.32265590326638777, Average precision = 0.9635499999456428, ROC-AUC = 0.9568613901435987, Elapsed Time = 1.1633805000001303 seconds Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 90, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 12968 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 4095 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4089 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4057 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3958 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4078 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4014 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4154 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4055 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3985 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3910 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3991 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4000 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4059 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4040 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4033 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4095 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3970 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4091 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 Trial 90, Fold 4: Log loss = 0.3344532381528901, Average precision = 0.9603714714322781, ROC-AUC = 0.9529044793522036, Elapsed Time = 1.1753873999987263 seconds Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 90, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 12970 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 4093 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4089 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4057 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3956 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4075 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4019 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4057 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3982 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Debug] Re-bagging, using 3977 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4014 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8 [LightGBM] [Debug] Re-bagging, using 4062 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4036 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5 [LightGBM] [Debug] Re-bagging, using 4036 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4099 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Debug] Re-bagging, using 3955 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6 [LightGBM] [Debug] Re-bagging, using 4109 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7 [LightGBM] [Debug] Re-bagging, using 4087 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7 Trial 90, Fold 5: Log loss = 0.3372951464810977, Average precision = 0.9593237982203765, ROC-AUC = 0.9517935455102837, Elapsed Time = 1.1603880000002391 seconds
Optimization Progress: 91%|#########1| 91/100 [21:21<02:02, 13.58s/it]
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 91, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 23584 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10304 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10526 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10397 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20 [LightGBM] [Debug] Re-bagging, using 10215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10288 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10280 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10471 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 Trial 91, Fold 1: Log loss = 0.3008881871994671, Average precision = 0.9718838331284857, ROC-AUC = 0.9666126274357981, Elapsed Time = 0.9970556000007491 seconds Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 91, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956 [LightGBM] [Info] Total Bins 23515 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10518 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10481 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10346 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10237 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10313 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10299 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10290 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10507 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10381 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 22 [LightGBM] [Debug] Re-bagging, using 10420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 Trial 91, Fold 2: Log loss = 0.2991729032591439, Average precision = 0.9707289483964904, ROC-AUC = 0.9667573713763008, Elapsed Time = 1.1395652999999584 seconds Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 91, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 23508 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10506 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10403 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10236 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 21 [LightGBM] [Debug] Re-bagging, using 10280 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10340 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10491 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10356 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10422 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10400 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14 Trial 91, Fold 3: Log loss = 0.2955545562487179, Average precision = 0.9719937847754485, ROC-AUC = 0.9677634960897998, Elapsed Time = 1.16592270000001 seconds Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 91, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 23522 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10492 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Debug] Re-bagging, using 10459 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10303 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10424 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19 [LightGBM] [Debug] Re-bagging, using 10396 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10295 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10283 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10472 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10402 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 20 Trial 91, Fold 4: Log loss = 0.3017551683302678, Average precision = 0.9715725587706239, ROC-AUC = 0.9661564327799117, Elapsed Time = 1.1777863999996043 seconds Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 91, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364 [LightGBM] [Info] Total Bins 23533 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10489 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10296 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10338 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10430 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10448 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10216 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10285 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10271 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10341 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 20 [LightGBM] [Debug] Re-bagging, using 10466 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10393 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 19 [LightGBM] [Debug] Re-bagging, using 10391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 Trial 91, Fold 5: Log loss = 0.3005683826155532, Average precision = 0.9699361551117898, ROC-AUC = 0.9658108986864351, Elapsed Time = 1.171874800000296 seconds
Optimization Progress: 92%|#########2| 92/100 [21:34<01:47, 13.41s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 92, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 24804 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 5347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5327 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5355 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5234 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5370 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5261 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5126 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5207 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 Trial 92, Fold 1: Log loss = 0.24369748691301674, Average precision = 0.9719584055289463, ROC-AUC = 0.9668471260329887, Elapsed Time = 1.0470143999991706 seconds Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 92, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 24808 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 5357 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5333 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5174 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5376 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5242 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5379 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5277 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5149 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5203 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5215 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 Trial 92, Fold 2: Log loss = 0.24594562479299417, Average precision = 0.9695862442338552, ROC-AUC = 0.9657001445549405, Elapsed Time = 1.1108922000003076 seconds Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 92, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 24804 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 5352 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5297 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5330 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5170 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5325 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5243 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5369 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5265 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5142 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5168 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5195 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5211 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 Trial 92, Fold 3: Log loss = 0.24196498044713488, Average precision = 0.9718823432725099, ROC-AUC = 0.9677592072695054, Elapsed Time = 1.1935706000003847 seconds Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 92, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 24794 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 5344 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5162 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5351 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5328 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5235 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5130 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5153 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5206 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 Trial 92, Fold 4: Log loss = 0.24702565761415668, Average precision = 0.9708847749496918, ROC-AUC = 0.9653432745337883, Elapsed Time = 1.2001282000001083 seconds Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 92, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 24798 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 5342 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8 [LightGBM] [Debug] Re-bagging, using 5292 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5324 data to train [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5240 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5255 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Debug] Re-bagging, using 5130 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5148 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9 [LightGBM] [Debug] Re-bagging, using 5195 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Debug] Re-bagging, using 5214 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 Trial 92, Fold 5: Log loss = 0.24949129955282773, Average precision = 0.9692402742286004, ROC-AUC = 0.9643114653586757, Elapsed Time = 1.1896702000012738 seconds
Optimization Progress: 93%|#########3| 93/100 [21:48<01:33, 13.35s/it]
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 93, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662 [LightGBM] [Info] Total Bins 15120 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 14380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14345 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14432 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14436 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14426 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14362 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14427 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14283 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14392 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14301 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14539 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14436 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14439 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14454 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 Trial 93, Fold 1: Log loss = 0.28890787041309307, Average precision = 0.9721419694650522, ROC-AUC = 0.9666721482580787, Elapsed Time = 1.2925502000016422 seconds Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 93, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298 [LightGBM] [Info] Total Bins 15133 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 14407 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14550 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14375 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14458 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14405 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14463 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14392 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14441 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14305 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14319 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14567 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14475 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14496 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12 Trial 93, Fold 2: Log loss = 0.2883054234552478, Average precision = 0.9700646790336668, ROC-AUC = 0.9665828614230118, Elapsed Time = 1.5125194000011106 seconds Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 93, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455 [LightGBM] [Info] Total Bins 15138 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14366 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14440 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14395 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14448 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14447 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14515 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14391 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14294 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14302 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14557 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14450 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14482 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 Trial 93, Fold 3: Log loss = 0.2847499990984428, Average precision = 0.9724436861937583, ROC-AUC = 0.9676233193843842, Elapsed Time = 1.528636799999731 seconds Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 93, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144 [LightGBM] [Info] Total Bins 15120 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 14375 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14514 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14343 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14387 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14430 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14498 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14360 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14281 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14390 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14286 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14431 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14433 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 Trial 93, Fold 4: Log loss = 0.28714861168790945, Average precision = 0.9713751042348534, ROC-AUC = 0.965691323848151, Elapsed Time = 1.502879200001189 seconds Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 93, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026 [LightGBM] [Info] Total Bins 15119 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 14371 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 14490 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14512 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14380 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14433 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14420 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14493 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14359 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Debug] Re-bagging, using 14274 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14386 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Debug] Re-bagging, using 14289 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14 [LightGBM] [Debug] Re-bagging, using 14425 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Debug] Re-bagging, using 14429 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10 [LightGBM] [Debug] Re-bagging, using 14443 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14 Trial 93, Fold 5: Log loss = 0.2910606291415502, Average precision = 0.969880620876491, ROC-AUC = 0.9651118666740985, Elapsed Time = 1.4681914000011602 seconds
Optimization Progress: 94%|#########3| 94/100 [22:02<01:22, 13.75s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 94, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 19014 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16662 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16552 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 19 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16528 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16562 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16634 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19 [LightGBM] [Debug] Re-bagging, using 16508 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16556 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16564 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16517 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16631 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16709 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16487 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 Trial 94, Fold 1: Log loss = 0.24722053119581827, Average precision = 0.9700217835372049, ROC-AUC = 0.964115889505456, Elapsed Time = 1.3311601000004885 seconds Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 94, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 19026 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16693 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16637 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16621 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16610 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16663 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16557 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16577 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 19 [LightGBM] [Debug] Re-bagging, using 16540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16657 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 115 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16548 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 20 [LightGBM] [Debug] Re-bagging, using 16665 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16718 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16524 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16674 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 20 Trial 94, Fold 2: Log loss = 0.2518173165478632, Average precision = 0.9688081030783827, ROC-AUC = 0.9652927017509569, Elapsed Time = 1.426636199999848 seconds Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 94, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 19029 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 16601 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16677 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16617 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16574 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16538 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 118 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 21 [LightGBM] [Debug] Re-bagging, using 16588 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16529 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16544 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16570 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16531 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16638 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16536 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16646 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 116 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16725 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16520 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16592 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16648 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 Trial 94, Fold 3: Log loss = 0.2580977406334601, Average precision = 0.9691910105800448, ROC-AUC = 0.964746649603683, Elapsed Time = 1.6245612000002438 seconds Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 94, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454 [LightGBM] [Info] Total Bins 19013 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 16579 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16655 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16599 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16549 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16593 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16561 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16623 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16505 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16522 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16547 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16511 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16619 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16563 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16608 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16503 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16624 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16707 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16483 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 121 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16516 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16633 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 17 Trial 94, Fold 4: Log loss = 0.24575069718207382, Average precision = 0.9701339402577004, ROC-AUC = 0.9641384769821002, Elapsed Time = 1.6441727000001265 seconds Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 94, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 19008 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 16573 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16654 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12 [LightGBM] [Debug] Re-bagging, using 16598 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16585 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11 [LightGBM] [Debug] Re-bagging, using 16553 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 117 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 20 [LightGBM] [Debug] Re-bagging, using 16500 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 21 [LightGBM] [Debug] Re-bagging, using 16519 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17 [LightGBM] [Debug] Re-bagging, using 16541 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 23 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16510 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16618 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16555 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15 [LightGBM] [Debug] Re-bagging, using 16604 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16499 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 18 [LightGBM] [Debug] Re-bagging, using 16616 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16699 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 19 [LightGBM] [Debug] Re-bagging, using 16480 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13 [LightGBM] [Debug] Re-bagging, using 16513 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 117 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14 [LightGBM] [Debug] Re-bagging, using 16560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16 [LightGBM] [Debug] Re-bagging, using 16629 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 18 Trial 94, Fold 5: Log loss = 0.24745709343345237, Average precision = 0.9671251946854702, ROC-AUC = 0.9629334671051409, Elapsed Time = 1.5948129999997036 seconds
Optimization Progress: 95%|#########5| 95/100 [22:18<01:11, 14.24s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 95, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 24150 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7527 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7589 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7461 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7455 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7603 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7337 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7348 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7403 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7458 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7636 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11 Trial 95, Fold 1: Log loss = 0.2819267212222898, Average precision = 0.9687208579186044, ROC-AUC = 0.962326082696453, Elapsed Time = 1.3650629999992816 seconds Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 95, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 24158 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7542 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7605 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7470 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7625 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7613 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7560 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7347 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7418 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7415 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7474 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7658 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7568 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 95, Fold 2: Log loss = 0.27767917005180226, Average precision = 0.9672489021741485, ROC-AUC = 0.9627532025611384, Elapsed Time = 1.6671499000003678 seconds Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 95, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 24152 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7595 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7467 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7468 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7609 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7525 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7543 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7606 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7361 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7349 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7416 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7401 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7469 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7643 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12 [LightGBM] [Debug] Re-bagging, using 7559 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 Trial 95, Fold 3: Log loss = 0.2742493392445868, Average precision = 0.9705207772076768, ROC-AUC = 0.9650506291473644, Elapsed Time = 1.686474399999497 seconds Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 95, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 24142 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7523 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7587 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7530 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7537 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7597 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7534 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7421 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7401 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7457 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7630 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 Trial 95, Fold 4: Log loss = 0.2786265672968445, Average precision = 0.9688874155910504, ROC-AUC = 0.9626656568691268, Elapsed Time = 1.6847445999992487 seconds Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 95, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 24146 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7456 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7451 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7586 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7521 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7546 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7591 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7532 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7339 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7332 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7413 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7406 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7465 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7628 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7540 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9 Trial 95, Fold 5: Log loss = 0.2833765556609774, Average precision = 0.9667874212231111, ROC-AUC = 0.96182643107965, Elapsed Time = 1.6412778000012622 seconds
Optimization Progress: 96%|#########6| 96/100 [22:33<00:58, 14.65s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 96, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 25868 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 10167 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10127 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10000 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10178 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10041 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10119 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10107 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10157 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18 [LightGBM] [Debug] Re-bagging, using 9877 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 24 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9954 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 20 Trial 96, Fold 1: Log loss = 0.19265856405157333, Average precision = 0.9761579744323005, ROC-AUC = 0.9717249955788799, Elapsed Time = 2.0507928999995784 seconds Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 96, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 25873 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 10188 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10146 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10128 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10026 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10205 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10126 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10125 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Debug] Re-bagging, using 10184 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 19 [LightGBM] [Debug] Re-bagging, using 9899 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 25 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 19 [LightGBM] [Debug] Re-bagging, using 9967 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 21 Trial 96, Fold 2: Log loss = 0.18734632777382243, Average precision = 0.9734495986173171, ROC-AUC = 0.9719588302188112, Elapsed Time = 2.1794813000014983 seconds Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 96, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 25869 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 10177 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10134 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10021 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10189 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10037 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10125 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10114 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 19 [LightGBM] [Debug] Re-bagging, using 10163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15 [LightGBM] [Debug] Re-bagging, using 9899 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Debug] Re-bagging, using 9959 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 22 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 19 Trial 96, Fold 3: Log loss = 0.1883741298901994, Average precision = 0.9752283440379164, ROC-AUC = 0.9723419246178886, Elapsed Time = 2.1102914000002784 seconds Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 96, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 25858 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 10163 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10123 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10110 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13 [LightGBM] [Debug] Re-bagging, using 9999 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10171 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10039 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10117 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 21 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12 [LightGBM] [Debug] Re-bagging, using 10106 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Debug] Re-bagging, using 10153 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Debug] Re-bagging, using 9876 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 20 [LightGBM] [Debug] Re-bagging, using 9950 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 Trial 96, Fold 4: Log loss = 0.19104105517645986, Average precision = 0.9752330648341077, ROC-AUC = 0.9707036512466887, Elapsed Time = 2.073414300000877 seconds Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 96, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 25865 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 10160 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10120 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10111 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12 [LightGBM] [Debug] Re-bagging, using 9992 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11 [LightGBM] [Debug] Re-bagging, using 10169 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13 [LightGBM] [Debug] Re-bagging, using 10032 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15 [LightGBM] [Debug] Re-bagging, using 10121 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17 [LightGBM] [Debug] Re-bagging, using 10101 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 20 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14 [LightGBM] [Debug] Re-bagging, using 10150 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16 [LightGBM] [Debug] Re-bagging, using 9879 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 23 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 19 [LightGBM] [Debug] Re-bagging, using 9940 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15 Trial 96, Fold 5: Log loss = 0.19329716201171468, Average precision = 0.9739603585368279, ROC-AUC = 0.9715274139308474, Elapsed Time = 2.0674993000011455 seconds
Optimization Progress: 97%|#########7| 97/100 [22:52<00:47, 15.98s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 97, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804600 [LightGBM] [Info] Total Bins 29002 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 19497 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19452 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19514 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14 [LightGBM] [Debug] Re-bagging, using 19469 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19477 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19483 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 33 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19435 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19467 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19496 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19436 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19473 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19432 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19449 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Re-bagging, using 19491 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19458 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19471 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19429 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19529 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19504 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19529 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19402 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19436 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19464 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19493 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19521 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 Trial 97, Fold 1: Log loss = 0.3439567228683248, Average precision = 0.9642014557062354, ROC-AUC = 0.9580364013516679, Elapsed Time = 1.6739467000006698 seconds Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 97, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804566 [LightGBM] [Info] Total Bins 29006 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 19534 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19485 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19553 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19503 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19517 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19506 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19475 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19514 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19532 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19470 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19505 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19468 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19526 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19493 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19506 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19569 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19540 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19560 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19439 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19469 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19504 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19535 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19554 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 Trial 97, Fold 2: Log loss = 0.3371119253550399, Average precision = 0.9651042928311725, ROC-AUC = 0.9614018885315396, Elapsed Time = 2.0464709000007133 seconds Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 97, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804456 [LightGBM] [Info] Total Bins 29001 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 19516 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19468 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19533 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19490 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19491 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19497 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Re-bagging, using 19453 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Re-bagging, using 19492 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Re-bagging, using 19513 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19457 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19451 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19466 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19507 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19480 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Re-bagging, using 19483 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19449 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19550 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19521 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19549 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19419 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19447 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19485 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19513 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19543 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 Trial 97, Fold 3: Log loss = 0.3337444841372523, Average precision = 0.9682855889749199, ROC-AUC = 0.963323212720671, Elapsed Time = 2.080849899999521 seconds Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 97, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155 [LightGBM] [Info] Total Bins 28992 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 19490 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19445 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19509 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19468 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19474 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19433 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 13 [LightGBM] [Debug] Re-bagging, using 19462 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19428 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19426 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19444 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19483 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19451 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19463 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19422 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19523 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19498 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19521 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19399 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19422 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19464 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19488 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19512 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 Trial 97, Fold 4: Log loss = 0.3345920525712018, Average precision = 0.9672589026802049, ROC-AUC = 0.961008492075414, Elapsed Time = 2.0492997000001196 seconds Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 97, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804939 [LightGBM] [Info] Total Bins 28998 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 19484 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19440 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19503 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Re-bagging, using 19460 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19463 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19465 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19433 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19454 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23 [LightGBM] [Debug] Re-bagging, using 19478 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19423 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19459 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19422 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19437 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19477 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Re-bagging, using 19449 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Re-bagging, using 19453 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Re-bagging, using 19419 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19518 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 [LightGBM] [Debug] Re-bagging, using 19489 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19518 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19393 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Re-bagging, using 19419 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 36 [LightGBM] [Debug] Re-bagging, using 19452 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15 [LightGBM] [Debug] Re-bagging, using 19487 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22 [LightGBM] [Debug] Re-bagging, using 19507 data to train [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16 Trial 97, Fold 5: Log loss = 0.34868921272817555, Average precision = 0.9628221406956591, ROC-AUC = 0.9572423685041711, Elapsed Time = 2.0245357000003423 seconds
Optimization Progress: 98%|#########8| 98/100 [23:10<00:32, 16.41s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 98, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817 [LightGBM] [Info] Total Bins 7969 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 6760 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6778 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6679 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6628 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6807 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6700 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 Trial 98, Fold 1: Log loss = 0.2059870375568024, Average precision = 0.9736278666055719, ROC-AUC = 0.9687441748756112, Elapsed Time = 1.1208126999990782 seconds Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 98, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987 [LightGBM] [Info] Total Bins 7985 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 6773 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6795 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6687 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6643 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6835 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6744 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6713 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 Trial 98, Fold 2: Log loss = 0.1950855705719745, Average precision = 0.9740338406308985, ROC-AUC = 0.9714351486111586, Elapsed Time = 1.53894020000007 seconds Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 98, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893 [LightGBM] [Info] Total Bins 7992 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 6766 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6785 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6681 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6641 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6823 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26 [LightGBM] [Debug] Re-bagging, using 6710 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 Trial 98, Fold 3: Log loss = 0.19211088925521713, Average precision = 0.974235235731083, ROC-AUC = 0.9724076865290712, Elapsed Time = 1.3739041999997426 seconds Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 98, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155 [LightGBM] [Info] Total Bins 7971 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 6757 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Re-bagging, using 6676 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Re-bagging, using 6630 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Re-bagging, using 6802 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6752 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Re-bagging, using 6701 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21 Trial 98, Fold 4: Log loss = 0.20014693335544856, Average precision = 0.97502289346449, ROC-AUC = 0.970527950051384, Elapsed Time = 1.3837733000000298 seconds Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 98, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356 [LightGBM] [Info] Total Bins 7973 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 6755 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6775 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Re-bagging, using 6674 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12 [LightGBM] [Debug] Re-bagging, using 6625 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Re-bagging, using 6801 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23 [LightGBM] [Debug] Re-bagging, using 6745 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 30 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14 [LightGBM] [Debug] Re-bagging, using 6709 data to train [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18 Trial 98, Fold 5: Log loss = 0.20402423794622532, Average precision = 0.9736777729507122, ROC-AUC = 0.9695342139977332, Elapsed Time = 1.3356522999984008 seconds
Optimization Progress: 99%|#########9| 99/100 [23:24<00:15, 15.83s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 99, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 [LightGBM] [Info] Number of positive: 10130, number of negative: 10533 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640 [LightGBM] [Info] Total Bins 11253 [LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012 [LightGBM] [Info] Start training from score -0.039012 [LightGBM] [Debug] Re-bagging, using 7912 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7981 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7859 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7810 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7975 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7910 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7908 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 Trial 99, Fold 1: Log loss = 0.4228630313718012, Average precision = 0.9653912293403434, ROC-AUC = 0.9590646104873748, Elapsed Time = 0.658761600001526 seconds Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 99, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 [LightGBM] [Info] Number of positive: 10230, number of negative: 10471 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600 [LightGBM] [Info] Total Bins 11266 [LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285 [LightGBM] [Info] Start training from score -0.023285 [LightGBM] [Debug] Re-bagging, using 7928 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7996 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7875 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7825 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 8007 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7902 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7918 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 Trial 99, Fold 2: Log loss = 0.42258646154902085, Average precision = 0.9609599053042722, ROC-AUC = 0.9579277747310195, Elapsed Time = 0.7333526000002166 seconds Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 99, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 [LightGBM] [Info] Number of positive: 10165, number of negative: 10517 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401 [LightGBM] [Info] Total Bins 11269 [LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043 [LightGBM] [Info] Start training from score -0.034043 [LightGBM] [Debug] Re-bagging, using 7919 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7987 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7866 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7825 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7990 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Re-bagging, using 7901 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7915 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 Trial 99, Fold 3: Log loss = 0.42016989402690114, Average precision = 0.9661421572245269, ROC-AUC = 0.9603194584228216, Elapsed Time = 0.7783467999997811 seconds Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 99, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 [LightGBM] [Info] Number of positive: 10177, number of negative: 10479 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090 [LightGBM] [Info] Total Bins 11250 [LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243 [LightGBM] [Info] Start training from score -0.029243 [LightGBM] [Debug] Re-bagging, using 7908 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7979 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7855 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7810 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7969 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7907 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7909 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 Trial 99, Fold 4: Log loss = 0.42064763482572876, Average precision = 0.9653052217148508, ROC-AUC = 0.9584859355589415, Elapsed Time = 0.8043866000007256 seconds Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 99, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 [LightGBM] [Info] Number of positive: 10150, number of negative: 10500 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012 [LightGBM] [Info] Total Bins 11253 [LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902 [LightGBM] [Info] Start training from score -0.033902 [LightGBM] [Debug] Re-bagging, using 7906 data to train [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7978 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7853 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7805 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Re-bagging, using 7968 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11 [LightGBM] [Debug] Re-bagging, using 7898 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10 [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 [LightGBM] [Debug] Re-bagging, using 7916 data to train [LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9 Trial 99, Fold 5: Log loss = 0.42703576123191167, Average precision = 0.9626720951126174, ROC-AUC = 0.9566728721921856, Elapsed Time = 0.772207600000911 seconds
Optimization Progress: 100%|##########| 100/100 [23:36<00:00, 14.51s/it]
Optuna Optimization Elapsed Time: 1416.1865574000003 seconds
Optimization Progress: 100%|##########| 100/100 [23:36<00:00, 14.17s/it]
Training with Best Trial 68
{'objective': 'binary', 'metric': ['binary_logloss', 'auc', 'average_precision'], 'device_type': 'cpu', 'verbosity': 2, 'random_state': 42, 'deterministic': True, 'bagging_seed': 42, 'feature_fraction_seed': 42, 'drop_seed': 42, 'force_col_wise': True, 'num_threads': -1, 'max_depth': -1, 'boosting_type': 'gbdt', 'lambda_l1': 0.001799212849506679, 'lambda_l2': 1.1881208494212355e-08, 'num_leaves': 81, 'feature_fraction': 0.8529067830109069, 'bagging_fraction': 0.7231722623902207, 'bagging_freq': 6, 'min_child_samples': 28, 'learning_rate': 0.0749127622821807, 'is_unbalance': True, 'max_bin': 184, 'min_sum_hessian_in_leaf': 0.005200324540154612, 'max_delta_step': 40.70003905844247, 'feature_fraction_bynode': 0.9524479541474791, 'num_boost_round': 88}
[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines [LightGBM] [Info] Number of positive: 12713, number of negative: 13125 [LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804279 [LightGBM] [Info] Total Bins 22087 [LightGBM] [Info] Number of data points in the train set: 25838, number of used features: 265 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492027 -> initscore=-0.031894 [LightGBM] [Info] Start training from score -0.031894 [LightGBM] [Debug] Re-bagging, using 18633 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 18737 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Re-bagging, using 18772 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 18702 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 18714 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Re-bagging, using 18601 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Re-bagging, using 18731 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Re-bagging, using 18744 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Re-bagging, using 18737 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 18635 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Re-bagging, using 18640 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23 [LightGBM] [Debug] Re-bagging, using 18506 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24 [LightGBM] [Debug] Re-bagging, using 18695 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26 [LightGBM] [Debug] Re-bagging, using 18539 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20 [LightGBM] [Debug] Re-bagging, using 18768 data to train [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22 [LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28 Training Elapsed Time: 3.8860796999997547 seconds Log loss: (Train) 0.18691289880514814 vs (Test) 0.19337372842261577 PR-AUC: (Train) 0.9769836663793002 vs (Test) 0.9751402347947055 ROC-AUC: (Train) 0.9735624134708025 vs (Test) 0.9717662835980452
In [18]:
save_results(clf_name = "LGBM",
best_trials = best_trials_lgbm,
exec_time = exec_time_lgbm,
lloss_auc_train = lloss_auc_train_lgbm,
lloss_auc_test = lloss_auc_test_lgbm,
df_metrics = df_metrics_lgbm,
cm_final = cm_final_lgbm,
cm_all = cm_lgbm_all,
cm_labels = cm_labels_lgbm_all)
Optuna with HistGradientBoostingClassifier¶
In [19]:
gc.collect();
In [20]:
X_df = clean_df.drop(columns = ["target", "anon_ssn"])
y_df = clean_df.target
anon_ssn = clean_df.anon_ssn;
In [21]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring that no anon_ssn (grouped by anon_ssn) appear in both sets
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = seed)
train_idx, test_idx = next(gss.split(X_df, y_df, groups = anon_ssn))
X_train, X_test = X_df.iloc[train_idx], X_df.iloc[test_idx]
y_train, y_test = y_df.iloc[train_idx], y_df.iloc[test_idx]
anon_ssn_train = anon_ssn[train_idx] # Keeping track of anon_ssn for cross-validation
In [22]:
del X_df, y_df, gss, train_idx, test_idx;
In [23]:
def objective(trial):
# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
# https://scikit-learn.org/stable/modules/ensemble.html#categorical-support-gbdt
param = {"loss": "log_loss",
"verbose": 2,
"learning_rate": trial.suggest_float("learning_rate", 1e-2, 1e-1, log = True),
"max_iter": trial.suggest_int("max_iter", 5, 100),
"max_leaf_nodes": trial.suggest_int("max_leaf_nodes", 2, 256),
"min_samples_leaf": trial.suggest_int("min_samples_leaf", 5, 200),
"l2_regularization": trial.suggest_float("l2_regularization", 1e-8, 1e1, log = True),
"class_weight": trial.suggest_categorical("class_weight", [None, "balanced"]), # classes are balance
"random_state": seed,
"categorical_features": "from_dtype",
"max_depth": None,
"max_features": trial.suggest_float("max_features", 1e-1, 1e0),
"max_bins": trial.suggest_int("max_bins", 40, 255),
#"monotonic_cst": trial.suggest_categorical("monotonic_cst", [None, -1, 0, 1]),
"interaction_cst": trial.suggest_categorical("interaction_cst",
["pairwise"
,"no_interactions"]),
"warm_start": trial.suggest_categorical("warm_start",[False, True])
}
sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):
# Extract train and validation sets
X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]
y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]
# Summarize the composition of classes in the train and validation sets
train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])
print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where 0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')
clf = HistGradientBoostingClassifier(**param)
start_fold = time.perf_counter()
clf.fit(X_train_fold, y_train_fold)
end_fold = time.perf_counter()
# Predict probabilities
y_prob_fold = clf.predict_proba(X_valid_fold)[:, 1]
print(f'Trial {trial.number}, Fold {fold_idx}: '
f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
f'Elapsed Time = {end_fold - start_fold} seconds')
# Calculate and store the evaluation metrics for this fold
lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))
del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, clf, start_fold, end_fold
gc.collect()
# Calculate average metrics across all folds for Optuna to optimize
mean_lloss = np.mean(lloss_scores)
mean_pr_auc = np.mean(pr_auc_scores)
mean_roc_auc = np.mean(roc_auc_scores)
del lloss_scores, pr_auc_scores, roc_auc_scores
gc.collect()
# Return the metrics to Optuna for optimization
return mean_lloss, mean_pr_auc, mean_roc_auc
In [24]:
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
ascii = True, # Plain text mode
dynamic_ncols = True # Auto-fit width
)
def update_progress(study_hgbc, trial):
trial_progress.update(1)
optuna.logging.disable_default_handler()
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)
logging.basicConfig(filename = "optuna_debug_HistGradientBoostingClassifier.log", filemode = "w", level = logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")
study_hgbc = optuna.create_study(study_name = "Optuna with HistGradientBoostingClassifier",
directions = ["minimize", "maximize", "maximize"],
sampler = module.AutoSampler(seed = seed)
)
start_optuna = time.perf_counter()
study_hgbc.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')
fig = plot_pareto_front(study_hgbc, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400) # Set desired width and height in pixels
fig.show()
trial_progress.close()
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
optuna.visualization.plot_optimization_history(study_hgbc,
target = lambda t: t.values[i], # Correctly target each objective
target_name = obj).show()
best_trials = study_hgbc.best_trials
best_trials_hgbc = {}
exec_time_hgbc, lloss_auc_train_hgbc, lloss_auc_test_hgbc, all_metrics = [], [], [], []
cm_hgbc_all, cm_labels_hgbc_all = [], []
for i, trial in enumerate(best_trials):
display(Markdown(f'<span style = "font-size: 18px; font-weight: bold;"> Training with Best Trial {trial.number}: </span>'))
best_params = trial.params
# Non-optimized and best Optuna optimized parameters
full_params = {"loss": "log_loss",
"verbose": 2,
"random_state": seed,
"categorical_features": "from_dtype",
"max_depth": None,
**best_params
}
print("Full_params:", full_params)
best_trials_hgbc[trial.number] = full_params
final_hgbc = HistGradientBoostingClassifier(**full_params)
start_train = time.perf_counter()
final_hgbc.fit(X_train, y_train)
end_train = time.perf_counter()
print(f'Training Elapsed Time: {end_train - start_train} seconds')
exec_time_hgbc.append({"Classifier": "HistGradientBoostingClassifier",
"Best Trial": trial.number,
"Optimization Elapsed Time (s)": end_optuna - start_optuna,
"Training Elapsed Time (s)": end_train - start_train})
y_prob_all = final_hgbc.predict_proba(X_test)[:, 1]
y_pred_all = final_hgbc.predict(X_test)
print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
print(f'PR-AUC: (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
print(f'ROC-AUC: (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')
lloss_auc_train_hgbc.append({"Classifier": "HistGradientBoostingClassifier",
"Best Trial": trial.number,
"Set": "Training",
"Log loss": trial.values[0],
"PR-AUC": trial.values[1],
"ROC-AUC": trial.values[2]})
lloss_auc_test_hgbc.append({"Classifier": "HistGradientBoostingClassifier",
"Best Trial": trial.number,
"Set": "Test",
"Log loss": log_loss(y_test, y_prob_all),
"PR-AUC": average_precision_score(y_test, y_prob_all),
"ROC-AUC": roc_auc_score(y_test, y_prob_all)})
report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
all_metrics.append({"Classifier": "HistGradientBoostingClassifier",
"Trial": trial.number,
"Accuracy": accuracy_score(y_test, y_pred_all),
"Precision (Safe)": report["Safe"]["precision"],
"Recall (Safe)": report["Safe"]["recall"],
"F1-score (Safe)": report["Safe"]["f1-score"],
"Precision (Risky)": report["Risky"]["precision"],
"Recall (Risky)": report["Risky"]["recall"],
"F1-score (Risky)": report["Risky"]["f1-score"],
"Precision (Macro avg)": report["macro avg"]["precision"],
"Recall (Macro avg)": report["macro avg"]["recall"],
"F1-score (Macro avg)": report["macro avg"]["f1-score"],
"Precision (Weighted avg)": report["weighted avg"]["precision"],
"Recall (Weighted avg)": report["weighted avg"]["recall"],
"F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})
# Store confusion matrix
cm_final_hgbc = confusion_matrix(y_test, y_pred_all)
cm_hgbc_all.append(cm_final_hgbc)
cm_labels_hgbc_all.append(f'HistGradientBoostingClassifier Confusion Matrix for Best Trial {trial.number}') # Store label for subplots
df_metrics_hgbc = pd.DataFrame(all_metrics)
gc.collect();
Optimization Progress: 0%| | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 0, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 19 leaves, max depth = 9, train loss: 0.68006, val loss: 0.67964, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.66763, val loss: 0.66685, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.65585, val loss: 0.65476, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.64478, val loss: 0.64325, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.63425, val loss: 0.63238, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.62427, val loss: 0.62208, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.61494, val loss: 0.61244, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.60610, val loss: 0.60325, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.59758, val loss: 0.59443, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.58933, val loss: 0.58588, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.58173, val loss: 0.57794, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.57455, val loss: 0.57044, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.56761, val loss: 0.56323, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.56109, val loss: 0.55642, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.55480, val loss: 0.54987, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.54838, val loss: 0.54389, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.54251, val loss: 0.53780, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.53693, val loss: 0.53200, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.53161, val loss: 0.52637, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.52641, val loss: 0.52093, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.52146, val loss: 0.51575, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.51675, val loss: 0.51081, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.51226, val loss: 0.50610, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.50798, val loss: 0.50161, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.50401, val loss: 0.49747, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.50023, val loss: 0.49352, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.49667, val loss: 0.48973, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.49329, val loss: 0.48618, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.49009, val loss: 0.48278, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.48493, val loss: 0.47801, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.48142, val loss: 0.47476, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.47846, val loss: 0.47159, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.47550, val loss: 0.46843, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.47081, val loss: 0.46413, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.46805, val loss: 0.46118, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.46553, val loss: 0.45850, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.46311, val loss: 0.45595, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.45874, val loss: 0.45193, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.45649, val loss: 0.44955, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.45361, val loss: 0.44691, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.45150, val loss: 0.44468, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.44940, val loss: 0.44241, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.44749, val loss: 0.44037, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44353, val loss: 0.43677, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44097, val loss: 0.43443, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.43853, val loss: 0.43222, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.43496, val loss: 0.42897, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.43316, val loss: 0.42704, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.43096, val loss: 0.42505, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.42767, val loss: 0.42206, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.42607, val loss: 0.42044, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.42298, val loss: 0.41764, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.42004, val loss: 0.41498, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.41825, val loss: 0.41338, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.41668, val loss: 0.41169, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.41396, val loss: 0.40923, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.41249, val loss: 0.40760, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.41109, val loss: 0.40610, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.40850, val loss: 0.40379, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40666, val loss: 0.40176, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.40505, val loss: 0.40035, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.40265, val loss: 0.39822, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.40143, val loss: 0.39688, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.40022, val loss: 0.39559, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.39912, val loss: 0.39437, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.39772, val loss: 0.39316, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39601, val loss: 0.39126, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39480, val loss: 0.39007, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.39263, val loss: 0.38815, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.39055, val loss: 0.38632, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.38947, val loss: 0.38513, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.38818, val loss: 0.38403, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38656, val loss: 0.38223, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.38551, val loss: 0.38120, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38397, val loss: 0.37949, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.38299, val loss: 0.37846, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38152, val loss: 0.37683, in 0.000s 1 tree, 19 leaves, max depth = 12, train loss: 0.38065, val loss: 0.37592, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.37978, val loss: 0.37496, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37839, val loss: 0.37341, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.37752, val loss: 0.37247, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.37562, val loss: 0.37082, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.37463, val loss: 0.36988, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.37284, val loss: 0.36834, in 0.016s Fit 84 trees in 1.032 s, (1476 total leaves) Time spent computing histograms: 0.355s Time spent finding best splits: 0.037s Time spent applying splits: 0.033s Time spent predicting: 0.016s Trial 0, Fold 1: Log loss = 0.3744440833225494, Average precision = 0.9491437057327736, ROC-AUC = 0.9445759578296841, Elapsed Time = 1.0311564000003273 seconds Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 0, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 19 leaves, max depth = 10, train loss: 0.68016, val loss: 0.67951, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.66784, val loss: 0.66660, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.65608, val loss: 0.65428, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.64497, val loss: 0.64262, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.63454, val loss: 0.63165, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.62463, val loss: 0.62126, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.61526, val loss: 0.61139, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.60638, val loss: 0.60202, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.59786, val loss: 0.59302, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.58966, val loss: 0.58432, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.58205, val loss: 0.57630, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.57483, val loss: 0.56866, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.56797, val loss: 0.56137, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.56146, val loss: 0.55444, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.55526, val loss: 0.54786, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.54889, val loss: 0.54169, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.54303, val loss: 0.53546, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.53745, val loss: 0.52952, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.53212, val loss: 0.52385, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.52696, val loss: 0.51831, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.52205, val loss: 0.51304, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.51737, val loss: 0.50801, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.51291, val loss: 0.50321, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.50867, val loss: 0.49863, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.50470, val loss: 0.49438, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.50093, val loss: 0.49032, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.49737, val loss: 0.48652, in 0.016s 1 tree, 19 leaves, max depth = 12, train loss: 0.49398, val loss: 0.48286, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.49076, val loss: 0.47936, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.48563, val loss: 0.47443, in 0.016s 1 tree, 19 leaves, max depth = 12, train loss: 0.48263, val loss: 0.47118, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.47976, val loss: 0.46810, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.47690, val loss: 0.46498, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.47213, val loss: 0.46041, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.46947, val loss: 0.45750, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.46702, val loss: 0.45481, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.46467, val loss: 0.45226, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.46022, val loss: 0.44801, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.45804, val loss: 0.44562, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.45509, val loss: 0.44287, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.45307, val loss: 0.44079, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.45098, val loss: 0.43848, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.44895, val loss: 0.43672, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44497, val loss: 0.43294, in 0.016s 1 tree, 19 leaves, max depth = 5, train loss: 0.44236, val loss: 0.43053, in 0.000s 1 tree, 19 leaves, max depth = 5, train loss: 0.43989, val loss: 0.42824, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.43631, val loss: 0.42485, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.43444, val loss: 0.42283, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.43221, val loss: 0.42079, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.42890, val loss: 0.41765, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.42728, val loss: 0.41590, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.42413, val loss: 0.41292, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.42113, val loss: 0.41008, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.41929, val loss: 0.40842, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.41780, val loss: 0.40682, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.41503, val loss: 0.40419, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.41356, val loss: 0.40257, in 0.016s 1 tree, 19 leaves, max depth = 13, train loss: 0.41225, val loss: 0.40113, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.40961, val loss: 0.39862, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40789, val loss: 0.39685, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.40653, val loss: 0.39571, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.40406, val loss: 0.39338, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.40258, val loss: 0.39209, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.40131, val loss: 0.39070, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.40018, val loss: 0.38943, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.39879, val loss: 0.38822, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39719, val loss: 0.38656, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.39597, val loss: 0.38556, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39378, val loss: 0.38351, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.39170, val loss: 0.38155, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39058, val loss: 0.38029, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.38924, val loss: 0.37911, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38773, val loss: 0.37754, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.38667, val loss: 0.37669, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38523, val loss: 0.37519, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.38419, val loss: 0.37406, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38282, val loss: 0.37263, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.38159, val loss: 0.37161, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.38065, val loss: 0.37060, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37934, val loss: 0.36924, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.37841, val loss: 0.36822, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.37655, val loss: 0.36649, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.37558, val loss: 0.36572, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.37384, val loss: 0.36411, in 0.016s Fit 84 trees in 0.940 s, (1462 total leaves) Time spent computing histograms: 0.374s Time spent finding best splits: 0.037s Time spent applying splits: 0.034s Time spent predicting: 0.000s Trial 0, Fold 2: Log loss = 0.375627800803536, Average precision = 0.9455558130432133, ROC-AUC = 0.9450537961074265, Elapsed Time = 0.9430072000013752 seconds Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 0, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 19 leaves, max depth = 10, train loss: 0.68016, val loss: 0.67973, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.66781, val loss: 0.66692, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.65615, val loss: 0.65478, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.64511, val loss: 0.64333, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.63465, val loss: 0.63246, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.62475, val loss: 0.62216, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.61545, val loss: 0.61249, in 0.016s 1 tree, 19 leaves, max depth = 12, train loss: 0.60665, val loss: 0.60333, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.59820, val loss: 0.59451, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.59006, val loss: 0.58607, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.58248, val loss: 0.57825, in 0.016s 1 tree, 19 leaves, max depth = 12, train loss: 0.57533, val loss: 0.57078, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.56844, val loss: 0.56358, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.56198, val loss: 0.55683, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.55574, val loss: 0.55029, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.54935, val loss: 0.54430, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.54356, val loss: 0.53819, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.53806, val loss: 0.53237, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.53275, val loss: 0.52682, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.52764, val loss: 0.52150, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.52278, val loss: 0.51642, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.51815, val loss: 0.51158, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.51375, val loss: 0.50696, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.50955, val loss: 0.50256, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.50563, val loss: 0.49841, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.50191, val loss: 0.49443, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.49836, val loss: 0.49075, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.49500, val loss: 0.48718, in 0.016s 1 tree, 19 leaves, max depth = 12, train loss: 0.49181, val loss: 0.48378, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.48668, val loss: 0.47902, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.48316, val loss: 0.47580, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.48022, val loss: 0.47274, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.47732, val loss: 0.46969, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.47266, val loss: 0.46538, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.46996, val loss: 0.46253, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.46747, val loss: 0.45984, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.46498, val loss: 0.45715, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.46065, val loss: 0.45316, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.45843, val loss: 0.45076, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.45556, val loss: 0.44816, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.45346, val loss: 0.44624, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.45133, val loss: 0.44397, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.44936, val loss: 0.44184, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.44543, val loss: 0.43824, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44288, val loss: 0.43595, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.44045, val loss: 0.43378, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.43691, val loss: 0.43055, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.43511, val loss: 0.42855, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.43292, val loss: 0.42662, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.42965, val loss: 0.42364, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.42798, val loss: 0.42217, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.42492, val loss: 0.41939, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.42199, val loss: 0.41674, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.42040, val loss: 0.41497, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.41888, val loss: 0.41327, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.41612, val loss: 0.41077, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.41465, val loss: 0.40918, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.41329, val loss: 0.40767, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.41066, val loss: 0.40531, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40887, val loss: 0.40365, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.40721, val loss: 0.40213, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.40478, val loss: 0.39997, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.40331, val loss: 0.39874, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.40210, val loss: 0.39738, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.40098, val loss: 0.39616, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.39960, val loss: 0.39502, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39793, val loss: 0.39348, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.39666, val loss: 0.39242, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.39450, val loss: 0.39052, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39244, val loss: 0.38871, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.39138, val loss: 0.38752, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.39009, val loss: 0.38635, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38852, val loss: 0.38489, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.38739, val loss: 0.38398, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38590, val loss: 0.38259, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.38491, val loss: 0.38148, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38349, val loss: 0.38016, in 0.000s 1 tree, 19 leaves, max depth = 14, train loss: 0.38257, val loss: 0.37915, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.38166, val loss: 0.37821, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38031, val loss: 0.37696, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.37943, val loss: 0.37600, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.37753, val loss: 0.37434, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.37647, val loss: 0.37350, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.37470, val loss: 0.37196, in 0.016s Fit 84 trees in 0.923 s, (1462 total leaves) Time spent computing histograms: 0.352s Time spent finding best splits: 0.037s Time spent applying splits: 0.033s Time spent predicting: 0.000s Trial 0, Fold 3: Log loss = 0.36979576733385733, Average precision = 0.9509140324614288, ROC-AUC = 0.9483565090146489, Elapsed Time = 0.9244606000011117 seconds Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 0, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 19 leaves, max depth = 9, train loss: 0.68017, val loss: 0.67941, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.66780, val loss: 0.66634, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.65611, val loss: 0.65397, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64506, val loss: 0.64229, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.63458, val loss: 0.63116, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.62466, val loss: 0.62060, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.61534, val loss: 0.61068, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.60650, val loss: 0.60129, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.59803, val loss: 0.59221, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.58990, val loss: 0.58345, in 0.000s 1 tree, 19 leaves, max depth = 13, train loss: 0.58231, val loss: 0.57527, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.57514, val loss: 0.56758, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.56823, val loss: 0.56014, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.56175, val loss: 0.55310, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.55550, val loss: 0.54633, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.54915, val loss: 0.54007, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.54333, val loss: 0.53375, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.53780, val loss: 0.52773, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.53249, val loss: 0.52198, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.52739, val loss: 0.51637, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.52253, val loss: 0.51101, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.51790, val loss: 0.50590, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.51350, val loss: 0.50102, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.50930, val loss: 0.49636, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.50537, val loss: 0.49201, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.50163, val loss: 0.48785, in 0.000s 1 tree, 19 leaves, max depth = 13, train loss: 0.49808, val loss: 0.48388, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.49470, val loss: 0.48013, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.49148, val loss: 0.47655, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.48639, val loss: 0.47158, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.48323, val loss: 0.46807, in 0.016s 1 tree, 19 leaves, max depth = 12, train loss: 0.48037, val loss: 0.46482, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.47754, val loss: 0.46161, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.47282, val loss: 0.45703, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.47020, val loss: 0.45403, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.46776, val loss: 0.45126, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.46544, val loss: 0.44861, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.46101, val loss: 0.44433, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.45885, val loss: 0.44185, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.45593, val loss: 0.43903, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.45393, val loss: 0.43689, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.45187, val loss: 0.43450, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.44987, val loss: 0.43245, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44592, val loss: 0.42865, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.44334, val loss: 0.42618, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.44090, val loss: 0.42382, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.43733, val loss: 0.42040, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.43548, val loss: 0.41828, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.43328, val loss: 0.41616, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.43000, val loss: 0.41302, in 0.016s 1 tree, 19 leaves, max depth = 13, train loss: 0.42836, val loss: 0.41112, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.42524, val loss: 0.40813, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.42226, val loss: 0.40528, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.42044, val loss: 0.40352, in 0.047s 1 tree, 19 leaves, max depth = 7, train loss: 0.41889, val loss: 0.40173, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.41613, val loss: 0.39910, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.41465, val loss: 0.39742, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.41326, val loss: 0.39579, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.41064, val loss: 0.39331, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40892, val loss: 0.39146, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.40727, val loss: 0.38990, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.40485, val loss: 0.38759, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.40342, val loss: 0.38621, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.40217, val loss: 0.38475, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.40103, val loss: 0.38338, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.39968, val loss: 0.38209, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39809, val loss: 0.38037, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.39686, val loss: 0.37911, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39472, val loss: 0.37709, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.39267, val loss: 0.37517, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.39158, val loss: 0.37386, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39029, val loss: 0.37265, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38878, val loss: 0.37102, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.38771, val loss: 0.36993, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38627, val loss: 0.36837, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.38525, val loss: 0.36718, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38388, val loss: 0.36570, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.38297, val loss: 0.36462, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.38207, val loss: 0.36354, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38077, val loss: 0.36214, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.37990, val loss: 0.36111, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.37802, val loss: 0.35936, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.37701, val loss: 0.35835, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.37526, val loss: 0.35672, in 0.000s Fit 84 trees in 1.126 s, (1461 total leaves) Time spent computing histograms: 0.462s Time spent finding best splits: 0.064s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 0, Fold 4: Log loss = 0.37527621904651487, Average precision = 0.9487464960271949, ROC-AUC = 0.9447211104911646, Elapsed Time = 1.1381462000008469 seconds Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 0, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.203 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 19 leaves, max depth = 10, train loss: 0.67998, val loss: 0.67905, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.66756, val loss: 0.66584, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.65575, val loss: 0.65320, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.64456, val loss: 0.64124, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.63405, val loss: 0.62998, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.62408, val loss: 0.61925, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.61465, val loss: 0.60911, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.60571, val loss: 0.59947, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.59715, val loss: 0.59025, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.58892, val loss: 0.58133, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.58125, val loss: 0.57300, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.57398, val loss: 0.56510, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.56707, val loss: 0.55758, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.56052, val loss: 0.55037, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.55426, val loss: 0.54354, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.54799, val loss: 0.53751, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.54209, val loss: 0.53104, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.53648, val loss: 0.52487, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53111, val loss: 0.51897, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.52593, val loss: 0.51324, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.52100, val loss: 0.50778, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.51631, val loss: 0.50257, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.51183, val loss: 0.49759, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.50757, val loss: 0.49283, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.50358, val loss: 0.48837, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.49978, val loss: 0.48410, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.49620, val loss: 0.48005, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.49278, val loss: 0.47621, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.48953, val loss: 0.47254, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.48449, val loss: 0.46783, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.48146, val loss: 0.46439, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.47856, val loss: 0.46108, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.47569, val loss: 0.45781, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.47101, val loss: 0.45347, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.46834, val loss: 0.45041, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.46589, val loss: 0.44760, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.46352, val loss: 0.44486, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.45915, val loss: 0.44084, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.45695, val loss: 0.43828, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.45404, val loss: 0.43575, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.45199, val loss: 0.43335, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.44995, val loss: 0.43097, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44796, val loss: 0.42898, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.44404, val loss: 0.42539, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.44149, val loss: 0.42320, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.43905, val loss: 0.42112, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.43555, val loss: 0.41794, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.43375, val loss: 0.41583, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.43156, val loss: 0.41399, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.42831, val loss: 0.41103, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.42673, val loss: 0.40915, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.42363, val loss: 0.40634, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.42070, val loss: 0.40372, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.41889, val loss: 0.40224, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.41744, val loss: 0.40050, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.41470, val loss: 0.39805, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.41327, val loss: 0.39635, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.41199, val loss: 0.39482, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.40938, val loss: 0.39249, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40759, val loss: 0.39077, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.40625, val loss: 0.38947, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.40382, val loss: 0.38730, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.40236, val loss: 0.38616, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.40115, val loss: 0.38468, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.40002, val loss: 0.38330, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.39865, val loss: 0.38225, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39698, val loss: 0.38065, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.39579, val loss: 0.37948, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.39363, val loss: 0.37758, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.39157, val loss: 0.37576, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.39049, val loss: 0.37445, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.38919, val loss: 0.37320, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38761, val loss: 0.37170, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.38656, val loss: 0.37069, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38506, val loss: 0.36925, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.38407, val loss: 0.36804, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38264, val loss: 0.36668, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.38140, val loss: 0.36558, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.38048, val loss: 0.36445, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37913, val loss: 0.36316, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.37824, val loss: 0.36206, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.37640, val loss: 0.36047, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.37544, val loss: 0.35956, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.37372, val loss: 0.35808, in 0.016s Fit 84 trees in 1.063 s, (1475 total leaves) Time spent computing histograms: 0.406s Time spent finding best splits: 0.041s Time spent applying splits: 0.037s Time spent predicting: 0.000s Trial 0, Fold 5: Log loss = 0.3795476722528022, Average precision = 0.9488541635377791, ROC-AUC = 0.9447077271798302, Elapsed Time = 1.0641242000001512 seconds
Optimization Progress: 1%|1 | 1/100 [00:12<21:16, 12.90s/it]
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 1, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 14, train loss: 0.67836, val loss: 0.67826, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.66426, val loss: 0.66417, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.65085, val loss: 0.65081, in 0.016s 1 tree, 91 leaves, max depth = 13, train loss: 0.63799, val loss: 0.63795, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.62573, val loss: 0.62569, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.61408, val loss: 0.61410, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.60293, val loss: 0.60296, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.59236, val loss: 0.59238, in 0.016s 1 tree, 118 leaves, max depth = 13, train loss: 0.58230, val loss: 0.58222, in 0.016s 1 tree, 136 leaves, max depth = 14, train loss: 0.57288, val loss: 0.57261, in 0.031s 1 tree, 122 leaves, max depth = 14, train loss: 0.56366, val loss: 0.56332, in 0.016s 1 tree, 67 leaves, max depth = 10, train loss: 0.55492, val loss: 0.55450, in 0.016s 1 tree, 67 leaves, max depth = 10, train loss: 0.54656, val loss: 0.54604, in 0.016s 1 tree, 96 leaves, max depth = 13, train loss: 0.53829, val loss: 0.53784, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.53042, val loss: 0.52995, in 0.031s 1 tree, 100 leaves, max depth = 14, train loss: 0.52282, val loss: 0.52240, in 0.016s 1 tree, 100 leaves, max depth = 14, train loss: 0.51552, val loss: 0.51515, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.50863, val loss: 0.50824, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.50187, val loss: 0.50153, in 0.016s 1 tree, 116 leaves, max depth = 16, train loss: 0.49555, val loss: 0.49519, in 0.031s 1 tree, 100 leaves, max depth = 14, train loss: 0.48929, val loss: 0.48900, in 0.016s 1 tree, 87 leaves, max depth = 14, train loss: 0.48314, val loss: 0.48283, in 0.016s 1 tree, 100 leaves, max depth = 14, train loss: 0.47735, val loss: 0.47712, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.47177, val loss: 0.47161, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.46641, val loss: 0.46630, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.46124, val loss: 0.46104, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.45627, val loss: 0.45613, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.45133, val loss: 0.45119, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.44670, val loss: 0.44663, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.44224, val loss: 0.44224, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.43553, val loss: 0.43579, in 0.031s 1 tree, 122 leaves, max depth = 16, train loss: 0.42920, val loss: 0.42964, in 0.016s 1 tree, 128 leaves, max depth = 13, train loss: 0.42524, val loss: 0.42570, in 0.016s 1 tree, 109 leaves, max depth = 14, train loss: 0.42125, val loss: 0.42170, in 0.032s 1 tree, 95 leaves, max depth = 15, train loss: 0.41533, val loss: 0.41599, in 0.016s 1 tree, 128 leaves, max depth = 13, train loss: 0.41175, val loss: 0.41244, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.40618, val loss: 0.40708, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.40082, val loss: 0.40196, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.39732, val loss: 0.39851, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.39398, val loss: 0.39516, in 0.016s 1 tree, 95 leaves, max depth = 15, train loss: 0.38903, val loss: 0.39042, in 0.031s 1 tree, 96 leaves, max depth = 15, train loss: 0.38431, val loss: 0.38591, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.38131, val loss: 0.38303, in 0.031s 1 tree, 99 leaves, max depth = 15, train loss: 0.37685, val loss: 0.37879, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.37255, val loss: 0.37468, in 0.016s 1 tree, 108 leaves, max depth = 15, train loss: 0.36979, val loss: 0.37203, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.36575, val loss: 0.36819, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.36315, val loss: 0.36572, in 0.016s 1 tree, 136 leaves, max depth = 13, train loss: 0.36072, val loss: 0.36338, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.35702, val loss: 0.35983, in 0.016s 1 tree, 108 leaves, max depth = 13, train loss: 0.35464, val loss: 0.35756, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.35126, val loss: 0.35421, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.34801, val loss: 0.35102, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.34491, val loss: 0.34793, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.34192, val loss: 0.34499, in 0.016s 1 tree, 119 leaves, max depth = 15, train loss: 0.33932, val loss: 0.34245, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.33652, val loss: 0.33968, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.33404, val loss: 0.33719, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.33131, val loss: 0.33475, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.32873, val loss: 0.33219, in 0.016s 1 tree, 121 leaves, max depth = 15, train loss: 0.32640, val loss: 0.32986, in 0.031s 1 tree, 109 leaves, max depth = 18, train loss: 0.32437, val loss: 0.32786, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.32189, val loss: 0.32565, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.31951, val loss: 0.32354, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.31724, val loss: 0.32133, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.31500, val loss: 0.31935, in 0.016s 1 tree, 119 leaves, max depth = 16, train loss: 0.31292, val loss: 0.31735, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.31092, val loss: 0.31534, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.30885, val loss: 0.31330, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.30681, val loss: 0.31151, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.30487, val loss: 0.30960, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.30300, val loss: 0.30775, in 0.016s 1 tree, 86 leaves, max depth = 13, train loss: 0.30111, val loss: 0.30607, in 0.016s 1 tree, 109 leaves, max depth = 17, train loss: 0.29951, val loss: 0.30454, in 0.016s Fit 74 trees in 1.690 s, (7096 total leaves) Time spent computing histograms: 0.503s Time spent finding best splits: 0.131s Time spent applying splits: 0.114s Time spent predicting: 0.000s Trial 1, Fold 1: Log loss = 0.306563213010577, Average precision = 0.9577684804959341, ROC-AUC = 0.9521826741084871, Elapsed Time = 1.7062987999997858 seconds Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 1, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 61 leaves, max depth = 11, train loss: 0.67837, val loss: 0.67810, in 0.016s 1 tree, 89 leaves, max depth = 11, train loss: 0.66391, val loss: 0.66349, in 0.016s 1 tree, 92 leaves, max depth = 13, train loss: 0.65038, val loss: 0.64974, in 0.016s 1 tree, 92 leaves, max depth = 13, train loss: 0.63750, val loss: 0.63666, in 0.031s 1 tree, 93 leaves, max depth = 11, train loss: 0.62502, val loss: 0.62406, in 0.016s 1 tree, 97 leaves, max depth = 11, train loss: 0.61313, val loss: 0.61206, in 0.016s 1 tree, 97 leaves, max depth = 11, train loss: 0.60177, val loss: 0.60059, in 0.031s 1 tree, 100 leaves, max depth = 12, train loss: 0.59093, val loss: 0.58965, in 0.016s 1 tree, 112 leaves, max depth = 11, train loss: 0.58067, val loss: 0.57935, in 0.031s 1 tree, 135 leaves, max depth = 17, train loss: 0.57143, val loss: 0.57008, in 0.016s 1 tree, 121 leaves, max depth = 11, train loss: 0.56202, val loss: 0.56064, in 0.016s 1 tree, 79 leaves, max depth = 11, train loss: 0.55309, val loss: 0.55160, in 0.016s 1 tree, 102 leaves, max depth = 12, train loss: 0.54433, val loss: 0.54274, in 0.016s 1 tree, 127 leaves, max depth = 12, train loss: 0.53606, val loss: 0.53444, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.52835, val loss: 0.52674, in 0.031s 1 tree, 142 leaves, max depth = 13, train loss: 0.52118, val loss: 0.51955, in 0.031s 1 tree, 103 leaves, max depth = 12, train loss: 0.51370, val loss: 0.51204, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.50653, val loss: 0.50479, in 0.016s 1 tree, 108 leaves, max depth = 12, train loss: 0.49963, val loss: 0.49787, in 0.031s 1 tree, 106 leaves, max depth = 12, train loss: 0.49301, val loss: 0.49117, in 0.031s 1 tree, 107 leaves, max depth = 15, train loss: 0.48666, val loss: 0.48475, in 0.016s 1 tree, 124 leaves, max depth = 17, train loss: 0.48090, val loss: 0.47893, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.47323, val loss: 0.47138, in 0.031s 1 tree, 102 leaves, max depth = 16, train loss: 0.46751, val loss: 0.46558, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.46199, val loss: 0.45998, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.45669, val loss: 0.45460, in 0.031s 1 tree, 99 leaves, max depth = 16, train loss: 0.44973, val loss: 0.44770, in 0.016s 1 tree, 100 leaves, max depth = 16, train loss: 0.44307, val loss: 0.44109, in 0.031s 1 tree, 99 leaves, max depth = 16, train loss: 0.43669, val loss: 0.43476, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.43200, val loss: 0.43003, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.42749, val loss: 0.42546, in 0.016s 1 tree, 121 leaves, max depth = 16, train loss: 0.42341, val loss: 0.42136, in 0.016s 1 tree, 108 leaves, max depth = 16, train loss: 0.41919, val loss: 0.41710, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.41512, val loss: 0.41301, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.41138, val loss: 0.40924, in 0.016s 1 tree, 100 leaves, max depth = 16, train loss: 0.40595, val loss: 0.40389, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.40226, val loss: 0.40018, in 0.031s 1 tree, 102 leaves, max depth = 16, train loss: 0.39873, val loss: 0.39665, in 0.016s 1 tree, 112 leaves, max depth = 16, train loss: 0.39533, val loss: 0.39331, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.39040, val loss: 0.38848, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.38715, val loss: 0.38524, in 0.016s 1 tree, 108 leaves, max depth = 16, train loss: 0.38400, val loss: 0.38210, in 0.031s 1 tree, 102 leaves, max depth = 16, train loss: 0.37939, val loss: 0.37756, in 0.016s 1 tree, 130 leaves, max depth = 12, train loss: 0.37649, val loss: 0.37472, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.37218, val loss: 0.37051, in 0.031s 1 tree, 128 leaves, max depth = 13, train loss: 0.36806, val loss: 0.36654, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.36532, val loss: 0.36378, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.36140, val loss: 0.35996, in 0.031s 1 tree, 103 leaves, max depth = 15, train loss: 0.35760, val loss: 0.35622, in 0.016s 1 tree, 108 leaves, max depth = 14, train loss: 0.35508, val loss: 0.35374, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.35274, val loss: 0.35142, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.34944, val loss: 0.34818, in 0.016s 1 tree, 54 leaves, max depth = 14, train loss: 0.34626, val loss: 0.34508, in 0.016s 1 tree, 119 leaves, max depth = 18, train loss: 0.34360, val loss: 0.34250, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.34061, val loss: 0.33958, in 0.016s 1 tree, 133 leaves, max depth = 13, train loss: 0.33851, val loss: 0.33762, in 0.016s 1 tree, 86 leaves, max depth = 13, train loss: 0.33558, val loss: 0.33494, in 0.031s 1 tree, 87 leaves, max depth = 13, train loss: 0.33276, val loss: 0.33236, in 0.016s 1 tree, 55 leaves, max depth = 15, train loss: 0.33011, val loss: 0.32978, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.32748, val loss: 0.32735, in 0.016s 1 tree, 121 leaves, max depth = 20, train loss: 0.32513, val loss: 0.32513, in 0.016s 1 tree, 122 leaves, max depth = 20, train loss: 0.32286, val loss: 0.32298, in 0.031s 1 tree, 57 leaves, max depth = 15, train loss: 0.32043, val loss: 0.32064, in 0.016s 1 tree, 88 leaves, max depth = 13, train loss: 0.31803, val loss: 0.31845, in 0.016s 1 tree, 144 leaves, max depth = 17, train loss: 0.31582, val loss: 0.31635, in 0.016s 1 tree, 57 leaves, max depth = 15, train loss: 0.31359, val loss: 0.31419, in 0.016s 1 tree, 87 leaves, max depth = 13, train loss: 0.31137, val loss: 0.31217, in 0.016s 1 tree, 52 leaves, max depth = 16, train loss: 0.30929, val loss: 0.31012, in 0.016s 1 tree, 145 leaves, max depth = 18, train loss: 0.30721, val loss: 0.30817, in 0.031s 1 tree, 87 leaves, max depth = 13, train loss: 0.30517, val loss: 0.30632, in 0.016s 1 tree, 56 leaves, max depth = 15, train loss: 0.30324, val loss: 0.30445, in 0.016s 1 tree, 117 leaves, max depth = 18, train loss: 0.30137, val loss: 0.30268, in 0.031s 1 tree, 86 leaves, max depth = 15, train loss: 0.29946, val loss: 0.30096, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.29763, val loss: 0.29931, in 0.016s Fit 74 trees in 1.830 s, (7413 total leaves) Time spent computing histograms: 0.529s Time spent finding best splits: 0.143s Time spent applying splits: 0.128s Time spent predicting: 0.000s Trial 1, Fold 2: Log loss = 0.30159064159157584, Average precision = 0.9600446233961784, ROC-AUC = 0.9570980178400766, Elapsed Time = 1.844349200000579 seconds Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 1, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.159 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 12, train loss: 0.67847, val loss: 0.67835, in 0.016s 1 tree, 99 leaves, max depth = 12, train loss: 0.66446, val loss: 0.66422, in 0.031s 1 tree, 95 leaves, max depth = 11, train loss: 0.65103, val loss: 0.65079, in 0.016s 1 tree, 97 leaves, max depth = 11, train loss: 0.63825, val loss: 0.63801, in 0.031s 1 tree, 96 leaves, max depth = 11, train loss: 0.62608, val loss: 0.62584, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.61427, val loss: 0.61405, in 0.016s 1 tree, 98 leaves, max depth = 11, train loss: 0.60321, val loss: 0.60293, in 0.016s 1 tree, 98 leaves, max depth = 11, train loss: 0.59264, val loss: 0.59231, in 0.031s 1 tree, 106 leaves, max depth = 12, train loss: 0.58264, val loss: 0.58227, in 0.016s 1 tree, 101 leaves, max depth = 12, train loss: 0.57299, val loss: 0.57254, in 0.016s 1 tree, 114 leaves, max depth = 15, train loss: 0.56362, val loss: 0.56323, in 0.016s 1 tree, 117 leaves, max depth = 15, train loss: 0.55466, val loss: 0.55433, in 0.031s 1 tree, 119 leaves, max depth = 13, train loss: 0.54612, val loss: 0.54583, in 0.016s 1 tree, 122 leaves, max depth = 15, train loss: 0.53790, val loss: 0.53767, in 0.016s 1 tree, 120 leaves, max depth = 16, train loss: 0.53006, val loss: 0.53001, in 0.031s 1 tree, 140 leaves, max depth = 17, train loss: 0.52281, val loss: 0.52283, in 0.031s 1 tree, 104 leaves, max depth = 15, train loss: 0.51538, val loss: 0.51546, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.50825, val loss: 0.50839, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.50140, val loss: 0.50159, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.49497, val loss: 0.49518, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.48863, val loss: 0.48890, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.48254, val loss: 0.48287, in 0.016s 1 tree, 100 leaves, max depth = 14, train loss: 0.47463, val loss: 0.47545, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.46896, val loss: 0.46984, in 0.031s 1 tree, 106 leaves, max depth = 15, train loss: 0.46349, val loss: 0.46445, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.45824, val loss: 0.45926, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.45116, val loss: 0.45265, in 0.031s 1 tree, 99 leaves, max depth = 14, train loss: 0.44438, val loss: 0.44634, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.43788, val loss: 0.44029, in 0.016s 1 tree, 108 leaves, max depth = 15, train loss: 0.43325, val loss: 0.43572, in 0.031s 1 tree, 111 leaves, max depth = 14, train loss: 0.42878, val loss: 0.43128, in 0.016s 1 tree, 108 leaves, max depth = 14, train loss: 0.42448, val loss: 0.42701, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.42032, val loss: 0.42292, in 0.031s 1 tree, 101 leaves, max depth = 14, train loss: 0.41458, val loss: 0.41761, in 0.031s 1 tree, 107 leaves, max depth = 15, train loss: 0.41068, val loss: 0.41377, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.40691, val loss: 0.41008, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.40161, val loss: 0.40520, in 0.016s 1 tree, 109 leaves, max depth = 14, train loss: 0.39807, val loss: 0.40174, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.39466, val loss: 0.39839, in 0.031s 1 tree, 107 leaves, max depth = 15, train loss: 0.39136, val loss: 0.39516, in 0.016s 1 tree, 100 leaves, max depth = 14, train loss: 0.38654, val loss: 0.39074, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.38343, val loss: 0.38771, in 0.031s 1 tree, 100 leaves, max depth = 14, train loss: 0.37889, val loss: 0.38356, in 0.031s 1 tree, 110 leaves, max depth = 14, train loss: 0.37596, val loss: 0.38071, in 0.016s 1 tree, 99 leaves, max depth = 16, train loss: 0.37170, val loss: 0.37682, in 0.031s 1 tree, 111 leaves, max depth = 14, train loss: 0.36894, val loss: 0.37413, in 0.016s 1 tree, 99 leaves, max depth = 16, train loss: 0.36491, val loss: 0.37047, in 0.031s 1 tree, 110 leaves, max depth = 14, train loss: 0.36231, val loss: 0.36795, in 0.016s 1 tree, 99 leaves, max depth = 16, train loss: 0.35850, val loss: 0.36451, in 0.031s 1 tree, 132 leaves, max depth = 14, train loss: 0.35609, val loss: 0.36223, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.35264, val loss: 0.35910, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.34934, val loss: 0.35610, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.34618, val loss: 0.35323, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.34294, val loss: 0.35032, in 0.016s 1 tree, 118 leaves, max depth = 18, train loss: 0.34035, val loss: 0.34762, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.33783, val loss: 0.34496, in 0.031s 1 tree, 53 leaves, max depth = 16, train loss: 0.33502, val loss: 0.34242, in 0.016s 1 tree, 112 leaves, max depth = 15, train loss: 0.33302, val loss: 0.34051, in 0.031s 1 tree, 53 leaves, max depth = 13, train loss: 0.33035, val loss: 0.33813, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.32766, val loss: 0.33595, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.32508, val loss: 0.33386, in 0.016s 1 tree, 55 leaves, max depth = 16, train loss: 0.32267, val loss: 0.33169, in 0.032s 1 tree, 110 leaves, max depth = 15, train loss: 0.32087, val loss: 0.32999, in 0.016s 1 tree, 120 leaves, max depth = 18, train loss: 0.31871, val loss: 0.32771, in 0.031s 1 tree, 53 leaves, max depth = 13, train loss: 0.31645, val loss: 0.32567, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.31416, val loss: 0.32380, in 0.016s 1 tree, 121 leaves, max depth = 18, train loss: 0.31213, val loss: 0.32169, in 0.031s 1 tree, 53 leaves, max depth = 13, train loss: 0.31005, val loss: 0.31982, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.30805, val loss: 0.31802, in 0.016s 1 tree, 121 leaves, max depth = 18, train loss: 0.30614, val loss: 0.31602, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.30409, val loss: 0.31437, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.30213, val loss: 0.31278, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.30023, val loss: 0.31130, in 0.031s 1 tree, 85 leaves, max depth = 14, train loss: 0.29841, val loss: 0.30983, in 0.016s Fit 74 trees in 1.956 s, (7280 total leaves) Time spent computing histograms: 0.587s Time spent finding best splits: 0.152s Time spent applying splits: 0.135s Time spent predicting: 0.000s Trial 1, Fold 3: Log loss = 0.29955521394130014, Average precision = 0.9582165465175596, ROC-AUC = 0.9550424788839557, Elapsed Time = 1.9552509000004648 seconds Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 1, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 72 leaves, max depth = 14, train loss: 0.67840, val loss: 0.67787, in 0.016s 1 tree, 96 leaves, max depth = 12, train loss: 0.66423, val loss: 0.66319, in 0.016s 1 tree, 100 leaves, max depth = 14, train loss: 0.65076, val loss: 0.64929, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.63793, val loss: 0.63605, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.62571, val loss: 0.62338, in 0.031s 1 tree, 124 leaves, max depth = 12, train loss: 0.61418, val loss: 0.61130, in 0.016s 1 tree, 100 leaves, max depth = 12, train loss: 0.60304, val loss: 0.59977, in 0.047s 1 tree, 101 leaves, max depth = 12, train loss: 0.59241, val loss: 0.58877, in 0.031s 1 tree, 105 leaves, max depth = 12, train loss: 0.58225, val loss: 0.57822, in 0.031s 1 tree, 104 leaves, max depth = 12, train loss: 0.57253, val loss: 0.56811, in 0.031s 1 tree, 99 leaves, max depth = 12, train loss: 0.56322, val loss: 0.55845, in 0.016s 1 tree, 113 leaves, max depth = 17, train loss: 0.55466, val loss: 0.54952, in 0.031s 1 tree, 102 leaves, max depth = 11, train loss: 0.54659, val loss: 0.54129, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.53839, val loss: 0.53273, in 0.031s 1 tree, 99 leaves, max depth = 11, train loss: 0.53035, val loss: 0.52436, in 0.016s 1 tree, 131 leaves, max depth = 13, train loss: 0.52272, val loss: 0.51636, in 0.031s 1 tree, 100 leaves, max depth = 13, train loss: 0.51530, val loss: 0.50864, in 0.031s 1 tree, 99 leaves, max depth = 12, train loss: 0.50821, val loss: 0.50120, in 0.016s 1 tree, 102 leaves, max depth = 12, train loss: 0.50137, val loss: 0.49406, in 0.031s 1 tree, 133 leaves, max depth = 13, train loss: 0.49489, val loss: 0.48724, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.48856, val loss: 0.48063, in 0.031s 1 tree, 102 leaves, max depth = 13, train loss: 0.48249, val loss: 0.47428, in 0.016s 1 tree, 134 leaves, max depth = 13, train loss: 0.47673, val loss: 0.46820, in 0.031s 1 tree, 137 leaves, max depth = 13, train loss: 0.47119, val loss: 0.46235, in 0.016s 1 tree, 133 leaves, max depth = 13, train loss: 0.46586, val loss: 0.45674, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.45868, val loss: 0.44940, in 0.031s 1 tree, 97 leaves, max depth = 13, train loss: 0.45172, val loss: 0.44237, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.44679, val loss: 0.43720, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.44024, val loss: 0.43056, in 0.016s 1 tree, 139 leaves, max depth = 13, train loss: 0.43572, val loss: 0.42577, in 0.031s 1 tree, 107 leaves, max depth = 13, train loss: 0.43126, val loss: 0.42112, in 0.031s 1 tree, 111 leaves, max depth = 13, train loss: 0.42696, val loss: 0.41662, in 0.016s 1 tree, 105 leaves, max depth = 12, train loss: 0.42281, val loss: 0.41228, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.41701, val loss: 0.40642, in 0.031s 1 tree, 136 leaves, max depth = 13, train loss: 0.41322, val loss: 0.40242, in 0.016s 1 tree, 116 leaves, max depth = 17, train loss: 0.40963, val loss: 0.39868, in 0.031s 1 tree, 97 leaves, max depth = 15, train loss: 0.40426, val loss: 0.39328, in 0.016s 1 tree, 113 leaves, max depth = 13, train loss: 0.40071, val loss: 0.38955, in 0.016s 1 tree, 112 leaves, max depth = 14, train loss: 0.39727, val loss: 0.38594, in 0.031s 1 tree, 110 leaves, max depth = 14, train loss: 0.39396, val loss: 0.38245, in 0.016s 1 tree, 130 leaves, max depth = 14, train loss: 0.38920, val loss: 0.37763, in 0.031s 1 tree, 99 leaves, max depth = 16, train loss: 0.38451, val loss: 0.37292, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.38146, val loss: 0.36973, in 0.031s 1 tree, 132 leaves, max depth = 14, train loss: 0.37716, val loss: 0.36538, in 0.016s 1 tree, 138 leaves, max depth = 14, train loss: 0.37438, val loss: 0.36240, in 0.031s 1 tree, 108 leaves, max depth = 12, train loss: 0.37160, val loss: 0.35948, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.36750, val loss: 0.35538, in 0.032s 1 tree, 110 leaves, max depth = 14, train loss: 0.36487, val loss: 0.35263, in 0.016s 1 tree, 98 leaves, max depth = 15, train loss: 0.36097, val loss: 0.34874, in 0.031s 1 tree, 136 leaves, max depth = 12, train loss: 0.35857, val loss: 0.34619, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.35510, val loss: 0.34261, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.35178, val loss: 0.33917, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.34908, val loss: 0.33648, in 0.031s 1 tree, 56 leaves, max depth = 13, train loss: 0.34595, val loss: 0.33324, in 0.016s 1 tree, 150 leaves, max depth = 18, train loss: 0.34328, val loss: 0.33054, in 0.031s 1 tree, 86 leaves, max depth = 13, train loss: 0.34026, val loss: 0.32767, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.33738, val loss: 0.32468, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.33455, val loss: 0.32203, in 0.016s 1 tree, 126 leaves, max depth = 18, train loss: 0.33218, val loss: 0.31967, in 0.031s 1 tree, 53 leaves, max depth = 14, train loss: 0.32953, val loss: 0.31689, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.32693, val loss: 0.31445, in 0.031s 1 tree, 56 leaves, max depth = 14, train loss: 0.32446, val loss: 0.31189, in 0.016s 1 tree, 124 leaves, max depth = 17, train loss: 0.32227, val loss: 0.30969, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.31993, val loss: 0.30726, in 0.031s 1 tree, 86 leaves, max depth = 14, train loss: 0.31758, val loss: 0.30508, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.31551, val loss: 0.30303, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.31329, val loss: 0.30093, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.31132, val loss: 0.29898, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.30922, val loss: 0.29703, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.30719, val loss: 0.29493, in 0.031s 1 tree, 85 leaves, max depth = 14, train loss: 0.30522, val loss: 0.29307, in 0.016s 1 tree, 108 leaves, max depth = 14, train loss: 0.30369, val loss: 0.29145, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.30182, val loss: 0.28948, in 0.031s 1 tree, 149 leaves, max depth = 17, train loss: 0.29991, val loss: 0.28754, in 0.031s Fit 74 trees in 2.096 s, (7611 total leaves) Time spent computing histograms: 0.623s Time spent finding best splits: 0.176s Time spent applying splits: 0.163s Time spent predicting: 0.016s Trial 1, Fold 4: Log loss = 0.3012262393830042, Average precision = 0.9599601894426121, ROC-AUC = 0.956310683228777, Elapsed Time = 2.0986773000004177 seconds Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 1, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 11, train loss: 0.67817, val loss: 0.67751, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.66462, val loss: 0.66343, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.65091, val loss: 0.64928, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.63782, val loss: 0.63568, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.62536, val loss: 0.62275, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.61347, val loss: 0.61038, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.60215, val loss: 0.59870, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.59131, val loss: 0.58742, in 0.016s 1 tree, 126 leaves, max depth = 13, train loss: 0.58106, val loss: 0.57674, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.57115, val loss: 0.56643, in 0.031s 1 tree, 97 leaves, max depth = 15, train loss: 0.56168, val loss: 0.55664, in 0.016s 1 tree, 130 leaves, max depth = 12, train loss: 0.55271, val loss: 0.54727, in 0.016s 1 tree, 128 leaves, max depth = 14, train loss: 0.54411, val loss: 0.53830, in 0.031s 1 tree, 103 leaves, max depth = 15, train loss: 0.53575, val loss: 0.52959, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.52774, val loss: 0.52126, in 0.016s 1 tree, 90 leaves, max depth = 13, train loss: 0.52066, val loss: 0.51381, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.51326, val loss: 0.50609, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.50625, val loss: 0.49876, in 0.016s 1 tree, 122 leaves, max depth = 15, train loss: 0.49976, val loss: 0.49204, in 0.031s 1 tree, 120 leaves, max depth = 15, train loss: 0.49354, val loss: 0.48558, in 0.016s 1 tree, 109 leaves, max depth = 15, train loss: 0.48717, val loss: 0.47899, in 0.016s 1 tree, 110 leaves, max depth = 15, train loss: 0.48104, val loss: 0.47262, in 0.016s 1 tree, 112 leaves, max depth = 15, train loss: 0.47514, val loss: 0.46647, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.46768, val loss: 0.45885, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.46251, val loss: 0.45347, in 0.031s 1 tree, 109 leaves, max depth = 15, train loss: 0.45718, val loss: 0.44794, in 0.016s 1 tree, 110 leaves, max depth = 15, train loss: 0.45205, val loss: 0.44260, in 0.016s 1 tree, 109 leaves, max depth = 15, train loss: 0.44712, val loss: 0.43748, in 0.031s 1 tree, 76 leaves, max depth = 15, train loss: 0.44255, val loss: 0.43264, in 0.016s 1 tree, 110 leaves, max depth = 15, train loss: 0.43794, val loss: 0.42791, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.43148, val loss: 0.42148, in 0.031s 1 tree, 112 leaves, max depth = 15, train loss: 0.42717, val loss: 0.41702, in 0.016s 1 tree, 109 leaves, max depth = 15, train loss: 0.42303, val loss: 0.41272, in 0.031s 1 tree, 102 leaves, max depth = 15, train loss: 0.41708, val loss: 0.40681, in 0.016s 1 tree, 112 leaves, max depth = 15, train loss: 0.41318, val loss: 0.40279, in 0.031s 1 tree, 134 leaves, max depth = 12, train loss: 0.40948, val loss: 0.39895, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.40398, val loss: 0.39350, in 0.031s 1 tree, 111 leaves, max depth = 15, train loss: 0.40044, val loss: 0.38985, in 0.016s 1 tree, 133 leaves, max depth = 11, train loss: 0.39709, val loss: 0.38640, in 0.031s 1 tree, 134 leaves, max depth = 11, train loss: 0.39385, val loss: 0.38307, in 0.016s 1 tree, 113 leaves, max depth = 15, train loss: 0.39066, val loss: 0.37977, in 0.031s 1 tree, 103 leaves, max depth = 15, train loss: 0.38572, val loss: 0.37491, in 0.031s 1 tree, 103 leaves, max depth = 15, train loss: 0.38099, val loss: 0.37025, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.37653, val loss: 0.36585, in 0.031s 1 tree, 102 leaves, max depth = 14, train loss: 0.37217, val loss: 0.36155, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.36806, val loss: 0.35752, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.36531, val loss: 0.35470, in 0.016s 1 tree, 101 leaves, max depth = 14, train loss: 0.36135, val loss: 0.35081, in 0.031s 1 tree, 128 leaves, max depth = 13, train loss: 0.35764, val loss: 0.34715, in 0.016s 1 tree, 109 leaves, max depth = 14, train loss: 0.35507, val loss: 0.34461, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.35147, val loss: 0.34109, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.34864, val loss: 0.33830, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.34542, val loss: 0.33503, in 0.016s 1 tree, 125 leaves, max depth = 17, train loss: 0.34273, val loss: 0.33238, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.33969, val loss: 0.32929, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.33713, val loss: 0.32678, in 0.016s 1 tree, 124 leaves, max depth = 17, train loss: 0.33467, val loss: 0.32436, in 0.031s 1 tree, 83 leaves, max depth = 13, train loss: 0.33178, val loss: 0.32175, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.32903, val loss: 0.31896, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.32632, val loss: 0.31653, in 0.016s 1 tree, 152 leaves, max depth = 17, train loss: 0.32393, val loss: 0.31411, in 0.031s 1 tree, 54 leaves, max depth = 16, train loss: 0.32139, val loss: 0.31155, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.31890, val loss: 0.30931, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.31654, val loss: 0.30691, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.31427, val loss: 0.30459, in 0.016s 1 tree, 110 leaves, max depth = 14, train loss: 0.31253, val loss: 0.30285, in 0.031s 1 tree, 83 leaves, max depth = 12, train loss: 0.31027, val loss: 0.30080, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.30817, val loss: 0.29867, in 0.016s 1 tree, 150 leaves, max depth = 17, train loss: 0.30609, val loss: 0.29656, in 0.031s 1 tree, 83 leaves, max depth = 12, train loss: 0.30400, val loss: 0.29471, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.30199, val loss: 0.29294, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.30011, val loss: 0.29106, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.29822, val loss: 0.28940, in 0.016s 1 tree, 156 leaves, max depth = 18, train loss: 0.29629, val loss: 0.28745, in 0.031s Fit 74 trees in 1.925 s, (7611 total leaves) Time spent computing histograms: 0.567s Time spent finding best splits: 0.152s Time spent applying splits: 0.134s Time spent predicting: 0.016s Trial 1, Fold 5: Log loss = 0.3059257783920364, Average precision = 0.9571162576778489, ROC-AUC = 0.952768147446259, Elapsed Time = 1.9239307999996527 seconds
Optimization Progress: 2%|2 | 2/100 [00:29<24:13, 14.83s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 2, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 8, train loss: 0.66988, val loss: 0.66936, in 0.016s 1 tree, 32 leaves, max depth = 7, train loss: 0.64735, val loss: 0.64677, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.62702, val loss: 0.62621, in 0.016s 1 tree, 33 leaves, max depth = 7, train loss: 0.60789, val loss: 0.60700, in 0.016s 1 tree, 37 leaves, max depth = 7, train loss: 0.59157, val loss: 0.59044, in 0.031s 1 tree, 27 leaves, max depth = 7, train loss: 0.57534, val loss: 0.57389, in 0.000s 1 tree, 40 leaves, max depth = 9, train loss: 0.56066, val loss: 0.55895, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.54615, val loss: 0.54432, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53325, val loss: 0.53123, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.52143, val loss: 0.51907, in 0.016s 1 tree, 39 leaves, max depth = 8, train loss: 0.50972, val loss: 0.50712, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.49942, val loss: 0.49652, in 0.016s Fit 12 trees in 0.486 s, (430 total leaves) Time spent computing histograms: 0.071s Time spent finding best splits: 0.009s Time spent applying splits: 0.008s Time spent predicting: 0.016s Trial 2, Fold 1: Log loss = 0.5002498264839964, Average precision = 0.9215519475609961, ROC-AUC = 0.9313925811215783, Elapsed Time = 0.48928890000024694 seconds Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 2, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 26 leaves, max depth = 7, train loss: 0.66975, val loss: 0.66923, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.64713, val loss: 0.64618, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.62625, val loss: 0.62494, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.60738, val loss: 0.60567, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.59063, val loss: 0.58876, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.57389, val loss: 0.57174, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.56004, val loss: 0.55744, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54544, val loss: 0.54262, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.53273, val loss: 0.52961, in 0.031s 1 tree, 32 leaves, max depth = 9, train loss: 0.52105, val loss: 0.51779, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.50992, val loss: 0.50640, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.49882, val loss: 0.49509, in 0.031s Fit 12 trees in 0.533 s, (422 total leaves) Time spent computing histograms: 0.087s Time spent finding best splits: 0.011s Time spent applying splits: 0.010s Time spent predicting: 0.000s Trial 2, Fold 2: Log loss = 0.4999592546829724, Average precision = 0.9167748509612672, ROC-AUC = 0.9306778179032644, Elapsed Time = 0.5291692999999213 seconds Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 2, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.127 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 8, train loss: 0.66986, val loss: 0.66962, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.64715, val loss: 0.64693, in 0.016s 1 tree, 24 leaves, max depth = 6, train loss: 0.62681, val loss: 0.62637, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.60765, val loss: 0.60713, in 0.016s 1 tree, 41 leaves, max depth = 7, train loss: 0.59130, val loss: 0.59069, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.57563, val loss: 0.57501, in 0.000s 1 tree, 39 leaves, max depth = 7, train loss: 0.56090, val loss: 0.56041, in 0.031s 1 tree, 36 leaves, max depth = 10, train loss: 0.54646, val loss: 0.54594, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.53362, val loss: 0.53316, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.52212, val loss: 0.52155, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51011, val loss: 0.50963, in 0.016s 1 tree, 46 leaves, max depth = 8, train loss: 0.49965, val loss: 0.49929, in 0.016s Fit 12 trees in 0.502 s, (443 total leaves) Time spent computing histograms: 0.082s Time spent finding best splits: 0.008s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 2, Fold 3: Log loss = 0.4973438762829475, Average precision = 0.924954635794069, ROC-AUC = 0.9353176676522426, Elapsed Time = 0.5145811000002141 seconds Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 2, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 8, train loss: 0.66985, val loss: 0.66880, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.64736, val loss: 0.64531, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.62709, val loss: 0.62403, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.60789, val loss: 0.60389, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.59126, val loss: 0.58663, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.57582, val loss: 0.57043, in 0.000s 1 tree, 38 leaves, max depth = 8, train loss: 0.56136, val loss: 0.55521, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54675, val loss: 0.53984, in 0.031s 1 tree, 37 leaves, max depth = 10, train loss: 0.53403, val loss: 0.52653, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.52218, val loss: 0.51412, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.51036, val loss: 0.50163, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.50014, val loss: 0.49096, in 0.016s Fit 12 trees in 0.501 s, (429 total leaves) Time spent computing histograms: 0.075s Time spent finding best splits: 0.009s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 2, Fold 4: Log loss = 0.499519425604958, Average precision = 0.9219241863020324, ROC-AUC = 0.9324585380945227, Elapsed Time = 0.5062674999990122 seconds Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 2, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 32 leaves, max depth = 8, train loss: 0.66967, val loss: 0.66820, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.64699, val loss: 0.64472, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.62652, val loss: 0.62337, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.60723, val loss: 0.60335, in 0.016s 1 tree, 26 leaves, max depth = 7, train loss: 0.58990, val loss: 0.58519, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.57447, val loss: 0.56880, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.55998, val loss: 0.55352, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.54538, val loss: 0.53844, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.53263, val loss: 0.52515, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.52105, val loss: 0.51313, in 0.000s 1 tree, 38 leaves, max depth = 8, train loss: 0.50947, val loss: 0.50111, in 0.016s 1 tree, 40 leaves, max depth = 7, train loss: 0.49927, val loss: 0.49066, in 0.016s Fit 12 trees in 0.485 s, (421 total leaves) Time spent computing histograms: 0.075s Time spent finding best splits: 0.008s Time spent applying splits: 0.008s Time spent predicting: 0.000s Trial 2, Fold 5: Log loss = 0.5021190230330362, Average precision = 0.9164841006897811, ROC-AUC = 0.9291524441224014, Elapsed Time = 0.5015739999998914 seconds
Optimization Progress: 3%|3 | 3/100 [00:38<19:37, 12.14s/it]
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 3, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.141 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 56 leaves, max depth = 11, train loss: 0.68265, val loss: 0.68255, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.67219, val loss: 0.67220, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.66265, val loss: 0.66258, in 0.031s 1 tree, 93 leaves, max depth = 12, train loss: 0.65301, val loss: 0.65292, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.64421, val loss: 0.64402, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.63505, val loss: 0.63491, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.62663, val loss: 0.62649, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.61810, val loss: 0.61803, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.60989, val loss: 0.60981, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.60188, val loss: 0.60184, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.59451, val loss: 0.59446, in 0.016s 1 tree, 102 leaves, max depth = 13, train loss: 0.58734, val loss: 0.58720, in 0.031s 1 tree, 85 leaves, max depth = 11, train loss: 0.58029, val loss: 0.58008, in 0.016s 1 tree, 74 leaves, max depth = 11, train loss: 0.57330, val loss: 0.57296, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.56644, val loss: 0.56610, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.55995, val loss: 0.55954, in 0.016s 1 tree, 72 leaves, max depth = 11, train loss: 0.55399, val loss: 0.55352, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.54771, val loss: 0.54725, in 0.016s 1 tree, 102 leaves, max depth = 13, train loss: 0.54172, val loss: 0.54122, in 0.016s 1 tree, 81 leaves, max depth = 11, train loss: 0.53582, val loss: 0.53531, in 0.031s 1 tree, 101 leaves, max depth = 16, train loss: 0.53045, val loss: 0.52987, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.52477, val loss: 0.52416, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.51987, val loss: 0.51922, in 0.016s 1 tree, 98 leaves, max depth = 12, train loss: 0.51475, val loss: 0.51405, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.50961, val loss: 0.50881, in 0.016s 1 tree, 102 leaves, max depth = 12, train loss: 0.50462, val loss: 0.50372, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.49996, val loss: 0.49904, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.49552, val loss: 0.49450, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.48950, val loss: 0.48863, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.48521, val loss: 0.48432, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.48083, val loss: 0.47996, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.47658, val loss: 0.47578, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.47263, val loss: 0.47181, in 0.016s 1 tree, 87 leaves, max depth = 11, train loss: 0.46881, val loss: 0.46802, in 0.031s 1 tree, 62 leaves, max depth = 12, train loss: 0.46516, val loss: 0.46424, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.46121, val loss: 0.46026, in 0.016s 1 tree, 86 leaves, max depth = 12, train loss: 0.45738, val loss: 0.45640, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.45382, val loss: 0.45275, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.45037, val loss: 0.44921, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.44689, val loss: 0.44565, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.44363, val loss: 0.44236, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.44028, val loss: 0.43901, in 0.031s 1 tree, 102 leaves, max depth = 12, train loss: 0.43709, val loss: 0.43577, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.43399, val loss: 0.43260, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.42936, val loss: 0.42815, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.42491, val loss: 0.42388, in 0.031s 1 tree, 85 leaves, max depth = 11, train loss: 0.42216, val loss: 0.42118, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.41925, val loss: 0.41828, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.41512, val loss: 0.41428, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.41263, val loss: 0.41170, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.41006, val loss: 0.40906, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.40613, val loss: 0.40525, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.40353, val loss: 0.40266, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.40097, val loss: 0.40011, in 0.031s 1 tree, 86 leaves, max depth = 13, train loss: 0.39845, val loss: 0.39760, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.39602, val loss: 0.39517, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.39382, val loss: 0.39300, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.39016, val loss: 0.38953, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.38807, val loss: 0.38746, in 0.016s Fit 59 trees in 1.344 s, (4932 total leaves) Time spent computing histograms: 0.405s Time spent finding best splits: 0.082s Time spent applying splits: 0.077s Time spent predicting: 0.000s Trial 3, Fold 1: Log loss = 0.3930796249502737, Average precision = 0.9518336436663236, ROC-AUC = 0.9472694057297716, Elapsed Time = 1.3563878999993904 seconds Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 3, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.141 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 61 leaves, max depth = 9, train loss: 0.68264, val loss: 0.68243, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.67194, val loss: 0.67151, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.66241, val loss: 0.66189, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.65243, val loss: 0.65170, in 0.016s 1 tree, 70 leaves, max depth = 14, train loss: 0.64361, val loss: 0.64272, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.63430, val loss: 0.63320, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.62583, val loss: 0.62465, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.61715, val loss: 0.61584, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.60929, val loss: 0.60787, in 0.016s 1 tree, 100 leaves, max depth = 16, train loss: 0.60179, val loss: 0.60030, in 0.016s 1 tree, 102 leaves, max depth = 13, train loss: 0.59396, val loss: 0.59233, in 0.031s 1 tree, 83 leaves, max depth = 12, train loss: 0.58629, val loss: 0.58449, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.57940, val loss: 0.57749, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.57243, val loss: 0.57036, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.56545, val loss: 0.56322, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.55918, val loss: 0.55686, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.55275, val loss: 0.55034, in 0.016s 1 tree, 100 leaves, max depth = 12, train loss: 0.54646, val loss: 0.54394, in 0.031s 1 tree, 84 leaves, max depth = 14, train loss: 0.54074, val loss: 0.53814, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.53520, val loss: 0.53252, in 0.031s 1 tree, 85 leaves, max depth = 13, train loss: 0.52974, val loss: 0.52703, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.52408, val loss: 0.52125, in 0.016s 1 tree, 101 leaves, max depth = 12, train loss: 0.51862, val loss: 0.51572, in 0.016s 1 tree, 84 leaves, max depth = 11, train loss: 0.51370, val loss: 0.51067, in 0.031s 1 tree, 83 leaves, max depth = 14, train loss: 0.50890, val loss: 0.50582, in 0.016s 1 tree, 82 leaves, max depth = 16, train loss: 0.50425, val loss: 0.50111, in 0.016s 1 tree, 99 leaves, max depth = 12, train loss: 0.49937, val loss: 0.49622, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.49498, val loss: 0.49179, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.49026, val loss: 0.48698, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.48569, val loss: 0.48231, in 0.016s 1 tree, 72 leaves, max depth = 16, train loss: 0.48178, val loss: 0.47836, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.47745, val loss: 0.47397, in 0.016s 1 tree, 71 leaves, max depth = 14, train loss: 0.47336, val loss: 0.46981, in 0.016s 1 tree, 99 leaves, max depth = 13, train loss: 0.46963, val loss: 0.46606, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.46566, val loss: 0.46201, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.46039, val loss: 0.45680, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.45528, val loss: 0.45176, in 0.016s 1 tree, 96 leaves, max depth = 17, train loss: 0.45042, val loss: 0.44700, in 0.016s 1 tree, 62 leaves, max depth = 14, train loss: 0.44571, val loss: 0.44232, in 0.016s 1 tree, 61 leaves, max depth = 18, train loss: 0.44263, val loss: 0.43922, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.43918, val loss: 0.43576, in 0.031s 1 tree, 83 leaves, max depth = 12, train loss: 0.43576, val loss: 0.43227, in 0.016s 1 tree, 102 leaves, max depth = 12, train loss: 0.43254, val loss: 0.42904, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.42942, val loss: 0.42589, in 0.016s 1 tree, 98 leaves, max depth = 17, train loss: 0.42519, val loss: 0.42176, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.42234, val loss: 0.41889, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.41816, val loss: 0.41477, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.41520, val loss: 0.41177, in 0.016s 1 tree, 96 leaves, max depth = 13, train loss: 0.41131, val loss: 0.40800, in 0.031s 1 tree, 69 leaves, max depth = 13, train loss: 0.40885, val loss: 0.40551, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.40623, val loss: 0.40288, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.40377, val loss: 0.40040, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.40004, val loss: 0.39674, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.39754, val loss: 0.39423, in 0.031s 1 tree, 82 leaves, max depth = 15, train loss: 0.39502, val loss: 0.39169, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.39252, val loss: 0.38917, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.39009, val loss: 0.38672, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.38778, val loss: 0.38440, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.38560, val loss: 0.38218, in 0.016s Fit 59 trees in 1.407 s, (4915 total leaves) Time spent computing histograms: 0.415s Time spent finding best splits: 0.089s Time spent applying splits: 0.083s Time spent predicting: 0.016s Trial 3, Fold 2: Log loss = 0.3883934943415345, Average precision = 0.9514920456568772, ROC-AUC = 0.949003108098142, Elapsed Time = 1.4139042999995581 seconds Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 3, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 11, train loss: 0.68269, val loss: 0.68256, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.67223, val loss: 0.67205, in 0.016s 1 tree, 98 leaves, max depth = 13, train loss: 0.66272, val loss: 0.66240, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.65294, val loss: 0.65264, in 0.016s 1 tree, 71 leaves, max depth = 14, train loss: 0.64410, val loss: 0.64378, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.63497, val loss: 0.63465, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.62656, val loss: 0.62628, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.61799, val loss: 0.61769, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.61000, val loss: 0.60975, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.60247, val loss: 0.60226, in 0.031s 1 tree, 99 leaves, max depth = 12, train loss: 0.59478, val loss: 0.59456, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.58723, val loss: 0.58703, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.58026, val loss: 0.58009, in 0.031s 1 tree, 71 leaves, max depth = 15, train loss: 0.57323, val loss: 0.57307, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.56628, val loss: 0.56618, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.55994, val loss: 0.55989, in 0.016s 1 tree, 98 leaves, max depth = 14, train loss: 0.55347, val loss: 0.55346, in 0.016s 1 tree, 98 leaves, max depth = 14, train loss: 0.54720, val loss: 0.54724, in 0.031s 1 tree, 83 leaves, max depth = 14, train loss: 0.54142, val loss: 0.54150, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.53581, val loss: 0.53593, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.53026, val loss: 0.53046, in 0.031s 1 tree, 82 leaves, max depth = 14, train loss: 0.52460, val loss: 0.52478, in 0.016s 1 tree, 98 leaves, max depth = 13, train loss: 0.51918, val loss: 0.51935, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.51421, val loss: 0.51444, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.50936, val loss: 0.50963, in 0.031s 1 tree, 84 leaves, max depth = 14, train loss: 0.50466, val loss: 0.50497, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.49979, val loss: 0.50017, in 0.062s 1 tree, 83 leaves, max depth = 14, train loss: 0.49535, val loss: 0.49576, in 0.047s 1 tree, 84 leaves, max depth = 14, train loss: 0.49066, val loss: 0.49113, in 0.031s 1 tree, 83 leaves, max depth = 14, train loss: 0.48611, val loss: 0.48662, in 0.016s 1 tree, 64 leaves, max depth = 15, train loss: 0.48043, val loss: 0.48129, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.47611, val loss: 0.47697, in 0.031s 1 tree, 82 leaves, max depth = 14, train loss: 0.47224, val loss: 0.47318, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.46846, val loss: 0.46943, in 0.016s 1 tree, 56 leaves, max depth = 15, train loss: 0.46324, val loss: 0.46457, in 0.031s 1 tree, 85 leaves, max depth = 14, train loss: 0.45930, val loss: 0.46070, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.45549, val loss: 0.45689, in 0.016s 1 tree, 70 leaves, max depth = 14, train loss: 0.45188, val loss: 0.45330, in 0.016s 1 tree, 71 leaves, max depth = 14, train loss: 0.44837, val loss: 0.44982, in 0.031s 1 tree, 71 leaves, max depth = 14, train loss: 0.44496, val loss: 0.44644, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.44181, val loss: 0.44333, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.43846, val loss: 0.43999, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.43526, val loss: 0.43682, in 0.016s 1 tree, 101 leaves, max depth = 12, train loss: 0.43214, val loss: 0.43378, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.42758, val loss: 0.42960, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.42320, val loss: 0.42559, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.42020, val loss: 0.42266, in 0.016s 1 tree, 99 leaves, max depth = 13, train loss: 0.41737, val loss: 0.41986, in 0.016s 1 tree, 95 leaves, max depth = 15, train loss: 0.41327, val loss: 0.41615, in 0.016s 1 tree, 69 leaves, max depth = 11, train loss: 0.41062, val loss: 0.41350, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.40666, val loss: 0.40989, in 0.016s 1 tree, 62 leaves, max depth = 14, train loss: 0.40414, val loss: 0.40739, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.40175, val loss: 0.40505, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.39948, val loss: 0.40281, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.39719, val loss: 0.40063, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.39484, val loss: 0.39835, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.39269, val loss: 0.39628, in 0.016s 1 tree, 73 leaves, max depth = 16, train loss: 0.38911, val loss: 0.39305, in 0.031s 1 tree, 80 leaves, max depth = 15, train loss: 0.38706, val loss: 0.39108, in 0.016s Fit 59 trees in 1.596 s, (4886 total leaves) Time spent computing histograms: 0.492s Time spent finding best splits: 0.115s Time spent applying splits: 0.093s Time spent predicting: 0.000s Trial 3, Fold 3: Log loss = 0.38656042904283894, Average precision = 0.9553353227323066, ROC-AUC = 0.9516027697652917, Elapsed Time = 1.598971100000199 seconds Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 3, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 58 leaves, max depth = 12, train loss: 0.68270, val loss: 0.68226, in 0.016s 1 tree, 80 leaves, max depth = 11, train loss: 0.67219, val loss: 0.67131, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.66270, val loss: 0.66134, in 0.031s 1 tree, 82 leaves, max depth = 11, train loss: 0.65294, val loss: 0.65123, in 0.016s 1 tree, 71 leaves, max depth = 13, train loss: 0.64426, val loss: 0.64216, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.63514, val loss: 0.63269, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.62685, val loss: 0.62410, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.61828, val loss: 0.61516, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.61046, val loss: 0.60700, in 0.031s 1 tree, 103 leaves, max depth = 13, train loss: 0.60307, val loss: 0.59923, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.59541, val loss: 0.59118, in 0.016s 1 tree, 79 leaves, max depth = 11, train loss: 0.58790, val loss: 0.58338, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.58095, val loss: 0.57613, in 0.031s 1 tree, 72 leaves, max depth = 12, train loss: 0.57391, val loss: 0.56877, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.56704, val loss: 0.56160, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.56071, val loss: 0.55499, in 0.031s 1 tree, 106 leaves, max depth = 12, train loss: 0.55434, val loss: 0.54828, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.54817, val loss: 0.54177, in 0.031s 1 tree, 73 leaves, max depth = 12, train loss: 0.54226, val loss: 0.53555, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.53639, val loss: 0.52941, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.53094, val loss: 0.52369, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.52540, val loss: 0.51788, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.52009, val loss: 0.51225, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.51519, val loss: 0.50723, in 0.031s 1 tree, 82 leaves, max depth = 14, train loss: 0.51033, val loss: 0.50212, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.50571, val loss: 0.49737, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.50083, val loss: 0.49226, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.49641, val loss: 0.48762, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.49214, val loss: 0.48322, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.48755, val loss: 0.47845, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.48363, val loss: 0.47431, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.47932, val loss: 0.46976, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.47538, val loss: 0.46571, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.47159, val loss: 0.46181, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.46639, val loss: 0.45651, in 0.016s 1 tree, 86 leaves, max depth = 12, train loss: 0.46245, val loss: 0.45240, in 0.031s 1 tree, 84 leaves, max depth = 12, train loss: 0.45861, val loss: 0.44840, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.45502, val loss: 0.44461, in 0.016s 1 tree, 105 leaves, max depth = 12, train loss: 0.45150, val loss: 0.44085, in 0.031s 1 tree, 63 leaves, max depth = 12, train loss: 0.44814, val loss: 0.43724, in 0.016s 1 tree, 86 leaves, max depth = 13, train loss: 0.44492, val loss: 0.43390, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.44160, val loss: 0.43038, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.43844, val loss: 0.42699, in 0.031s 1 tree, 107 leaves, max depth = 12, train loss: 0.43531, val loss: 0.42371, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.43074, val loss: 0.41907, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.42633, val loss: 0.41463, in 0.016s 1 tree, 86 leaves, max depth = 12, train loss: 0.42333, val loss: 0.41150, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.42053, val loss: 0.40850, in 0.031s 1 tree, 95 leaves, max depth = 12, train loss: 0.41643, val loss: 0.40431, in 0.031s 1 tree, 69 leaves, max depth = 12, train loss: 0.41381, val loss: 0.40148, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.40982, val loss: 0.39747, in 0.016s 1 tree, 72 leaves, max depth = 12, train loss: 0.40732, val loss: 0.39477, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.40494, val loss: 0.39235, in 0.031s 1 tree, 86 leaves, max depth = 11, train loss: 0.40267, val loss: 0.39003, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.40049, val loss: 0.38773, in 0.031s 1 tree, 72 leaves, max depth = 11, train loss: 0.39820, val loss: 0.38529, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.39454, val loss: 0.38160, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.39098, val loss: 0.37802, in 0.031s 1 tree, 84 leaves, max depth = 12, train loss: 0.38876, val loss: 0.37568, in 0.016s Fit 59 trees in 1.533 s, (4986 total leaves) Time spent computing histograms: 0.452s Time spent finding best splits: 0.096s Time spent applying splits: 0.090s Time spent predicting: 0.000s Trial 3, Fold 4: Log loss = 0.38953950043832297, Average precision = 0.9538868791853735, ROC-AUC = 0.9492232487106304, Elapsed Time = 1.5329940999999963 seconds Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 3, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 61 leaves, max depth = 14, train loss: 0.68267, val loss: 0.68208, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.67200, val loss: 0.67100, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.66251, val loss: 0.66098, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.65257, val loss: 0.65070, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.64343, val loss: 0.64118, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.63415, val loss: 0.63158, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.62570, val loss: 0.62276, in 0.031s 1 tree, 79 leaves, max depth = 11, train loss: 0.61705, val loss: 0.61379, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.60916, val loss: 0.60554, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.60165, val loss: 0.59769, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.59376, val loss: 0.58952, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.58638, val loss: 0.58179, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.57945, val loss: 0.57456, in 0.031s 1 tree, 73 leaves, max depth = 11, train loss: 0.57243, val loss: 0.56719, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.56547, val loss: 0.55997, in 0.016s 1 tree, 102 leaves, max depth = 13, train loss: 0.55927, val loss: 0.55349, in 0.016s 1 tree, 102 leaves, max depth = 12, train loss: 0.55278, val loss: 0.54674, in 0.031s 1 tree, 102 leaves, max depth = 12, train loss: 0.54652, val loss: 0.54024, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.54076, val loss: 0.53426, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.53518, val loss: 0.52846, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.52969, val loss: 0.52273, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.52400, val loss: 0.51686, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.51858, val loss: 0.51124, in 0.031s 1 tree, 85 leaves, max depth = 13, train loss: 0.51357, val loss: 0.50614, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.50875, val loss: 0.50113, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.50407, val loss: 0.49626, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.49919, val loss: 0.49119, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.49477, val loss: 0.48660, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.49006, val loss: 0.48171, in 0.031s 1 tree, 83 leaves, max depth = 13, train loss: 0.48548, val loss: 0.47696, in 0.016s 1 tree, 72 leaves, max depth = 11, train loss: 0.48157, val loss: 0.47280, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.47725, val loss: 0.46832, in 0.016s 1 tree, 86 leaves, max depth = 12, train loss: 0.47334, val loss: 0.46434, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.46954, val loss: 0.46047, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.46432, val loss: 0.45519, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.46038, val loss: 0.45112, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.45654, val loss: 0.44716, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.45297, val loss: 0.44341, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.44949, val loss: 0.43978, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.44605, val loss: 0.43621, in 0.031s 1 tree, 84 leaves, max depth = 12, train loss: 0.44292, val loss: 0.43296, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.43958, val loss: 0.42950, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.43640, val loss: 0.42620, in 0.031s 1 tree, 103 leaves, max depth = 13, train loss: 0.43329, val loss: 0.42301, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.42871, val loss: 0.41845, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.42427, val loss: 0.41406, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.42128, val loss: 0.41097, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.41838, val loss: 0.40798, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.41553, val loss: 0.40508, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.41295, val loss: 0.40244, in 0.031s 1 tree, 74 leaves, max depth = 14, train loss: 0.40888, val loss: 0.39840, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.40626, val loss: 0.39575, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.40369, val loss: 0.39310, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.40142, val loss: 0.39076, in 0.031s 1 tree, 102 leaves, max depth = 12, train loss: 0.39923, val loss: 0.38857, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.39554, val loss: 0.38494, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.39337, val loss: 0.38278, in 0.031s 1 tree, 80 leaves, max depth = 14, train loss: 0.38972, val loss: 0.37921, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.38757, val loss: 0.37696, in 0.016s Fit 59 trees in 1.502 s, (4930 total leaves) Time spent computing histograms: 0.441s Time spent finding best splits: 0.095s Time spent applying splits: 0.089s Time spent predicting: 0.000s Trial 3, Fold 5: Log loss = 0.39399982523344507, Average precision = 0.9492866062937685, ROC-AUC = 0.9478948404956988, Elapsed Time = 1.5117119999995339 seconds
Optimization Progress: 4%|4 | 4/100 [00:51<20:32, 12.84s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 4, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.174 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 7, train loss: 0.67412, val loss: 0.67382, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65525, val loss: 0.65467, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.63726, val loss: 0.63628, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.62082, val loss: 0.61942, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.60647, val loss: 0.60492, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.59321, val loss: 0.59147, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.57988, val loss: 0.57792, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56780, val loss: 0.56557, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55604, val loss: 0.55344, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.54339, val loss: 0.54088, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.53386, val loss: 0.53113, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.52390, val loss: 0.52099, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.51381, val loss: 0.51081, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50556, val loss: 0.50232, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.49718, val loss: 0.49367, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48948, val loss: 0.48578, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48109, val loss: 0.47722, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47319, val loss: 0.46920, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46678, val loss: 0.46247, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46041, val loss: 0.45609, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.45454, val loss: 0.44995, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44794, val loss: 0.44327, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44206, val loss: 0.43727, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.43525, val loss: 0.43046, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43039, val loss: 0.42546, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42565, val loss: 0.42064, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41986, val loss: 0.41492, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.41536, val loss: 0.41028, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.41087, val loss: 0.40555, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40634, val loss: 0.40100, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40269, val loss: 0.39725, in 0.031s 1 tree, 17 leaves, max depth = 9, train loss: 0.39968, val loss: 0.39406, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39481, val loss: 0.38923, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39169, val loss: 0.38601, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38506, val loss: 0.37956, in 0.016s Fit 35 trees in 0.987 s, (570 total leaves) Time spent computing histograms: 0.281s Time spent finding best splits: 0.023s Time spent applying splits: 0.018s Time spent predicting: 0.000s Trial 4, Fold 1: Log loss = 0.3857294285343602, Average precision = 0.9546373970653965, ROC-AUC = 0.9465977987601746, Elapsed Time = 1.0007836999993742 seconds Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 4, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.205 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 7, train loss: 0.67433, val loss: 0.67347, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.65446, val loss: 0.65314, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63657, val loss: 0.63500, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.62000, val loss: 0.61798, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.60582, val loss: 0.60311, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.59264, val loss: 0.58920, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.57918, val loss: 0.57533, in 0.031s 1 tree, 12 leaves, max depth = 6, train loss: 0.56709, val loss: 0.56271, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.55458, val loss: 0.55005, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.54171, val loss: 0.53693, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.53217, val loss: 0.52690, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.52228, val loss: 0.51665, in 0.031s 1 tree, 17 leaves, max depth = 5, train loss: 0.51238, val loss: 0.50663, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50429, val loss: 0.49824, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.49574, val loss: 0.48940, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.48818, val loss: 0.48147, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47964, val loss: 0.47268, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.47165, val loss: 0.46456, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46514, val loss: 0.45802, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45833, val loss: 0.45112, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.45239, val loss: 0.44500, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.44376, val loss: 0.43639, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.43665, val loss: 0.42944, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43129, val loss: 0.42406, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42487, val loss: 0.41774, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.41908, val loss: 0.41184, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.41473, val loss: 0.40730, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41004, val loss: 0.40265, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.40493, val loss: 0.39749, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.40137, val loss: 0.39379, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.39769, val loss: 0.39010, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39364, val loss: 0.38605, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39037, val loss: 0.38272, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.38742, val loss: 0.37971, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.38473, val loss: 0.37692, in 0.031s Fit 35 trees in 1.283 s, (573 total leaves) Time spent computing histograms: 0.329s Time spent finding best splits: 0.031s Time spent applying splits: 0.023s Time spent predicting: 0.000s Trial 4, Fold 2: Log loss = 0.3869140975171336, Average precision = 0.950777794365889, ROC-AUC = 0.9465078433877876, Elapsed Time = 1.288409300001149 seconds Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 4, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67429, val loss: 0.67398, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65557, val loss: 0.65486, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63772, val loss: 0.63694, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.62128, val loss: 0.62002, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.60715, val loss: 0.60547, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.59404, val loss: 0.59203, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.58058, val loss: 0.57846, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56862, val loss: 0.56621, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55634, val loss: 0.55353, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.54372, val loss: 0.54080, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.53417, val loss: 0.53103, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.52413, val loss: 0.52081, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51363, val loss: 0.51032, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.50477, val loss: 0.50132, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.49335, val loss: 0.49047, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48428, val loss: 0.48145, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47658, val loss: 0.47356, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46858, val loss: 0.46559, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46269, val loss: 0.45952, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45526, val loss: 0.45214, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44841, val loss: 0.44531, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44198, val loss: 0.43894, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.43709, val loss: 0.43393, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43167, val loss: 0.42869, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.42544, val loss: 0.42215, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41996, val loss: 0.41667, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41561, val loss: 0.41227, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41079, val loss: 0.40763, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.40642, val loss: 0.40342, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.40199, val loss: 0.39868, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39832, val loss: 0.39492, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39498, val loss: 0.39150, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.39030, val loss: 0.38667, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.38721, val loss: 0.38377, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38087, val loss: 0.37797, in 0.016s Fit 35 trees in 1.002 s, (559 total leaves) Time spent computing histograms: 0.279s Time spent finding best splits: 0.022s Time spent applying splits: 0.018s Time spent predicting: 0.000s Trial 4, Fold 3: Log loss = 0.3760721421921893, Average precision = 0.956812154332084, ROC-AUC = 0.9499740714478335, Elapsed Time = 1.0188118000005488 seconds Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 4, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.172 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 9, train loss: 0.67437, val loss: 0.67377, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.65373, val loss: 0.65215, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63749, val loss: 0.63533, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.62145, val loss: 0.61868, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.60728, val loss: 0.60401, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.59418, val loss: 0.59029, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.58072, val loss: 0.57638, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56861, val loss: 0.56359, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55655, val loss: 0.55088, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54381, val loss: 0.53749, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.53429, val loss: 0.52760, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.52443, val loss: 0.51726, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.51608, val loss: 0.50850, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.50802, val loss: 0.50002, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49940, val loss: 0.49062, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.49184, val loss: 0.48259, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.48338, val loss: 0.47340, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.47538, val loss: 0.46475, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46876, val loss: 0.45746, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46164, val loss: 0.44983, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45553, val loss: 0.44322, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44877, val loss: 0.43603, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.44179, val loss: 0.42898, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.43637, val loss: 0.42311, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.43005, val loss: 0.41659, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42451, val loss: 0.41068, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41982, val loss: 0.40562, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41511, val loss: 0.40067, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.41073, val loss: 0.39602, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40627, val loss: 0.39127, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39866, val loss: 0.38385, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39524, val loss: 0.38019, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39052, val loss: 0.37538, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.38737, val loss: 0.37202, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.38404, val loss: 0.36837, in 0.016s Fit 35 trees in 0.985 s, (582 total leaves) Time spent computing histograms: 0.270s Time spent finding best splits: 0.022s Time spent applying splits: 0.018s Time spent predicting: 0.016s Trial 4, Fold 4: Log loss = 0.3838827584958714, Average precision = 0.9537464555030243, ROC-AUC = 0.9472256349087129, Elapsed Time = 0.9993679999988672 seconds Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 4, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 7, train loss: 0.67408, val loss: 0.67314, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.65417, val loss: 0.65205, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63645, val loss: 0.63339, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.62000, val loss: 0.61584, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.60572, val loss: 0.60077, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.59242, val loss: 0.58659, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.57897, val loss: 0.57263, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56682, val loss: 0.55988, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.55445, val loss: 0.54666, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.54181, val loss: 0.53346, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.53230, val loss: 0.52316, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.52232, val loss: 0.51280, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51193, val loss: 0.50179, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50264, val loss: 0.49164, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.49117, val loss: 0.47989, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.48224, val loss: 0.47039, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47453, val loss: 0.46197, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46784, val loss: 0.45473, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.46093, val loss: 0.44757, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45322, val loss: 0.43958, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.44623, val loss: 0.43221, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43958, val loss: 0.42519, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.43457, val loss: 0.41991, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.42919, val loss: 0.41417, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.42288, val loss: 0.40770, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41731, val loss: 0.40193, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41294, val loss: 0.39738, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.40817, val loss: 0.39247, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40400, val loss: 0.38794, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39875, val loss: 0.38266, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39373, val loss: 0.37764, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.38908, val loss: 0.37311, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38617, val loss: 0.36985, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.38333, val loss: 0.36672, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.37712, val loss: 0.36046, in 0.000s Fit 35 trees in 0.987 s, (563 total leaves) Time spent computing histograms: 0.277s Time spent finding best splits: 0.021s Time spent applying splits: 0.017s Time spent predicting: 0.000s Trial 4, Fold 5: Log loss = 0.3835427485060661, Average precision = 0.953093001420166, ROC-AUC = 0.9466856176727422, Elapsed Time = 1.0019355999993422 seconds
Optimization Progress: 5%|5 | 5/100 [01:05<20:30, 12.96s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 5, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 12 leaves, max depth = 7, train loss: 0.68648, val loss: 0.68626, in 0.016s 1 tree, 15 leaves, max depth = 9, train loss: 0.67994, val loss: 0.67954, in 0.000s 1 tree, 12 leaves, max depth = 8, train loss: 0.67354, val loss: 0.67299, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66732, val loss: 0.66654, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.66132, val loss: 0.66036, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.65548, val loss: 0.65430, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.64981, val loss: 0.64846, in 0.000s 1 tree, 15 leaves, max depth = 9, train loss: 0.64430, val loss: 0.64278, in 0.000s 1 tree, 15 leaves, max depth = 10, train loss: 0.63891, val loss: 0.63720, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.63355, val loss: 0.63167, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.62846, val loss: 0.62638, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.62352, val loss: 0.62128, in 0.000s 1 tree, 18 leaves, max depth = 10, train loss: 0.61872, val loss: 0.61632, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61406, val loss: 0.61148, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.60951, val loss: 0.60677, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.60508, val loss: 0.60217, in 0.000s 1 tree, 12 leaves, max depth = 8, train loss: 0.60070, val loss: 0.59766, in 0.016s 1 tree, 12 leaves, max depth = 8, train loss: 0.59644, val loss: 0.59328, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.59229, val loss: 0.58894, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.58817, val loss: 0.58465, in 0.000s Fit 20 trees in 0.408 s, (282 total leaves) Time spent computing histograms: 0.082s Time spent finding best splits: 0.006s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 5, Fold 1: Log loss = 0.5874254284962492, Average precision = 0.8367587948394197, ROC-AUC = 0.8674685622517603, Elapsed Time = 0.42420400000082736 seconds Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 5, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 15 leaves, max depth = 8, train loss: 0.68647, val loss: 0.68613, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.67988, val loss: 0.67921, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.67348, val loss: 0.67249, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.66725, val loss: 0.66596, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.66120, val loss: 0.65960, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.65533, val loss: 0.65341, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.64964, val loss: 0.64744, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.64412, val loss: 0.64165, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.63871, val loss: 0.63595, in 0.000s 1 tree, 11 leaves, max depth = 7, train loss: 0.63340, val loss: 0.63035, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.62831, val loss: 0.62498, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.62336, val loss: 0.61978, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.61852, val loss: 0.61466, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.61386, val loss: 0.60974, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.60927, val loss: 0.60488, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.60482, val loss: 0.60020, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.60047, val loss: 0.59560, in 0.000s 1 tree, 18 leaves, max depth = 9, train loss: 0.59625, val loss: 0.59113, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.59209, val loss: 0.58675, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.58804, val loss: 0.58246, in 0.000s Fit 20 trees in 0.440 s, (277 total leaves) Time spent computing histograms: 0.087s Time spent finding best splits: 0.006s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 5, Fold 2: Log loss = 0.5878867246029013, Average precision = 0.8337268872759109, ROC-AUC = 0.869113741338616, Elapsed Time = 0.45685410000078264 seconds Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 5, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 14 leaves, max depth = 9, train loss: 0.68650, val loss: 0.68627, in 0.016s 1 tree, 15 leaves, max depth = 11, train loss: 0.68001, val loss: 0.67955, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.67368, val loss: 0.67297, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66748, val loss: 0.66656, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.66152, val loss: 0.66038, in 0.000s 1 tree, 14 leaves, max depth = 9, train loss: 0.65570, val loss: 0.65439, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.65007, val loss: 0.64853, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.64460, val loss: 0.64285, in 0.000s 1 tree, 12 leaves, max depth = 7, train loss: 0.63921, val loss: 0.63726, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.63393, val loss: 0.63179, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.62889, val loss: 0.62654, in 0.016s 1 tree, 20 leaves, max depth = 12, train loss: 0.62399, val loss: 0.62145, in 0.000s 1 tree, 22 leaves, max depth = 13, train loss: 0.61922, val loss: 0.61650, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.61459, val loss: 0.61168, in 0.000s 1 tree, 20 leaves, max depth = 11, train loss: 0.61008, val loss: 0.60698, in 0.016s 1 tree, 22 leaves, max depth = 13, train loss: 0.60568, val loss: 0.60241, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.60136, val loss: 0.59789, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.59715, val loss: 0.59349, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.59301, val loss: 0.58920, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.58895, val loss: 0.58499, in 0.016s Fit 20 trees in 0.439 s, (313 total leaves) Time spent computing histograms: 0.079s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 5, Fold 3: Log loss = 0.5860022740246483, Average precision = 0.8476111167806911, ROC-AUC = 0.8774352975026725, Elapsed Time = 0.4495236999991903 seconds Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 5, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.157 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 7 leaves, max depth = 5, train loss: 0.68650, val loss: 0.68611, in 0.000s 1 tree, 15 leaves, max depth = 9, train loss: 0.67998, val loss: 0.67924, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.67362, val loss: 0.67253, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.66742, val loss: 0.66599, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.66144, val loss: 0.65967, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.65560, val loss: 0.65347, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.64995, val loss: 0.64749, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.64445, val loss: 0.64167, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.63909, val loss: 0.63596, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.63379, val loss: 0.63032, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.62872, val loss: 0.62491, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.62379, val loss: 0.61967, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.61900, val loss: 0.61458, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.61436, val loss: 0.60960, in 0.000s 1 tree, 15 leaves, max depth = 9, train loss: 0.60982, val loss: 0.60477, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.60540, val loss: 0.60004, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.60106, val loss: 0.59540, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.59683, val loss: 0.59088, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.59269, val loss: 0.58647, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.58862, val loss: 0.58209, in 0.016s Fit 20 trees in 0.454 s, (294 total leaves) Time spent computing histograms: 0.086s Time spent finding best splits: 0.006s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 5, Fold 4: Log loss = 0.5879750852345283, Average precision = 0.8255683557170996, ROC-AUC = 0.8667012853160894, Elapsed Time = 0.45265290000133973 seconds Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 5, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 9, train loss: 0.68641, val loss: 0.68594, in 0.016s 1 tree, 16 leaves, max depth = 10, train loss: 0.67980, val loss: 0.67887, in 0.000s 1 tree, 11 leaves, max depth = 6, train loss: 0.67335, val loss: 0.67201, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66708, val loss: 0.66532, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.66102, val loss: 0.65881, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.65511, val loss: 0.65247, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.64939, val loss: 0.64636, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.64383, val loss: 0.64037, in 0.016s 1 tree, 14 leaves, max depth = 8, train loss: 0.63838, val loss: 0.63455, in 0.000s 1 tree, 11 leaves, max depth = 7, train loss: 0.63305, val loss: 0.62880, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.62791, val loss: 0.62326, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.62292, val loss: 0.61787, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.61808, val loss: 0.61263, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.61339, val loss: 0.60755, in 0.016s 1 tree, 16 leaves, max depth = 10, train loss: 0.60878, val loss: 0.60257, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.60431, val loss: 0.59775, in 0.000s 1 tree, 11 leaves, max depth = 7, train loss: 0.59994, val loss: 0.59301, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.59568, val loss: 0.58838, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.59150, val loss: 0.58386, in 0.016s 1 tree, 12 leaves, max depth = 8, train loss: 0.58740, val loss: 0.57940, in 0.000s Fit 20 trees in 0.439 s, (292 total leaves) Time spent computing histograms: 0.083s Time spent finding best splits: 0.006s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 5, Fold 5: Log loss = 0.5906025603832106, Average precision = 0.829664343872623, ROC-AUC = 0.8594015941140405, Elapsed Time = 0.4455651999996917 seconds
Optimization Progress: 6%|6 | 6/100 [01:14<18:16, 11.67s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 6, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 7, train loss: 0.64682, val loss: 0.64584, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.60923, val loss: 0.60826, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57663, val loss: 0.57493, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.54702, val loss: 0.54501, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.52327, val loss: 0.52099, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50163, val loss: 0.49901, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.48332, val loss: 0.48045, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.46715, val loss: 0.46376, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.45198, val loss: 0.44809, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.43849, val loss: 0.43484, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.42493, val loss: 0.42120, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.41304, val loss: 0.40976, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.40354, val loss: 0.40020, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.38656, val loss: 0.38455, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.37841, val loss: 0.37587, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.37063, val loss: 0.36800, in 0.000s 1 tree, 31 leaves, max depth = 13, train loss: 0.36308, val loss: 0.36019, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.35007, val loss: 0.34797, in 0.016s Fit 18 trees in 0.564 s, (514 total leaves) Time spent computing histograms: 0.110s Time spent finding best splits: 0.014s Time spent applying splits: 0.011s Time spent predicting: 0.000s Trial 6, Fold 1: Log loss = 0.351445931369262, Average precision = 0.9549264452505613, ROC-AUC = 0.9488231456388638, Elapsed Time = 0.5713984000012715 seconds Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 6, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 8, train loss: 0.64672, val loss: 0.64567, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.60891, val loss: 0.60705, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.57563, val loss: 0.57322, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.54689, val loss: 0.54408, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.52281, val loss: 0.51955, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.50099, val loss: 0.49712, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.48171, val loss: 0.47747, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.46327, val loss: 0.45859, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.44737, val loss: 0.44247, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.43278, val loss: 0.42760, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.41305, val loss: 0.40819, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39614, val loss: 0.39161, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.38659, val loss: 0.38186, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37596, val loss: 0.37114, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36871, val loss: 0.36346, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.36135, val loss: 0.35629, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.35377, val loss: 0.34874, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.34666, val loss: 0.34195, in 0.016s Fit 18 trees in 0.626 s, (479 total leaves) Time spent computing histograms: 0.125s Time spent finding best splits: 0.015s Time spent applying splits: 0.011s Time spent predicting: 0.000s Trial 6, Fold 2: Log loss = 0.3485951215531161, Average precision = 0.9527528591451231, ROC-AUC = 0.9498124549119393, Elapsed Time = 0.6378046999998332 seconds Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 6, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 9, train loss: 0.64689, val loss: 0.64606, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.60951, val loss: 0.60831, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57613, val loss: 0.57510, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.54743, val loss: 0.54646, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.52314, val loss: 0.52207, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.50169, val loss: 0.50025, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.48170, val loss: 0.47987, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.46353, val loss: 0.46166, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.44786, val loss: 0.44578, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.43362, val loss: 0.43174, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.41372, val loss: 0.41334, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.39648, val loss: 0.39754, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.38458, val loss: 0.38508, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.37387, val loss: 0.37399, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.36103, val loss: 0.36236, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.34986, val loss: 0.35234, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34225, val loss: 0.34499, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33422, val loss: 0.33678, in 0.016s Fit 18 trees in 0.642 s, (481 total leaves) Time spent computing histograms: 0.125s Time spent finding best splits: 0.016s Time spent applying splits: 0.012s Time spent predicting: 0.000s Trial 6, Fold 3: Log loss = 0.33131520081959953, Average precision = 0.9602025195875663, ROC-AUC = 0.9562246884360162, Elapsed Time = 0.6537537000003795 seconds Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 6, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 9, train loss: 0.64797, val loss: 0.64605, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.61040, val loss: 0.60692, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.57731, val loss: 0.57218, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.54897, val loss: 0.54247, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.52528, val loss: 0.51725, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50382, val loss: 0.49495, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.48282, val loss: 0.47276, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.46470, val loss: 0.45353, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.44889, val loss: 0.43673, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.43514, val loss: 0.42176, in 0.031s 1 tree, 20 leaves, max depth = 9, train loss: 0.41537, val loss: 0.40175, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.40489, val loss: 0.39095, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.39317, val loss: 0.37912, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.38347, val loss: 0.36885, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37073, val loss: 0.35523, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.35966, val loss: 0.34315, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.35111, val loss: 0.33455, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.34035, val loss: 0.32368, in 0.016s Fit 18 trees in 0.657 s, (483 total leaves) Time spent computing histograms: 0.125s Time spent finding best splits: 0.016s Time spent applying splits: 0.012s Time spent predicting: 0.000s Trial 6, Fold 4: Log loss = 0.33944525800884284, Average precision = 0.9586036453769143, ROC-AUC = 0.9534304652460353, Elapsed Time = 0.6549568999998883 seconds Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 6, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.172 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 9, train loss: 0.64656, val loss: 0.64368, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.60851, val loss: 0.60445, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57553, val loss: 0.56993, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.54676, val loss: 0.53985, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.52258, val loss: 0.51387, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.50078, val loss: 0.49148, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.48064, val loss: 0.47014, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.46273, val loss: 0.45134, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44693, val loss: 0.43461, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.43251, val loss: 0.41911, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.41342, val loss: 0.39988, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39633, val loss: 0.38292, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.38660, val loss: 0.37296, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37632, val loss: 0.36253, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36899, val loss: 0.35500, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.36171, val loss: 0.34769, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35397, val loss: 0.33972, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.34685, val loss: 0.33242, in 0.016s Fit 18 trees in 0.641 s, (472 total leaves) Time spent computing histograms: 0.119s Time spent finding best splits: 0.015s Time spent applying splits: 0.011s Time spent predicting: 0.000s Trial 6, Fold 5: Log loss = 0.3542236616621092, Average precision = 0.9494919234787114, ROC-AUC = 0.9467516860821583, Elapsed Time = 0.6471075999997993 seconds
Optimization Progress: 7%|7 | 7/100 [01:24<17:07, 11.05s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 7, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 Binning 0.040 GB of training data: 0.220 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 11, train loss: 0.66112, val loss: 0.66111, in 0.031s 1 tree, 49 leaves, max depth = 11, train loss: 0.63276, val loss: 0.63277, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.60721, val loss: 0.60719, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.58504, val loss: 0.58493, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.56405, val loss: 0.56390, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.54583, val loss: 0.54557, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.52809, val loss: 0.52771, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.51192, val loss: 0.51143, in 0.031s [9/36] 1 tree, 47 leaves, max depth = 11, train loss: 0.49707, val loss: 0.49647, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.48348, val loss: 0.48268, in 0.031s 1 tree, 47 leaves, max depth = 10, train loss: 0.47105, val loss: 0.47016, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.45957, val loss: 0.45860, in 0.031s 1 tree, 56 leaves, max depth = 11, train loss: 0.44915, val loss: 0.44785, in 0.031s 1 tree, 45 leaves, max depth = 10, train loss: 0.43945, val loss: 0.43808, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.42548, val loss: 0.42457, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.41704, val loss: 0.41610, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.40486, val loss: 0.40436, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.39745, val loss: 0.39694, in 0.031s 1 tree, 52 leaves, max depth = 12, train loss: 0.38679, val loss: 0.38671, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.38032, val loss: 0.38026, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.37095, val loss: 0.37131, in 0.031s 1 tree, 49 leaves, max depth = 12, train loss: 0.36527, val loss: 0.36567, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35741, val loss: 0.35787, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35027, val loss: 0.35078, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.34466, val loss: 0.34528, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33837, val loss: 0.33904, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.33325, val loss: 0.33402, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.32750, val loss: 0.32895, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.32226, val loss: 0.32375, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.31730, val loss: 0.31944, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.31299, val loss: 0.31518, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.30861, val loss: 0.31083, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.30462, val loss: 0.30681, in 0.031s 1 tree, 51 leaves, max depth = 13, train loss: 0.30056, val loss: 0.30334, in 0.031s 1 tree, 30 leaves, max depth = 9, train loss: 0.29687, val loss: 0.29966, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.29335, val loss: 0.29669, in 0.016s Fit 36 trees in 1.330 s, (1686 total leaves) Time spent computing histograms: 0.323s Time spent finding best splits: 0.057s Time spent applying splits: 0.052s Time spent predicting: 0.000s Trial 7, Fold 1: Log loss = 0.2971298608703609, Average precision = 0.9601508407125532, ROC-AUC = 0.954229981287908, Elapsed Time = 1.3452409000001353 seconds Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 7, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.236 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 11, train loss: 0.66131, val loss: 0.66052, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.63253, val loss: 0.63114, in 0.031s 1 tree, 49 leaves, max depth = 10, train loss: 0.60688, val loss: 0.60477, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.58478, val loss: 0.58232, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.56350, val loss: 0.56065, in 0.031s 1 tree, 51 leaves, max depth = 13, train loss: 0.54533, val loss: 0.54218, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.52756, val loss: 0.52399, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.51136, val loss: 0.50739, in 0.031s 1 tree, 49 leaves, max depth = 11, train loss: 0.49658, val loss: 0.49232, in 0.031s 1 tree, 49 leaves, max depth = 9, train loss: 0.48304, val loss: 0.47846, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.47059, val loss: 0.46569, in 0.031s 1 tree, 46 leaves, max depth = 10, train loss: 0.45917, val loss: 0.45407, in 0.031s 1 tree, 49 leaves, max depth = 10, train loss: 0.44866, val loss: 0.44341, in 0.031s 1 tree, 46 leaves, max depth = 10, train loss: 0.43895, val loss: 0.43343, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.42508, val loss: 0.41974, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.41672, val loss: 0.41110, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.40472, val loss: 0.39931, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.39751, val loss: 0.39189, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.38699, val loss: 0.38159, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.37742, val loss: 0.37220, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.37146, val loss: 0.36610, in 0.031s 1 tree, 50 leaves, max depth = 10, train loss: 0.36584, val loss: 0.36035, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.35803, val loss: 0.35271, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35094, val loss: 0.34576, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.34451, val loss: 0.33951, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.33911, val loss: 0.33410, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.33332, val loss: 0.32871, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.32796, val loss: 0.32343, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.32326, val loss: 0.31872, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.31891, val loss: 0.31445, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.31421, val loss: 0.31009, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.31019, val loss: 0.30625, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.30598, val loss: 0.30218, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.30203, val loss: 0.29852, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29839, val loss: 0.29499, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.29495, val loss: 0.29180, in 0.016s Fit 36 trees in 1.346 s, (1694 total leaves) Time spent computing histograms: 0.313s Time spent finding best splits: 0.055s Time spent applying splits: 0.050s Time spent predicting: 0.031s Trial 7, Fold 2: Log loss = 0.29597727707207266, Average precision = 0.9594641087138922, ROC-AUC = 0.9563577921425602, Elapsed Time = 1.3618537000002107 seconds Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 7, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.237 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 11, train loss: 0.66100, val loss: 0.66112, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.63240, val loss: 0.63261, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.60692, val loss: 0.60694, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.58480, val loss: 0.58485, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.56367, val loss: 0.56370, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.54464, val loss: 0.54451, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.52803, val loss: 0.52794, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.51196, val loss: 0.51182, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.49740, val loss: 0.49713, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.48393, val loss: 0.48361, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.47159, val loss: 0.47124, in 0.031s 1 tree, 53 leaves, max depth = 10, train loss: 0.46022, val loss: 0.45986, in 0.016s 1 tree, 64 leaves, max depth = 10, train loss: 0.44987, val loss: 0.44958, in 0.031s 1 tree, 52 leaves, max depth = 12, train loss: 0.43527, val loss: 0.43601, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.42617, val loss: 0.42690, in 0.031s 1 tree, 54 leaves, max depth = 11, train loss: 0.41773, val loss: 0.41836, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.40551, val loss: 0.40712, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.39816, val loss: 0.39972, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.38746, val loss: 0.38993, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.38115, val loss: 0.38360, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.37173, val loss: 0.37505, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.36608, val loss: 0.36932, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.35813, val loss: 0.36208, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.35091, val loss: 0.35552, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.34535, val loss: 0.34960, in 0.031s 1 tree, 49 leaves, max depth = 13, train loss: 0.33903, val loss: 0.34420, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.33303, val loss: 0.33877, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.32756, val loss: 0.33420, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.32242, val loss: 0.32949, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.31769, val loss: 0.32553, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.31329, val loss: 0.32079, in 0.031s 1 tree, 28 leaves, max depth = 10, train loss: 0.30897, val loss: 0.31685, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.30501, val loss: 0.31263, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.30119, val loss: 0.30945, in 0.016s [35/36] 1 tree, 31 leaves, max depth = 10, train loss: 0.29756, val loss: 0.30616, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.29408, val loss: 0.30241, in 0.016s Fit 36 trees in 1.222 s, (1727 total leaves) Time spent computing histograms: 0.293s Time spent finding best splits: 0.047s Time spent applying splits: 0.044s Time spent predicting: 0.016s Trial 7, Fold 3: Log loss = 0.2932838393195154, Average precision = 0.9595868858698278, ROC-AUC = 0.9563772801475475, Elapsed Time = 1.2324594000001525 seconds Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 7, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.173 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 46 leaves, max depth = 9, train loss: 0.66148, val loss: 0.66016, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.63340, val loss: 0.63081, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.60809, val loss: 0.60438, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.58620, val loss: 0.58132, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.56504, val loss: 0.55906, in 0.031s 1 tree, 54 leaves, max depth = 13, train loss: 0.54696, val loss: 0.54001, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.52929, val loss: 0.52137, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.51313, val loss: 0.50434, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.49843, val loss: 0.48873, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.48494, val loss: 0.47441, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.47255, val loss: 0.46141, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.46121, val loss: 0.44939, in 0.031s 1 tree, 49 leaves, max depth = 10, train loss: 0.45075, val loss: 0.43820, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.44110, val loss: 0.42796, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.42733, val loss: 0.41394, in 0.031s 1 tree, 51 leaves, max depth = 9, train loss: 0.41898, val loss: 0.40503, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.40697, val loss: 0.39281, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.39972, val loss: 0.38506, in 0.031s 1 tree, 54 leaves, max depth = 13, train loss: 0.38923, val loss: 0.37444, in 0.016s 1 tree, 49 leaves, max depth = 9, train loss: 0.38286, val loss: 0.36757, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.37360, val loss: 0.35817, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.36802, val loss: 0.35209, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.36019, val loss: 0.34397, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.35269, val loss: 0.33636, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.34619, val loss: 0.32964, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.34083, val loss: 0.32413, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.33513, val loss: 0.31871, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.33031, val loss: 0.31376, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.32506, val loss: 0.30829, in 0.031s 1 tree, 49 leaves, max depth = 12, train loss: 0.32029, val loss: 0.30378, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.31577, val loss: 0.29909, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.31162, val loss: 0.29473, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.30762, val loss: 0.29098, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.30380, val loss: 0.28696, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.30019, val loss: 0.28327, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.29681, val loss: 0.28013, in 0.031s Fit 36 trees in 1.111 s, (1731 total leaves) Time spent computing histograms: 0.286s Time spent finding best splits: 0.046s Time spent applying splits: 0.043s Time spent predicting: 0.016s Trial 7, Fold 4: Log loss = 0.2960871708318758, Average precision = 0.9603063654296586, ROC-AUC = 0.9556672651535202, Elapsed Time = 1.122271999998702 seconds Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 7, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 10, train loss: 0.66088, val loss: 0.65953, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.63219, val loss: 0.62956, in 0.016s 1 tree, 49 leaves, max depth = 9, train loss: 0.60645, val loss: 0.60265, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.58433, val loss: 0.57944, in 0.031s 1 tree, 52 leaves, max depth = 10, train loss: 0.56314, val loss: 0.55732, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.54495, val loss: 0.53821, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.52727, val loss: 0.51973, in 0.031s 1 tree, 53 leaves, max depth = 10, train loss: 0.51118, val loss: 0.50295, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.49644, val loss: 0.48754, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.48294, val loss: 0.47345, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.47053, val loss: 0.46045, in 0.031s 1 tree, 49 leaves, max depth = 11, train loss: 0.45914, val loss: 0.44850, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.44865, val loss: 0.43752, in 0.031s 1 tree, 49 leaves, max depth = 9, train loss: 0.43897, val loss: 0.42744, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.42513, val loss: 0.41360, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.41675, val loss: 0.40490, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.40467, val loss: 0.39284, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.39734, val loss: 0.38523, in 0.031s 1 tree, 54 leaves, max depth = 9, train loss: 0.39059, val loss: 0.37825, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.38430, val loss: 0.37173, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.37444, val loss: 0.36192, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.36544, val loss: 0.35301, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.35723, val loss: 0.34485, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.35224, val loss: 0.33979, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.34525, val loss: 0.33263, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.33889, val loss: 0.32610, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.33310, val loss: 0.32015, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.32816, val loss: 0.31523, in 0.031s 1 tree, 50 leaves, max depth = 12, train loss: 0.32291, val loss: 0.31059, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.31809, val loss: 0.30561, in 0.000s 1 tree, 52 leaves, max depth = 14, train loss: 0.31376, val loss: 0.30131, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.30951, val loss: 0.29696, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.30552, val loss: 0.29298, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.30149, val loss: 0.28942, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.29780, val loss: 0.28617, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.29439, val loss: 0.28269, in 0.016s Fit 36 trees in 1.095 s, (1734 total leaves) Time spent computing histograms: 0.281s Time spent finding best splits: 0.044s Time spent applying splits: 0.042s Time spent predicting: 0.000s Trial 7, Fold 5: Log loss = 0.30208427335159127, Average precision = 0.9562072059339609, ROC-AUC = 0.9511322668747562, Elapsed Time = 1.1022063000000344 seconds
Optimization Progress: 8%|8 | 8/100 [01:38<18:50, 12.29s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 8, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.141 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 5 leaves, max depth = 3, train loss: 0.67012, val loss: 0.66942, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.64882, val loss: 0.64743, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.62956, val loss: 0.62755, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61219, val loss: 0.60943, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.59636, val loss: 0.59301, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58199, val loss: 0.57806, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.56910, val loss: 0.56462, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.55737, val loss: 0.55235, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.54646, val loss: 0.54095, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.53625, val loss: 0.53025, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.52727, val loss: 0.52078, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.51911, val loss: 0.51220, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.51148, val loss: 0.50416, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.50457, val loss: 0.49684, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.49815, val loss: 0.49003, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.48850, val loss: 0.48109, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.48286, val loss: 0.47511, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.47769, val loss: 0.46962, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.47284, val loss: 0.46432, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.46828, val loss: 0.45940, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.46285, val loss: 0.45438, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45729, val loss: 0.44932, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.45363, val loss: 0.44533, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.45002, val loss: 0.44139, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.44541, val loss: 0.43721, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.44204, val loss: 0.43345, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.43875, val loss: 0.43013, in 0.000s 1 tree, 23 leaves, max depth = 11, train loss: 0.43533, val loss: 0.42704, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.43109, val loss: 0.42332, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.42498, val loss: 0.41784, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.42142, val loss: 0.41476, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.41863, val loss: 0.41202, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.41591, val loss: 0.40902, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.41336, val loss: 0.40652, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.41089, val loss: 0.40379, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.40857, val loss: 0.40153, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.40629, val loss: 0.39913, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.40155, val loss: 0.39492, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.39952, val loss: 0.39278, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.39685, val loss: 0.39055, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39408, val loss: 0.38745, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.39227, val loss: 0.38544, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.39033, val loss: 0.38357, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.38634, val loss: 0.38010, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.38400, val loss: 0.37818, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.38193, val loss: 0.37652, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.37860, val loss: 0.37367, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.37707, val loss: 0.37201, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.37533, val loss: 0.37067, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.37236, val loss: 0.36811, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.37099, val loss: 0.36685, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.36835, val loss: 0.36461, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.36698, val loss: 0.36318, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.36572, val loss: 0.36183, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.36458, val loss: 0.36060, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.36212, val loss: 0.35854, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.36107, val loss: 0.35719, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.36006, val loss: 0.35610, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.35773, val loss: 0.35399, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35523, val loss: 0.35119, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.35382, val loss: 0.35011, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.35174, val loss: 0.34827, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.35077, val loss: 0.34707, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.34990, val loss: 0.34613, in 0.000s 1 tree, 10 leaves, max depth = 4, train loss: 0.34906, val loss: 0.34510, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.34775, val loss: 0.34396, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.34544, val loss: 0.34136, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.34421, val loss: 0.34047, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.34241, val loss: 0.33890, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.34075, val loss: 0.33745, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.33989, val loss: 0.33641, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.33912, val loss: 0.33549, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33696, val loss: 0.33304, in 0.000s 1 tree, 46 leaves, max depth = 13, train loss: 0.33604, val loss: 0.33229, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.33403, val loss: 0.33002, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.33329, val loss: 0.32912, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.33144, val loss: 0.32701, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.33079, val loss: 0.32635, in 0.016s 1 tree, 14 leaves, max depth = 12, train loss: 0.33011, val loss: 0.32554, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.32840, val loss: 0.32357, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.32779, val loss: 0.32288, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.32613, val loss: 0.32148, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.32505, val loss: 0.32075, in 0.000s 1 tree, 26 leaves, max depth = 7, train loss: 0.32356, val loss: 0.31948, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.32218, val loss: 0.31832, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.32052, val loss: 0.31642, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.31988, val loss: 0.31570, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.31932, val loss: 0.31516, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.31852, val loss: 0.31459, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.31697, val loss: 0.31280, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.31605, val loss: 0.31222, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.31482, val loss: 0.31123, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.31423, val loss: 0.31051, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.31341, val loss: 0.30999, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.31191, val loss: 0.30827, in 0.000s Fit 95 trees in 0.923 s, (1545 total leaves) Time spent computing histograms: 0.382s Time spent finding best splits: 0.039s Time spent applying splits: 0.032s Time spent predicting: 0.000s Trial 8, Fold 1: Log loss = 0.3164149077430296, Average precision = 0.9534281493802323, ROC-AUC = 0.9489805733595255, Elapsed Time = 0.9312449999997625 seconds Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 8, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 5 leaves, max depth = 4, train loss: 0.67009, val loss: 0.66893, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.64885, val loss: 0.64658, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62968, val loss: 0.62634, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61225, val loss: 0.60801, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.59649, val loss: 0.59130, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.58217, val loss: 0.57609, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.56921, val loss: 0.56239, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.55742, val loss: 0.54988, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.54658, val loss: 0.53831, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53648, val loss: 0.52750, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.52750, val loss: 0.51786, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.51929, val loss: 0.50907, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.51172, val loss: 0.50090, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50483, val loss: 0.49343, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.49847, val loss: 0.48653, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.48885, val loss: 0.47729, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.48328, val loss: 0.47118, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47818, val loss: 0.46558, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.47333, val loss: 0.46031, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.46885, val loss: 0.45540, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46341, val loss: 0.45040, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.45792, val loss: 0.44531, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.45427, val loss: 0.44129, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.45073, val loss: 0.43737, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.44612, val loss: 0.43319, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.44280, val loss: 0.42977, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.43989, val loss: 0.42653, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.43644, val loss: 0.42327, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.43209, val loss: 0.41927, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.42582, val loss: 0.41336, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.42217, val loss: 0.41006, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.41920, val loss: 0.40756, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.41646, val loss: 0.40524, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.41394, val loss: 0.40315, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.41150, val loss: 0.40041, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.40920, val loss: 0.39852, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.40711, val loss: 0.39617, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.40233, val loss: 0.39169, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.40047, val loss: 0.38959, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.39775, val loss: 0.38717, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39504, val loss: 0.38436, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.39334, val loss: 0.38242, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.39141, val loss: 0.38097, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.38736, val loss: 0.37718, in 0.000s 1 tree, 33 leaves, max depth = 10, train loss: 0.38502, val loss: 0.37517, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38289, val loss: 0.37339, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.37949, val loss: 0.37024, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.37795, val loss: 0.36856, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.37615, val loss: 0.36714, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.37316, val loss: 0.36438, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.37179, val loss: 0.36342, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.36904, val loss: 0.36096, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.36776, val loss: 0.35947, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.36656, val loss: 0.35868, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.36532, val loss: 0.35727, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.36284, val loss: 0.35504, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.36159, val loss: 0.35361, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.36058, val loss: 0.35254, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.35840, val loss: 0.35058, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.35596, val loss: 0.34804, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.35462, val loss: 0.34697, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.35258, val loss: 0.34516, in 0.016s 1 tree, 10 leaves, max depth = 4, train loss: 0.35152, val loss: 0.34386, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.35056, val loss: 0.34267, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.34969, val loss: 0.34161, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.34832, val loss: 0.34057, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34606, val loss: 0.33821, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.34487, val loss: 0.33730, in 0.013s 1 tree, 26 leaves, max depth = 8, train loss: 0.34305, val loss: 0.33571, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.34151, val loss: 0.33438, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.34063, val loss: 0.33330, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.33987, val loss: 0.33254, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33776, val loss: 0.33033, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.33699, val loss: 0.32938, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33504, val loss: 0.32734, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.33432, val loss: 0.32643, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.33252, val loss: 0.32453, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.33132, val loss: 0.32366, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.33059, val loss: 0.32275, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.32891, val loss: 0.32098, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.32829, val loss: 0.32019, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32665, val loss: 0.31876, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.32563, val loss: 0.31802, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.32415, val loss: 0.31672, in 0.000s 1 tree, 22 leaves, max depth = 8, train loss: 0.32278, val loss: 0.31555, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.32115, val loss: 0.31383, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.32049, val loss: 0.31301, in 0.000s 1 tree, 11 leaves, max depth = 7, train loss: 0.31990, val loss: 0.31230, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.31931, val loss: 0.31156, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.31779, val loss: 0.30995, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.31687, val loss: 0.30931, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.31558, val loss: 0.30819, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.31501, val loss: 0.30755, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.31420, val loss: 0.30700, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.31274, val loss: 0.30545, in 0.016s Fit 95 trees in 1.049 s, (1419 total leaves) Time spent computing histograms: 0.435s Time spent finding best splits: 0.048s Time spent applying splits: 0.041s Time spent predicting: 0.000s Trial 8, Fold 2: Log loss = 0.31504257772127, Average precision = 0.9514261562429316, ROC-AUC = 0.9503154229468751, Elapsed Time = 1.0518565000002127 seconds Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 8, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.205 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 4 leaves, max depth = 3, train loss: 0.67019, val loss: 0.66930, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.64909, val loss: 0.64730, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.63001, val loss: 0.62740, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61268, val loss: 0.60942, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.59702, val loss: 0.59306, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58285, val loss: 0.57835, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.57002, val loss: 0.56486, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.55835, val loss: 0.55256, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.54756, val loss: 0.54123, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.53749, val loss: 0.53072, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.52855, val loss: 0.52138, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.52044, val loss: 0.51277, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.51290, val loss: 0.50479, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50606, val loss: 0.49749, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.49973, val loss: 0.49077, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.49007, val loss: 0.48179, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.48451, val loss: 0.47582, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.47943, val loss: 0.47033, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.47460, val loss: 0.46526, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.47014, val loss: 0.46055, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.46466, val loss: 0.45538, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.45910, val loss: 0.45038, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.45547, val loss: 0.44655, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.45196, val loss: 0.44281, in 0.000s 1 tree, 34 leaves, max depth = 8, train loss: 0.44729, val loss: 0.43846, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.44388, val loss: 0.43533, in 0.000s 1 tree, 12 leaves, max depth = 5, train loss: 0.44099, val loss: 0.43228, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.43751, val loss: 0.42915, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.43311, val loss: 0.42523, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.42685, val loss: 0.41949, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42315, val loss: 0.41627, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.42011, val loss: 0.41367, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.41733, val loss: 0.41131, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.41479, val loss: 0.40917, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.41237, val loss: 0.40657, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.41003, val loss: 0.40465, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.40782, val loss: 0.40215, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40308, val loss: 0.39793, in 0.000s 1 tree, 12 leaves, max depth = 7, train loss: 0.40112, val loss: 0.39570, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.39841, val loss: 0.39344, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39561, val loss: 0.39086, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.39375, val loss: 0.38930, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.39187, val loss: 0.38784, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.38794, val loss: 0.38442, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.38562, val loss: 0.38236, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.38361, val loss: 0.38077, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.38033, val loss: 0.37794, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.37872, val loss: 0.37613, in 0.000s 1 tree, 15 leaves, max depth = 9, train loss: 0.37725, val loss: 0.37456, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.37423, val loss: 0.37197, in 0.000s 1 tree, 40 leaves, max depth = 14, train loss: 0.37285, val loss: 0.37099, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.37009, val loss: 0.36867, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.36878, val loss: 0.36713, in 0.000s 1 tree, 45 leaves, max depth = 13, train loss: 0.36757, val loss: 0.36630, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.36634, val loss: 0.36490, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.36392, val loss: 0.36284, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.36268, val loss: 0.36147, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.36169, val loss: 0.36043, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.35939, val loss: 0.35854, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35689, val loss: 0.35622, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.35547, val loss: 0.35506, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.35336, val loss: 0.35334, in 0.016s 1 tree, 11 leaves, max depth = 4, train loss: 0.35237, val loss: 0.35202, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.35152, val loss: 0.35114, in 0.000s 1 tree, 10 leaves, max depth = 4, train loss: 0.35066, val loss: 0.34992, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.34962, val loss: 0.34925, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.34730, val loss: 0.34711, in 0.000s 1 tree, 69 leaves, max depth = 16, train loss: 0.34606, val loss: 0.34613, in 0.016s 1 tree, 27 leaves, max depth = 14, train loss: 0.34436, val loss: 0.34475, in 0.016s 1 tree, 38 leaves, max depth = 15, train loss: 0.34264, val loss: 0.34340, in 0.000s 1 tree, 11 leaves, max depth = 4, train loss: 0.34180, val loss: 0.34220, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.34105, val loss: 0.34108, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.33888, val loss: 0.33907, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.33815, val loss: 0.33820, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.33616, val loss: 0.33635, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.33547, val loss: 0.33542, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.33363, val loss: 0.33371, in 0.000s 1 tree, 34 leaves, max depth = 13, train loss: 0.33240, val loss: 0.33277, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.33170, val loss: 0.33185, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.32998, val loss: 0.33025, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.32938, val loss: 0.32946, in 0.000s 1 tree, 26 leaves, max depth = 8, train loss: 0.32769, val loss: 0.32816, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.32662, val loss: 0.32737, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.32514, val loss: 0.32623, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.32376, val loss: 0.32521, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.32210, val loss: 0.32366, in 0.000s 1 tree, 12 leaves, max depth = 5, train loss: 0.32147, val loss: 0.32280, in 0.016s 1 tree, 18 leaves, max depth = 11, train loss: 0.32084, val loss: 0.32218, in 0.000s 1 tree, 46 leaves, max depth = 14, train loss: 0.32004, val loss: 0.32181, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.31848, val loss: 0.32035, in 0.000s 1 tree, 73 leaves, max depth = 17, train loss: 0.31756, val loss: 0.31971, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31630, val loss: 0.31883, in 0.000s 1 tree, 12 leaves, max depth = 5, train loss: 0.31571, val loss: 0.31800, in 0.016s 1 tree, 74 leaves, max depth = 17, train loss: 0.31489, val loss: 0.31744, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.31339, val loss: 0.31605, in 0.000s Fit 95 trees in 1.080 s, (1767 total leaves) Time spent computing histograms: 0.428s Time spent finding best splits: 0.049s Time spent applying splits: 0.040s Time spent predicting: 0.000s Trial 8, Fold 3: Log loss = 0.3097361123640661, Average precision = 0.956834791175193, ROC-AUC = 0.9536477846211633, Elapsed Time = 1.09967609999876 seconds Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 8, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.189 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 5 leaves, max depth = 4, train loss: 0.67015, val loss: 0.66875, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.64905, val loss: 0.64636, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62994, val loss: 0.62599, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.61261, val loss: 0.60759, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.59694, val loss: 0.59086, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.58276, val loss: 0.57562, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.56991, val loss: 0.56179, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.55817, val loss: 0.54917, in 0.000s 1 tree, 7 leaves, max depth = 3, train loss: 0.54736, val loss: 0.53745, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.53729, val loss: 0.52640, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.52834, val loss: 0.51658, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.52016, val loss: 0.50764, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.51261, val loss: 0.49931, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50573, val loss: 0.49160, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.49938, val loss: 0.48452, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.48977, val loss: 0.47517, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.48419, val loss: 0.46893, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.47907, val loss: 0.46318, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.47424, val loss: 0.45776, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.46977, val loss: 0.45262, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.46438, val loss: 0.44729, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45894, val loss: 0.44208, in 0.000s 1 tree, 9 leaves, max depth = 4, train loss: 0.45530, val loss: 0.43787, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.45178, val loss: 0.43377, in 0.000s 1 tree, 26 leaves, max depth = 8, train loss: 0.44719, val loss: 0.42925, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.44391, val loss: 0.42577, in 0.000s 1 tree, 9 leaves, max depth = 4, train loss: 0.44101, val loss: 0.42235, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.43760, val loss: 0.41871, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.43327, val loss: 0.41459, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.42702, val loss: 0.40859, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.42338, val loss: 0.40515, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.42041, val loss: 0.40213, in 0.000s 1 tree, 22 leaves, max depth = 8, train loss: 0.41770, val loss: 0.39935, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.41521, val loss: 0.39680, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.41279, val loss: 0.39393, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.41052, val loss: 0.39159, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40825, val loss: 0.38892, in 0.000s 1 tree, 18 leaves, max depth = 9, train loss: 0.40355, val loss: 0.38449, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.40154, val loss: 0.38210, in 0.000s 1 tree, 24 leaves, max depth = 8, train loss: 0.39887, val loss: 0.37972, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39618, val loss: 0.37684, in 0.000s 1 tree, 11 leaves, max depth = 6, train loss: 0.39442, val loss: 0.37470, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.39252, val loss: 0.37275, in 0.000s 1 tree, 16 leaves, max depth = 9, train loss: 0.38855, val loss: 0.36905, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.38625, val loss: 0.36691, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.38417, val loss: 0.36509, in 0.000s 1 tree, 12 leaves, max depth = 5, train loss: 0.38086, val loss: 0.36199, in 0.016s 1 tree, 15 leaves, max depth = 8, train loss: 0.37935, val loss: 0.36015, in 0.000s 1 tree, 24 leaves, max depth = 8, train loss: 0.37761, val loss: 0.35861, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.37468, val loss: 0.35593, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.37336, val loss: 0.35458, in 0.000s 1 tree, 22 leaves, max depth = 8, train loss: 0.37076, val loss: 0.35222, in 0.016s 1 tree, 15 leaves, max depth = 8, train loss: 0.36941, val loss: 0.35057, in 0.000s 1 tree, 11 leaves, max depth = 8, train loss: 0.36818, val loss: 0.34905, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.36705, val loss: 0.34766, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.36464, val loss: 0.34547, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36346, val loss: 0.34408, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.36247, val loss: 0.34302, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.36025, val loss: 0.34099, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.35783, val loss: 0.33837, in 0.016s 1 tree, 54 leaves, max depth = 16, train loss: 0.35643, val loss: 0.33721, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.35447, val loss: 0.33550, in 0.031s 1 tree, 8 leaves, max depth = 4, train loss: 0.35348, val loss: 0.33439, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.35264, val loss: 0.33352, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.35178, val loss: 0.33252, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.35038, val loss: 0.33118, in 0.031s 1 tree, 3 leaves, max depth = 2, train loss: 0.34814, val loss: 0.32875, in 0.000s 1 tree, 57 leaves, max depth = 16, train loss: 0.34692, val loss: 0.32779, in 0.031s 1 tree, 21 leaves, max depth = 8, train loss: 0.34520, val loss: 0.32619, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.34362, val loss: 0.32479, in 0.016s 1 tree, 10 leaves, max depth = 4, train loss: 0.34274, val loss: 0.32380, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.34195, val loss: 0.32292, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33986, val loss: 0.32064, in 0.016s 1 tree, 44 leaves, max depth = 16, train loss: 0.33895, val loss: 0.31979, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33701, val loss: 0.31768, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.33626, val loss: 0.31685, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33447, val loss: 0.31490, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.33382, val loss: 0.31422, in 0.000s 1 tree, 11 leaves, max depth = 6, train loss: 0.33315, val loss: 0.31351, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33149, val loss: 0.31170, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.33091, val loss: 0.31108, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.32933, val loss: 0.30964, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.32823, val loss: 0.30883, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.32685, val loss: 0.30759, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.32561, val loss: 0.30654, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.32401, val loss: 0.30478, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.32340, val loss: 0.30414, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.32282, val loss: 0.30359, in 0.000s 1 tree, 12 leaves, max depth = 5, train loss: 0.32226, val loss: 0.30301, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.32076, val loss: 0.30136, in 0.000s 1 tree, 57 leaves, max depth = 14, train loss: 0.31977, val loss: 0.30067, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.31859, val loss: 0.29964, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.31806, val loss: 0.29909, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.31719, val loss: 0.29848, in 0.000s 1 tree, 4 leaves, max depth = 2, train loss: 0.31575, val loss: 0.29690, in 0.016s Fit 95 trees in 1.283 s, (1467 total leaves) Time spent computing histograms: 0.535s Time spent finding best splits: 0.066s Time spent applying splits: 0.054s Time spent predicting: 0.000s Trial 8, Fold 4: Log loss = 0.31759633195988035, Average precision = 0.9537953575007956, ROC-AUC = 0.9493490263262684, Elapsed Time = 1.2855565000008937 seconds Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 8, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 4 leaves, max depth = 3, train loss: 0.66986, val loss: 0.66819, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.64852, val loss: 0.64533, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62922, val loss: 0.62458, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.61167, val loss: 0.60572, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.59591, val loss: 0.58863, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.58158, val loss: 0.57303, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.56856, val loss: 0.55885, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.55669, val loss: 0.54582, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.54576, val loss: 0.53380, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.53560, val loss: 0.52259, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.52656, val loss: 0.51251, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.51830, val loss: 0.50327, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.51066, val loss: 0.49473, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50372, val loss: 0.48684, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.49730, val loss: 0.47961, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48781, val loss: 0.47076, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.48217, val loss: 0.46431, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.47700, val loss: 0.45837, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.47212, val loss: 0.45278, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.46762, val loss: 0.44761, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.46233, val loss: 0.44243, in 0.016s 1 tree, 13 leaves, max depth = 8, train loss: 0.45696, val loss: 0.43780, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.45327, val loss: 0.43346, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.44970, val loss: 0.42931, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.44521, val loss: 0.42495, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.44191, val loss: 0.42174, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.43898, val loss: 0.41824, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.43562, val loss: 0.41489, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.43134, val loss: 0.41136, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.42517, val loss: 0.40587, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.42158, val loss: 0.40297, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.41864, val loss: 0.40007, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.41594, val loss: 0.39742, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.41347, val loss: 0.39499, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.41099, val loss: 0.39206, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.40871, val loss: 0.38981, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.40659, val loss: 0.38728, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.40195, val loss: 0.38325, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.39993, val loss: 0.38079, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.39729, val loss: 0.37880, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39456, val loss: 0.37617, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.39275, val loss: 0.37399, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.39085, val loss: 0.37215, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.38694, val loss: 0.36881, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.38466, val loss: 0.36674, in 0.000s 1 tree, 33 leaves, max depth = 10, train loss: 0.38261, val loss: 0.36528, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.37934, val loss: 0.36254, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.37780, val loss: 0.36066, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.37608, val loss: 0.35949, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37320, val loss: 0.35705, in 0.000s 1 tree, 35 leaves, max depth = 15, train loss: 0.37184, val loss: 0.35574, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.36925, val loss: 0.35349, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.36788, val loss: 0.35177, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36668, val loss: 0.35026, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.36547, val loss: 0.34889, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.36304, val loss: 0.34682, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.36183, val loss: 0.34533, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.36086, val loss: 0.34421, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.35863, val loss: 0.34233, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35619, val loss: 0.33997, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.35481, val loss: 0.33880, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.35284, val loss: 0.33716, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.35182, val loss: 0.33599, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.35090, val loss: 0.33493, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.35005, val loss: 0.33381, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.34866, val loss: 0.33268, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34640, val loss: 0.33050, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.34519, val loss: 0.32951, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.34347, val loss: 0.32813, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.34192, val loss: 0.32690, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.34104, val loss: 0.32586, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.34025, val loss: 0.32492, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33814, val loss: 0.32288, in 0.000s 1 tree, 31 leaves, max depth = 13, train loss: 0.33697, val loss: 0.32198, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33502, val loss: 0.32009, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.33424, val loss: 0.31920, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33244, val loss: 0.31746, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.33178, val loss: 0.31671, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.33107, val loss: 0.31590, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.32939, val loss: 0.31429, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.32879, val loss: 0.31360, in 0.000s 1 tree, 19 leaves, max depth = 7, train loss: 0.32714, val loss: 0.31229, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.32609, val loss: 0.31145, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.32464, val loss: 0.31030, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.32330, val loss: 0.30930, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.32168, val loss: 0.30773, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.32106, val loss: 0.30700, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.32049, val loss: 0.30636, in 0.000s 1 tree, 43 leaves, max depth = 14, train loss: 0.31967, val loss: 0.30566, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.31816, val loss: 0.30418, in 0.000s 1 tree, 52 leaves, max depth = 14, train loss: 0.31725, val loss: 0.30347, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.31607, val loss: 0.30257, in 0.000s 1 tree, 12 leaves, max depth = 5, train loss: 0.31547, val loss: 0.30191, in 0.000s 1 tree, 54 leaves, max depth = 14, train loss: 0.31467, val loss: 0.30128, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.31321, val loss: 0.29987, in 0.000s Fit 95 trees in 1.033 s, (1455 total leaves) Time spent computing histograms: 0.434s Time spent finding best splits: 0.044s Time spent applying splits: 0.036s Time spent predicting: 0.000s Trial 8, Fold 5: Log loss = 0.319615800834132, Average precision = 0.9529594062011224, ROC-AUC = 0.9482054883599947, Elapsed Time = 1.0457816000016464 seconds
Optimization Progress: 9%|9 | 9/100 [01:51<18:51, 12.44s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 9, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.159 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 17, train loss: 0.64898, val loss: 0.64781, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.61250, val loss: 0.61028, in 0.016s 1 tree, 76 leaves, max depth = 18, train loss: 0.58242, val loss: 0.57932, in 0.000s 1 tree, 77 leaves, max depth = 18, train loss: 0.55778, val loss: 0.55395, in 0.016s 1 tree, 127 leaves, max depth = 13, train loss: 0.53453, val loss: 0.53256, in 0.016s 1 tree, 76 leaves, max depth = 17, train loss: 0.51534, val loss: 0.51258, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.49704, val loss: 0.49590, in 0.016s 1 tree, 77 leaves, max depth = 17, train loss: 0.48227, val loss: 0.48041, in 0.000s 1 tree, 76 leaves, max depth = 16, train loss: 0.46965, val loss: 0.46711, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.45857, val loss: 0.45533, in 0.000s 1 tree, 127 leaves, max depth = 14, train loss: 0.44484, val loss: 0.44306, in 0.016s 1 tree, 77 leaves, max depth = 16, train loss: 0.43659, val loss: 0.43430, in 0.016s 1 tree, 76 leaves, max depth = 17, train loss: 0.42911, val loss: 0.42639, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42246, val loss: 0.41905, in 0.000s 1 tree, 168 leaves, max depth = 16, train loss: 0.41469, val loss: 0.41243, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.40459, val loss: 0.40373, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39914, val loss: 0.39767, in 0.000s 1 tree, 76 leaves, max depth = 15, train loss: 0.39378, val loss: 0.39209, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38923, val loss: 0.38700, in 0.000s 1 tree, 76 leaves, max depth = 13, train loss: 0.38458, val loss: 0.38209, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38077, val loss: 0.37779, in 0.000s 1 tree, 168 leaves, max depth = 15, train loss: 0.37539, val loss: 0.37369, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.36746, val loss: 0.36718, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.36381, val loss: 0.36332, in 0.000s 1 tree, 153 leaves, max depth = 19, train loss: 0.35998, val loss: 0.35994, in 0.000s 1 tree, 127 leaves, max depth = 15, train loss: 0.35365, val loss: 0.35484, in 0.031s 1 tree, 127 leaves, max depth = 15, train loss: 0.34825, val loss: 0.35056, in 0.000s 1 tree, 46 leaves, max depth = 11, train loss: 0.34571, val loss: 0.34787, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.34113, val loss: 0.34431, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.33723, val loss: 0.34132, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.33447, val loss: 0.33823, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33099, val loss: 0.33425, in 0.000s 1 tree, 76 leaves, max depth = 13, train loss: 0.32862, val loss: 0.33170, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.32508, val loss: 0.32909, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.32291, val loss: 0.32668, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.32095, val loss: 0.32436, in 0.000s 1 tree, 164 leaves, max depth = 17, train loss: 0.31834, val loss: 0.32270, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.31546, val loss: 0.32062, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.31375, val loss: 0.31863, in 0.016s 1 tree, 164 leaves, max depth = 17, train loss: 0.31169, val loss: 0.31743, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30852, val loss: 0.31378, in 0.000s 1 tree, 96 leaves, max depth = 16, train loss: 0.30688, val loss: 0.31184, in 0.016s 1 tree, 153 leaves, max depth = 22, train loss: 0.30509, val loss: 0.31056, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.30282, val loss: 0.30899, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30000, val loss: 0.30573, in 0.000s 1 tree, 76 leaves, max depth = 15, train loss: 0.29847, val loss: 0.30409, in 0.016s Fit 46 trees in 0.799 s, (4201 total leaves) Time spent computing histograms: 0.215s Time spent finding best splits: 0.084s Time spent applying splits: 0.060s Time spent predicting: 0.016s Trial 9, Fold 1: Log loss = 0.30940654560773445, Average precision = 0.9546708274374668, ROC-AUC = 0.9510657438474738, Elapsed Time = 0.8121594000003824 seconds Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 9, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 20, train loss: 0.64881, val loss: 0.64702, in 0.015s 1 tree, 77 leaves, max depth = 15, train loss: 0.61244, val loss: 0.60872, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.58246, val loss: 0.57713, in 0.016s 1 tree, 77 leaves, max depth = 17, train loss: 0.55774, val loss: 0.55117, in 0.000s 1 tree, 126 leaves, max depth = 19, train loss: 0.53504, val loss: 0.52940, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.51590, val loss: 0.50923, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.49800, val loss: 0.49216, in 0.016s 1 tree, 77 leaves, max depth = 20, train loss: 0.48316, val loss: 0.47640, in 0.000s 1 tree, 77 leaves, max depth = 13, train loss: 0.47050, val loss: 0.46276, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.45947, val loss: 0.45086, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.44605, val loss: 0.43823, in 0.016s 1 tree, 77 leaves, max depth = 22, train loss: 0.43775, val loss: 0.42930, in 0.000s 1 tree, 77 leaves, max depth = 19, train loss: 0.43073, val loss: 0.42172, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42425, val loss: 0.41501, in 0.000s 1 tree, 168 leaves, max depth = 13, train loss: 0.41656, val loss: 0.40836, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.40664, val loss: 0.39923, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40134, val loss: 0.39370, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.39587, val loss: 0.38800, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39144, val loss: 0.38338, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.38684, val loss: 0.37835, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38314, val loss: 0.37445, in 0.000s 1 tree, 153 leaves, max depth = 23, train loss: 0.37833, val loss: 0.37084, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.37046, val loss: 0.36376, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.36682, val loss: 0.35978, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.36212, val loss: 0.35617, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.35600, val loss: 0.35075, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.35080, val loss: 0.34618, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.34775, val loss: 0.34290, in 0.000s 1 tree, 126 leaves, max depth = 20, train loss: 0.34327, val loss: 0.33899, in 0.031s 1 tree, 126 leaves, max depth = 20, train loss: 0.33945, val loss: 0.33571, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.33677, val loss: 0.33296, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33336, val loss: 0.32938, in 0.016s 1 tree, 97 leaves, max depth = 18, train loss: 0.33103, val loss: 0.32673, in 0.000s 1 tree, 126 leaves, max depth = 20, train loss: 0.32760, val loss: 0.32381, in 0.016s 1 tree, 96 leaves, max depth = 16, train loss: 0.32559, val loss: 0.32149, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32372, val loss: 0.31953, in 0.016s 1 tree, 163 leaves, max depth = 17, train loss: 0.32131, val loss: 0.31803, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.31853, val loss: 0.31571, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.31682, val loss: 0.31383, in 0.000s 1 tree, 162 leaves, max depth = 16, train loss: 0.31493, val loss: 0.31275, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31181, val loss: 0.30948, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.31011, val loss: 0.30765, in 0.000s 1 tree, 152 leaves, max depth = 24, train loss: 0.30812, val loss: 0.30663, in 0.016s 1 tree, 126 leaves, max depth = 21, train loss: 0.30594, val loss: 0.30486, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30317, val loss: 0.30194, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.30164, val loss: 0.30029, in 0.000s Fit 46 trees in 0.798 s, (4239 total leaves) Time spent computing histograms: 0.224s Time spent finding best splits: 0.082s Time spent applying splits: 0.059s Time spent predicting: 0.016s Trial 9, Fold 2: Log loss = 0.30944503444618643, Average precision = 0.952180089458451, ROC-AUC = 0.9511007936105684, Elapsed Time = 0.8179369999998016 seconds Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 9, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 15, train loss: 0.64923, val loss: 0.64769, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.61303, val loss: 0.61001, in 0.000s 1 tree, 76 leaves, max depth = 14, train loss: 0.58324, val loss: 0.57897, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.55878, val loss: 0.55332, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.53606, val loss: 0.53243, in 0.016s 1 tree, 76 leaves, max depth = 15, train loss: 0.51700, val loss: 0.51227, in 0.000s 1 tree, 121 leaves, max depth = 14, train loss: 0.49913, val loss: 0.49593, in 0.016s 1 tree, 76 leaves, max depth = 19, train loss: 0.48443, val loss: 0.48029, in 0.016s 1 tree, 76 leaves, max depth = 15, train loss: 0.47188, val loss: 0.46681, in 0.000s 1 tree, 76 leaves, max depth = 13, train loss: 0.46090, val loss: 0.45537, in 0.016s 1 tree, 121 leaves, max depth = 15, train loss: 0.44753, val loss: 0.44321, in 0.016s 1 tree, 76 leaves, max depth = 19, train loss: 0.43929, val loss: 0.43432, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.43195, val loss: 0.42628, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.42523, val loss: 0.42008, in 0.016s 1 tree, 168 leaves, max depth = 14, train loss: 0.41756, val loss: 0.41361, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.40779, val loss: 0.40487, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40230, val loss: 0.39978, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.39701, val loss: 0.39394, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39243, val loss: 0.38970, in 0.000s 1 tree, 76 leaves, max depth = 13, train loss: 0.38784, val loss: 0.38491, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38401, val loss: 0.38136, in 0.000s 1 tree, 153 leaves, max depth = 25, train loss: 0.37916, val loss: 0.37772, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.37143, val loss: 0.37093, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.36780, val loss: 0.36713, in 0.016s 1 tree, 153 leaves, max depth = 25, train loss: 0.36418, val loss: 0.36460, in 0.016s 1 tree, 121 leaves, max depth = 15, train loss: 0.35802, val loss: 0.35925, in 0.000s 1 tree, 121 leaves, max depth = 14, train loss: 0.35279, val loss: 0.35475, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.35018, val loss: 0.35204, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.34577, val loss: 0.34829, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.34203, val loss: 0.34514, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.33936, val loss: 0.34210, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33587, val loss: 0.33883, in 0.000s 1 tree, 96 leaves, max depth = 16, train loss: 0.33348, val loss: 0.33595, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.33012, val loss: 0.33316, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.32806, val loss: 0.33085, in 0.000s 1 tree, 96 leaves, max depth = 16, train loss: 0.32598, val loss: 0.32826, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.32344, val loss: 0.32641, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.32075, val loss: 0.32421, in 0.016s 1 tree, 92 leaves, max depth = 22, train loss: 0.31898, val loss: 0.32186, in 0.000s 1 tree, 162 leaves, max depth = 18, train loss: 0.31699, val loss: 0.32049, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31381, val loss: 0.31751, in 0.016s 1 tree, 96 leaves, max depth = 20, train loss: 0.31209, val loss: 0.31529, in 0.016s 1 tree, 153 leaves, max depth = 24, train loss: 0.31029, val loss: 0.31449, in 0.016s 1 tree, 121 leaves, max depth = 19, train loss: 0.30820, val loss: 0.31283, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30538, val loss: 0.31019, in 0.000s 1 tree, 96 leaves, max depth = 20, train loss: 0.30384, val loss: 0.30819, in 0.016s Fit 46 trees in 0.830 s, (4096 total leaves) Time spent computing histograms: 0.227s Time spent finding best splits: 0.087s Time spent applying splits: 0.062s Time spent predicting: 0.000s Trial 9, Fold 3: Log loss = 0.3068492381650382, Average precision = 0.9564745796419276, ROC-AUC = 0.9535465835107049, Elapsed Time = 0.8307597000002716 seconds Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 9, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 14, train loss: 0.64907, val loss: 0.64680, in 0.000s 1 tree, 76 leaves, max depth = 14, train loss: 0.61275, val loss: 0.60823, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.58283, val loss: 0.57639, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.55820, val loss: 0.55014, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.53569, val loss: 0.52826, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.51655, val loss: 0.50746, in 0.000s 1 tree, 124 leaves, max depth = 16, train loss: 0.49887, val loss: 0.49034, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.48407, val loss: 0.47406, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.47145, val loss: 0.46009, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.46054, val loss: 0.44777, in 0.000s 1 tree, 124 leaves, max depth = 16, train loss: 0.44735, val loss: 0.43517, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.43906, val loss: 0.42575, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.43157, val loss: 0.41737, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42510, val loss: 0.41044, in 0.016s 1 tree, 168 leaves, max depth = 14, train loss: 0.41760, val loss: 0.40375, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.40800, val loss: 0.39474, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40270, val loss: 0.38905, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.39727, val loss: 0.38279, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39286, val loss: 0.37804, in 0.000s 1 tree, 76 leaves, max depth = 13, train loss: 0.38832, val loss: 0.37273, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38463, val loss: 0.36875, in 0.016s 1 tree, 152 leaves, max depth = 27, train loss: 0.37991, val loss: 0.36440, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.37232, val loss: 0.35750, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.36873, val loss: 0.35327, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.36407, val loss: 0.34928, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.35821, val loss: 0.34402, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.35324, val loss: 0.33957, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.35063, val loss: 0.33688, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.34645, val loss: 0.33317, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.34291, val loss: 0.33005, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.34010, val loss: 0.32708, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33672, val loss: 0.32338, in 0.000s 1 tree, 97 leaves, max depth = 14, train loss: 0.33440, val loss: 0.32095, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.33119, val loss: 0.31816, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.32896, val loss: 0.31561, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.32694, val loss: 0.31360, in 0.016s 1 tree, 163 leaves, max depth = 22, train loss: 0.32451, val loss: 0.31184, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.32193, val loss: 0.30963, in 0.016s 1 tree, 97 leaves, max depth = 19, train loss: 0.32023, val loss: 0.30776, in 0.000s 1 tree, 163 leaves, max depth = 21, train loss: 0.31830, val loss: 0.30644, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.31521, val loss: 0.30306, in 0.000s 1 tree, 97 leaves, max depth = 16, train loss: 0.31352, val loss: 0.30142, in 0.016s 1 tree, 152 leaves, max depth = 30, train loss: 0.31152, val loss: 0.29988, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.30952, val loss: 0.29822, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30679, val loss: 0.29522, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.30525, val loss: 0.29346, in 0.016s Fit 46 trees in 0.923 s, (4149 total leaves) Time spent computing histograms: 0.242s Time spent finding best splits: 0.098s Time spent applying splits: 0.070s Time spent predicting: 0.000s Trial 9, Fold 4: Log loss = 0.3106716960208532, Average precision = 0.9542641250120021, ROC-AUC = 0.9510418082409973, Elapsed Time = 0.9211533999987296 seconds Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 9, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 76 leaves, max depth = 12, train loss: 0.64857, val loss: 0.64569, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.61206, val loss: 0.60642, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.58208, val loss: 0.57397, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.55722, val loss: 0.54677, in 0.016s 1 tree, 121 leaves, max depth = 17, train loss: 0.53504, val loss: 0.52573, in 0.000s 1 tree, 76 leaves, max depth = 15, train loss: 0.51560, val loss: 0.50445, in 0.016s 1 tree, 121 leaves, max depth = 18, train loss: 0.49819, val loss: 0.48810, in 0.031s 1 tree, 76 leaves, max depth = 14, train loss: 0.48315, val loss: 0.47132, in 0.000s 1 tree, 76 leaves, max depth = 15, train loss: 0.47030, val loss: 0.45701, in 0.016s 1 tree, 76 leaves, max depth = 15, train loss: 0.45915, val loss: 0.44442, in 0.016s 1 tree, 121 leaves, max depth = 19, train loss: 0.44613, val loss: 0.43252, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.43768, val loss: 0.42285, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.43054, val loss: 0.41463, in 0.016s [14/46] 1 tree, 5 leaves, max depth = 3, train loss: 0.42404, val loss: 0.40839, in 0.000s 1 tree, 168 leaves, max depth = 13, train loss: 0.41646, val loss: 0.40243, in 0.016s 1 tree, 121 leaves, max depth = 20, train loss: 0.40693, val loss: 0.39395, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40160, val loss: 0.38887, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.39611, val loss: 0.38223, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39169, val loss: 0.37802, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.38699, val loss: 0.37250, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38329, val loss: 0.36900, in 0.016s 1 tree, 152 leaves, max depth = 30, train loss: 0.37850, val loss: 0.36462, in 0.016s 1 tree, 121 leaves, max depth = 20, train loss: 0.37098, val loss: 0.35818, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.36725, val loss: 0.35376, in 0.016s 1 tree, 164 leaves, max depth = 18, train loss: 0.36264, val loss: 0.34991, in 0.016s 1 tree, 121 leaves, max depth = 20, train loss: 0.35684, val loss: 0.34505, in 0.016s 1 tree, 121 leaves, max depth = 19, train loss: 0.35193, val loss: 0.34098, in 0.016s 1 tree, 97 leaves, max depth = 13, train loss: 0.34875, val loss: 0.33742, in 0.016s 1 tree, 121 leaves, max depth = 21, train loss: 0.34455, val loss: 0.33397, in 0.016s 1 tree, 121 leaves, max depth = 21, train loss: 0.34099, val loss: 0.33109, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.33828, val loss: 0.32763, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33488, val loss: 0.32438, in 0.016s 1 tree, 97 leaves, max depth = 13, train loss: 0.33245, val loss: 0.32181, in 0.016s 1 tree, 121 leaves, max depth = 21, train loss: 0.32925, val loss: 0.31928, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.32715, val loss: 0.31705, in 0.000s 1 tree, 76 leaves, max depth = 17, train loss: 0.32524, val loss: 0.31468, in 0.016s 1 tree, 164 leaves, max depth = 17, train loss: 0.32281, val loss: 0.31290, in 0.063s 1 tree, 121 leaves, max depth = 20, train loss: 0.32024, val loss: 0.31091, in 0.031s 1 tree, 94 leaves, max depth = 18, train loss: 0.31844, val loss: 0.30896, in 0.031s 1 tree, 97 leaves, max depth = 15, train loss: 0.31676, val loss: 0.30707, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.31371, val loss: 0.30412, in 0.000s 1 tree, 46 leaves, max depth = 11, train loss: 0.31235, val loss: 0.30254, in 0.016s 1 tree, 152 leaves, max depth = 28, train loss: 0.31024, val loss: 0.30088, in 0.031s 1 tree, 121 leaves, max depth = 19, train loss: 0.30806, val loss: 0.29925, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30536, val loss: 0.29662, in 0.016s 1 tree, 121 leaves, max depth = 20, train loss: 0.30346, val loss: 0.29523, in 0.016s Fit 46 trees in 1.017 s, (4076 total leaves) Time spent computing histograms: 0.284s Time spent finding best splits: 0.123s Time spent applying splits: 0.090s Time spent predicting: 0.000s Trial 9, Fold 5: Log loss = 0.31477065490313444, Average precision = 0.9525868854943338, ROC-AUC = 0.9487047544729948, Elapsed Time = 1.028342600000542 seconds
Optimization Progress: 10%|# | 10/100 [02:02<18:03, 12.04s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 10, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.190 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 19, train loss: 0.68188, val loss: 0.68157, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.67108, val loss: 0.67043, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.66079, val loss: 0.65980, in 0.016s 1 tree, 76 leaves, max depth = 17, train loss: 0.65117, val loss: 0.64997, in 0.000s 1 tree, 80 leaves, max depth = 15, train loss: 0.64190, val loss: 0.64041, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.63304, val loss: 0.63127, in 0.016s 1 tree, 73 leaves, max depth = 19, train loss: 0.62468, val loss: 0.62268, in 0.000s 1 tree, 78 leaves, max depth = 18, train loss: 0.61668, val loss: 0.61449, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.60894, val loss: 0.60648, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.60142, val loss: 0.59868, in 0.000s 1 tree, 79 leaves, max depth = 16, train loss: 0.59439, val loss: 0.59138, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.58759, val loss: 0.58439, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.58109, val loss: 0.57766, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.57477, val loss: 0.57107, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.56881, val loss: 0.56489, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.56310, val loss: 0.55896, in 0.000s 1 tree, 80 leaves, max depth = 14, train loss: 0.55761, val loss: 0.55332, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.55235, val loss: 0.54791, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.54737, val loss: 0.54280, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.54244, val loss: 0.53763, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.53772, val loss: 0.53267, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.53319, val loss: 0.52790, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.52786, val loss: 0.52299, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.52360, val loss: 0.51850, in 0.000s 1 tree, 80 leaves, max depth = 15, train loss: 0.51959, val loss: 0.51436, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.51461, val loss: 0.50978, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.50983, val loss: 0.50539, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.50614, val loss: 0.50151, in 0.000s 1 tree, 122 leaves, max depth = 19, train loss: 0.50161, val loss: 0.49735, in 0.016s 1 tree, 123 leaves, max depth = 19, train loss: 0.49725, val loss: 0.49336, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.49386, val loss: 0.48984, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.49058, val loss: 0.48637, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.48734, val loss: 0.48293, in 0.000s 1 tree, 123 leaves, max depth = 19, train loss: 0.48328, val loss: 0.47923, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.48023, val loss: 0.47597, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.47736, val loss: 0.47301, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.47454, val loss: 0.46999, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.47074, val loss: 0.46654, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.46815, val loss: 0.46385, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.46565, val loss: 0.46120, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.46325, val loss: 0.45872, in 0.000s 1 tree, 123 leaves, max depth = 14, train loss: 0.45968, val loss: 0.45549, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.45738, val loss: 0.45306, in 0.016s 1 tree, 122 leaves, max depth = 13, train loss: 0.45397, val loss: 0.45000, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.45180, val loss: 0.44770, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.44855, val loss: 0.44478, in 0.000s 1 tree, 123 leaves, max depth = 14, train loss: 0.44542, val loss: 0.44198, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.44336, val loss: 0.43974, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.44037, val loss: 0.43707, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.43749, val loss: 0.43450, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.43554, val loss: 0.43245, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.43279, val loss: 0.43001, in 0.016s 1 tree, 122 leaves, max depth = 14, train loss: 0.43015, val loss: 0.42766, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.42834, val loss: 0.42569, in 0.000s 1 tree, 123 leaves, max depth = 14, train loss: 0.42581, val loss: 0.42345, in 0.016s Fit 55 trees in 0.987 s, (5055 total leaves) Time spent computing histograms: 0.277s Time spent finding best splits: 0.073s Time spent applying splits: 0.086s Time spent predicting: 0.016s Trial 10, Fold 1: Log loss = 0.42790914008392067, Average precision = 0.9175382538763569, ROC-AUC = 0.9268080588654212, Elapsed Time = 0.9964141999989806 seconds Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 10, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.221 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 76 leaves, max depth = 19, train loss: 0.68194, val loss: 0.68148, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.67119, val loss: 0.67021, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.66094, val loss: 0.65946, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.65128, val loss: 0.64939, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.64206, val loss: 0.63972, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.63327, val loss: 0.63048, in 0.016s 1 tree, 76 leaves, max depth = 19, train loss: 0.62486, val loss: 0.62170, in 0.016s 1 tree, 77 leaves, max depth = 18, train loss: 0.61683, val loss: 0.61326, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.60909, val loss: 0.60513, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.60161, val loss: 0.59725, in 0.000s 1 tree, 79 leaves, max depth = 16, train loss: 0.59458, val loss: 0.58988, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.58786, val loss: 0.58279, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.58136, val loss: 0.57594, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.57507, val loss: 0.56929, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.56915, val loss: 0.56308, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.56344, val loss: 0.55704, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.55800, val loss: 0.55129, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.55279, val loss: 0.54578, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.54781, val loss: 0.54048, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.54291, val loss: 0.53528, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.53820, val loss: 0.53028, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.53369, val loss: 0.52548, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.52845, val loss: 0.52047, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.52421, val loss: 0.51595, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.52024, val loss: 0.51172, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.51534, val loss: 0.50705, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.51064, val loss: 0.50257, in 0.016s 1 tree, 79 leaves, max depth = 19, train loss: 0.50697, val loss: 0.49866, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.50251, val loss: 0.49441, in 0.000s 1 tree, 123 leaves, max depth = 17, train loss: 0.49824, val loss: 0.49035, in 0.016s 1 tree, 76 leaves, max depth = 18, train loss: 0.49482, val loss: 0.48674, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.49157, val loss: 0.48325, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.48834, val loss: 0.47979, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.48436, val loss: 0.47601, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.48131, val loss: 0.47275, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.47843, val loss: 0.46966, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.47562, val loss: 0.46664, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.47190, val loss: 0.46312, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.46932, val loss: 0.46036, in 0.000s 1 tree, 79 leaves, max depth = 16, train loss: 0.46684, val loss: 0.45771, in 0.016s 1 tree, 79 leaves, max depth = 25, train loss: 0.46445, val loss: 0.45518, in 0.016s 1 tree, 123 leaves, max depth = 18, train loss: 0.46095, val loss: 0.45188, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.45867, val loss: 0.44942, in 0.000s 1 tree, 123 leaves, max depth = 17, train loss: 0.45532, val loss: 0.44628, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.45320, val loss: 0.44400, in 0.016s [46/55] 1 tree, 123 leaves, max depth = 17, train loss: 0.45000, val loss: 0.44100, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.44692, val loss: 0.43812, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.44487, val loss: 0.43590, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.44193, val loss: 0.43316, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.43911, val loss: 0.43052, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.43726, val loss: 0.42853, in 0.000s 1 tree, 123 leaves, max depth = 15, train loss: 0.43455, val loss: 0.42601, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.43195, val loss: 0.42360, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.43015, val loss: 0.42165, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.42766, val loss: 0.41934, in 0.016s Fit 55 trees in 1.049 s, (5042 total leaves) Time spent computing histograms: 0.283s Time spent finding best splits: 0.071s Time spent applying splits: 0.085s Time spent predicting: 0.000s Trial 10, Fold 2: Log loss = 0.4291473245433425, Average precision = 0.9123761684258548, ROC-AUC = 0.9273861049408776, Elapsed Time = 1.0500720000000001 seconds Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 10, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 72 leaves, max depth = 13, train loss: 0.68197, val loss: 0.68161, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.67128, val loss: 0.67061, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.66109, val loss: 0.66011, in 0.016s 1 tree, 77 leaves, max depth = 16, train loss: 0.65154, val loss: 0.65022, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.64238, val loss: 0.64081, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.63361, val loss: 0.63168, in 0.016s 1 tree, 77 leaves, max depth = 16, train loss: 0.62527, val loss: 0.62304, in 0.016s 1 tree, 77 leaves, max depth = 17, train loss: 0.61732, val loss: 0.61480, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.60965, val loss: 0.60680, in 0.000s 1 tree, 79 leaves, max depth = 15, train loss: 0.60221, val loss: 0.59913, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.59521, val loss: 0.59193, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.58847, val loss: 0.58492, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.58204, val loss: 0.57819, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.57578, val loss: 0.57173, in 0.000s 1 tree, 79 leaves, max depth = 12, train loss: 0.56988, val loss: 0.56554, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.56423, val loss: 0.55962, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.55880, val loss: 0.55394, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.55360, val loss: 0.54860, in 0.016s 1 tree, 77 leaves, max depth = 18, train loss: 0.54865, val loss: 0.54344, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.54378, val loss: 0.53839, in 0.000s 1 tree, 79 leaves, max depth = 15, train loss: 0.53911, val loss: 0.53355, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.53463, val loss: 0.52890, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.52929, val loss: 0.52398, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.52508, val loss: 0.51960, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.52111, val loss: 0.51542, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.51612, val loss: 0.51082, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.51133, val loss: 0.50642, in 0.016s 1 tree, 78 leaves, max depth = 17, train loss: 0.50767, val loss: 0.50263, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.50312, val loss: 0.49845, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.49876, val loss: 0.49445, in 0.016s 1 tree, 73 leaves, max depth = 18, train loss: 0.49539, val loss: 0.49090, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.49213, val loss: 0.48752, in 0.000s 1 tree, 79 leaves, max depth = 15, train loss: 0.48894, val loss: 0.48417, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.48487, val loss: 0.48045, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.48187, val loss: 0.47729, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.47902, val loss: 0.47427, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.47624, val loss: 0.47135, in 0.000s 1 tree, 120 leaves, max depth = 15, train loss: 0.47244, val loss: 0.46788, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.46986, val loss: 0.46520, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.46739, val loss: 0.46262, in 0.000s 1 tree, 79 leaves, max depth = 13, train loss: 0.46501, val loss: 0.46008, in 0.000s 1 tree, 120 leaves, max depth = 14, train loss: 0.46144, val loss: 0.45681, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.45918, val loss: 0.45440, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.45577, val loss: 0.45129, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.45364, val loss: 0.44901, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.45038, val loss: 0.44605, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.44724, val loss: 0.44320, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.44522, val loss: 0.44105, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.44222, val loss: 0.43833, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.43934, val loss: 0.43573, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.43741, val loss: 0.43367, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.43466, val loss: 0.43118, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.43201, val loss: 0.42880, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.43024, val loss: 0.42690, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.42770, val loss: 0.42462, in 0.000s Fit 55 trees in 1.065 s, (4972 total leaves) Time spent computing histograms: 0.309s Time spent finding best splits: 0.079s Time spent applying splits: 0.095s Time spent predicting: 0.000s Trial 10, Fold 3: Log loss = 0.4249057059562685, Average precision = 0.9207502708543311, ROC-AUC = 0.9313444142404484, Elapsed Time = 1.0818947999996453 seconds Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 10, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 15, train loss: 0.68197, val loss: 0.68140, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.67128, val loss: 0.67007, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.66109, val loss: 0.65926, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.65149, val loss: 0.64915, in 0.016s 1 tree, 79 leaves, max depth = 19, train loss: 0.64228, val loss: 0.63940, in 0.016s 1 tree, 79 leaves, max depth = 19, train loss: 0.63349, val loss: 0.63008, in 0.000s 1 tree, 78 leaves, max depth = 14, train loss: 0.62513, val loss: 0.62123, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.61714, val loss: 0.61276, in 0.016s 1 tree, 79 leaves, max depth = 19, train loss: 0.60945, val loss: 0.60458, in 0.000s 1 tree, 78 leaves, max depth = 16, train loss: 0.60202, val loss: 0.59662, in 0.016s 1 tree, 80 leaves, max depth = 18, train loss: 0.59500, val loss: 0.58908, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.58824, val loss: 0.58189, in 0.000s 1 tree, 79 leaves, max depth = 19, train loss: 0.58179, val loss: 0.57499, in 0.000s 1 tree, 78 leaves, max depth = 16, train loss: 0.57554, val loss: 0.56825, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.56966, val loss: 0.56195, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.56402, val loss: 0.55591, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.55856, val loss: 0.55007, in 0.000s 1 tree, 79 leaves, max depth = 13, train loss: 0.55333, val loss: 0.54446, in 0.016s 1 tree, 74 leaves, max depth = 15, train loss: 0.54835, val loss: 0.53911, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.54349, val loss: 0.53381, in 0.000s 1 tree, 78 leaves, max depth = 16, train loss: 0.53882, val loss: 0.52872, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.53434, val loss: 0.52382, in 0.016s 1 tree, 124 leaves, max depth = 17, train loss: 0.52907, val loss: 0.51871, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.52486, val loss: 0.51410, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.52087, val loss: 0.50978, in 0.000s 1 tree, 124 leaves, max depth = 16, train loss: 0.51595, val loss: 0.50500, in 0.031s 1 tree, 124 leaves, max depth = 16, train loss: 0.51122, val loss: 0.50043, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.50754, val loss: 0.49637, in 0.000s 1 tree, 124 leaves, max depth = 13, train loss: 0.50306, val loss: 0.49203, in 0.031s 1 tree, 124 leaves, max depth = 13, train loss: 0.49877, val loss: 0.48787, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.49537, val loss: 0.48416, in 0.000s 1 tree, 78 leaves, max depth = 13, train loss: 0.49199, val loss: 0.48048, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.48879, val loss: 0.47694, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.48480, val loss: 0.47308, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.48179, val loss: 0.46973, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.47894, val loss: 0.46660, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.47616, val loss: 0.46349, in 0.000s 1 tree, 124 leaves, max depth = 14, train loss: 0.47242, val loss: 0.45989, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.46973, val loss: 0.45693, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.46723, val loss: 0.45413, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.46484, val loss: 0.45149, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.46134, val loss: 0.44812, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.45907, val loss: 0.44556, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.45573, val loss: 0.44236, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.45358, val loss: 0.43994, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.45039, val loss: 0.43688, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.44733, val loss: 0.43394, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.44530, val loss: 0.43163, in 0.000s 1 tree, 124 leaves, max depth = 13, train loss: 0.44237, val loss: 0.42882, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.43955, val loss: 0.42612, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.43761, val loss: 0.42396, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.43492, val loss: 0.42139, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.43233, val loss: 0.41891, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.43056, val loss: 0.41688, in 0.016s 1 tree, 124 leaves, max depth = 13, train loss: 0.42808, val loss: 0.41451, in 0.016s Fit 55 trees in 1.017 s, (5039 total leaves) Time spent computing histograms: 0.294s Time spent finding best splits: 0.075s Time spent applying splits: 0.091s Time spent predicting: 0.000s Trial 10, Fold 4: Log loss = 0.42822515332766903, Average precision = 0.9172057006370695, ROC-AUC = 0.9287372940764108, Elapsed Time = 1.0293785000012576 seconds Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 10, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 78 leaves, max depth = 13, train loss: 0.68180, val loss: 0.68110, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.67100, val loss: 0.66961, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.66078, val loss: 0.65869, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.65106, val loss: 0.64833, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.64177, val loss: 0.63840, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.63291, val loss: 0.62891, in 0.000s 1 tree, 77 leaves, max depth = 14, train loss: 0.62445, val loss: 0.61984, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.61637, val loss: 0.61118, in 0.000s 1 tree, 79 leaves, max depth = 13, train loss: 0.60861, val loss: 0.60283, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.60110, val loss: 0.59474, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.59401, val loss: 0.58709, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.58719, val loss: 0.57971, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.58068, val loss: 0.57266, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.57436, val loss: 0.56582, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.56840, val loss: 0.55936, in 0.000s 1 tree, 78 leaves, max depth = 13, train loss: 0.56269, val loss: 0.55316, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.55719, val loss: 0.54715, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.55191, val loss: 0.54138, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.54688, val loss: 0.53590, in 0.000s 1 tree, 79 leaves, max depth = 16, train loss: 0.54195, val loss: 0.53052, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.53723, val loss: 0.52535, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.53270, val loss: 0.52039, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.52751, val loss: 0.51548, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.52324, val loss: 0.51080, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.51921, val loss: 0.50634, in 0.016s 1 tree, 122 leaves, max depth = 18, train loss: 0.51436, val loss: 0.50177, in 0.016s 1 tree, 122 leaves, max depth = 18, train loss: 0.50970, val loss: 0.49740, in 0.031s 1 tree, 78 leaves, max depth = 15, train loss: 0.50598, val loss: 0.49328, in 0.000s 1 tree, 122 leaves, max depth = 19, train loss: 0.50156, val loss: 0.48914, in 0.016s 1 tree, 122 leaves, max depth = 19, train loss: 0.49732, val loss: 0.48517, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.49388, val loss: 0.48135, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.49056, val loss: 0.47767, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.48732, val loss: 0.47408, in 0.000s 1 tree, 122 leaves, max depth = 17, train loss: 0.48337, val loss: 0.47040, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.48031, val loss: 0.46699, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.47743, val loss: 0.46379, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.47461, val loss: 0.46063, in 0.000s 1 tree, 122 leaves, max depth = 17, train loss: 0.47091, val loss: 0.45721, in 0.031s 1 tree, 78 leaves, max depth = 15, train loss: 0.46828, val loss: 0.45427, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.46577, val loss: 0.45143, in 0.000s 1 tree, 78 leaves, max depth = 14, train loss: 0.46335, val loss: 0.44870, in 0.016s 1 tree, 122 leaves, max depth = 18, train loss: 0.45987, val loss: 0.44551, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.45758, val loss: 0.44292, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.45426, val loss: 0.43988, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.45210, val loss: 0.43743, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.44892, val loss: 0.43453, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.44586, val loss: 0.43174, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.44380, val loss: 0.42940, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.44088, val loss: 0.42675, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.43807, val loss: 0.42421, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.43618, val loss: 0.42206, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.43349, val loss: 0.41963, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.43090, val loss: 0.41729, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.42909, val loss: 0.41523, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.42661, val loss: 0.41300, in 0.016s Fit 55 trees in 1.018 s, (5007 total leaves) Time spent computing histograms: 0.299s Time spent finding best splits: 0.075s Time spent applying splits: 0.090s Time spent predicting: 0.016s Trial 10, Fold 5: Log loss = 0.4337304957267745, Average precision = 0.912977732762567, ROC-AUC = 0.9249993497203797, Elapsed Time = 1.0223001000013028 seconds
Optimization Progress: 11%|#1 | 11/100 [02:15<18:14, 12.30s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 11, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 43 leaves, max depth = 11, train loss: 0.68296, val loss: 0.68266, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.67751, val loss: 0.67678, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.66797, val loss: 0.66700, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.65892, val loss: 0.65761, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.65025, val loss: 0.64861, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.64185, val loss: 0.63997, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.63387, val loss: 0.63168, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.62624, val loss: 0.62380, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.62171, val loss: 0.61895, in 0.000s 1 tree, 42 leaves, max depth = 14, train loss: 0.61435, val loss: 0.61134, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.60746, val loss: 0.60426, in 0.000s 1 tree, 24 leaves, max depth = 8, train loss: 0.60343, val loss: 0.59992, in 0.016s 1 tree, 42 leaves, max depth = 13, train loss: 0.59686, val loss: 0.59314, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.59066, val loss: 0.58673, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.58468, val loss: 0.58055, in 0.000s 1 tree, 43 leaves, max depth = 11, train loss: 0.57893, val loss: 0.57461, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.57362, val loss: 0.56912, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.56826, val loss: 0.56351, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.56314, val loss: 0.55820, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.55802, val loss: 0.55290, in 0.000s 1 tree, 42 leaves, max depth = 14, train loss: 0.55310, val loss: 0.54779, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.54836, val loss: 0.54288, in 0.000s 1 tree, 40 leaves, max depth = 10, train loss: 0.54417, val loss: 0.53856, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.53976, val loss: 0.53398, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.53564, val loss: 0.52965, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.53161, val loss: 0.52549, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.52779, val loss: 0.52146, in 0.000s 1 tree, 43 leaves, max depth = 14, train loss: 0.52413, val loss: 0.51767, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.52061, val loss: 0.51400, in 0.000s 1 tree, 72 leaves, max depth = 11, train loss: 0.51605, val loss: 0.50979, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.51271, val loss: 0.50633, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.50949, val loss: 0.50290, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.50631, val loss: 0.49959, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.50331, val loss: 0.49640, in 0.000s 1 tree, 42 leaves, max depth = 14, train loss: 0.50029, val loss: 0.49323, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.49752, val loss: 0.49034, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.49484, val loss: 0.48756, in 0.000s 1 tree, 72 leaves, max depth = 11, train loss: 0.49072, val loss: 0.48378, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.48811, val loss: 0.48107, in 0.000s 1 tree, 43 leaves, max depth = 14, train loss: 0.48568, val loss: 0.47853, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.48325, val loss: 0.47600, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.48095, val loss: 0.47358, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.47877, val loss: 0.47128, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.47664, val loss: 0.46898, in 0.000s 1 tree, 89 leaves, max depth = 14, train loss: 0.47393, val loss: 0.46651, in 0.016s 1 tree, 88 leaves, max depth = 10, train loss: 0.47132, val loss: 0.46414, in 0.016s 1 tree, 72 leaves, max depth = 11, train loss: 0.46768, val loss: 0.46083, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.46576, val loss: 0.45898, in 0.016s 1 tree, 87 leaves, max depth = 10, train loss: 0.46338, val loss: 0.45684, in 0.016s 1 tree, 42 leaves, max depth = 15, train loss: 0.46146, val loss: 0.45483, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.45964, val loss: 0.45290, in 0.000s 1 tree, 71 leaves, max depth = 11, train loss: 0.45630, val loss: 0.44988, in 0.016s Fit 52 trees in 0.735 s, (2175 total leaves) Time spent computing histograms: 0.228s Time spent finding best splits: 0.036s Time spent applying splits: 0.039s Time spent predicting: 0.000s Trial 11, Fold 1: Log loss = 0.45635692276042383, Average precision = 0.9041750786722952, ROC-AUC = 0.9112115870387099, Elapsed Time = 0.7370410000003176 seconds Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 11, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 40 leaves, max depth = 11, train loss: 0.68307, val loss: 0.68258, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.67755, val loss: 0.67652, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.66803, val loss: 0.66655, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.65895, val loss: 0.65703, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.65025, val loss: 0.64790, in 0.016s 1 tree, 40 leaves, max depth = 13, train loss: 0.64190, val loss: 0.63917, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.63391, val loss: 0.63077, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.62624, val loss: 0.62275, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.62168, val loss: 0.61800, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.61436, val loss: 0.61029, in 0.000s 1 tree, 45 leaves, max depth = 19, train loss: 0.60745, val loss: 0.60302, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.60339, val loss: 0.59878, in 0.000s 1 tree, 45 leaves, max depth = 14, train loss: 0.59688, val loss: 0.59195, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.59069, val loss: 0.58541, in 0.000s 1 tree, 42 leaves, max depth = 12, train loss: 0.58469, val loss: 0.57912, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.57892, val loss: 0.57303, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.57365, val loss: 0.56751, in 0.000s 1 tree, 39 leaves, max depth = 13, train loss: 0.56830, val loss: 0.56187, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.56318, val loss: 0.55645, in 0.000s 1 tree, 43 leaves, max depth = 10, train loss: 0.55811, val loss: 0.55106, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.55324, val loss: 0.54588, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.54855, val loss: 0.54089, in 0.000s 1 tree, 38 leaves, max depth = 16, train loss: 0.54433, val loss: 0.53648, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.53997, val loss: 0.53183, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.53585, val loss: 0.52748, in 0.016s [26/52] 1 tree, 41 leaves, max depth = 11, train loss: 0.53185, val loss: 0.52323, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.52804, val loss: 0.51920, in 0.016s 1 tree, 46 leaves, max depth = 19, train loss: 0.52436, val loss: 0.51528, in 0.000s 1 tree, 44 leaves, max depth = 14, train loss: 0.52083, val loss: 0.51151, in 0.016s 1 tree, 73 leaves, max depth = 15, train loss: 0.51626, val loss: 0.50714, in 0.016s 1 tree, 46 leaves, max depth = 19, train loss: 0.51291, val loss: 0.50357, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.50967, val loss: 0.50011, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.50652, val loss: 0.49674, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.50351, val loss: 0.49351, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.50054, val loss: 0.49031, in 0.000s 1 tree, 41 leaves, max depth = 16, train loss: 0.49775, val loss: 0.48735, in 0.016s 1 tree, 46 leaves, max depth = 19, train loss: 0.49507, val loss: 0.48448, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.49093, val loss: 0.48054, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48835, val loss: 0.47779, in 0.000s 1 tree, 46 leaves, max depth = 19, train loss: 0.48591, val loss: 0.47516, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.48351, val loss: 0.47260, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48121, val loss: 0.47012, in 0.000s 1 tree, 45 leaves, max depth = 17, train loss: 0.47903, val loss: 0.46776, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.47690, val loss: 0.46545, in 0.016s 1 tree, 92 leaves, max depth = 11, train loss: 0.47423, val loss: 0.46300, in 0.016s 1 tree, 92 leaves, max depth = 11, train loss: 0.47166, val loss: 0.46066, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.46801, val loss: 0.45720, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.46599, val loss: 0.45540, in 0.000s 1 tree, 92 leaves, max depth = 11, train loss: 0.46366, val loss: 0.45327, in 0.016s 1 tree, 47 leaves, max depth = 15, train loss: 0.46176, val loss: 0.45122, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.45994, val loss: 0.44925, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.45659, val loss: 0.44610, in 0.000s Fit 52 trees in 0.766 s, (2202 total leaves) Time spent computing histograms: 0.251s Time spent finding best splits: 0.040s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 11, Fold 2: Log loss = 0.45779518972191213, Average precision = 0.9013062927885805, ROC-AUC = 0.9160451583199821, Elapsed Time = 0.7835273000000598 seconds Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 11, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 40 leaves, max depth = 11, train loss: 0.68304, val loss: 0.68274, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.67750, val loss: 0.67707, in 0.000s 1 tree, 42 leaves, max depth = 10, train loss: 0.66806, val loss: 0.66726, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.65903, val loss: 0.65791, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.65039, val loss: 0.64895, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.64211, val loss: 0.64041, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.63416, val loss: 0.63216, in 0.000s 1 tree, 44 leaves, max depth = 16, train loss: 0.62657, val loss: 0.62429, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.62201, val loss: 0.61967, in 0.000s 1 tree, 45 leaves, max depth = 11, train loss: 0.61473, val loss: 0.61214, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.60788, val loss: 0.60504, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.60381, val loss: 0.60092, in 0.000s 1 tree, 40 leaves, max depth = 9, train loss: 0.59732, val loss: 0.59412, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.59117, val loss: 0.58775, in 0.000s 1 tree, 43 leaves, max depth = 10, train loss: 0.58523, val loss: 0.58155, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.57952, val loss: 0.57561, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.57426, val loss: 0.57013, in 0.000s 1 tree, 42 leaves, max depth = 12, train loss: 0.56894, val loss: 0.56462, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.56386, val loss: 0.55934, in 0.000s 1 tree, 45 leaves, max depth = 11, train loss: 0.55882, val loss: 0.55411, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.55397, val loss: 0.54907, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.54931, val loss: 0.54422, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.54515, val loss: 0.53987, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.54081, val loss: 0.53535, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.53672, val loss: 0.53110, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.53275, val loss: 0.52693, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52896, val loss: 0.52298, in 0.016s 1 tree, 44 leaves, max depth = 15, train loss: 0.52533, val loss: 0.51920, in 0.000s 1 tree, 44 leaves, max depth = 15, train loss: 0.52183, val loss: 0.51553, in 0.016s 1 tree, 70 leaves, max depth = 13, train loss: 0.51720, val loss: 0.51122, in 0.016s 1 tree, 43 leaves, max depth = 15, train loss: 0.51390, val loss: 0.50776, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.51068, val loss: 0.50438, in 0.016s 1 tree, 42 leaves, max depth = 9, train loss: 0.50756, val loss: 0.50107, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.50457, val loss: 0.49792, in 0.000s 1 tree, 45 leaves, max depth = 10, train loss: 0.50161, val loss: 0.49482, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.49886, val loss: 0.49192, in 0.000s 1 tree, 44 leaves, max depth = 15, train loss: 0.49621, val loss: 0.48915, in 0.016s 1 tree, 70 leaves, max depth = 12, train loss: 0.49202, val loss: 0.48526, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.48947, val loss: 0.48255, in 0.000s 1 tree, 44 leaves, max depth = 14, train loss: 0.48705, val loss: 0.48000, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.48467, val loss: 0.47747, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.48240, val loss: 0.47507, in 0.000s 1 tree, 42 leaves, max depth = 14, train loss: 0.48024, val loss: 0.47280, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.47812, val loss: 0.47055, in 0.000s 1 tree, 96 leaves, max depth = 10, train loss: 0.47541, val loss: 0.46812, in 0.016s 1 tree, 96 leaves, max depth = 10, train loss: 0.47280, val loss: 0.46579, in 0.016s 1 tree, 70 leaves, max depth = 11, train loss: 0.46911, val loss: 0.46238, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.46713, val loss: 0.46059, in 0.016s 1 tree, 96 leaves, max depth = 12, train loss: 0.46475, val loss: 0.45850, in 0.016s 1 tree, 43 leaves, max depth = 17, train loss: 0.46288, val loss: 0.45652, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.46108, val loss: 0.45462, in 0.000s 1 tree, 71 leaves, max depth = 11, train loss: 0.45769, val loss: 0.45150, in 0.016s Fit 52 trees in 0.831 s, (2202 total leaves) Time spent computing histograms: 0.260s Time spent finding best splits: 0.041s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 11, Fold 3: Log loss = 0.4524066912549197, Average precision = 0.9099322068591883, ROC-AUC = 0.9202060469657424, Elapsed Time = 0.8413478000002215 seconds Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 11, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 40 leaves, max depth = 12, train loss: 0.68307, val loss: 0.68251, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.67749, val loss: 0.67657, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.66801, val loss: 0.66657, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.65898, val loss: 0.65704, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.65034, val loss: 0.64791, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.64199, val loss: 0.63907, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.63404, val loss: 0.63064, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.62641, val loss: 0.62254, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.62181, val loss: 0.61779, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.61454, val loss: 0.61001, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.60766, val loss: 0.60268, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.60356, val loss: 0.59843, in 0.000s 1 tree, 40 leaves, max depth = 11, train loss: 0.59704, val loss: 0.59148, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.59088, val loss: 0.58488, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.58490, val loss: 0.57850, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.57917, val loss: 0.57234, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.57396, val loss: 0.56663, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.56865, val loss: 0.56091, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.56356, val loss: 0.55542, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.55852, val loss: 0.54996, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.55367, val loss: 0.54469, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.54900, val loss: 0.53962, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54479, val loss: 0.53504, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.54045, val loss: 0.53031, in 0.000s 1 tree, 40 leaves, max depth = 15, train loss: 0.53636, val loss: 0.52582, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.53238, val loss: 0.52150, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.52859, val loss: 0.51735, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.52493, val loss: 0.51336, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.52142, val loss: 0.50950, in 0.016s 1 tree, 72 leaves, max depth = 15, train loss: 0.51677, val loss: 0.50500, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.51344, val loss: 0.50134, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.51023, val loss: 0.49782, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.50709, val loss: 0.49436, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.50411, val loss: 0.49108, in 0.000s 1 tree, 40 leaves, max depth = 12, train loss: 0.50114, val loss: 0.48779, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.49837, val loss: 0.48474, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.49570, val loss: 0.48176, in 0.000s 1 tree, 72 leaves, max depth = 15, train loss: 0.49149, val loss: 0.47770, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48892, val loss: 0.47486, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.48649, val loss: 0.47214, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48410, val loss: 0.46950, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.48183, val loss: 0.46695, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.47965, val loss: 0.46451, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.47754, val loss: 0.46214, in 0.000s 1 tree, 93 leaves, max depth = 11, train loss: 0.47488, val loss: 0.45964, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.47234, val loss: 0.45723, in 0.016s 1 tree, 72 leaves, max depth = 15, train loss: 0.46862, val loss: 0.45367, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.46660, val loss: 0.45167, in 0.016s 1 tree, 94 leaves, max depth = 11, train loss: 0.46428, val loss: 0.44948, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46240, val loss: 0.44734, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.46058, val loss: 0.44528, in 0.016s 1 tree, 71 leaves, max depth = 15, train loss: 0.45717, val loss: 0.44201, in 0.031s Fit 52 trees in 0.846 s, (2130 total leaves) Time spent computing histograms: 0.263s Time spent finding best splits: 0.047s Time spent applying splits: 0.051s Time spent predicting: 0.000s Trial 11, Fold 4: Log loss = 0.45743982263479455, Average precision = 0.9038198909903736, ROC-AUC = 0.9152758106397132, Elapsed Time = 0.8614232999989326 seconds Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 11, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 39 leaves, max depth = 13, train loss: 0.68291, val loss: 0.68219, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.67746, val loss: 0.67617, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.66789, val loss: 0.66593, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.65874, val loss: 0.65617, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.64997, val loss: 0.64681, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.64158, val loss: 0.63786, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.63352, val loss: 0.62923, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.62581, val loss: 0.62098, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.62129, val loss: 0.61613, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.61394, val loss: 0.60821, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.60697, val loss: 0.60075, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.60294, val loss: 0.59641, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.59636, val loss: 0.58931, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.59012, val loss: 0.58251, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.58407, val loss: 0.57600, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.57825, val loss: 0.56971, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.57295, val loss: 0.56397, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.56756, val loss: 0.55816, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.56241, val loss: 0.55250, in 0.000s 1 tree, 43 leaves, max depth = 14, train loss: 0.55731, val loss: 0.54694, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.55241, val loss: 0.54159, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.54769, val loss: 0.53643, in 0.000s 1 tree, 38 leaves, max depth = 9, train loss: 0.54345, val loss: 0.53178, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.53906, val loss: 0.52696, in 0.000s 1 tree, 40 leaves, max depth = 11, train loss: 0.53492, val loss: 0.52244, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.53089, val loss: 0.51800, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.52706, val loss: 0.51380, in 0.000s 1 tree, 44 leaves, max depth = 12, train loss: 0.52335, val loss: 0.50973, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.51978, val loss: 0.50580, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.51520, val loss: 0.50151, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.51182, val loss: 0.49779, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.50856, val loss: 0.49416, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.50540, val loss: 0.49065, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.50237, val loss: 0.48726, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.49937, val loss: 0.48391, in 0.000s 1 tree, 44 leaves, max depth = 12, train loss: 0.49657, val loss: 0.48079, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.49386, val loss: 0.47777, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.48971, val loss: 0.47392, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.48712, val loss: 0.47101, in 0.000s 1 tree, 44 leaves, max depth = 12, train loss: 0.48465, val loss: 0.46824, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.48225, val loss: 0.46553, in 0.016s [42/52] 1 tree, 39 leaves, max depth = 11, train loss: 0.47995, val loss: 0.46291, in 0.000s 1 tree, 44 leaves, max depth = 13, train loss: 0.47774, val loss: 0.46042, in 0.016s [44/52] 1 tree, 7 leaves, max depth = 5, train loss: 0.47559, val loss: 0.45797, in 0.000s 1 tree, 93 leaves, max depth = 10, train loss: 0.47296, val loss: 0.45572, in 0.031s 1 tree, 93 leaves, max depth = 10, train loss: 0.47044, val loss: 0.45356, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.46678, val loss: 0.45019, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.46480, val loss: 0.44834, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.46286, val loss: 0.44614, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.46098, val loss: 0.44400, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.45919, val loss: 0.44194, in 0.000s 1 tree, 72 leaves, max depth = 13, train loss: 0.45577, val loss: 0.43881, in 0.016s Fit 52 trees in 0.845 s, (2144 total leaves) Time spent computing histograms: 0.254s Time spent finding best splits: 0.043s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 11, Fold 5: Log loss = 0.46344583690742613, Average precision = 0.8988737071441618, ROC-AUC = 0.9086291734017056, Elapsed Time = 0.8489270999998553 seconds
Optimization Progress: 12%|#2 | 12/100 [02:26<17:21, 11.83s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 12, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 13 leaves, max depth = 7, train loss: 0.67249, val loss: 0.67184, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.66180, val loss: 0.66028, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.64349, val loss: 0.64145, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62687, val loss: 0.62418, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.61165, val loss: 0.60833, in 0.000s 1 tree, 14 leaves, max depth = 9, train loss: 0.59754, val loss: 0.59373, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58471, val loss: 0.58032, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.57300, val loss: 0.56817, in 0.016s 1 tree, 34 leaves, max depth = 7, train loss: 0.56509, val loss: 0.56073, in 0.000s 1 tree, 26 leaves, max depth = 10, train loss: 0.56039, val loss: 0.55528, in 0.016s 1 tree, 18 leaves, max depth = 10, train loss: 0.55022, val loss: 0.54472, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.54603, val loss: 0.53972, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.53694, val loss: 0.53028, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.52853, val loss: 0.52149, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.52079, val loss: 0.51342, in 0.000s 1 tree, 18 leaves, max depth = 9, train loss: 0.51364, val loss: 0.50592, in 0.016s Fit 16 trees in 0.407 s, (256 total leaves) Time spent computing histograms: 0.073s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 12, Fold 1: Log loss = 0.5121890687203764, Average precision = 0.8792070370375461, ROC-AUC = 0.8829421836987328, Elapsed Time = 0.41822590000083437 seconds Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 12, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 8, train loss: 0.67249, val loss: 0.67142, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66164, val loss: 0.65946, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.64336, val loss: 0.64029, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.62670, val loss: 0.62279, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.61144, val loss: 0.60674, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.59742, val loss: 0.59188, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58454, val loss: 0.57830, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.57280, val loss: 0.56587, in 0.016s 1 tree, 42 leaves, max depth = 8, train loss: 0.56496, val loss: 0.55839, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.56033, val loss: 0.55325, in 0.016s 1 tree, 21 leaves, max depth = 13, train loss: 0.55010, val loss: 0.54242, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.54594, val loss: 0.53788, in 0.016s 1 tree, 21 leaves, max depth = 13, train loss: 0.53681, val loss: 0.52818, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.52843, val loss: 0.51920, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.52066, val loss: 0.51091, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.51347, val loss: 0.50322, in 0.016s Fit 16 trees in 0.423 s, (287 total leaves) Time spent computing histograms: 0.068s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 12, Fold 2: Log loss = 0.5132259896944191, Average precision = 0.8776855526440213, ROC-AUC = 0.8887734256850123, Elapsed Time = 0.4338880999985122 seconds Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 12, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.143 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 15 leaves, max depth = 8, train loss: 0.67258, val loss: 0.67186, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66170, val loss: 0.66070, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.64355, val loss: 0.64184, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.62700, val loss: 0.62466, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61184, val loss: 0.60891, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.59796, val loss: 0.59448, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58518, val loss: 0.58117, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.57352, val loss: 0.56894, in 0.016s 1 tree, 39 leaves, max depth = 7, train loss: 0.56559, val loss: 0.56164, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.56103, val loss: 0.55613, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.55092, val loss: 0.54549, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.54682, val loss: 0.54073, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.53780, val loss: 0.53121, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.52948, val loss: 0.52248, in 0.000s 1 tree, 20 leaves, max depth = 11, train loss: 0.52178, val loss: 0.51434, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.51468, val loss: 0.50681, in 0.000s Fit 16 trees in 0.393 s, (297 total leaves) Time spent computing histograms: 0.060s Time spent finding best splits: 0.006s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 12, Fold 3: Log loss = 0.509757626563093, Average precision = 0.8850018372669946, ROC-AUC = 0.8930221495747899, Elapsed Time = 0.39575520000107645 seconds Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 12, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.126 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 11 leaves, max depth = 6, train loss: 0.67255, val loss: 0.67132, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66159, val loss: 0.65961, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.64345, val loss: 0.64035, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.62691, val loss: 0.62281, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61175, val loss: 0.60669, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.59786, val loss: 0.59179, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58509, val loss: 0.57813, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.57336, val loss: 0.56553, in 0.016s 1 tree, 38 leaves, max depth = 8, train loss: 0.56555, val loss: 0.55778, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.56087, val loss: 0.55270, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.55072, val loss: 0.54175, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.54660, val loss: 0.53713, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.53752, val loss: 0.52731, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.52917, val loss: 0.51816, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.52143, val loss: 0.50968, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.51428, val loss: 0.50184, in 0.000s Fit 16 trees in 0.360 s, (283 total leaves) Time spent computing histograms: 0.062s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 12, Fold 4: Log loss = 0.5130938883208326, Average precision = 0.8756620847959189, ROC-AUC = 0.8863719953456319, Elapsed Time = 0.37896379999983765 seconds Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 12, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 14 leaves, max depth = 7, train loss: 0.67229, val loss: 0.67077, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66158, val loss: 0.65890, in 0.000s 1 tree, 11 leaves, max depth = 6, train loss: 0.64322, val loss: 0.63926, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62642, val loss: 0.62127, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.61104, val loss: 0.60474, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.59699, val loss: 0.58945, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58402, val loss: 0.57542, in 0.000s 1 tree, 23 leaves, max depth = 9, train loss: 0.57218, val loss: 0.56244, in 0.000s 1 tree, 45 leaves, max depth = 7, train loss: 0.56445, val loss: 0.55537, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.55972, val loss: 0.55005, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.54943, val loss: 0.53871, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.54517, val loss: 0.53409, in 0.000s 1 tree, 18 leaves, max depth = 10, train loss: 0.53597, val loss: 0.52391, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.52752, val loss: 0.51448, in 0.000s 1 tree, 19 leaves, max depth = 11, train loss: 0.51969, val loss: 0.50570, in 0.000s 1 tree, 19 leaves, max depth = 12, train loss: 0.51245, val loss: 0.49755, in 0.016s Fit 16 trees in 0.376 s, (269 total leaves) Time spent computing histograms: 0.058s Time spent finding best splits: 0.006s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 12, Fold 5: Log loss = 0.5175040686108638, Average precision = 0.8760296722679743, ROC-AUC = 0.8810527841257455, Elapsed Time = 0.38353559999995923 seconds
Optimization Progress: 13%|#3 | 13/100 [02:35<15:56, 11.00s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 13, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.67771, val loss: 0.67720, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66296, val loss: 0.66204, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.64917, val loss: 0.64781, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.63630, val loss: 0.63442, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62422, val loss: 0.62195, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.61289, val loss: 0.61025, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.60240, val loss: 0.59937, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.59256, val loss: 0.58915, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.58317, val loss: 0.57942, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.57418, val loss: 0.57006, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.56596, val loss: 0.56146, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.55827, val loss: 0.55344, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.55091, val loss: 0.54577, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.54406, val loss: 0.53859, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53752, val loss: 0.53176, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.53026, val loss: 0.52504, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.52424, val loss: 0.51875, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.51857, val loss: 0.51282, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.51318, val loss: 0.50707, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.50799, val loss: 0.50157, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50309, val loss: 0.49638, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.49848, val loss: 0.49146, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.49412, val loss: 0.48682, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.49001, val loss: 0.48242, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.48628, val loss: 0.47842, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.48272, val loss: 0.47465, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.47940, val loss: 0.47107, in 0.000s 1 tree, 9 leaves, max depth = 7, train loss: 0.47628, val loss: 0.46775, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.47213, val loss: 0.46399, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.46651, val loss: 0.45890, in 0.000s 1 tree, 20 leaves, max depth = 6, train loss: 0.46282, val loss: 0.45556, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.46007, val loss: 0.45257, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.45732, val loss: 0.44960, in 0.016s 1 tree, 9 leaves, max depth = 7, train loss: 0.45489, val loss: 0.44698, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.45244, val loss: 0.44430, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.44999, val loss: 0.44186, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.44738, val loss: 0.43949, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.44264, val loss: 0.43519, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.44039, val loss: 0.43280, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.43741, val loss: 0.43016, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.43518, val loss: 0.42771, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.43317, val loss: 0.42551, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.43111, val loss: 0.42347, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.42695, val loss: 0.41972, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.42437, val loss: 0.41747, in 0.000s 1 tree, 26 leaves, max depth = 8, train loss: 0.42194, val loss: 0.41537, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.41828, val loss: 0.41216, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.41651, val loss: 0.41028, in 0.016s 1 tree, 26 leaves, max depth = 7, train loss: 0.41437, val loss: 0.40844, in 0.000s 1 tree, 26 leaves, max depth = 11, train loss: 0.41100, val loss: 0.40548, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.40942, val loss: 0.40392, in 0.000s Fit 51 trees in 0.610 s, (590 total leaves) Time spent computing histograms: 0.197s Time spent finding best splits: 0.016s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 13, Fold 1: Log loss = 0.4108587589253095, Average precision = 0.9460757513044896, ROC-AUC = 0.9415504778394344, Elapsed Time = 0.6094315999998798 seconds Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 13, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.159 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 4 leaves, max depth = 3, train loss: 0.67769, val loss: 0.67689, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.66307, val loss: 0.66155, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.64932, val loss: 0.64706, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.63642, val loss: 0.63352, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.62445, val loss: 0.62088, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61321, val loss: 0.60901, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.60268, val loss: 0.59791, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.59281, val loss: 0.58744, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58347, val loss: 0.57750, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.57454, val loss: 0.56801, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.56632, val loss: 0.55927, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.55860, val loss: 0.55103, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.55133, val loss: 0.54329, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.54436, val loss: 0.53586, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.53785, val loss: 0.52886, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.53061, val loss: 0.52180, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.52464, val loss: 0.51534, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.51904, val loss: 0.50933, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.51363, val loss: 0.50355, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50848, val loss: 0.49799, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.50362, val loss: 0.49274, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.49903, val loss: 0.48779, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.49470, val loss: 0.48310, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.49062, val loss: 0.47866, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.48690, val loss: 0.47461, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.48335, val loss: 0.47072, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.48003, val loss: 0.46710, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.47689, val loss: 0.46368, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.47283, val loss: 0.45983, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.46721, val loss: 0.45443, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46360, val loss: 0.45104, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.46086, val loss: 0.44803, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.45813, val loss: 0.44505, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.45569, val loss: 0.44236, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.45325, val loss: 0.43968, in 0.016s 1 tree, 27 leaves, max depth = 13, train loss: 0.45075, val loss: 0.43753, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.44859, val loss: 0.43515, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.44376, val loss: 0.43052, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.44179, val loss: 0.42833, in 0.000s 1 tree, 23 leaves, max depth = 8, train loss: 0.43879, val loss: 0.42556, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.43662, val loss: 0.42331, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.43470, val loss: 0.42118, in 0.016s 1 tree, 27 leaves, max depth = 13, train loss: 0.43253, val loss: 0.41937, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42826, val loss: 0.41531, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.42558, val loss: 0.41290, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.42307, val loss: 0.41062, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.41932, val loss: 0.40704, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.41765, val loss: 0.40519, in 0.000s 1 tree, 29 leaves, max depth = 7, train loss: 0.41543, val loss: 0.40321, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.41201, val loss: 0.39996, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.41034, val loss: 0.39862, in 0.016s Fit 51 trees in 0.643 s, (558 total leaves) Time spent computing histograms: 0.225s Time spent finding best splits: 0.017s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 13, Fold 2: Log loss = 0.411491134302576, Average precision = 0.9436506570711057, ROC-AUC = 0.9425052679243726, Elapsed Time = 0.6547442999999475 seconds Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 13, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 4 leaves, max depth = 3, train loss: 0.67776, val loss: 0.67719, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.66317, val loss: 0.66199, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.64951, val loss: 0.64775, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.63668, val loss: 0.63446, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.62472, val loss: 0.62196, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61357, val loss: 0.61043, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.60315, val loss: 0.59955, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.59337, val loss: 0.58930, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58408, val loss: 0.57956, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.57519, val loss: 0.57031, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.56703, val loss: 0.56183, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.55940, val loss: 0.55379, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.55212, val loss: 0.54612, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.54533, val loss: 0.53897, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.53886, val loss: 0.53216, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.53147, val loss: 0.52526, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.52554, val loss: 0.51897, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.51995, val loss: 0.51305, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.51458, val loss: 0.50742, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.50948, val loss: 0.50206, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.50466, val loss: 0.49700, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.50012, val loss: 0.49222, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.49583, val loss: 0.48771, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.49178, val loss: 0.48344, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.48810, val loss: 0.47956, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.48460, val loss: 0.47580, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.48131, val loss: 0.47233, in 0.016s 1 tree, 6 leaves, max depth = 5, train loss: 0.47823, val loss: 0.46898, in 0.000s 1 tree, 22 leaves, max depth = 6, train loss: 0.47408, val loss: 0.46521, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.46837, val loss: 0.45993, in 0.000s 1 tree, 23 leaves, max depth = 6, train loss: 0.46467, val loss: 0.45659, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.46195, val loss: 0.45371, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.45927, val loss: 0.45086, in 0.016s 1 tree, 6 leaves, max depth = 5, train loss: 0.45688, val loss: 0.44822, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.45448, val loss: 0.44566, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.45195, val loss: 0.44342, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.44935, val loss: 0.44102, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.44453, val loss: 0.43661, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.44235, val loss: 0.43420, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.43937, val loss: 0.43152, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.43711, val loss: 0.42946, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.43516, val loss: 0.42736, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.43301, val loss: 0.42549, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.42876, val loss: 0.42162, in 0.000s 1 tree, 36 leaves, max depth = 9, train loss: 0.42614, val loss: 0.41920, in 0.016s 1 tree, 21 leaves, max depth = 6, train loss: 0.42365, val loss: 0.41699, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.41985, val loss: 0.41356, in 0.016s 1 tree, 11 leaves, max depth = 8, train loss: 0.41815, val loss: 0.41167, in 0.000s 1 tree, 23 leaves, max depth = 6, train loss: 0.41595, val loss: 0.40975, in 0.000s 1 tree, 26 leaves, max depth = 10, train loss: 0.41248, val loss: 0.40663, in 0.000s 1 tree, 38 leaves, max depth = 16, train loss: 0.41082, val loss: 0.40525, in 0.016s Fit 51 trees in 0.658 s, (612 total leaves) Time spent computing histograms: 0.224s Time spent finding best splits: 0.017s Time spent applying splits: 0.016s Time spent predicting: 0.000s Trial 13, Fold 3: Log loss = 0.4052344243260207, Average precision = 0.951163209490254, ROC-AUC = 0.9481140778043167, Elapsed Time = 0.6698638999987452 seconds Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 13, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 4 leaves, max depth = 3, train loss: 0.67774, val loss: 0.67683, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66312, val loss: 0.66140, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64942, val loss: 0.64689, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.63659, val loss: 0.63332, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.62462, val loss: 0.62060, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.61340, val loss: 0.60865, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.60294, val loss: 0.59749, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.59312, val loss: 0.58701, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.58382, val loss: 0.57704, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.57493, val loss: 0.56742, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.56676, val loss: 0.55856, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.55908, val loss: 0.55030, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.55180, val loss: 0.54240, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.54499, val loss: 0.53494, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.53851, val loss: 0.52789, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.53121, val loss: 0.52067, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.52525, val loss: 0.51415, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.51965, val loss: 0.50798, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.51427, val loss: 0.50211, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.50916, val loss: 0.49645, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.50435, val loss: 0.49110, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.49980, val loss: 0.48603, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.49551, val loss: 0.48124, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.49146, val loss: 0.47669, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.48775, val loss: 0.47252, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.48424, val loss: 0.46853, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.48093, val loss: 0.46479, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.47781, val loss: 0.46128, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.47377, val loss: 0.45737, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46812, val loss: 0.45187, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.46453, val loss: 0.44840, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.46162, val loss: 0.44512, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.45893, val loss: 0.44204, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.45650, val loss: 0.43926, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.45409, val loss: 0.43647, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.45162, val loss: 0.43391, in 0.016s [37/51] 1 tree, 8 leaves, max depth = 4, train loss: 0.44946, val loss: 0.43141, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.44463, val loss: 0.42671, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.44248, val loss: 0.42425, in 0.000s 1 tree, 23 leaves, max depth = 7, train loss: 0.43954, val loss: 0.42146, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.43737, val loss: 0.41917, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.43549, val loss: 0.41696, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.43336, val loss: 0.41476, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42910, val loss: 0.41066, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.42647, val loss: 0.40812, in 0.000s 1 tree, 23 leaves, max depth = 7, train loss: 0.42401, val loss: 0.40581, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.42025, val loss: 0.40219, in 0.000s 1 tree, 13 leaves, max depth = 8, train loss: 0.41859, val loss: 0.40025, in 0.016s 1 tree, 22 leaves, max depth = 6, train loss: 0.41642, val loss: 0.39824, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.41299, val loss: 0.39493, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.41135, val loss: 0.39325, in 0.000s Fit 51 trees in 0.658 s, (556 total leaves) Time spent computing histograms: 0.222s Time spent finding best splits: 0.017s Time spent applying splits: 0.014s Time spent predicting: 0.016s Trial 13, Fold 4: Log loss = 0.4107153777528465, Average precision = 0.945980256226045, ROC-AUC = 0.9420119739097883, Elapsed Time = 0.6631233000007342 seconds Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 13, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 4 leaves, max depth = 3, train loss: 0.67755, val loss: 0.67641, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.66285, val loss: 0.66070, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.64902, val loss: 0.64585, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.63603, val loss: 0.63194, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.62399, val loss: 0.61899, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.61269, val loss: 0.60682, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.60211, val loss: 0.59540, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.59218, val loss: 0.58463, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58277, val loss: 0.57439, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.57379, val loss: 0.56461, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.56552, val loss: 0.55559, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.55776, val loss: 0.54710, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.55039, val loss: 0.53899, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.54351, val loss: 0.53135, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53696, val loss: 0.52411, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.52974, val loss: 0.51731, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.52371, val loss: 0.51062, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.51803, val loss: 0.50429, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.51261, val loss: 0.49827, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.50743, val loss: 0.49250, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.50256, val loss: 0.48704, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.49795, val loss: 0.48188, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.49361, val loss: 0.47698, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.48951, val loss: 0.47235, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.48577, val loss: 0.46809, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.48220, val loss: 0.46400, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.47886, val loss: 0.46018, in 0.016s 1 tree, 6 leaves, max depth = 5, train loss: 0.47572, val loss: 0.45657, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.47170, val loss: 0.45301, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.46612, val loss: 0.44789, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.46254, val loss: 0.44475, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.45978, val loss: 0.44155, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.45705, val loss: 0.43838, in 0.016s 1 tree, 6 leaves, max depth = 5, train loss: 0.45460, val loss: 0.43551, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.45216, val loss: 0.43265, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.44969, val loss: 0.43019, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.44750, val loss: 0.42760, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.44270, val loss: 0.42327, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.44070, val loss: 0.42089, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.43773, val loss: 0.41836, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.43556, val loss: 0.41626, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.43365, val loss: 0.41398, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.43152, val loss: 0.41187, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.42728, val loss: 0.40810, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.42466, val loss: 0.40560, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.42218, val loss: 0.40354, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.41843, val loss: 0.40023, in 0.016s 1 tree, 15 leaves, max depth = 9, train loss: 0.41676, val loss: 0.39827, in 0.000s 1 tree, 27 leaves, max depth = 7, train loss: 0.41457, val loss: 0.39648, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.41115, val loss: 0.39349, in 0.000s 1 tree, 36 leaves, max depth = 14, train loss: 0.40950, val loss: 0.39189, in 0.016s Fit 51 trees in 0.642 s, (569 total leaves) Time spent computing histograms: 0.214s Time spent finding best splits: 0.017s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 13, Fold 5: Log loss = 0.4152681992329296, Average precision = 0.9466220671297173, ROC-AUC = 0.9429014361889898, Elapsed Time = 0.6470339000006788 seconds
Optimization Progress: 14%|#4 | 14/100 [02:45<15:11, 10.60s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 14, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 37 leaves, max depth = 11, train loss: 0.68557, val loss: 0.68534, in 0.000s 1 tree, 37 leaves, max depth = 14, train loss: 0.67812, val loss: 0.67769, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.67091, val loss: 0.67028, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66395, val loss: 0.66305, in 0.016s 1 tree, 38 leaves, max depth = 14, train loss: 0.65719, val loss: 0.65609, in 0.000s 1 tree, 38 leaves, max depth = 14, train loss: 0.65064, val loss: 0.64936, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.64434, val loss: 0.64289, in 0.000s 1 tree, 37 leaves, max depth = 12, train loss: 0.63825, val loss: 0.63663, in 0.016s 1 tree, 38 leaves, max depth = 14, train loss: 0.63229, val loss: 0.63049, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.62642, val loss: 0.62444, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.62084, val loss: 0.61867, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.61545, val loss: 0.61309, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.61017, val loss: 0.60764, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.60512, val loss: 0.60240, in 0.016s 1 tree, 36 leaves, max depth = 14, train loss: 0.60020, val loss: 0.59733, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.59543, val loss: 0.59238, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.59074, val loss: 0.58754, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.58619, val loss: 0.58284, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58177, val loss: 0.57820, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.57739, val loss: 0.57367, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.57314, val loss: 0.56927, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.56902, val loss: 0.56499, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.56511, val loss: 0.56092, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.56122, val loss: 0.55688, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.55754, val loss: 0.55303, in 0.000s 1 tree, 37 leaves, max depth = 12, train loss: 0.55393, val loss: 0.54930, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.55046, val loss: 0.54567, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.54708, val loss: 0.54218, in 0.000s 1 tree, 34 leaves, max depth = 14, train loss: 0.54381, val loss: 0.53878, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.54006, val loss: 0.53530, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.53692, val loss: 0.53205, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53383, val loss: 0.52877, in 0.000s 1 tree, 38 leaves, max depth = 13, train loss: 0.53075, val loss: 0.52554, in 0.016s 1 tree, 36 leaves, max depth = 18, train loss: 0.52787, val loss: 0.52255, in 0.000s 1 tree, 38 leaves, max depth = 13, train loss: 0.52496, val loss: 0.51949, in 0.016s 1 tree, 36 leaves, max depth = 16, train loss: 0.52220, val loss: 0.51661, in 0.000s 1 tree, 35 leaves, max depth = 16, train loss: 0.51954, val loss: 0.51386, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.51612, val loss: 0.51070, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.51354, val loss: 0.50801, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.51106, val loss: 0.50540, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.50863, val loss: 0.50286, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.50619, val loss: 0.50028, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.50389, val loss: 0.49788, in 0.000s 1 tree, 57 leaves, max depth = 10, train loss: 0.50068, val loss: 0.49493, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49845, val loss: 0.49254, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.49630, val loss: 0.49029, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.49321, val loss: 0.48746, in 0.000s 1 tree, 36 leaves, max depth = 9, train loss: 0.49123, val loss: 0.48538, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.48922, val loss: 0.48326, in 0.000s 1 tree, 57 leaves, max depth = 10, train loss: 0.48627, val loss: 0.48056, in 0.016s 1 tree, 37 leaves, max depth = 15, train loss: 0.48436, val loss: 0.47856, in 0.000s 1 tree, 57 leaves, max depth = 10, train loss: 0.48150, val loss: 0.47594, in 0.016s 1 tree, 36 leaves, max depth = 17, train loss: 0.47967, val loss: 0.47403, in 0.016s 1 tree, 36 leaves, max depth = 16, train loss: 0.47789, val loss: 0.47216, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.47613, val loss: 0.47031, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.47340, val loss: 0.46781, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47169, val loss: 0.46597, in 0.000s 1 tree, 38 leaves, max depth = 16, train loss: 0.47005, val loss: 0.46423, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.46741, val loss: 0.46184, in 0.000s 1 tree, 36 leaves, max depth = 15, train loss: 0.46586, val loss: 0.46020, in 0.016s 1 tree, 38 leaves, max depth = 16, train loss: 0.46433, val loss: 0.45858, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.46179, val loss: 0.45628, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.46003, val loss: 0.45469, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.45857, val loss: 0.45314, in 0.016s 1 tree, 37 leaves, max depth = 15, train loss: 0.45718, val loss: 0.45167, in 0.000s 1 tree, 57 leaves, max depth = 10, train loss: 0.45549, val loss: 0.45015, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.45412, val loss: 0.44870, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.45279, val loss: 0.44729, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.45043, val loss: 0.44517, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44910, val loss: 0.44372, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44780, val loss: 0.44231, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.44659, val loss: 0.44103, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.44542, val loss: 0.43975, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.44424, val loss: 0.43848, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.44311, val loss: 0.43724, in 0.016s Fit 75 trees in 0.908 s, (2782 total leaves) Time spent computing histograms: 0.325s Time spent finding best splits: 0.042s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 14, Fold 1: Log loss = 0.4434066575603427, Average precision = 0.9299184649791674, ROC-AUC = 0.9270560000573551, Elapsed Time = 0.9077753000001394 seconds Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 14, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 36 leaves, max depth = 10, train loss: 0.68557, val loss: 0.68520, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.67813, val loss: 0.67738, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.67091, val loss: 0.66982, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66392, val loss: 0.66250, in 0.000s 1 tree, 36 leaves, max depth = 14, train loss: 0.65720, val loss: 0.65546, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.65067, val loss: 0.64863, in 0.000s 1 tree, 39 leaves, max depth = 18, train loss: 0.64436, val loss: 0.64202, in 0.016s 1 tree, 36 leaves, max depth = 14, train loss: 0.63824, val loss: 0.63561, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.63229, val loss: 0.62933, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.62645, val loss: 0.62317, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.62086, val loss: 0.61732, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.61548, val loss: 0.61165, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.61021, val loss: 0.60608, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.60517, val loss: 0.60075, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.60023, val loss: 0.59557, in 0.000s 1 tree, 37 leaves, max depth = 16, train loss: 0.59544, val loss: 0.59053, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.59075, val loss: 0.58558, in 0.000s 1 tree, 33 leaves, max depth = 10, train loss: 0.58620, val loss: 0.58078, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58178, val loss: 0.57611, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.57746, val loss: 0.57150, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.57324, val loss: 0.56701, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.56914, val loss: 0.56265, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.56523, val loss: 0.55854, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.56138, val loss: 0.55443, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.55770, val loss: 0.55054, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.55409, val loss: 0.54672, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.55062, val loss: 0.54305, in 0.016s 1 tree, 38 leaves, max depth = 15, train loss: 0.54724, val loss: 0.53947, in 0.016s 1 tree, 37 leaves, max depth = 17, train loss: 0.54396, val loss: 0.53599, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.54027, val loss: 0.53241, in 0.016s 1 tree, 38 leaves, max depth = 14, train loss: 0.53712, val loss: 0.52908, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53403, val loss: 0.52579, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.53100, val loss: 0.52252, in 0.000s 1 tree, 39 leaves, max depth = 14, train loss: 0.52811, val loss: 0.51945, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.52524, val loss: 0.51635, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.52249, val loss: 0.51340, in 0.000s 1 tree, 38 leaves, max depth = 15, train loss: 0.51984, val loss: 0.51058, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.51645, val loss: 0.50731, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.51388, val loss: 0.50457, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.51140, val loss: 0.50193, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.50897, val loss: 0.49932, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.50657, val loss: 0.49672, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50427, val loss: 0.49426, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.50110, val loss: 0.49121, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49887, val loss: 0.48882, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.49673, val loss: 0.48653, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.49369, val loss: 0.48360, in 0.016s 1 tree, 38 leaves, max depth = 14, train loss: 0.49170, val loss: 0.48150, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.48971, val loss: 0.47937, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.48679, val loss: 0.47657, in 0.000s 1 tree, 36 leaves, max depth = 14, train loss: 0.48488, val loss: 0.47452, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.48206, val loss: 0.47182, in 0.016s 1 tree, 37 leaves, max depth = 17, train loss: 0.48023, val loss: 0.46985, in 0.000s 1 tree, 37 leaves, max depth = 17, train loss: 0.47844, val loss: 0.46794, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.47669, val loss: 0.46605, in 0.000s 1 tree, 57 leaves, max depth = 13, train loss: 0.47399, val loss: 0.46347, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47229, val loss: 0.46162, in 0.000s 1 tree, 38 leaves, max depth = 17, train loss: 0.47067, val loss: 0.45988, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.46807, val loss: 0.45739, in 0.016s 1 tree, 36 leaves, max depth = 16, train loss: 0.46652, val loss: 0.45573, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.46501, val loss: 0.45410, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.46250, val loss: 0.45171, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.46103, val loss: 0.45012, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.45960, val loss: 0.44856, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.45823, val loss: 0.44707, in 0.000s 1 tree, 57 leaves, max depth = 9, train loss: 0.45652, val loss: 0.44551, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.45519, val loss: 0.44406, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.45390, val loss: 0.44265, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.45155, val loss: 0.44042, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.45024, val loss: 0.43900, in 0.000s 1 tree, 37 leaves, max depth = 9, train loss: 0.44898, val loss: 0.43760, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.44779, val loss: 0.43630, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44664, val loss: 0.43512, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.44550, val loss: 0.43388, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44440, val loss: 0.43273, in 0.000s Fit 75 trees in 0.955 s, (2737 total leaves) Time spent computing histograms: 0.343s Time spent finding best splits: 0.045s Time spent applying splits: 0.054s Time spent predicting: 0.016s Trial 14, Fold 2: Log loss = 0.4453315201550713, Average precision = 0.9283547951673355, ROC-AUC = 0.9304742482490582, Elapsed Time = 0.9605992000015249 seconds Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 14, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 12, train loss: 0.68561, val loss: 0.68537, in 0.000s 1 tree, 34 leaves, max depth = 9, train loss: 0.67820, val loss: 0.67771, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.67104, val loss: 0.67028, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66409, val loss: 0.66309, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.65738, val loss: 0.65614, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.65087, val loss: 0.64940, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.64461, val loss: 0.64287, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.63854, val loss: 0.63656, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.63262, val loss: 0.63042, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.62680, val loss: 0.62441, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.62125, val loss: 0.61866, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.61589, val loss: 0.61307, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.61065, val loss: 0.60763, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.60563, val loss: 0.60243, in 0.000s 1 tree, 38 leaves, max depth = 13, train loss: 0.60074, val loss: 0.59732, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.59599, val loss: 0.59238, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.59134, val loss: 0.58753, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.58683, val loss: 0.58282, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58243, val loss: 0.57824, in 0.000s 1 tree, 36 leaves, max depth = 9, train loss: 0.57810, val loss: 0.57376, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.57390, val loss: 0.56941, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.56982, val loss: 0.56518, in 0.000s 1 tree, 36 leaves, max depth = 12, train loss: 0.56593, val loss: 0.56115, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.56208, val loss: 0.55715, in 0.000s 1 tree, 37 leaves, max depth = 12, train loss: 0.55841, val loss: 0.55335, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.55484, val loss: 0.54961, in 0.000s 1 tree, 37 leaves, max depth = 12, train loss: 0.55139, val loss: 0.54601, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.54804, val loss: 0.54249, in 0.000s 1 tree, 39 leaves, max depth = 14, train loss: 0.54479, val loss: 0.53910, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.54107, val loss: 0.53566, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.53795, val loss: 0.53238, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53487, val loss: 0.52916, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.53184, val loss: 0.52600, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.52898, val loss: 0.52299, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.52611, val loss: 0.52000, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.52338, val loss: 0.51713, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.52075, val loss: 0.51436, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.51735, val loss: 0.51122, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.51480, val loss: 0.50853, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.51233, val loss: 0.50595, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50993, val loss: 0.50341, in 0.000s 1 tree, 37 leaves, max depth = 9, train loss: 0.50753, val loss: 0.50091, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50525, val loss: 0.49850, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.50206, val loss: 0.49557, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49985, val loss: 0.49324, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.49773, val loss: 0.49099, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.49466, val loss: 0.48818, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.49271, val loss: 0.48611, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.49072, val loss: 0.48400, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.48778, val loss: 0.48131, in 0.000s 1 tree, 35 leaves, max depth = 14, train loss: 0.48589, val loss: 0.47932, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.48306, val loss: 0.47672, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.48124, val loss: 0.47480, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.47948, val loss: 0.47293, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47775, val loss: 0.47108, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.47503, val loss: 0.46859, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47333, val loss: 0.46679, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.47171, val loss: 0.46507, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.46909, val loss: 0.46267, in 0.000s 1 tree, 36 leaves, max depth = 14, train loss: 0.46755, val loss: 0.46104, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.46604, val loss: 0.45942, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.46352, val loss: 0.45712, in 0.016s 1 tree, 57 leaves, max depth = 8, train loss: 0.46176, val loss: 0.45556, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.46033, val loss: 0.45402, in 0.000s 1 tree, 36 leaves, max depth = 14, train loss: 0.45894, val loss: 0.45254, in 0.016s 1 tree, 57 leaves, max depth = 8, train loss: 0.45726, val loss: 0.45104, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.45592, val loss: 0.44959, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.45461, val loss: 0.44818, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.45227, val loss: 0.44606, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.45095, val loss: 0.44468, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.44968, val loss: 0.44333, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.44849, val loss: 0.44204, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44731, val loss: 0.44096, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.44614, val loss: 0.43972, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44500, val loss: 0.43867, in 0.000s Fit 75 trees in 1.017 s, (2741 total leaves) Time spent computing histograms: 0.352s Time spent finding best splits: 0.049s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 14, Fold 3: Log loss = 0.4398167373771425, Average precision = 0.9355649438911422, ROC-AUC = 0.9350192560506977, Elapsed Time = 1.0301173999996536 seconds Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 14, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 14, train loss: 0.68559, val loss: 0.68516, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.67818, val loss: 0.67735, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.67100, val loss: 0.66979, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66406, val loss: 0.66247, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.65733, val loss: 0.65535, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.65081, val loss: 0.64846, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.64452, val loss: 0.64181, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.63843, val loss: 0.63536, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.63250, val loss: 0.62906, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.62670, val loss: 0.62287, in 0.000s 1 tree, 35 leaves, max depth = 14, train loss: 0.62114, val loss: 0.61693, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.61575, val loss: 0.61123, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.61050, val loss: 0.60563, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.60547, val loss: 0.60025, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.60054, val loss: 0.59500, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.59577, val loss: 0.58990, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.59111, val loss: 0.58493, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.58659, val loss: 0.58011, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58219, val loss: 0.57540, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.57787, val loss: 0.57074, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.57368, val loss: 0.56621, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.56961, val loss: 0.56181, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.56572, val loss: 0.55760, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.56187, val loss: 0.55343, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.55820, val loss: 0.54945, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.55461, val loss: 0.54559, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.55115, val loss: 0.54182, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.54779, val loss: 0.53819, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.54452, val loss: 0.53465, in 0.016s [30/75] 1 tree, 57 leaves, max depth = 12, train loss: 0.54078, val loss: 0.53100, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.53765, val loss: 0.52761, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53457, val loss: 0.52427, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.53154, val loss: 0.52096, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.52867, val loss: 0.51784, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.52581, val loss: 0.51470, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.52307, val loss: 0.51171, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.52043, val loss: 0.50882, in 0.000s 1 tree, 57 leaves, max depth = 13, train loss: 0.51701, val loss: 0.50550, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.51445, val loss: 0.50271, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.51199, val loss: 0.49998, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.50956, val loss: 0.49734, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.50717, val loss: 0.49470, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.50489, val loss: 0.49220, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.50169, val loss: 0.48907, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.49947, val loss: 0.48664, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.49734, val loss: 0.48429, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.49427, val loss: 0.48130, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.49227, val loss: 0.47908, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.49028, val loss: 0.47686, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.48734, val loss: 0.47399, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.48544, val loss: 0.47188, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.48260, val loss: 0.46911, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.48078, val loss: 0.46708, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.47901, val loss: 0.46512, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.47727, val loss: 0.46318, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.47454, val loss: 0.46054, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.47284, val loss: 0.45865, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.47122, val loss: 0.45682, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.46860, val loss: 0.45428, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.46697, val loss: 0.45247, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.46546, val loss: 0.45075, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.46293, val loss: 0.44830, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.46147, val loss: 0.44667, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.46006, val loss: 0.44505, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.45867, val loss: 0.44347, in 0.016s 1 tree, 57 leaves, max depth = 9, train loss: 0.45698, val loss: 0.44187, in 0.000s 1 tree, 36 leaves, max depth = 11, train loss: 0.45565, val loss: 0.44037, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.45436, val loss: 0.43890, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.45200, val loss: 0.43663, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.45072, val loss: 0.43515, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.44947, val loss: 0.43371, in 0.000s 1 tree, 39 leaves, max depth = 13, train loss: 0.44830, val loss: 0.43236, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44716, val loss: 0.43116, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.44603, val loss: 0.42987, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44493, val loss: 0.42871, in 0.000s Fit 75 trees in 0.971 s, (2757 total leaves) Time spent computing histograms: 0.343s Time spent finding best splits: 0.044s Time spent applying splits: 0.053s Time spent predicting: 0.000s Trial 14, Fold 4: Log loss = 0.44463370939777047, Average precision = 0.9301654073756966, ROC-AUC = 0.9295039563319194, Elapsed Time = 0.9744702000007237 seconds Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 14, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 36 leaves, max depth = 12, train loss: 0.68550, val loss: 0.68497, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.67801, val loss: 0.67699, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.67075, val loss: 0.66924, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66371, val loss: 0.66174, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.65694, val loss: 0.65447, in 0.000s 1 tree, 36 leaves, max depth = 12, train loss: 0.65037, val loss: 0.64742, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.64401, val loss: 0.64062, in 0.000s 1 tree, 37 leaves, max depth = 12, train loss: 0.63785, val loss: 0.63399, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.63186, val loss: 0.62757, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.62600, val loss: 0.62126, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.62038, val loss: 0.61520, in 0.000s 1 tree, 37 leaves, max depth = 9, train loss: 0.61496, val loss: 0.60933, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.60967, val loss: 0.60362, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.60459, val loss: 0.59809, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.59962, val loss: 0.59269, in 0.000s 1 tree, 34 leaves, max depth = 10, train loss: 0.59480, val loss: 0.58745, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.59008, val loss: 0.58235, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.58553, val loss: 0.57740, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58108, val loss: 0.57258, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.57673, val loss: 0.56783, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.57250, val loss: 0.56322, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.56839, val loss: 0.55873, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.56446, val loss: 0.55442, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.56059, val loss: 0.55018, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.55688, val loss: 0.54610, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.55326, val loss: 0.54215, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.54976, val loss: 0.53829, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.54636, val loss: 0.53457, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.54305, val loss: 0.53092, in 0.000s 1 tree, 57 leaves, max depth = 14, train loss: 0.53940, val loss: 0.52745, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.53624, val loss: 0.52397, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53312, val loss: 0.52054, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.53007, val loss: 0.51716, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.52716, val loss: 0.51396, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.52427, val loss: 0.51075, in 0.000s 1 tree, 37 leaves, max depth = 12, train loss: 0.52152, val loss: 0.50769, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.51885, val loss: 0.50473, in 0.016s 1 tree, 57 leaves, max depth = 14, train loss: 0.51551, val loss: 0.50157, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.51293, val loss: 0.49871, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.51043, val loss: 0.49591, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.50801, val loss: 0.49321, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.50559, val loss: 0.49051, in 0.000s 1 tree, 36 leaves, max depth = 15, train loss: 0.50329, val loss: 0.48795, in 0.016s 1 tree, 57 leaves, max depth = 14, train loss: 0.50017, val loss: 0.48501, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49792, val loss: 0.48251, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.49577, val loss: 0.48009, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.49277, val loss: 0.47728, in 0.000s 1 tree, 34 leaves, max depth = 10, train loss: 0.49076, val loss: 0.47505, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.48874, val loss: 0.47278, in 0.031s 1 tree, 57 leaves, max depth = 13, train loss: 0.48586, val loss: 0.47009, in 0.032s 1 tree, 36 leaves, max depth = 12, train loss: 0.48394, val loss: 0.46793, in 0.047s 1 tree, 57 leaves, max depth = 13, train loss: 0.48115, val loss: 0.46533, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.47930, val loss: 0.46325, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.47751, val loss: 0.46122, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47574, val loss: 0.45923, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.47307, val loss: 0.45674, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47135, val loss: 0.45479, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46971, val loss: 0.45293, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.46714, val loss: 0.45055, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.46557, val loss: 0.44874, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46403, val loss: 0.44699, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.46156, val loss: 0.44471, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.46009, val loss: 0.44304, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.45865, val loss: 0.44139, in 0.000s 1 tree, 36 leaves, max depth = 12, train loss: 0.45727, val loss: 0.43978, in 0.016s 1 tree, 57 leaves, max depth = 9, train loss: 0.45559, val loss: 0.43837, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.45423, val loss: 0.43682, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.45293, val loss: 0.43530, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.45061, val loss: 0.43318, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.44930, val loss: 0.43167, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.44803, val loss: 0.43019, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.44683, val loss: 0.42881, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44569, val loss: 0.42770, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.44455, val loss: 0.42638, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.44344, val loss: 0.42531, in 0.000s Fit 75 trees in 1.096 s, (2748 total leaves) Time spent computing histograms: 0.393s Time spent finding best splits: 0.071s Time spent applying splits: 0.082s Time spent predicting: 0.000s Trial 14, Fold 5: Log loss = 0.4511288488395596, Average precision = 0.925886440789031, ROC-AUC = 0.924868922207978, Elapsed Time = 1.1130646000001434 seconds
Optimization Progress: 15%|#5 | 15/100 [02:56<15:20, 10.83s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 15, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 49 leaves, max depth = 10, train loss: 0.66220, val loss: 0.66243, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.63453, val loss: 0.63502, in 0.015s 1 tree, 48 leaves, max depth = 11, train loss: 0.60959, val loss: 0.61019, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.58790, val loss: 0.58839, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.56691, val loss: 0.56733, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.54896, val loss: 0.54928, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.53166, val loss: 0.53207, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.51556, val loss: 0.51590, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.50083, val loss: 0.50105, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.48729, val loss: 0.48745, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.47482, val loss: 0.47483, in 0.031s 1 tree, 51 leaves, max depth = 10, train loss: 0.46334, val loss: 0.46327, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.45272, val loss: 0.45257, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.44290, val loss: 0.44274, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.42909, val loss: 0.42941, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.42059, val loss: 0.42090, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.41303, val loss: 0.41326, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.40140, val loss: 0.40211, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.39446, val loss: 0.39517, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.38798, val loss: 0.38875, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.37808, val loss: 0.37933, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.36904, val loss: 0.37072, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.36375, val loss: 0.36547, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.35613, val loss: 0.35795, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.34919, val loss: 0.35110, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.34368, val loss: 0.34559, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.33755, val loss: 0.33954, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.33259, val loss: 0.33460, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.32679, val loss: 0.32945, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.32149, val loss: 0.32476, in 0.031s 1 tree, 26 leaves, max depth = 15, train loss: 0.31666, val loss: 0.32003, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.31236, val loss: 0.31581, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.30788, val loss: 0.31189, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.30379, val loss: 0.30784, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.30006, val loss: 0.30426, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.29624, val loss: 0.30096, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.29278, val loss: 0.29754, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.28941, val loss: 0.29468, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.28638, val loss: 0.29168, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.28314, val loss: 0.28844, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.28028, val loss: 0.28603, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.27770, val loss: 0.28343, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.27489, val loss: 0.28071, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.27243, val loss: 0.27870, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.27023, val loss: 0.27649, in 0.016s Fit 45 trees in 1.095 s, (2018 total leaves) Time spent computing histograms: 0.297s Time spent finding best splits: 0.064s Time spent applying splits: 0.043s Time spent predicting: 0.000s Trial 15, Fold 1: Log loss = 0.2758642335299996, Average precision = 0.963677897916902, ROC-AUC = 0.9577225914454912, Elapsed Time = 1.1047766000010597 seconds Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 15, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 49 leaves, max depth = 10, train loss: 0.66197, val loss: 0.66118, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.63397, val loss: 0.63262, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.60884, val loss: 0.60702, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.58717, val loss: 0.58512, in 0.031s 1 tree, 50 leaves, max depth = 10, train loss: 0.56631, val loss: 0.56377, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.54836, val loss: 0.54555, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.53069, val loss: 0.52757, in 0.031s 1 tree, 50 leaves, max depth = 10, train loss: 0.51452, val loss: 0.51102, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.49971, val loss: 0.49597, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.48612, val loss: 0.48203, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.47362, val loss: 0.46922, in 0.026s 1 tree, 49 leaves, max depth = 10, train loss: 0.46217, val loss: 0.45764, in 0.022s 1 tree, 48 leaves, max depth = 11, train loss: 0.45155, val loss: 0.44675, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.44169, val loss: 0.43673, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.42806, val loss: 0.42331, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.41963, val loss: 0.41470, in 0.031s 1 tree, 54 leaves, max depth = 12, train loss: 0.41218, val loss: 0.40725, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.40070, val loss: 0.39601, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.39372, val loss: 0.38892, in 0.031s 1 tree, 52 leaves, max depth = 10, train loss: 0.38727, val loss: 0.38246, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.37751, val loss: 0.37294, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.36854, val loss: 0.36413, in 0.016s 1 tree, 59 leaves, max depth = 9, train loss: 0.36320, val loss: 0.35876, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.35564, val loss: 0.35134, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.34879, val loss: 0.34467, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.34330, val loss: 0.33931, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.33707, val loss: 0.33352, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.33134, val loss: 0.32790, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.32654, val loss: 0.32323, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.32131, val loss: 0.31844, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.31654, val loss: 0.31376, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.31223, val loss: 0.30962, in 0.031s 1 tree, 43 leaves, max depth = 13, train loss: 0.30782, val loss: 0.30560, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.30377, val loss: 0.30165, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.29991, val loss: 0.29813, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.29639, val loss: 0.29472, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.29297, val loss: 0.29158, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.28989, val loss: 0.28860, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.28646, val loss: 0.28534, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.28332, val loss: 0.28228, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.28050, val loss: 0.27975, in 0.016s 1 tree, 26 leaves, max depth = 16, train loss: 0.27799, val loss: 0.27728, in 0.016s 1 tree, 61 leaves, max depth = 15, train loss: 0.27512, val loss: 0.27458, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.27271, val loss: 0.27241, in 0.031s 1 tree, 28 leaves, max depth = 10, train loss: 0.27055, val loss: 0.27030, in 0.016s Fit 45 trees in 1.189 s, (2034 total leaves) Time spent computing histograms: 0.317s Time spent finding best splits: 0.067s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 15, Fold 2: Log loss = 0.2732321948794839, Average precision = 0.9636508261454557, ROC-AUC = 0.9604291852266854, Elapsed Time = 1.1898820999995223 seconds Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 15, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 49 leaves, max depth = 11, train loss: 0.66244, val loss: 0.66232, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.63447, val loss: 0.63435, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.60927, val loss: 0.60911, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.58756, val loss: 0.58750, in 0.031s 1 tree, 50 leaves, max depth = 12, train loss: 0.56673, val loss: 0.56670, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.54872, val loss: 0.54872, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.53142, val loss: 0.53127, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.51548, val loss: 0.51535, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.50085, val loss: 0.50067, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.48813, val loss: 0.48804, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.47569, val loss: 0.47557, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.46421, val loss: 0.46414, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.45352, val loss: 0.45332, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.44369, val loss: 0.44344, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.42989, val loss: 0.43066, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.42134, val loss: 0.42206, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.41391, val loss: 0.41472, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.40229, val loss: 0.40410, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.39528, val loss: 0.39704, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.38505, val loss: 0.38776, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.37884, val loss: 0.38151, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.36979, val loss: 0.37332, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.36441, val loss: 0.36789, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.35673, val loss: 0.36089, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.34974, val loss: 0.35454, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.34325, val loss: 0.34905, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.33724, val loss: 0.34358, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.33215, val loss: 0.33813, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.32741, val loss: 0.33303, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.32212, val loss: 0.32858, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.31726, val loss: 0.32414, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.31312, val loss: 0.31975, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.30865, val loss: 0.31605, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.30456, val loss: 0.31233, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.30066, val loss: 0.30912, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.29713, val loss: 0.30587, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.29369, val loss: 0.30226, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.29050, val loss: 0.29944, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.28723, val loss: 0.29587, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.28417, val loss: 0.29338, in 0.031s 1 tree, 43 leaves, max depth = 10, train loss: 0.28134, val loss: 0.29112, in 0.016s 1 tree, 26 leaves, max depth = 12, train loss: 0.27878, val loss: 0.28881, in 0.000s 1 tree, 42 leaves, max depth = 10, train loss: 0.27624, val loss: 0.28681, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.27398, val loss: 0.28477, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.27135, val loss: 0.28186, in 0.016s Fit 45 trees in 1.143 s, (2028 total leaves) Time spent computing histograms: 0.312s Time spent finding best splits: 0.064s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 15, Fold 3: Log loss = 0.27160769915984334, Average precision = 0.9628420835833131, ROC-AUC = 0.9593151972917529, Elapsed Time = 1.1477941000011924 seconds Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 15, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.166 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 10, train loss: 0.66237, val loss: 0.66117, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.63481, val loss: 0.63244, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.60993, val loss: 0.60653, in 0.031s 1 tree, 49 leaves, max depth = 13, train loss: 0.58852, val loss: 0.58411, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.56757, val loss: 0.56228, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.54987, val loss: 0.54374, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.53227, val loss: 0.52533, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.51624, val loss: 0.50850, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.50151, val loss: 0.49305, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.48804, val loss: 0.47885, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.47561, val loss: 0.46574, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.46415, val loss: 0.45366, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.45363, val loss: 0.44252, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.44388, val loss: 0.43219, in 0.031s 1 tree, 47 leaves, max depth = 11, train loss: 0.43489, val loss: 0.42258, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.42176, val loss: 0.40926, in 0.016s 1 tree, 54 leaves, max depth = 14, train loss: 0.41434, val loss: 0.40140, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.40284, val loss: 0.38972, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.39587, val loss: 0.38238, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.38939, val loss: 0.37551, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.37960, val loss: 0.36560, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.37065, val loss: 0.35655, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.36536, val loss: 0.35091, in 0.032s 1 tree, 26 leaves, max depth = 11, train loss: 0.35778, val loss: 0.34306, in 0.000s 1 tree, 26 leaves, max depth = 11, train loss: 0.35087, val loss: 0.33590, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.34544, val loss: 0.33042, in 0.031s 1 tree, 44 leaves, max depth = 9, train loss: 0.33917, val loss: 0.32456, in 0.031s 1 tree, 26 leaves, max depth = 10, train loss: 0.33342, val loss: 0.31856, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.32856, val loss: 0.31359, in 0.031s 1 tree, 44 leaves, max depth = 11, train loss: 0.32328, val loss: 0.30871, in 0.031s 1 tree, 26 leaves, max depth = 12, train loss: 0.31847, val loss: 0.30369, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.31431, val loss: 0.29953, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.31021, val loss: 0.29529, in 0.031s 1 tree, 46 leaves, max depth = 11, train loss: 0.30588, val loss: 0.29131, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.30193, val loss: 0.28718, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.29813, val loss: 0.28361, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.29468, val loss: 0.27991, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.29135, val loss: 0.27689, in 0.031s 1 tree, 25 leaves, max depth = 10, train loss: 0.28834, val loss: 0.27371, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.28506, val loss: 0.27032, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.28221, val loss: 0.26775, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.27968, val loss: 0.26501, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.27689, val loss: 0.26223, in 0.031s 1 tree, 47 leaves, max depth = 11, train loss: 0.27444, val loss: 0.26005, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.27226, val loss: 0.25774, in 0.016s Fit 45 trees in 1.236 s, (1998 total leaves) Time spent computing histograms: 0.333s Time spent finding best splits: 0.074s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 15, Fold 4: Log loss = 0.2721552688535955, Average precision = 0.9637009760932421, ROC-AUC = 0.9597320132048613, Elapsed Time = 1.2341109000008146 seconds Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 15, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 12, train loss: 0.66188, val loss: 0.66069, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.63385, val loss: 0.63157, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.60861, val loss: 0.60532, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.58686, val loss: 0.58256, in 0.031s 1 tree, 50 leaves, max depth = 12, train loss: 0.56588, val loss: 0.56065, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.54790, val loss: 0.54184, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.53037, val loss: 0.52360, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.51431, val loss: 0.50682, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.49961, val loss: 0.49151, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.48604, val loss: 0.47746, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.47363, val loss: 0.46450, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.46218, val loss: 0.45253, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.45166, val loss: 0.44155, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.44191, val loss: 0.43140, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.42819, val loss: 0.41770, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.41969, val loss: 0.40884, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.41221, val loss: 0.40106, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.40065, val loss: 0.38961, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.39365, val loss: 0.38248, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.38718, val loss: 0.37591, in 0.031s 1 tree, 49 leaves, max depth = 15, train loss: 0.37734, val loss: 0.36618, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.36833, val loss: 0.35731, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.36316, val loss: 0.35204, in 0.031s 1 tree, 25 leaves, max depth = 10, train loss: 0.35550, val loss: 0.34428, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.34857, val loss: 0.33709, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.34312, val loss: 0.33164, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.33694, val loss: 0.32538, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.33113, val loss: 0.32024, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.32637, val loss: 0.31550, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.32124, val loss: 0.31018, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.31684, val loss: 0.30572, in 0.031s 1 tree, 43 leaves, max depth = 12, train loss: 0.31210, val loss: 0.30158, in 0.016s 1 tree, 26 leaves, max depth = 14, train loss: 0.30776, val loss: 0.29715, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.30395, val loss: 0.29341, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.29995, val loss: 0.28997, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.29629, val loss: 0.28623, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.29279, val loss: 0.28312, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.28960, val loss: 0.27986, in 0.031s 1 tree, 58 leaves, max depth = 11, train loss: 0.28627, val loss: 0.27655, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.28329, val loss: 0.27394, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.28057, val loss: 0.27106, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.27773, val loss: 0.26832, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.27518, val loss: 0.26612, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.27287, val loss: 0.26377, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.27023, val loss: 0.26117, in 0.016s Fit 45 trees in 1.173 s, (2029 total leaves) Time spent computing histograms: 0.307s Time spent finding best splits: 0.063s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 15, Fold 5: Log loss = 0.27970886264491346, Average precision = 0.959729690231182, ROC-AUC = 0.9547875815171952, Elapsed Time = 1.1758101999985229 seconds
Optimization Progress: 16%|#6 | 16/100 [03:10<16:26, 11.75s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 16, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 37 leaves, max depth = 12, train loss: 0.68427, val loss: 0.68410, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.67611, val loss: 0.67582, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.66815, val loss: 0.66773, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.66014, val loss: 0.65970, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.65280, val loss: 0.65227, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.64479, val loss: 0.64426, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.63793, val loss: 0.63733, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.63064, val loss: 0.63002, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.62411, val loss: 0.62344, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.61737, val loss: 0.61652, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.61094, val loss: 0.61000, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.60445, val loss: 0.60347, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.59835, val loss: 0.59723, in 0.000s 1 tree, 36 leaves, max depth = 12, train loss: 0.59276, val loss: 0.59162, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.58708, val loss: 0.58584, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.58167, val loss: 0.58031, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.57597, val loss: 0.57453, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.57082, val loss: 0.56935, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.56533, val loss: 0.56382, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.55986, val loss: 0.55835, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.55472, val loss: 0.55317, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.54950, val loss: 0.54795, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.54478, val loss: 0.54320, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.54038, val loss: 0.53870, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.53565, val loss: 0.53392, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.53171, val loss: 0.52989, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.52715, val loss: 0.52526, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52292, val loss: 0.52092, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.51857, val loss: 0.51648, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.51442, val loss: 0.51231, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.51021, val loss: 0.50804, in 0.031s 1 tree, 33 leaves, max depth = 10, train loss: 0.50655, val loss: 0.50431, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.50243, val loss: 0.50015, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.49858, val loss: 0.49630, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.49472, val loss: 0.49246, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.49125, val loss: 0.48899, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.48788, val loss: 0.48549, in 0.016s 1 tree, 14 leaves, max depth = 9, train loss: 0.48334, val loss: 0.48101, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.48006, val loss: 0.47769, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.47557, val loss: 0.47331, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47264, val loss: 0.47034, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.46937, val loss: 0.46706, in 0.016s 1 tree, 38 leaves, max depth = 15, train loss: 0.46641, val loss: 0.46403, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46330, val loss: 0.46083, in 0.031s 1 tree, 8 leaves, max depth = 5, train loss: 0.46063, val loss: 0.45800, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.45789, val loss: 0.45523, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.45502, val loss: 0.45233, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45250, val loss: 0.44973, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.45008, val loss: 0.44713, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.44732, val loss: 0.44428, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44465, val loss: 0.44161, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.44226, val loss: 0.43910, in 0.016s 1 tree, 15 leaves, max depth = 9, train loss: 0.43848, val loss: 0.43544, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.43591, val loss: 0.43278, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.43237, val loss: 0.42937, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.42989, val loss: 0.42688, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.42780, val loss: 0.42479, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.42575, val loss: 0.42272, in 0.016s 1 tree, 34 leaves, max depth = 14, train loss: 0.42357, val loss: 0.42047, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.42173, val loss: 0.41859, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.41953, val loss: 0.41639, in 0.031s 1 tree, 38 leaves, max depth = 13, train loss: 0.41718, val loss: 0.41403, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.41505, val loss: 0.41194, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.41193, val loss: 0.40893, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.40956, val loss: 0.40656, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.40755, val loss: 0.40454, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.40584, val loss: 0.40279, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.40391, val loss: 0.40084, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.40186, val loss: 0.39886, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39963, val loss: 0.39667, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.39750, val loss: 0.39455, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.39565, val loss: 0.39262, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.39398, val loss: 0.39086, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.39232, val loss: 0.38908, in 0.016s Fit 74 trees in 1.517 s, (2394 total leaves) Time spent computing histograms: 0.512s Time spent finding best splits: 0.062s Time spent applying splits: 0.054s Time spent predicting: 0.000s Trial 16, Fold 1: Log loss = 0.3938402636701185, Average precision = 0.9528827845173964, ROC-AUC = 0.9465832359492026, Elapsed Time = 1.5249160999992455 seconds Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 16, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 9, train loss: 0.68432, val loss: 0.68414, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.67584, val loss: 0.67557, in 0.016s 1 tree, 38 leaves, max depth = 16, train loss: 0.66782, val loss: 0.66742, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.66017, val loss: 0.65948, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.65277, val loss: 0.65180, in 0.031s 1 tree, 32 leaves, max depth = 11, train loss: 0.64499, val loss: 0.64385, in 0.000s 1 tree, 28 leaves, max depth = 8, train loss: 0.63818, val loss: 0.63678, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.63110, val loss: 0.62954, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.62431, val loss: 0.62272, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.61733, val loss: 0.61556, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.61098, val loss: 0.60907, in 0.016s 1 tree, 34 leaves, max depth = 13, train loss: 0.60466, val loss: 0.60265, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.59875, val loss: 0.59650, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.59236, val loss: 0.58996, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.58671, val loss: 0.58416, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.58109, val loss: 0.57838, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.57564, val loss: 0.57282, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.56987, val loss: 0.56703, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.56460, val loss: 0.56162, in 0.031s 1 tree, 38 leaves, max depth = 11, train loss: 0.55918, val loss: 0.55608, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.55427, val loss: 0.55105, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.54940, val loss: 0.54610, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.54474, val loss: 0.54129, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.54002, val loss: 0.53651, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.53567, val loss: 0.53205, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.53104, val loss: 0.52730, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.52689, val loss: 0.52308, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.52270, val loss: 0.51882, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.51853, val loss: 0.51460, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.51482, val loss: 0.51076, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.51100, val loss: 0.50684, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.50715, val loss: 0.50292, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.50312, val loss: 0.49884, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.49957, val loss: 0.49515, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.49614, val loss: 0.49160, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.49264, val loss: 0.48812, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.48930, val loss: 0.48475, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.48582, val loss: 0.48123, in 0.031s 1 tree, 36 leaves, max depth = 9, train loss: 0.48249, val loss: 0.47789, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.47936, val loss: 0.47471, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.47651, val loss: 0.47175, in 0.016s 1 tree, 40 leaves, max depth = 13, train loss: 0.47316, val loss: 0.46838, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.47023, val loss: 0.46542, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.46703, val loss: 0.46216, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.46429, val loss: 0.45934, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.46157, val loss: 0.45661, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.45754, val loss: 0.45257, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.45489, val loss: 0.44988, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45204, val loss: 0.44700, in 0.016s 1 tree, 36 leaves, max depth = 14, train loss: 0.44930, val loss: 0.44419, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.44660, val loss: 0.44143, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.44422, val loss: 0.43895, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.44045, val loss: 0.43522, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.43783, val loss: 0.43258, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.43430, val loss: 0.42904, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.43178, val loss: 0.42651, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42955, val loss: 0.42422, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.42743, val loss: 0.42205, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42530, val loss: 0.41996, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.42347, val loss: 0.41808, in 0.031s 1 tree, 36 leaves, max depth = 11, train loss: 0.42104, val loss: 0.41563, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.41847, val loss: 0.41307, in 0.031s 1 tree, 37 leaves, max depth = 12, train loss: 0.41598, val loss: 0.41061, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.41433, val loss: 0.40884, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.41189, val loss: 0.40643, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.40998, val loss: 0.40454, in 0.031s 1 tree, 33 leaves, max depth = 12, train loss: 0.40837, val loss: 0.40289, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.40608, val loss: 0.40062, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.40401, val loss: 0.39851, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.40178, val loss: 0.39631, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.39906, val loss: 0.39371, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.39722, val loss: 0.39184, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.39560, val loss: 0.39021, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.39299, val loss: 0.38773, in 0.016s Fit 74 trees in 1.706 s, (2392 total leaves) Time spent computing histograms: 0.574s Time spent finding best splits: 0.070s Time spent applying splits: 0.062s Time spent predicting: 0.000s Trial 16, Fold 2: Log loss = 0.3954167607410999, Average precision = 0.9511083064377481, ROC-AUC = 0.9473437460640419, Elapsed Time = 1.711078099999213 seconds Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 16, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 13, train loss: 0.68432, val loss: 0.68415, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.67628, val loss: 0.67603, in 0.016s 1 tree, 33 leaves, max depth = 13, train loss: 0.66836, val loss: 0.66792, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.66030, val loss: 0.65979, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.65290, val loss: 0.65227, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.64492, val loss: 0.64428, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.63789, val loss: 0.63716, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.63093, val loss: 0.63018, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.62433, val loss: 0.62348, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.61767, val loss: 0.61671, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.61120, val loss: 0.61010, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.60466, val loss: 0.60351, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.59862, val loss: 0.59745, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.59273, val loss: 0.59144, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.58705, val loss: 0.58572, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.58160, val loss: 0.58016, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.57574, val loss: 0.57422, in 0.031s 1 tree, 30 leaves, max depth = 11, train loss: 0.57043, val loss: 0.56882, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.56522, val loss: 0.56357, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.56029, val loss: 0.55855, in 0.016s 1 tree, 34 leaves, max depth = 14, train loss: 0.55492, val loss: 0.55315, in 0.031s 1 tree, 43 leaves, max depth = 11, train loss: 0.55041, val loss: 0.54858, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.54545, val loss: 0.54357, in 0.031s 1 tree, 26 leaves, max depth = 8, train loss: 0.54085, val loss: 0.53897, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.53646, val loss: 0.53456, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.53253, val loss: 0.53054, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.52793, val loss: 0.52592, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.52374, val loss: 0.52160, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.51941, val loss: 0.51727, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.51518, val loss: 0.51302, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.51136, val loss: 0.50924, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.50748, val loss: 0.50530, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.50364, val loss: 0.50148, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.50002, val loss: 0.49790, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.49611, val loss: 0.49400, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.49242, val loss: 0.49027, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.48908, val loss: 0.48692, in 0.031s 1 tree, 19 leaves, max depth = 9, train loss: 0.48455, val loss: 0.48265, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48125, val loss: 0.47931, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.47685, val loss: 0.47515, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.47404, val loss: 0.47235, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.47072, val loss: 0.46902, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.46783, val loss: 0.46610, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.46473, val loss: 0.46299, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.46201, val loss: 0.46028, in 0.016s 1 tree, 39 leaves, max depth = 14, train loss: 0.45926, val loss: 0.45753, in 0.031s 1 tree, 10 leaves, max depth = 5, train loss: 0.45538, val loss: 0.45391, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.45288, val loss: 0.45130, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.45026, val loss: 0.44870, in 0.016s 1 tree, 34 leaves, max depth = 13, train loss: 0.44774, val loss: 0.44621, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.44533, val loss: 0.44383, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.44278, val loss: 0.44125, in 0.031s 1 tree, 4 leaves, max depth = 2, train loss: 0.44061, val loss: 0.43910, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.43827, val loss: 0.43681, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.43604, val loss: 0.43457, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.43353, val loss: 0.43207, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.43116, val loss: 0.42978, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.42879, val loss: 0.42740, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.42612, val loss: 0.42469, in 0.031s 1 tree, 33 leaves, max depth = 12, train loss: 0.42426, val loss: 0.42284, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.42167, val loss: 0.42020, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.41935, val loss: 0.41791, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.41717, val loss: 0.41570, in 0.031s 1 tree, 40 leaves, max depth = 12, train loss: 0.41470, val loss: 0.41315, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.41263, val loss: 0.41112, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.41081, val loss: 0.40927, in 0.031s 1 tree, 35 leaves, max depth = 14, train loss: 0.40876, val loss: 0.40725, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.40682, val loss: 0.40532, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.40474, val loss: 0.40324, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.40253, val loss: 0.40096, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.40068, val loss: 0.39916, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.39868, val loss: 0.39730, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.39575, val loss: 0.39462, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39411, val loss: 0.39301, in 0.016s Fit 74 trees in 1.752 s, (2308 total leaves) Time spent computing histograms: 0.604s Time spent finding best splits: 0.070s Time spent applying splits: 0.061s Time spent predicting: 0.000s Trial 16, Fold 3: Log loss = 0.39023381825865555, Average precision = 0.9561549356669634, ROC-AUC = 0.9512085745105027, Elapsed Time = 1.7605428999995638 seconds Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 16, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 32 leaves, max depth = 11, train loss: 0.68448, val loss: 0.68410, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.67644, val loss: 0.67568, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.66844, val loss: 0.66726, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.66060, val loss: 0.65906, in 0.016s 1 tree, 38 leaves, max depth = 17, train loss: 0.65309, val loss: 0.65120, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.64586, val loss: 0.64373, in 0.031s 1 tree, 34 leaves, max depth = 10, train loss: 0.63842, val loss: 0.63592, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.63161, val loss: 0.62882, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.62463, val loss: 0.62156, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.61781, val loss: 0.61437, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.61140, val loss: 0.60764, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.60514, val loss: 0.60107, in 0.031s 1 tree, 32 leaves, max depth = 15, train loss: 0.59923, val loss: 0.59500, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.59306, val loss: 0.58853, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.58740, val loss: 0.58263, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.58194, val loss: 0.57695, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.57594, val loss: 0.57071, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.57038, val loss: 0.56485, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.56533, val loss: 0.55956, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.55999, val loss: 0.55390, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.55519, val loss: 0.54875, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.55030, val loss: 0.54363, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.54530, val loss: 0.53835, in 0.031s 1 tree, 35 leaves, max depth = 13, train loss: 0.54065, val loss: 0.53345, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.53616, val loss: 0.52865, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53199, val loss: 0.52431, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.52779, val loss: 0.51979, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.52372, val loss: 0.51546, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.51958, val loss: 0.51108, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.51595, val loss: 0.50717, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.51220, val loss: 0.50325, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50836, val loss: 0.49918, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.50437, val loss: 0.49499, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.50082, val loss: 0.49131, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.49743, val loss: 0.48775, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.49433, val loss: 0.48450, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.49088, val loss: 0.48085, in 0.031s 1 tree, 35 leaves, max depth = 13, train loss: 0.48760, val loss: 0.47738, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.48309, val loss: 0.47272, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.48005, val loss: 0.46954, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.47590, val loss: 0.46526, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.47310, val loss: 0.46239, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.46987, val loss: 0.45896, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.46659, val loss: 0.45552, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.46387, val loss: 0.45269, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.46120, val loss: 0.44990, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.45833, val loss: 0.44684, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.45548, val loss: 0.44376, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.45294, val loss: 0.44102, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.45015, val loss: 0.43806, in 0.016s 1 tree, 37 leaves, max depth = 15, train loss: 0.44769, val loss: 0.43542, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.44502, val loss: 0.43257, in 0.031s 1 tree, 30 leaves, max depth = 14, train loss: 0.44293, val loss: 0.43040, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.44039, val loss: 0.42769, in 0.016s 1 tree, 11 leaves, max depth = 8, train loss: 0.43693, val loss: 0.42415, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.43439, val loss: 0.42143, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.43216, val loss: 0.41904, in 0.031s 1 tree, 33 leaves, max depth = 11, train loss: 0.42983, val loss: 0.41656, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.42790, val loss: 0.41456, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.42588, val loss: 0.41243, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.42370, val loss: 0.41011, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.42174, val loss: 0.40800, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.41976, val loss: 0.40596, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.41752, val loss: 0.40364, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.41569, val loss: 0.40166, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.41395, val loss: 0.39977, in 0.031s 1 tree, 31 leaves, max depth = 13, train loss: 0.41219, val loss: 0.39786, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.41006, val loss: 0.39568, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.40784, val loss: 0.39347, in 0.031s 1 tree, 40 leaves, max depth = 13, train loss: 0.40561, val loss: 0.39121, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.40360, val loss: 0.38915, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.40171, val loss: 0.38717, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.40018, val loss: 0.38555, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.39847, val loss: 0.38367, in 0.016s Fit 74 trees in 1.751 s, (2447 total leaves) Time spent computing histograms: 0.598s Time spent finding best splits: 0.072s Time spent applying splits: 0.064s Time spent predicting: 0.000s Trial 16, Fold 4: Log loss = 0.3980620539886425, Average precision = 0.951851052319919, ROC-AUC = 0.9459866359793129, Elapsed Time = 1.7685146000003442 seconds Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 16, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 11, train loss: 0.68426, val loss: 0.68370, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.67617, val loss: 0.67516, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.66823, val loss: 0.66676, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.66016, val loss: 0.65826, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.65272, val loss: 0.65057, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.64472, val loss: 0.64225, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.63796, val loss: 0.63515, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.63079, val loss: 0.62764, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.62391, val loss: 0.62044, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.61718, val loss: 0.61326, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.61083, val loss: 0.60659, in 0.016s 1 tree, 34 leaves, max depth = 13, train loss: 0.60419, val loss: 0.59965, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.59815, val loss: 0.59330, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59213, val loss: 0.58705, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.58642, val loss: 0.58118, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.58092, val loss: 0.57545, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.57514, val loss: 0.56935, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.56979, val loss: 0.56366, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.56468, val loss: 0.55828, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.55916, val loss: 0.55257, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.55392, val loss: 0.54711, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.54862, val loss: 0.54158, in 0.031s 1 tree, 28 leaves, max depth = 9, train loss: 0.54397, val loss: 0.53668, in 0.031s 1 tree, 27 leaves, max depth = 7, train loss: 0.53959, val loss: 0.53213, in 0.031s 1 tree, 34 leaves, max depth = 13, train loss: 0.53524, val loss: 0.52760, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.53128, val loss: 0.52340, in 0.032s 1 tree, 34 leaves, max depth = 12, train loss: 0.52661, val loss: 0.51851, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.52240, val loss: 0.51400, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.51810, val loss: 0.50953, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.51382, val loss: 0.50500, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.50992, val loss: 0.50086, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.50612, val loss: 0.49682, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.50201, val loss: 0.49254, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.49802, val loss: 0.48838, in 0.031s 1 tree, 41 leaves, max depth = 13, train loss: 0.49408, val loss: 0.48431, in 0.016s 1 tree, 39 leaves, max depth = 14, train loss: 0.49034, val loss: 0.48044, in 0.031s 1 tree, 38 leaves, max depth = 11, train loss: 0.48725, val loss: 0.47718, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.48394, val loss: 0.47367, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.48064, val loss: 0.47009, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.47614, val loss: 0.46555, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.47315, val loss: 0.46246, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.46988, val loss: 0.45909, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.46698, val loss: 0.45599, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.46389, val loss: 0.45271, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.46123, val loss: 0.44994, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.45850, val loss: 0.44717, in 0.031s 1 tree, 9 leaves, max depth = 5, train loss: 0.45461, val loss: 0.44322, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.45204, val loss: 0.44062, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.44935, val loss: 0.43771, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.44656, val loss: 0.43475, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.44416, val loss: 0.43224, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.44160, val loss: 0.42949, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.43792, val loss: 0.42574, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.43554, val loss: 0.42323, in 0.031s 1 tree, 39 leaves, max depth = 11, train loss: 0.43333, val loss: 0.42091, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.43080, val loss: 0.41827, in 0.031s 1 tree, 32 leaves, max depth = 11, train loss: 0.42855, val loss: 0.41591, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.42641, val loss: 0.41369, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.42427, val loss: 0.41141, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.42244, val loss: 0.40947, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.41985, val loss: 0.40685, in 0.031s 1 tree, 33 leaves, max depth = 11, train loss: 0.41770, val loss: 0.40461, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.41524, val loss: 0.40211, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.41362, val loss: 0.40034, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.41120, val loss: 0.39789, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.40942, val loss: 0.39594, in 0.031s 1 tree, 37 leaves, max depth = 12, train loss: 0.40741, val loss: 0.39380, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40550, val loss: 0.39180, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.40345, val loss: 0.38970, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.40175, val loss: 0.38799, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.39960, val loss: 0.38582, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39673, val loss: 0.38290, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39411, val loss: 0.38014, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.39244, val loss: 0.37839, in 0.016s Fit 74 trees in 1.846 s, (2384 total leaves) Time spent computing histograms: 0.627s Time spent finding best splits: 0.081s Time spent applying splits: 0.078s Time spent predicting: 0.000s Trial 16, Fold 5: Log loss = 0.3984597419290703, Average precision = 0.9524122235739496, ROC-AUC = 0.9470420452223027, Elapsed Time = 1.8619557000001805 seconds
Optimization Progress: 17%|#7 | 17/100 [03:25<17:42, 12.80s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 17, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 32 leaves, max depth = 9, train loss: 0.66507, val loss: 0.66472, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.63841, val loss: 0.63797, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.61633, val loss: 0.61549, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.59559, val loss: 0.59439, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.57693, val loss: 0.57546, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.55961, val loss: 0.55818, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.54219, val loss: 0.54094, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.52658, val loss: 0.52496, in 0.000s 1 tree, 22 leaves, max depth = 8, train loss: 0.51319, val loss: 0.51126, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.50033, val loss: 0.49815, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.48853, val loss: 0.48650, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.47672, val loss: 0.47460, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.46670, val loss: 0.46432, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.45308, val loss: 0.45113, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.44400, val loss: 0.44195, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.43655, val loss: 0.43440, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.42880, val loss: 0.42653, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.42121, val loss: 0.41879, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.41505, val loss: 0.41242, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.40483, val loss: 0.40253, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.39548, val loss: 0.39352, in 0.016s Fit 21 trees in 0.596 s, (468 total leaves) Time spent computing histograms: 0.123s Time spent finding best splits: 0.013s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 17, Fold 1: Log loss = 0.39756739316111345, Average precision = 0.9541704790921671, ROC-AUC = 0.9475914305549582, Elapsed Time = 0.5942180999991251 seconds Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 17, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 9, train loss: 0.66539, val loss: 0.66473, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.64026, val loss: 0.63928, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.61738, val loss: 0.61586, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.59498, val loss: 0.59293, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.57621, val loss: 0.57385, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.55887, val loss: 0.55629, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.54307, val loss: 0.54021, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.52964, val loss: 0.52629, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.51611, val loss: 0.51239, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.50330, val loss: 0.49944, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.49164, val loss: 0.48767, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.47942, val loss: 0.47515, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.46952, val loss: 0.46529, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.46090, val loss: 0.45620, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.45187, val loss: 0.44693, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.44443, val loss: 0.43914, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.43653, val loss: 0.43132, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.42810, val loss: 0.42291, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.42129, val loss: 0.41596, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.41343, val loss: 0.40812, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.40291, val loss: 0.39803, in 0.016s Fit 21 trees in 0.610 s, (526 total leaves) Time spent computing histograms: 0.122s Time spent finding best splits: 0.014s Time spent applying splits: 0.010s Time spent predicting: 0.000s Trial 17, Fold 2: Log loss = 0.40518344337243584, Average precision = 0.9527342274576989, ROC-AUC = 0.949551524594826, Elapsed Time = 0.6115257000001293 seconds Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 17, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 9, train loss: 0.66559, val loss: 0.66505, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.64046, val loss: 0.63985, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.61773, val loss: 0.61666, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.59592, val loss: 0.59467, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.57611, val loss: 0.57468, in 0.031s 1 tree, 12 leaves, max depth = 6, train loss: 0.55841, val loss: 0.55707, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.54289, val loss: 0.54170, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.52858, val loss: 0.52736, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.51444, val loss: 0.51341, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.50159, val loss: 0.50054, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49013, val loss: 0.48903, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.47809, val loss: 0.47697, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.46787, val loss: 0.46705, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.45428, val loss: 0.45429, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.44552, val loss: 0.44544, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.43799, val loss: 0.43780, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.43013, val loss: 0.42978, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.42302, val loss: 0.42272, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41618, val loss: 0.41609, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.40647, val loss: 0.40718, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.39716, val loss: 0.39861, in 0.016s Fit 21 trees in 0.673 s, (481 total leaves) Time spent computing histograms: 0.130s Time spent finding best splits: 0.013s Time spent applying splits: 0.010s Time spent predicting: 0.000s Trial 17, Fold 3: Log loss = 0.39428931547288537, Average precision = 0.9567107082320997, ROC-AUC = 0.9522550466623647, Elapsed Time = 0.6815104000015708 seconds Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 17, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 8, train loss: 0.66547, val loss: 0.66418, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.64126, val loss: 0.63883, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.61845, val loss: 0.61484, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.59738, val loss: 0.59317, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.58069, val loss: 0.57572, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.56350, val loss: 0.55780, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.54596, val loss: 0.53959, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.52977, val loss: 0.52266, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.51579, val loss: 0.50794, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50278, val loss: 0.49436, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.49103, val loss: 0.48235, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.48049, val loss: 0.47128, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46961, val loss: 0.45968, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.45616, val loss: 0.44584, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.44701, val loss: 0.43613, in 0.016s 1 tree, 42 leaves, max depth = 9, train loss: 0.43925, val loss: 0.42805, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.43107, val loss: 0.41942, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.42377, val loss: 0.41158, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.41728, val loss: 0.40452, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.41171, val loss: 0.39847, in 0.016s 1 tree, 26 leaves, max depth = 8, train loss: 0.40425, val loss: 0.39089, in 0.000s Fit 21 trees in 0.674 s, (588 total leaves) Time spent computing histograms: 0.143s Time spent finding best splits: 0.016s Time spent applying splits: 0.012s Time spent predicting: 0.000s Trial 17, Fold 4: Log loss = 0.4040348495124786, Average precision = 0.954412483575146, ROC-AUC = 0.9494533442539301, Elapsed Time = 0.6907933000002231 seconds Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 17, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 11, train loss: 0.66527, val loss: 0.66365, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.63975, val loss: 0.63669, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.61722, val loss: 0.61283, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.59465, val loss: 0.58947, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.57630, val loss: 0.57058, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.55935, val loss: 0.55280, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.54307, val loss: 0.53549, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.52860, val loss: 0.52063, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.51506, val loss: 0.50630, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.50106, val loss: 0.49176, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.48909, val loss: 0.47910, in 0.016s 1 tree, 13 leaves, max depth = 8, train loss: 0.47812, val loss: 0.46755, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.46845, val loss: 0.45713, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.45849, val loss: 0.44673, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.44993, val loss: 0.43794, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.44236, val loss: 0.42992, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.43443, val loss: 0.42171, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.42614, val loss: 0.41325, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.41917, val loss: 0.40599, in 0.031s 1 tree, 21 leaves, max depth = 8, train loss: 0.41301, val loss: 0.39956, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.40720, val loss: 0.39350, in 0.016s Fit 21 trees in 0.690 s, (556 total leaves) Time spent computing histograms: 0.138s Time spent finding best splits: 0.015s Time spent applying splits: 0.012s Time spent predicting: 0.000s Trial 17, Fold 5: Log loss = 0.41314811982724015, Average precision = 0.9197794119472843, ROC-AUC = 0.9302998346431822, Elapsed Time = 0.6928602000007231 seconds
Optimization Progress: 18%|#8 | 18/100 [03:35<16:17, 11.92s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 18, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 52 leaves, max depth = 16, train loss: 0.66318, val loss: 0.66227, in 0.015s 1 tree, 52 leaves, max depth = 16, train loss: 0.63692, val loss: 0.63517, in 0.000s 1 tree, 51 leaves, max depth = 16, train loss: 0.61382, val loss: 0.61127, in 0.016s 1 tree, 54 leaves, max depth = 17, train loss: 0.59360, val loss: 0.59055, in 0.016s 1 tree, 52 leaves, max depth = 17, train loss: 0.57550, val loss: 0.57175, in 0.000s Fit 5 trees in 0.314 s, (261 total leaves) Time spent computing histograms: 0.024s Time spent finding best splits: 0.005s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 18, Fold 1: Log loss = 0.5758696069682718, Average precision = 0.8140119097748746, ROC-AUC = 0.8614314929524958, Elapsed Time = 0.3241126000011718 seconds Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 18, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 52 leaves, max depth = 11, train loss: 0.66356, val loss: 0.66219, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.63764, val loss: 0.63497, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.61482, val loss: 0.61105, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.59466, val loss: 0.58981, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.57680, val loss: 0.57101, in 0.016s Fit 5 trees in 0.313 s, (262 total leaves) Time spent computing histograms: 0.023s Time spent finding best splits: 0.005s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 18, Fold 2: Log loss = 0.5768084023705091, Average precision = 0.8172011369396084, ROC-AUC = 0.8660990439586019, Elapsed Time = 0.3173349999997299 seconds Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 18, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.126 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 10, train loss: 0.66371, val loss: 0.66260, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.63793, val loss: 0.63579, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.61525, val loss: 0.61217, in 0.016s 1 tree, 52 leaves, max depth = 15, train loss: 0.59520, val loss: 0.59141, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.57718, val loss: 0.57284, in 0.016s Fit 5 trees in 0.314 s, (259 total leaves) Time spent computing histograms: 0.021s Time spent finding best splits: 0.005s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 18, Fold 3: Log loss = 0.5743287091850737, Average precision = 0.8266571807986699, ROC-AUC = 0.8716865477314095, Elapsed Time = 0.31165280000095663 seconds Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 18, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.143 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 53 leaves, max depth = 12, train loss: 0.66348, val loss: 0.66164, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.63749, val loss: 0.63393, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.61463, val loss: 0.60946, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.59452, val loss: 0.58806, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.57663, val loss: 0.56873, in 0.000s Fit 5 trees in 0.315 s, (264 total leaves) Time spent computing histograms: 0.023s Time spent finding best splits: 0.005s Time spent applying splits: 0.005s Time spent predicting: 0.000s Trial 18, Fold 4: Log loss = 0.5755665976093852, Average precision = 0.8188990668810218, ROC-AUC = 0.8676090003123578, Elapsed Time = 0.3268798999997671 seconds Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 18, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 54 leaves, max depth = 15, train loss: 0.66343, val loss: 0.66149, in 0.000s 1 tree, 48 leaves, max depth = 14, train loss: 0.63709, val loss: 0.63332, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.61391, val loss: 0.60844, in 0.016s 1 tree, 54 leaves, max depth = 17, train loss: 0.59365, val loss: 0.58660, in 0.000s 1 tree, 48 leaves, max depth = 15, train loss: 0.57548, val loss: 0.56694, in 0.016s Fit 5 trees in 0.344 s, (252 total leaves) Time spent computing histograms: 0.024s Time spent finding best splits: 0.005s Time spent applying splits: 0.005s Time spent predicting: 0.000s Trial 18, Fold 5: Log loss = 0.5793771384317353, Average precision = 0.8077174872619299, ROC-AUC = 0.858010664585772, Elapsed Time = 0.34722619999956805 seconds
Optimization Progress: 19%|#9 | 19/100 [03:43<14:33, 10.79s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 19, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.126 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 42 leaves, max depth = 9, train loss: 0.68003, val loss: 0.68007, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.66750, val loss: 0.66754, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.65551, val loss: 0.65557, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.64402, val loss: 0.64410, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.63301, val loss: 0.63311, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.62247, val loss: 0.62257, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.61234, val loss: 0.61250, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.60261, val loss: 0.60279, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.59328, val loss: 0.59340, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.58429, val loss: 0.58446, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.57564, val loss: 0.57588, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.56743, val loss: 0.56764, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.55974, val loss: 0.55988, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.55200, val loss: 0.55216, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.54454, val loss: 0.54470, in 0.031s 1 tree, 46 leaves, max depth = 11, train loss: 0.53745, val loss: 0.53753, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.53080, val loss: 0.53082, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.52438, val loss: 0.52435, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.51789, val loss: 0.51787, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.51162, val loss: 0.51162, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.50558, val loss: 0.50559, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.49989, val loss: 0.49982, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.49424, val loss: 0.49420, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.48901, val loss: 0.48894, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.48372, val loss: 0.48366, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.47860, val loss: 0.47858, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.47365, val loss: 0.47367, in 0.016s Fit 27 trees in 0.736 s, (1351 total leaves) Time spent computing histograms: 0.188s Time spent finding best splits: 0.033s Time spent applying splits: 0.023s Time spent predicting: 0.000s Trial 19, Fold 1: Log loss = 0.4759809969926131, Average precision = 0.9192054238246357, ROC-AUC = 0.9301531738862362, Elapsed Time = 0.743226999999024 seconds Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 19, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.127 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 38 leaves, max depth = 9, train loss: 0.68007, val loss: 0.67983, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.66738, val loss: 0.66688, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.65524, val loss: 0.65450, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.64361, val loss: 0.64263, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.63247, val loss: 0.63127, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.62183, val loss: 0.62049, in 0.016s 1 tree, 40 leaves, max depth = 8, train loss: 0.61157, val loss: 0.61003, in 0.016s 1 tree, 42 leaves, max depth = 8, train loss: 0.60173, val loss: 0.60000, in 0.016s 1 tree, 40 leaves, max depth = 8, train loss: 0.59227, val loss: 0.59034, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.58318, val loss: 0.58107, in 0.016s 1 tree, 42 leaves, max depth = 8, train loss: 0.57445, val loss: 0.57215, in 0.016s 1 tree, 42 leaves, max depth = 8, train loss: 0.56605, val loss: 0.56358, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.55795, val loss: 0.55534, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.55023, val loss: 0.54752, in 0.031s 1 tree, 43 leaves, max depth = 9, train loss: 0.54272, val loss: 0.53985, in 0.016s 1 tree, 43 leaves, max depth = 9, train loss: 0.53548, val loss: 0.53245, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.52885, val loss: 0.52577, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.52210, val loss: 0.51887, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.51559, val loss: 0.51221, in 0.016s 1 tree, 49 leaves, max depth = 9, train loss: 0.50926, val loss: 0.50588, in 0.031s 1 tree, 43 leaves, max depth = 8, train loss: 0.50320, val loss: 0.49971, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.49734, val loss: 0.49376, in 0.016s 1 tree, 43 leaves, max depth = 9, train loss: 0.49168, val loss: 0.48796, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.48621, val loss: 0.48239, in 0.000s 1 tree, 55 leaves, max depth = 9, train loss: 0.48103, val loss: 0.47720, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.47591, val loss: 0.47200, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.47091, val loss: 0.46702, in 0.016s Fit 27 trees in 0.750 s, (1174 total leaves) Time spent computing histograms: 0.182s Time spent finding best splits: 0.032s Time spent applying splits: 0.022s Time spent predicting: 0.000s Trial 19, Fold 2: Log loss = 0.47331780547530794, Average precision = 0.915780116516474, ROC-AUC = 0.9320602402288841, Elapsed Time = 0.765905100000964 seconds Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 19, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 8, train loss: 0.67995, val loss: 0.67999, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.66734, val loss: 0.66740, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.65528, val loss: 0.65533, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.64434, val loss: 0.64440, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.63324, val loss: 0.63329, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62318, val loss: 0.62325, in 0.000s 1 tree, 38 leaves, max depth = 8, train loss: 0.61294, val loss: 0.61299, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.60311, val loss: 0.60314, in 0.031s 1 tree, 37 leaves, max depth = 9, train loss: 0.59366, val loss: 0.59371, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.58507, val loss: 0.58519, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.57630, val loss: 0.57648, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.56786, val loss: 0.56810, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.55974, val loss: 0.56005, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.55191, val loss: 0.55228, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.54436, val loss: 0.54481, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.53711, val loss: 0.53760, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.53009, val loss: 0.53064, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.52333, val loss: 0.52395, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.51682, val loss: 0.51748, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.51084, val loss: 0.51157, in 0.031s 1 tree, 56 leaves, max depth = 13, train loss: 0.50474, val loss: 0.50550, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.49891, val loss: 0.49972, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.49322, val loss: 0.49406, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.48772, val loss: 0.48861, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.48246, val loss: 0.48339, in 0.016s 1 tree, 62 leaves, max depth = 14, train loss: 0.47732, val loss: 0.47833, in 0.016s 1 tree, 64 leaves, max depth = 14, train loss: 0.47231, val loss: 0.47339, in 0.016s Fit 27 trees in 0.799 s, (1280 total leaves) Time spent computing histograms: 0.187s Time spent finding best splits: 0.033s Time spent applying splits: 0.023s Time spent predicting: 0.000s Trial 19, Fold 3: Log loss = 0.47063638550940856, Average precision = 0.9229946765596092, ROC-AUC = 0.9352773376929819, Elapsed Time = 0.803773600000568 seconds Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 19, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 38 leaves, max depth = 8, train loss: 0.68012, val loss: 0.67960, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.66767, val loss: 0.66664, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.65575, val loss: 0.65424, in 0.016s 1 tree, 38 leaves, max depth = 8, train loss: 0.64433, val loss: 0.64235, in 0.031s 1 tree, 46 leaves, max depth = 10, train loss: 0.63339, val loss: 0.63096, in 0.000s 1 tree, 44 leaves, max depth = 8, train loss: 0.62290, val loss: 0.62002, in 0.031s 1 tree, 43 leaves, max depth = 9, train loss: 0.61284, val loss: 0.60952, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.60318, val loss: 0.59943, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.59390, val loss: 0.58973, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.58498, val loss: 0.58043, in 0.016s 1 tree, 45 leaves, max depth = 8, train loss: 0.57640, val loss: 0.57147, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.56816, val loss: 0.56285, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.56022, val loss: 0.55455, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.55268, val loss: 0.54658, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.54531, val loss: 0.53886, in 0.016s 1 tree, 45 leaves, max depth = 9, train loss: 0.53821, val loss: 0.53141, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.53165, val loss: 0.52453, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.52503, val loss: 0.51760, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.51874, val loss: 0.51094, in 0.031s 1 tree, 53 leaves, max depth = 10, train loss: 0.51242, val loss: 0.50437, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.50647, val loss: 0.49810, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.50071, val loss: 0.49207, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.49515, val loss: 0.48623, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.48977, val loss: 0.48059, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.48465, val loss: 0.47513, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.47962, val loss: 0.46983, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.47474, val loss: 0.46470, in 0.016s Fit 27 trees in 0.814 s, (1296 total leaves) Time spent computing histograms: 0.194s Time spent finding best splits: 0.036s Time spent applying splits: 0.025s Time spent predicting: 0.000s Trial 19, Fold 4: Log loss = 0.47506838673715585, Average precision = 0.9201397556504622, ROC-AUC = 0.9309760313168379, Elapsed Time = 0.8261987999994744 seconds Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 19, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 9, train loss: 0.67988, val loss: 0.67937, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.66720, val loss: 0.66622, in 0.016s 1 tree, 43 leaves, max depth = 9, train loss: 0.65514, val loss: 0.65370, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.64419, val loss: 0.64229, in 0.031s 1 tree, 46 leaves, max depth = 9, train loss: 0.63301, val loss: 0.63067, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.62230, val loss: 0.61952, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.61263, val loss: 0.60946, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.60273, val loss: 0.59917, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.59322, val loss: 0.58929, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.58408, val loss: 0.57978, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.57529, val loss: 0.57064, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.56684, val loss: 0.56184, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.55870, val loss: 0.55339, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.55086, val loss: 0.54521, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.54331, val loss: 0.53738, in 0.016s 1 tree, 45 leaves, max depth = 9, train loss: 0.53613, val loss: 0.52987, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.52911, val loss: 0.52258, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.52271, val loss: 0.51592, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.51616, val loss: 0.50911, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.50984, val loss: 0.50253, in 0.016s 1 tree, 55 leaves, max depth = 9, train loss: 0.50383, val loss: 0.49623, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.49792, val loss: 0.49014, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.49223, val loss: 0.48420, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.48672, val loss: 0.47850, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.48140, val loss: 0.47298, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.47625, val loss: 0.46764, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.47153, val loss: 0.46276, in 0.016s Fit 27 trees in 0.799 s, (1325 total leaves) Time spent computing histograms: 0.182s Time spent finding best splits: 0.034s Time spent applying splits: 0.024s Time spent predicting: 0.000s Trial 19, Fold 5: Log loss = 0.47660104352388494, Average precision = 0.9162011773787254, ROC-AUC = 0.9303857458707244, Elapsed Time = 0.8032679999996617 seconds
Optimization Progress: 20%|## | 20/100 [03:54<14:10, 10.63s/it]
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 20, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 101 leaves, max depth = 15, train loss: 0.68483, val loss: 0.68463, in 0.000s 1 tree, 104 leaves, max depth = 17, train loss: 0.67663, val loss: 0.67620, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.66871, val loss: 0.66805, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.66116, val loss: 0.66033, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.65386, val loss: 0.65283, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.64681, val loss: 0.64558, in 0.000s 1 tree, 104 leaves, max depth = 21, train loss: 0.64007, val loss: 0.63868, in 0.016s 1 tree, 104 leaves, max depth = 19, train loss: 0.63356, val loss: 0.63201, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.62719, val loss: 0.62545, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.62095, val loss: 0.61901, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.61505, val loss: 0.61289, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.60928, val loss: 0.60695, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.60371, val loss: 0.60120, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.59824, val loss: 0.59554, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.59302, val loss: 0.59015, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.58797, val loss: 0.58494, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.58300, val loss: 0.57979, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.57827, val loss: 0.57493, in 0.031s 1 tree, 103 leaves, max depth = 20, train loss: 0.57372, val loss: 0.57026, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.56920, val loss: 0.56557, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.56483, val loss: 0.56102, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.56059, val loss: 0.55661, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.55648, val loss: 0.55233, in 0.031s 1 tree, 105 leaves, max depth = 18, train loss: 0.55250, val loss: 0.54818, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.54870, val loss: 0.54425, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.54448, val loss: 0.54029, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.54039, val loss: 0.53647, in 0.031s 1 tree, 106 leaves, max depth = 17, train loss: 0.53684, val loss: 0.53276, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.53292, val loss: 0.52909, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.52913, val loss: 0.52555, in 0.031s 1 tree, 104 leaves, max depth = 16, train loss: 0.52578, val loss: 0.52210, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.52253, val loss: 0.51869, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.51931, val loss: 0.51530, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.51573, val loss: 0.51197, in 0.031s 1 tree, 159 leaves, max depth = 15, train loss: 0.51227, val loss: 0.50875, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.50928, val loss: 0.50563, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.50632, val loss: 0.50251, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.50302, val loss: 0.49945, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.50025, val loss: 0.49654, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.49757, val loss: 0.49371, in 0.000s 1 tree, 106 leaves, max depth = 21, train loss: 0.49495, val loss: 0.49101, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.49183, val loss: 0.48812, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.48933, val loss: 0.48550, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.48632, val loss: 0.48271, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.48340, val loss: 0.48001, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.48057, val loss: 0.47739, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.47782, val loss: 0.47485, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.47546, val loss: 0.47234, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.47281, val loss: 0.46990, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.47024, val loss: 0.46752, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.46805, val loss: 0.46519, in 0.000s 1 tree, 159 leaves, max depth = 16, train loss: 0.46557, val loss: 0.46291, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.46316, val loss: 0.46069, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.46102, val loss: 0.45841, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45870, val loss: 0.45628, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45644, val loss: 0.45421, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.45424, val loss: 0.45220, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.45228, val loss: 0.45010, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45016, val loss: 0.44816, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.44821, val loss: 0.44612, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.44638, val loss: 0.44415, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.44434, val loss: 0.44229, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.44236, val loss: 0.44049, in 0.031s 1 tree, 104 leaves, max depth = 20, train loss: 0.44062, val loss: 0.43867, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43871, val loss: 0.43693, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.43697, val loss: 0.43508, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.43533, val loss: 0.43331, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.43369, val loss: 0.43158, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43186, val loss: 0.42992, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.43008, val loss: 0.42832, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.42835, val loss: 0.42675, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.42667, val loss: 0.42524, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.42517, val loss: 0.42361, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.42372, val loss: 0.42209, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.42230, val loss: 0.42057, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.42093, val loss: 0.41907, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41955, val loss: 0.41757, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.41821, val loss: 0.41615, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.41690, val loss: 0.41475, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41565, val loss: 0.41338, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41439, val loss: 0.41200, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.41280, val loss: 0.41058, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41159, val loss: 0.40925, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.41005, val loss: 0.40788, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.40856, val loss: 0.40655, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40737, val loss: 0.40524, in 0.000s 1 tree, 159 leaves, max depth = 16, train loss: 0.40592, val loss: 0.40395, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40476, val loss: 0.40268, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40362, val loss: 0.40143, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.40222, val loss: 0.40019, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40112, val loss: 0.39898, in 0.016s Fit 91 trees in 1.815 s, (10974 total leaves) Time spent computing histograms: 0.564s Time spent finding best splits: 0.175s Time spent applying splits: 0.207s Time spent predicting: 0.000s Trial 20, Fold 1: Log loss = 0.4053307122495676, Average precision = 0.9457528949247934, ROC-AUC = 0.9421825785167024, Elapsed Time = 1.8230208000004495 seconds Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 20, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 105 leaves, max depth = 18, train loss: 0.68479, val loss: 0.68447, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.67669, val loss: 0.67601, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.66888, val loss: 0.66784, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.66134, val loss: 0.65995, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.65408, val loss: 0.65235, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.64707, val loss: 0.64501, in 0.016s 1 tree, 106 leaves, max depth = 21, train loss: 0.64030, val loss: 0.63796, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.63376, val loss: 0.63114, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.62743, val loss: 0.62449, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.62123, val loss: 0.61798, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.61531, val loss: 0.61177, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.60956, val loss: 0.60573, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.60402, val loss: 0.59991, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.59858, val loss: 0.59419, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.59340, val loss: 0.58877, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.58837, val loss: 0.58353, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.58342, val loss: 0.57832, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.57869, val loss: 0.57334, in 0.016s 1 tree, 104 leaves, max depth = 23, train loss: 0.57413, val loss: 0.56857, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.56963, val loss: 0.56383, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.56528, val loss: 0.55923, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.56106, val loss: 0.55478, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.55697, val loss: 0.55046, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.55300, val loss: 0.54628, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.54921, val loss: 0.54227, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.54499, val loss: 0.53820, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.54089, val loss: 0.53427, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.53734, val loss: 0.53051, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.53342, val loss: 0.52674, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.52961, val loss: 0.52309, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.52626, val loss: 0.51957, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.52301, val loss: 0.51613, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.51980, val loss: 0.51272, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.51622, val loss: 0.50930, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.51275, val loss: 0.50598, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.50977, val loss: 0.50281, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.50683, val loss: 0.49968, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.50352, val loss: 0.49652, in 0.031s 1 tree, 106 leaves, max depth = 15, train loss: 0.50075, val loss: 0.49358, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.49807, val loss: 0.49073, in 0.000s 1 tree, 106 leaves, max depth = 15, train loss: 0.49547, val loss: 0.48795, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48496, in 0.032s 1 tree, 106 leaves, max depth = 15, train loss: 0.48984, val loss: 0.48230, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.48683, val loss: 0.47943, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.48391, val loss: 0.47665, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.48107, val loss: 0.47394, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.47832, val loss: 0.47132, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.47597, val loss: 0.46882, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.47331, val loss: 0.46629, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.47074, val loss: 0.46384, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.46855, val loss: 0.46151, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.46606, val loss: 0.45915, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.46365, val loss: 0.45686, in 0.031s 1 tree, 106 leaves, max depth = 14, train loss: 0.46153, val loss: 0.45459, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45920, val loss: 0.45238, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45694, val loss: 0.45024, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.45474, val loss: 0.44816, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.45278, val loss: 0.44607, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45066, val loss: 0.44406, in 0.031s 1 tree, 106 leaves, max depth = 13, train loss: 0.44877, val loss: 0.44204, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.44694, val loss: 0.44008, in 0.000s 1 tree, 159 leaves, max depth = 16, train loss: 0.44490, val loss: 0.43816, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.44292, val loss: 0.43629, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.44117, val loss: 0.43444, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.43925, val loss: 0.43264, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.43757, val loss: 0.43084, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.43594, val loss: 0.42909, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.43436, val loss: 0.42742, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43252, val loss: 0.42569, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43073, val loss: 0.42402, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.42899, val loss: 0.42239, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.42730, val loss: 0.42081, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.42581, val loss: 0.41921, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.42436, val loss: 0.41768, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.42295, val loss: 0.41617, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.42159, val loss: 0.41471, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.42023, val loss: 0.41324, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.41894, val loss: 0.41185, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.41770, val loss: 0.41051, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41648, val loss: 0.40924, in 0.000s 1 tree, 106 leaves, max depth = 18, train loss: 0.41524, val loss: 0.40790, in 0.000s 1 tree, 159 leaves, max depth = 15, train loss: 0.41364, val loss: 0.40642, in 0.031s 1 tree, 106 leaves, max depth = 18, train loss: 0.41245, val loss: 0.40512, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.41089, val loss: 0.40368, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.40939, val loss: 0.40229, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40822, val loss: 0.40108, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.40676, val loss: 0.39972, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40563, val loss: 0.39855, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.40452, val loss: 0.39736, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.40310, val loss: 0.39604, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40198, val loss: 0.39488, in 0.016s Fit 91 trees in 1.830 s, (10912 total leaves) Time spent computing histograms: 0.555s Time spent finding best splits: 0.167s Time spent applying splits: 0.202s Time spent predicting: 0.000s Trial 20, Fold 2: Log loss = 0.4061719563790699, Average precision = 0.9421895019520601, ROC-AUC = 0.9427703713055864, Elapsed Time = 1.8393180000002758 seconds Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 20, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 99 leaves, max depth = 15, train loss: 0.68486, val loss: 0.68458, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.67682, val loss: 0.67626, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.66905, val loss: 0.66824, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.66155, val loss: 0.66049, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.65433, val loss: 0.65307, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.64736, val loss: 0.64591, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.64065, val loss: 0.63895, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.63417, val loss: 0.63224, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.62786, val loss: 0.62569, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.62168, val loss: 0.61932, in 0.016s 1 tree, 102 leaves, max depth = 20, train loss: 0.61582, val loss: 0.61326, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.61011, val loss: 0.60733, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.60460, val loss: 0.60167, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.59917, val loss: 0.59608, in 0.016s 1 tree, 102 leaves, max depth = 20, train loss: 0.59404, val loss: 0.59076, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.58903, val loss: 0.58553, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.58411, val loss: 0.58045, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.57941, val loss: 0.57557, in 0.016s 1 tree, 103 leaves, max depth = 20, train loss: 0.57489, val loss: 0.57087, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.57041, val loss: 0.56624, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.56607, val loss: 0.56176, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.56187, val loss: 0.55742, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.55739, val loss: 0.55329, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.55338, val loss: 0.54914, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.54955, val loss: 0.54515, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.54531, val loss: 0.54125, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.54121, val loss: 0.53748, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.53761, val loss: 0.53376, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.53368, val loss: 0.53015, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.52987, val loss: 0.52665, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.52649, val loss: 0.52312, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.52321, val loss: 0.51972, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.51996, val loss: 0.51634, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.51637, val loss: 0.51305, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.51289, val loss: 0.50987, in 0.031s 1 tree, 104 leaves, max depth = 15, train loss: 0.50989, val loss: 0.50671, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.50692, val loss: 0.50361, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.50360, val loss: 0.50058, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.50081, val loss: 0.49764, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.49810, val loss: 0.49482, in 0.031s 1 tree, 104 leaves, max depth = 15, train loss: 0.49548, val loss: 0.49204, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48918, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.48983, val loss: 0.48652, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.48681, val loss: 0.48378, in 0.016s [45/91] 1 tree, 159 leaves, max depth = 18, train loss: 0.48389, val loss: 0.48111, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.48104, val loss: 0.47853, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.47828, val loss: 0.47603, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.47591, val loss: 0.47353, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.47325, val loss: 0.47112, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.47067, val loss: 0.46878, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.46846, val loss: 0.46647, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.46596, val loss: 0.46421, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.46354, val loss: 0.46203, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.46141, val loss: 0.45977, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.45907, val loss: 0.45766, in 0.031s 1 tree, 159 leaves, max depth = 15, train loss: 0.45680, val loss: 0.45562, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.45459, val loss: 0.45364, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.45261, val loss: 0.45156, in 0.031s 1 tree, 159 leaves, max depth = 17, train loss: 0.45048, val loss: 0.44965, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.44859, val loss: 0.44761, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.44674, val loss: 0.44567, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.44469, val loss: 0.44383, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.44270, val loss: 0.44205, in 0.031s 1 tree, 105 leaves, max depth = 15, train loss: 0.44095, val loss: 0.44016, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43903, val loss: 0.43845, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.43735, val loss: 0.43663, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.43571, val loss: 0.43486, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.43407, val loss: 0.43310, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43222, val loss: 0.43146, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43043, val loss: 0.42986, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.42869, val loss: 0.42832, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.42700, val loss: 0.42682, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.42551, val loss: 0.42520, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.42406, val loss: 0.42364, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.42266, val loss: 0.42211, in 0.031s 1 tree, 104 leaves, max depth = 13, train loss: 0.42128, val loss: 0.42062, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.42000, val loss: 0.41943, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.41863, val loss: 0.41795, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.41735, val loss: 0.41656, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41612, val loss: 0.41541, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.41483, val loss: 0.41403, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.41323, val loss: 0.41262, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.41200, val loss: 0.41129, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.41045, val loss: 0.40993, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.40895, val loss: 0.40861, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40777, val loss: 0.40751, in 0.000s 1 tree, 159 leaves, max depth = 16, train loss: 0.40631, val loss: 0.40623, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40517, val loss: 0.40517, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.40404, val loss: 0.40394, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.40262, val loss: 0.40271, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.40150, val loss: 0.40149, in 0.016s Fit 91 trees in 1.955 s, (10896 total leaves) Time spent computing histograms: 0.608s Time spent finding best splits: 0.184s Time spent applying splits: 0.224s Time spent predicting: 0.063s Trial 20, Fold 3: Log loss = 0.40054153446053187, Average precision = 0.948156783525392, ROC-AUC = 0.9469457128651367, Elapsed Time = 1.969958299998325 seconds Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 20, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 96 leaves, max depth = 16, train loss: 0.68486, val loss: 0.68446, in 0.016s 1 tree, 102 leaves, max depth = 20, train loss: 0.67674, val loss: 0.67586, in 0.016s 1 tree, 102 leaves, max depth = 20, train loss: 0.66890, val loss: 0.66754, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.66138, val loss: 0.65964, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.65413, val loss: 0.65198, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.64716, val loss: 0.64458, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.64044, val loss: 0.63746, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.63394, val loss: 0.63057, in 0.000s 1 tree, 105 leaves, max depth = 13, train loss: 0.62762, val loss: 0.62386, in 0.031s 1 tree, 105 leaves, max depth = 20, train loss: 0.62144, val loss: 0.61727, in 0.000s 1 tree, 103 leaves, max depth = 19, train loss: 0.61556, val loss: 0.61098, in 0.016s 1 tree, 104 leaves, max depth = 19, train loss: 0.60981, val loss: 0.60492, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.60428, val loss: 0.59902, in 0.000s 1 tree, 105 leaves, max depth = 20, train loss: 0.59887, val loss: 0.59321, in 0.031s 1 tree, 104 leaves, max depth = 13, train loss: 0.59368, val loss: 0.58768, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.58867, val loss: 0.58231, in 0.000s 1 tree, 104 leaves, max depth = 19, train loss: 0.58376, val loss: 0.57702, in 0.000s 1 tree, 104 leaves, max depth = 18, train loss: 0.57903, val loss: 0.57201, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.57450, val loss: 0.56716, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.57003, val loss: 0.56233, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.56571, val loss: 0.55765, in 0.047s 1 tree, 105 leaves, max depth = 20, train loss: 0.56152, val loss: 0.55310, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.55746, val loss: 0.54870, in 0.063s 1 tree, 105 leaves, max depth = 20, train loss: 0.55352, val loss: 0.54443, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.54974, val loss: 0.54038, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.54552, val loss: 0.53628, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.54143, val loss: 0.53232, in 0.031s 1 tree, 103 leaves, max depth = 21, train loss: 0.53789, val loss: 0.52848, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.53397, val loss: 0.52468, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.53018, val loss: 0.52100, in 0.031s 1 tree, 103 leaves, max depth = 15, train loss: 0.52685, val loss: 0.51742, in 0.016s 1 tree, 103 leaves, max depth = 20, train loss: 0.52361, val loss: 0.51390, in 0.016s 1 tree, 104 leaves, max depth = 20, train loss: 0.52042, val loss: 0.51041, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.51685, val loss: 0.50696, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.51339, val loss: 0.50361, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.51041, val loss: 0.50040, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.50750, val loss: 0.49720, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.50420, val loss: 0.49401, in 0.031s 1 tree, 104 leaves, max depth = 21, train loss: 0.50144, val loss: 0.49103, in 0.016s 1 tree, 103 leaves, max depth = 22, train loss: 0.49877, val loss: 0.48810, in 0.031s 1 tree, 104 leaves, max depth = 21, train loss: 0.49616, val loss: 0.48529, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.49304, val loss: 0.48228, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.49055, val loss: 0.47959, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.48755, val loss: 0.47670, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.48464, val loss: 0.47389, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.48181, val loss: 0.47117, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.47907, val loss: 0.46853, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.47674, val loss: 0.46594, in 0.016s 1 tree, 159 leaves, max depth = 19, train loss: 0.47410, val loss: 0.46340, in 0.031s 1 tree, 159 leaves, max depth = 19, train loss: 0.47153, val loss: 0.46093, in 0.016s 1 tree, 103 leaves, max depth = 20, train loss: 0.46935, val loss: 0.45852, in 0.016s 1 tree, 159 leaves, max depth = 19, train loss: 0.46688, val loss: 0.45614, in 0.031s 1 tree, 159 leaves, max depth = 19, train loss: 0.46447, val loss: 0.45383, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.46237, val loss: 0.45149, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.46006, val loss: 0.44927, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.45780, val loss: 0.44710, in 0.031s 1 tree, 159 leaves, max depth = 21, train loss: 0.45561, val loss: 0.44501, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.45360, val loss: 0.44281, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.45149, val loss: 0.44079, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.44961, val loss: 0.43871, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.44778, val loss: 0.43668, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.44576, val loss: 0.43474, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.44379, val loss: 0.43286, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.44199, val loss: 0.43089, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.44009, val loss: 0.42908, in 0.031s 1 tree, 103 leaves, max depth = 17, train loss: 0.43841, val loss: 0.42720, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.43672, val loss: 0.42536, in 0.031s 1 tree, 104 leaves, max depth = 16, train loss: 0.43515, val loss: 0.42360, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.43333, val loss: 0.42186, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.43156, val loss: 0.42018, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.42985, val loss: 0.41855, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.42818, val loss: 0.41696, in 0.031s 1 tree, 103 leaves, max depth = 17, train loss: 0.42668, val loss: 0.41528, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.42523, val loss: 0.41367, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.42382, val loss: 0.41209, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.42246, val loss: 0.41057, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.42111, val loss: 0.40902, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.41977, val loss: 0.40755, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.41852, val loss: 0.40613, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41730, val loss: 0.40482, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.41607, val loss: 0.40341, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.41449, val loss: 0.40192, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.41331, val loss: 0.40056, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.41178, val loss: 0.39911, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.41029, val loss: 0.39771, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40913, val loss: 0.39646, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.40769, val loss: 0.39510, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40656, val loss: 0.39389, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.40545, val loss: 0.39261, in 0.031s 1 tree, 159 leaves, max depth = 20, train loss: 0.40405, val loss: 0.39130, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.40298, val loss: 0.39005, in 0.016s Fit 91 trees in 2.221 s, (10918 total leaves) Time spent computing histograms: 0.679s Time spent finding best splits: 0.226s Time spent applying splits: 0.279s Time spent predicting: 0.000s Trial 20, Fold 4: Log loss = 0.40511396654818094, Average precision = 0.9467197122369408, ROC-AUC = 0.9438916339406426, Elapsed Time = 2.220715299999938 seconds Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 20, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 102 leaves, max depth = 13, train loss: 0.68473, val loss: 0.68423, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.67653, val loss: 0.67551, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.66861, val loss: 0.66709, in 0.016s 1 tree, 101 leaves, max depth = 14, train loss: 0.66105, val loss: 0.65905, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.65374, val loss: 0.65127, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.64667, val loss: 0.64375, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.63986, val loss: 0.63650, in 0.016s 1 tree, 103 leaves, max depth = 18, train loss: 0.63328, val loss: 0.62949, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.62689, val loss: 0.62263, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.62065, val loss: 0.61595, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.61469, val loss: 0.60952, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.60891, val loss: 0.60330, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.60333, val loss: 0.59727, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.59785, val loss: 0.59138, in 0.016s 1 tree, 100 leaves, max depth = 20, train loss: 0.59263, val loss: 0.58579, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.58756, val loss: 0.58030, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.58258, val loss: 0.57494, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.57783, val loss: 0.56982, in 0.016s 1 tree, 103 leaves, max depth = 18, train loss: 0.57324, val loss: 0.56487, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.56871, val loss: 0.55997, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.56433, val loss: 0.55523, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.56008, val loss: 0.55062, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.55597, val loss: 0.54615, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.55198, val loss: 0.54182, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.54816, val loss: 0.53768, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.54406, val loss: 0.53379, in 0.016s 1 tree, 158 leaves, max depth = 17, train loss: 0.54008, val loss: 0.53004, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.53649, val loss: 0.52613, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.53267, val loss: 0.52254, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.52898, val loss: 0.51906, in 0.031s 1 tree, 102 leaves, max depth = 15, train loss: 0.52559, val loss: 0.51536, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.52231, val loss: 0.51174, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.51907, val loss: 0.50820, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.51560, val loss: 0.50494, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.51223, val loss: 0.50179, in 0.031s 1 tree, 103 leaves, max depth = 18, train loss: 0.50922, val loss: 0.49850, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.50625, val loss: 0.49523, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.50304, val loss: 0.49224, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.50024, val loss: 0.48916, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.49752, val loss: 0.48617, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.49489, val loss: 0.48326, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.49185, val loss: 0.48044, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.48933, val loss: 0.47765, in 0.016s 1 tree, 158 leaves, max depth = 17, train loss: 0.48640, val loss: 0.47495, in 0.031s 1 tree, 159 leaves, max depth = 17, train loss: 0.48357, val loss: 0.47232, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.48082, val loss: 0.46978, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.47815, val loss: 0.46732, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.47576, val loss: 0.46468, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.47319, val loss: 0.46231, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.47069, val loss: 0.46001, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.46847, val loss: 0.45754, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.46606, val loss: 0.45534, in 0.031s 1 tree, 159 leaves, max depth = 17, train loss: 0.46372, val loss: 0.45320, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.46157, val loss: 0.45080, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45931, val loss: 0.44874, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.45712, val loss: 0.44674, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45500, val loss: 0.44481, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.45300, val loss: 0.44258, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45095, val loss: 0.44071, in 0.031s 1 tree, 103 leaves, max depth = 16, train loss: 0.44903, val loss: 0.43857, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.44716, val loss: 0.43648, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.44519, val loss: 0.43470, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.44327, val loss: 0.43297, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.44149, val loss: 0.43099, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.43964, val loss: 0.42932, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.43792, val loss: 0.42739, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.43625, val loss: 0.42551, in 0.016s 1 tree, 103 leaves, max depth = 17, train loss: 0.43464, val loss: 0.42369, in 0.000s 1 tree, 159 leaves, max depth = 16, train loss: 0.43287, val loss: 0.42211, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43114, val loss: 0.42057, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.42947, val loss: 0.41907, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.42784, val loss: 0.41763, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.42631, val loss: 0.41589, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.42483, val loss: 0.41423, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42339, val loss: 0.41259, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42199, val loss: 0.41100, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.42059, val loss: 0.40942, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.41928, val loss: 0.40792, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.41800, val loss: 0.40645, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41678, val loss: 0.40527, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.41550, val loss: 0.40382, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.41397, val loss: 0.40247, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.41274, val loss: 0.40107, in 0.016s 1 tree, 159 leaves, max depth = 19, train loss: 0.41124, val loss: 0.39977, in 0.031s 1 tree, 159 leaves, max depth = 17, train loss: 0.40980, val loss: 0.39850, in 0.063s 1 tree, 5 leaves, max depth = 3, train loss: 0.40863, val loss: 0.39738, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.40723, val loss: 0.39616, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40610, val loss: 0.39508, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.40494, val loss: 0.39375, in 0.031s 1 tree, 159 leaves, max depth = 19, train loss: 0.40358, val loss: 0.39258, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.40246, val loss: 0.39130, in 0.031s Fit 91 trees in 2.017 s, (10928 total leaves) Time spent computing histograms: 0.633s Time spent finding best splits: 0.190s Time spent applying splits: 0.236s Time spent predicting: 0.031s Trial 20, Fold 5: Log loss = 0.41079164018892395, Average precision = 0.9446465371422839, ROC-AUC = 0.941807182802891, Elapsed Time = 2.021551100000579 seconds
Optimization Progress: 21%|##1 | 21/100 [04:10<16:19, 12.40s/it]
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 21, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 8, train loss: 0.67808, val loss: 0.67764, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.66413, val loss: 0.66327, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.65103, val loss: 0.64976, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.63883, val loss: 0.63723, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.62725, val loss: 0.62526, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.61634, val loss: 0.61397, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.60617, val loss: 0.60351, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.59648, val loss: 0.59347, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.58734, val loss: 0.58398, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57871, val loss: 0.57502, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.57057, val loss: 0.56655, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56287, val loss: 0.55853, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.55573, val loss: 0.55108, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.54884, val loss: 0.54389, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.54233, val loss: 0.53709, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.53543, val loss: 0.53057, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.52942, val loss: 0.52428, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.52301, val loss: 0.51826, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51758, val loss: 0.51255, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.51162, val loss: 0.50695, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.50598, val loss: 0.50168, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.50064, val loss: 0.49668, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49559, val loss: 0.49197, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49077, val loss: 0.48687, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.48604, val loss: 0.48246, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.48164, val loss: 0.47778, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.47723, val loss: 0.47370, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47313, val loss: 0.46935, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.46899, val loss: 0.46552, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46509, val loss: 0.46137, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46143, val loss: 0.45750, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45760, val loss: 0.45397, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45415, val loss: 0.45027, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.45054, val loss: 0.44694, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.44712, val loss: 0.44379, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.44388, val loss: 0.44082, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.44081, val loss: 0.43759, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.43776, val loss: 0.43480, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.43479, val loss: 0.43167, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43192, val loss: 0.42909, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.42922, val loss: 0.42617, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42651, val loss: 0.42372, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.42396, val loss: 0.42098, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42141, val loss: 0.41871, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.41899, val loss: 0.41654, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.41662, val loss: 0.41397, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41433, val loss: 0.41191, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.41213, val loss: 0.40956, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40994, val loss: 0.40714, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.40778, val loss: 0.40519, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.40565, val loss: 0.40294, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40361, val loss: 0.40115, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.40165, val loss: 0.39903, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.39979, val loss: 0.39700, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.39785, val loss: 0.39529, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39584, val loss: 0.39306, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.39400, val loss: 0.39145, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39210, val loss: 0.38933, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39030, val loss: 0.38733, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38859, val loss: 0.38542, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38697, val loss: 0.38361, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.38522, val loss: 0.38211, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38356, val loss: 0.38070, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38202, val loss: 0.37897, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38057, val loss: 0.37733, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.37887, val loss: 0.37556, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37749, val loss: 0.37400, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.37593, val loss: 0.37237, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.37437, val loss: 0.37115, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37306, val loss: 0.36967, in 0.000s Fit 70 trees in 0.736 s, (1070 total leaves) Time spent computing histograms: 0.281s Time spent finding best splits: 0.029s Time spent applying splits: 0.023s Time spent predicting: 0.000s Trial 21, Fold 1: Log loss = 0.374868091326567, Average precision = 0.9452746861639605, ROC-AUC = 0.9428602346539338, Elapsed Time = 0.7437253999996756 seconds Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 21, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 8, train loss: 0.67826, val loss: 0.67752, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.66446, val loss: 0.66307, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.65141, val loss: 0.64934, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.63924, val loss: 0.63656, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.62769, val loss: 0.62439, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.61683, val loss: 0.61291, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.60668, val loss: 0.60222, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.59703, val loss: 0.59201, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.58793, val loss: 0.58236, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57934, val loss: 0.57325, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.57123, val loss: 0.56465, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.56364, val loss: 0.55660, in 0.000s 1 tree, 17 leaves, max depth = 12, train loss: 0.55648, val loss: 0.54903, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.54969, val loss: 0.54182, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.54321, val loss: 0.53489, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.53621, val loss: 0.52810, in 0.016s 1 tree, 17 leaves, max depth = 12, train loss: 0.53031, val loss: 0.52184, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.52381, val loss: 0.51553, in 0.000s 1 tree, 17 leaves, max depth = 12, train loss: 0.51836, val loss: 0.50974, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.51231, val loss: 0.50388, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.50658, val loss: 0.49834, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.50117, val loss: 0.49311, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.49604, val loss: 0.48816, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49132, val loss: 0.48310, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48656, val loss: 0.47856, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.48216, val loss: 0.47386, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.47767, val loss: 0.46954, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.47358, val loss: 0.46518, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46941, val loss: 0.46122, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46543, val loss: 0.45739, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.46168, val loss: 0.45335, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45793, val loss: 0.44978, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.45443, val loss: 0.44604, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45091, val loss: 0.44268, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.44764, val loss: 0.43917, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.44433, val loss: 0.43603, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.44121, val loss: 0.43310, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.43819, val loss: 0.42986, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.43533, val loss: 0.42679, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.43239, val loss: 0.42397, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.42971, val loss: 0.42109, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42692, val loss: 0.41846, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.42441, val loss: 0.41572, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.42178, val loss: 0.41321, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41931, val loss: 0.41089, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.41693, val loss: 0.40865, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41464, val loss: 0.40612, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.41239, val loss: 0.40400, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41026, val loss: 0.40179, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.40812, val loss: 0.39943, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.40599, val loss: 0.39745, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.40399, val loss: 0.39523, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.40197, val loss: 0.39336, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.40005, val loss: 0.39126, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.39814, val loss: 0.38948, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39619, val loss: 0.38746, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.39437, val loss: 0.38578, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39252, val loss: 0.38386, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39077, val loss: 0.38204, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38911, val loss: 0.38030, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38754, val loss: 0.37866, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.38581, val loss: 0.37708, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38406, val loss: 0.37518, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38257, val loss: 0.37362, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38092, val loss: 0.37212, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37951, val loss: 0.37064, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.37783, val loss: 0.36885, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37649, val loss: 0.36744, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.37492, val loss: 0.36602, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37337, val loss: 0.36434, in 0.000s Fit 70 trees in 0.768 s, (1082 total leaves) Time spent computing histograms: 0.291s Time spent finding best splits: 0.032s Time spent applying splits: 0.025s Time spent predicting: 0.000s Trial 21, Fold 2: Log loss = 0.3752575024880809, Average precision = 0.9417755109207956, ROC-AUC = 0.9443603087278346, Elapsed Time = 0.7794021999998222 seconds Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 21, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.141 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 8, train loss: 0.67827, val loss: 0.67781, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.66471, val loss: 0.66373, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.65184, val loss: 0.65036, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.63975, val loss: 0.63779, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.62838, val loss: 0.62596, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.61755, val loss: 0.61477, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.60747, val loss: 0.60425, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.59800, val loss: 0.59445, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58903, val loss: 0.58508, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58045, val loss: 0.57619, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.57255, val loss: 0.56790, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.56489, val loss: 0.55994, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.55775, val loss: 0.55245, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55090, val loss: 0.54533, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.54442, val loss: 0.53860, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.53733, val loss: 0.53192, in 0.000s 1 tree, 17 leaves, max depth = 12, train loss: 0.53149, val loss: 0.52576, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.52490, val loss: 0.51956, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.51948, val loss: 0.51389, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.51335, val loss: 0.50813, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50755, val loss: 0.50269, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.50205, val loss: 0.49756, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49685, val loss: 0.49272, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.49219, val loss: 0.48775, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48733, val loss: 0.48321, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.48272, val loss: 0.47894, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47843, val loss: 0.47440, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.47441, val loss: 0.47010, in 0.016s [29/70] 1 tree, 17 leaves, max depth = 7, train loss: 0.47015, val loss: 0.46615, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46610, val loss: 0.46241, in 0.016s [31/70] 1 tree, 17 leaves, max depth = 9, train loss: 0.46243, val loss: 0.45848, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45863, val loss: 0.45498, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.45516, val loss: 0.45132, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45158, val loss: 0.44804, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.44818, val loss: 0.44494, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.44499, val loss: 0.44156, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.44179, val loss: 0.43864, in 0.016s 1 tree, 17 leaves, max depth = 12, train loss: 0.43880, val loss: 0.43545, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.43596, val loss: 0.43242, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43296, val loss: 0.42970, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.43026, val loss: 0.42675, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42744, val loss: 0.42419, in 0.000s 1 tree, 17 leaves, max depth = 12, train loss: 0.42494, val loss: 0.42152, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42227, val loss: 0.41911, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.41973, val loss: 0.41682, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41732, val loss: 0.41465, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.41507, val loss: 0.41219, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41279, val loss: 0.41015, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41057, val loss: 0.40809, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40841, val loss: 0.40617, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.40627, val loss: 0.40382, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40422, val loss: 0.40201, in 0.000s 1 tree, 17 leaves, max depth = 12, train loss: 0.40227, val loss: 0.39987, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.40040, val loss: 0.39779, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.39846, val loss: 0.39609, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39643, val loss: 0.39420, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.39459, val loss: 0.39260, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39267, val loss: 0.39081, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39085, val loss: 0.38912, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38912, val loss: 0.38751, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38749, val loss: 0.38599, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.38573, val loss: 0.38448, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38406, val loss: 0.38304, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38251, val loss: 0.38159, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38103, val loss: 0.38022, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37963, val loss: 0.37892, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.37792, val loss: 0.37706, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.37635, val loss: 0.37532, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.37475, val loss: 0.37396, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37342, val loss: 0.37272, in 0.016s Fit 70 trees in 0.829 s, (1070 total leaves) Time spent computing histograms: 0.313s Time spent finding best splits: 0.032s Time spent applying splits: 0.026s Time spent predicting: 0.000s Trial 21, Fold 3: Log loss = 0.3701317586291271, Average precision = 0.9496235379991602, ROC-AUC = 0.9494132893837101, Elapsed Time = 0.8211824000009074 seconds Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 21, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 7, train loss: 0.67830, val loss: 0.67744, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.66447, val loss: 0.66278, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.65149, val loss: 0.64898, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.63937, val loss: 0.63615, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.62790, val loss: 0.62391, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.61709, val loss: 0.61236, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.60699, val loss: 0.60160, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.59739, val loss: 0.59130, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.58834, val loss: 0.58157, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.57980, val loss: 0.57236, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.57174, val loss: 0.56365, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.56412, val loss: 0.55541, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.55699, val loss: 0.54765, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.55018, val loss: 0.54024, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.54374, val loss: 0.53323, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.53663, val loss: 0.52625, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.53070, val loss: 0.51976, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.52409, val loss: 0.51329, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.51867, val loss: 0.50732, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.51253, val loss: 0.50132, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50671, val loss: 0.49561, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.50120, val loss: 0.49021, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49599, val loss: 0.48511, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.49128, val loss: 0.47999, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48641, val loss: 0.47523, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.48179, val loss: 0.47070, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47751, val loss: 0.46601, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.47346, val loss: 0.46149, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46919, val loss: 0.45732, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46513, val loss: 0.45336, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46140, val loss: 0.44921, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45760, val loss: 0.44550, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.45414, val loss: 0.44164, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45056, val loss: 0.43818, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44716, val loss: 0.43487, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.44398, val loss: 0.43130, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.44078, val loss: 0.42820, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.43781, val loss: 0.42487, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.43489, val loss: 0.42161, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.43190, val loss: 0.41872, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42926, val loss: 0.41574, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42644, val loss: 0.41302, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42396, val loss: 0.41018, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.42130, val loss: 0.40760, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.41877, val loss: 0.40517, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41636, val loss: 0.40285, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.41410, val loss: 0.40029, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41183, val loss: 0.39807, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40969, val loss: 0.39577, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.40753, val loss: 0.39368, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.40542, val loss: 0.39127, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.40337, val loss: 0.38930, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.40140, val loss: 0.38702, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.39952, val loss: 0.38484, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39758, val loss: 0.38298, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39561, val loss: 0.38086, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39377, val loss: 0.37909, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39191, val loss: 0.37708, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39015, val loss: 0.37518, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38848, val loss: 0.37337, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38690, val loss: 0.37166, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38515, val loss: 0.36999, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.38347, val loss: 0.36839, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38197, val loss: 0.36676, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38054, val loss: 0.36522, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37883, val loss: 0.36328, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37748, val loss: 0.36182, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.37591, val loss: 0.36000, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37430, val loss: 0.35849, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37302, val loss: 0.35710, in 0.016s Fit 70 trees in 0.830 s, (1070 total leaves) Time spent computing histograms: 0.304s Time spent finding best splits: 0.032s Time spent applying splits: 0.026s Time spent predicting: 0.000s Trial 21, Fold 4: Log loss = 0.3720529426817586, Average precision = 0.948352786118597, ROC-AUC = 0.9466053903159581, Elapsed Time = 0.8287339999988035 seconds Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 21, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 8, train loss: 0.67829, val loss: 0.67724, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.66452, val loss: 0.66246, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.65138, val loss: 0.64845, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.63921, val loss: 0.63531, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.62759, val loss: 0.62286, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.61665, val loss: 0.61113, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.60649, val loss: 0.60012, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.59677, val loss: 0.58961, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.58760, val loss: 0.57970, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.57894, val loss: 0.57032, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.57077, val loss: 0.56146, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.56316, val loss: 0.55310, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.55597, val loss: 0.54518, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.54905, val loss: 0.53759, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.54251, val loss: 0.53042, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.53552, val loss: 0.52372, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.52949, val loss: 0.51706, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.52301, val loss: 0.51087, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51753, val loss: 0.50481, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.51150, val loss: 0.49908, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50578, val loss: 0.49365, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.50038, val loss: 0.48851, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49526, val loss: 0.48366, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.49050, val loss: 0.47837, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.48573, val loss: 0.47386, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48129, val loss: 0.46892, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47682, val loss: 0.46472, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.47269, val loss: 0.46010, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46850, val loss: 0.45616, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.46465, val loss: 0.45185, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.46101, val loss: 0.44778, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45712, val loss: 0.44415, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.45372, val loss: 0.44031, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45006, val loss: 0.43690, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.44658, val loss: 0.43367, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.44328, val loss: 0.43061, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.44022, val loss: 0.42714, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43711, val loss: 0.42427, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.43423, val loss: 0.42098, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43130, val loss: 0.41828, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.42859, val loss: 0.41520, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42582, val loss: 0.41266, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.42329, val loss: 0.40976, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42067, val loss: 0.40738, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.41817, val loss: 0.40513, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.41582, val loss: 0.40242, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.41346, val loss: 0.40030, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.41121, val loss: 0.39828, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40906, val loss: 0.39620, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.40690, val loss: 0.39376, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.40477, val loss: 0.39182, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.40275, val loss: 0.38947, in 0.047s 1 tree, 17 leaves, max depth = 8, train loss: 0.40073, val loss: 0.38765, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39880, val loss: 0.38540, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39688, val loss: 0.38367, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39490, val loss: 0.38177, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.39307, val loss: 0.38014, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39120, val loss: 0.37834, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38942, val loss: 0.37664, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38774, val loss: 0.37503, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38615, val loss: 0.37350, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.38440, val loss: 0.37195, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.38266, val loss: 0.36990, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38116, val loss: 0.36846, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.37949, val loss: 0.36698, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37806, val loss: 0.36561, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.37639, val loss: 0.36368, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37504, val loss: 0.36238, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.37345, val loss: 0.36099, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.37188, val loss: 0.35917, in 0.000s Fit 70 trees in 0.892 s, (1082 total leaves) Time spent computing histograms: 0.331s Time spent finding best splits: 0.038s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 21, Fold 5: Log loss = 0.37708270831005164, Average precision = 0.9454919453985955, ROC-AUC = 0.9430249521579993, Elapsed Time = 0.8994215000002441 seconds
Optimization Progress: 22%|##2 | 22/100 [04:21<15:25, 11.86s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 22, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 11, train loss: 0.66298, val loss: 0.66325, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.63598, val loss: 0.63648, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.61156, val loss: 0.61214, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.59031, val loss: 0.59090, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.57004, val loss: 0.57076, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.55147, val loss: 0.55217, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.53410, val loss: 0.53461, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.51818, val loss: 0.51851, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.50361, val loss: 0.50378, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.49016, val loss: 0.49020, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.47775, val loss: 0.47766, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.46631, val loss: 0.46614, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.45572, val loss: 0.45536, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.44593, val loss: 0.44558, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.43681, val loss: 0.43644, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.42369, val loss: 0.42385, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.41572, val loss: 0.41585, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.40408, val loss: 0.40468, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.39707, val loss: 0.39776, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.38678, val loss: 0.38794, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.38060, val loss: 0.38185, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.37148, val loss: 0.37323, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.36600, val loss: 0.36783, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.36089, val loss: 0.36290, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.35341, val loss: 0.35553, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.34659, val loss: 0.34879, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.34034, val loss: 0.34263, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.33428, val loss: 0.33721, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.32888, val loss: 0.33187, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.32410, val loss: 0.32731, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.31893, val loss: 0.32273, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.31438, val loss: 0.31823, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.31018, val loss: 0.31424, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.30577, val loss: 0.31037, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.30192, val loss: 0.30656, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.29789, val loss: 0.30251, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.29413, val loss: 0.29926, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.29084, val loss: 0.29601, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.28726, val loss: 0.29245, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.28403, val loss: 0.28970, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.28123, val loss: 0.28694, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.27825, val loss: 0.28410, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.27546, val loss: 0.28176, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.27252, val loss: 0.27874, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.27001, val loss: 0.27664, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.26779, val loss: 0.27445, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.26573, val loss: 0.27247, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.26311, val loss: 0.26984, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.26101, val loss: 0.26806, in 0.016s Fit 49 trees in 1.127 s, (2350 total leaves) Time spent computing histograms: 0.317s Time spent finding best splits: 0.069s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 22, Fold 1: Log loss = 0.2685903231207794, Average precision = 0.9631391826305978, ROC-AUC = 0.9585063694001137, Elapsed Time = 1.1325500999992073 seconds Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 22, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 10, train loss: 0.66256, val loss: 0.66211, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.63534, val loss: 0.63440, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.61048, val loss: 0.60922, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.58929, val loss: 0.58780, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.56878, val loss: 0.56697, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.54985, val loss: 0.54777, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.53252, val loss: 0.53025, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.51664, val loss: 0.51419, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.50203, val loss: 0.49938, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.48862, val loss: 0.48574, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.47624, val loss: 0.47313, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.46481, val loss: 0.46151, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.45427, val loss: 0.45082, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.44450, val loss: 0.44090, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.43091, val loss: 0.42745, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.42238, val loss: 0.41882, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.41448, val loss: 0.41078, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.40304, val loss: 0.39959, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.39610, val loss: 0.39252, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.38602, val loss: 0.38265, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.37669, val loss: 0.37346, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.37085, val loss: 0.36759, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.36546, val loss: 0.36231, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.35748, val loss: 0.35458, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.35262, val loss: 0.34966, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.34589, val loss: 0.34315, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.33971, val loss: 0.33715, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.33374, val loss: 0.33164, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.32840, val loss: 0.32647, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.32355, val loss: 0.32177, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.31849, val loss: 0.31711, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.31398, val loss: 0.31275, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.30971, val loss: 0.30860, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.30537, val loss: 0.30460, in 0.016s 1 tree, 48 leaves, max depth = 18, train loss: 0.30156, val loss: 0.30092, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.29784, val loss: 0.29724, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.29413, val loss: 0.29384, in 0.016s 1 tree, 48 leaves, max depth = 19, train loss: 0.29090, val loss: 0.29073, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.28749, val loss: 0.28747, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.28431, val loss: 0.28459, in 0.016s 1 tree, 48 leaves, max depth = 22, train loss: 0.28154, val loss: 0.28190, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.27849, val loss: 0.27898, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.27574, val loss: 0.27649, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.27338, val loss: 0.27422, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.27072, val loss: 0.27165, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.26834, val loss: 0.26950, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.26632, val loss: 0.26759, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.26421, val loss: 0.26571, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.26242, val loss: 0.26399, in 0.016s Fit 49 trees in 1.221 s, (2352 total leaves) Time spent computing histograms: 0.351s Time spent finding best splits: 0.074s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 22, Fold 2: Log loss = 0.26610610846642424, Average precision = 0.9634199782399377, ROC-AUC = 0.960638900666203, Elapsed Time = 1.2277723000006517 seconds Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 22, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 12, train loss: 0.66322, val loss: 0.66334, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.63585, val loss: 0.63615, in 0.016s 1 tree, 48 leaves, max depth = 8, train loss: 0.61153, val loss: 0.61171, in 0.031s 1 tree, 48 leaves, max depth = 8, train loss: 0.58904, val loss: 0.58938, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.56858, val loss: 0.56905, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.54991, val loss: 0.55048, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.53351, val loss: 0.53414, in 0.031s 1 tree, 48 leaves, max depth = 9, train loss: 0.51787, val loss: 0.51852, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.50330, val loss: 0.50410, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.48989, val loss: 0.49086, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.47757, val loss: 0.47859, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.46616, val loss: 0.46729, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.45561, val loss: 0.45676, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.44584, val loss: 0.44707, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.43209, val loss: 0.43434, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.42358, val loss: 0.42593, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.41564, val loss: 0.41810, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.40399, val loss: 0.40739, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.39333, val loss: 0.39761, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.38673, val loss: 0.39117, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.37729, val loss: 0.38253, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.37147, val loss: 0.37676, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.36309, val loss: 0.36917, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.35789, val loss: 0.36400, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.35048, val loss: 0.35747, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.34394, val loss: 0.35150, in 0.028s 1 tree, 47 leaves, max depth = 14, train loss: 0.33795, val loss: 0.34605, in 0.004s 1 tree, 48 leaves, max depth = 15, train loss: 0.33289, val loss: 0.34080, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.32712, val loss: 0.33591, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.32209, val loss: 0.33135, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.31766, val loss: 0.32673, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.31274, val loss: 0.32259, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.30848, val loss: 0.31878, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.30459, val loss: 0.31476, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.30038, val loss: 0.31122, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.29649, val loss: 0.30794, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.29306, val loss: 0.30488, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.28972, val loss: 0.30139, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.28636, val loss: 0.29851, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.28346, val loss: 0.29593, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.28024, val loss: 0.29230, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.27735, val loss: 0.28991, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.27487, val loss: 0.28771, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.27230, val loss: 0.28560, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.27009, val loss: 0.28369, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.26733, val loss: 0.28056, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.26537, val loss: 0.27883, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.26301, val loss: 0.27628, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.26091, val loss: 0.27460, in 0.016s Fit 49 trees in 1.268 s, (2348 total leaves) Time spent computing histograms: 0.353s Time spent finding best splits: 0.077s Time spent applying splits: 0.051s Time spent predicting: 0.016s Trial 22, Fold 3: Log loss = 0.2634322686868408, Average precision = 0.9640817460306497, ROC-AUC = 0.9609534514017972, Elapsed Time = 1.279824600000211 seconds Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 22, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 11, train loss: 0.66321, val loss: 0.66211, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.63633, val loss: 0.63405, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.61205, val loss: 0.60880, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.59116, val loss: 0.58710, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.57097, val loss: 0.56607, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.55206, val loss: 0.54636, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.53478, val loss: 0.52829, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.51894, val loss: 0.51173, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.50438, val loss: 0.49650, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.49180, val loss: 0.48327, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.47950, val loss: 0.47023, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.46798, val loss: 0.45819, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.45734, val loss: 0.44703, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.44749, val loss: 0.43668, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.43837, val loss: 0.42708, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.42536, val loss: 0.41391, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.41739, val loss: 0.40548, in 0.031s 1 tree, 48 leaves, max depth = 14, train loss: 0.40595, val loss: 0.39390, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.39896, val loss: 0.38648, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.38885, val loss: 0.37627, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.38269, val loss: 0.36967, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.37373, val loss: 0.36062, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.36838, val loss: 0.35498, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.36326, val loss: 0.34953, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.35586, val loss: 0.34188, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.34910, val loss: 0.33492, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.34291, val loss: 0.32850, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.33782, val loss: 0.32340, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.33192, val loss: 0.31791, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.32673, val loss: 0.31251, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.32227, val loss: 0.30803, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.31785, val loss: 0.30341, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.31297, val loss: 0.29893, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.30872, val loss: 0.29452, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.30444, val loss: 0.29062, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.30049, val loss: 0.28703, in 0.031s 1 tree, 48 leaves, max depth = 14, train loss: 0.29697, val loss: 0.28339, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.29348, val loss: 0.28023, in 0.031s 1 tree, 48 leaves, max depth = 13, train loss: 0.29038, val loss: 0.27699, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.28691, val loss: 0.27332, in 0.032s 1 tree, 48 leaves, max depth = 13, train loss: 0.28392, val loss: 0.27063, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.28127, val loss: 0.26784, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.27818, val loss: 0.26452, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.27560, val loss: 0.26221, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.27334, val loss: 0.25982, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.27073, val loss: 0.25724, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.26871, val loss: 0.25510, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.26613, val loss: 0.25235, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.26404, val loss: 0.25051, in 0.031s Fit 49 trees in 1.377 s, (2352 total leaves) Time spent computing histograms: 0.393s Time spent finding best splits: 0.091s Time spent applying splits: 0.061s Time spent predicting: 0.000s Trial 22, Fold 4: Log loss = 0.2667903100689803, Average precision = 0.9642265018935711, ROC-AUC = 0.9601925023426827, Elapsed Time = 1.3921172999998817 seconds Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 22, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 14, train loss: 0.66293, val loss: 0.66200, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.63540, val loss: 0.63352, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.61051, val loss: 0.60776, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.58918, val loss: 0.58564, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.56846, val loss: 0.56419, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.54957, val loss: 0.54461, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.53228, val loss: 0.52670, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.51643, val loss: 0.51028, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.50187, val loss: 0.49512, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.48916, val loss: 0.48194, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.47670, val loss: 0.46903, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.46519, val loss: 0.45709, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.45456, val loss: 0.44609, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.44472, val loss: 0.43584, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.43559, val loss: 0.42640, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.42252, val loss: 0.41342, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.41452, val loss: 0.40521, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.40301, val loss: 0.39379, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.39598, val loss: 0.38658, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.38580, val loss: 0.37649, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.37959, val loss: 0.37013, in 0.031s 1 tree, 48 leaves, max depth = 15, train loss: 0.37057, val loss: 0.36122, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.36507, val loss: 0.35558, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.35994, val loss: 0.35036, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.35252, val loss: 0.34274, in 0.016s 1 tree, 48 leaves, max depth = 8, train loss: 0.34798, val loss: 0.33825, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.34136, val loss: 0.33158, in 0.016s 1 tree, 46 leaves, max depth = 15, train loss: 0.33531, val loss: 0.32546, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.32939, val loss: 0.32014, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.32416, val loss: 0.31481, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.31955, val loss: 0.31025, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.31452, val loss: 0.30578, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.31012, val loss: 0.30134, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.30590, val loss: 0.29708, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.30161, val loss: 0.29318, in 0.031s 1 tree, 46 leaves, max depth = 13, train loss: 0.29788, val loss: 0.28928, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.29444, val loss: 0.28567, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.29077, val loss: 0.28200, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.28725, val loss: 0.27887, in 0.031s 1 tree, 48 leaves, max depth = 14, train loss: 0.28398, val loss: 0.27606, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.28120, val loss: 0.27314, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.27809, val loss: 0.27016, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.27528, val loss: 0.26767, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.27267, val loss: 0.26547, in 0.031s 1 tree, 47 leaves, max depth = 11, train loss: 0.27036, val loss: 0.26309, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.26754, val loss: 0.26026, in 0.031s 1 tree, 48 leaves, max depth = 15, train loss: 0.26548, val loss: 0.25812, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.26289, val loss: 0.25549, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.26078, val loss: 0.25368, in 0.031s Fit 49 trees in 1.298 s, (2340 total leaves) Time spent computing histograms: 0.360s Time spent finding best splits: 0.078s Time spent applying splits: 0.053s Time spent predicting: 0.000s Trial 22, Fold 5: Log loss = 0.27211823887263314, Average precision = 0.9607680900911164, ROC-AUC = 0.9565815357746688, Elapsed Time = 1.3137683000004472 seconds
Optimization Progress: 23%|##3 | 23/100 [04:34<15:40, 12.21s/it]
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 23, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 15, train loss: 0.67715, val loss: 0.67667, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.66222, val loss: 0.66127, in 0.000s 1 tree, 51 leaves, max depth = 15, train loss: 0.64827, val loss: 0.64687, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.63522, val loss: 0.63339, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.62300, val loss: 0.62075, in 0.000s 1 tree, 50 leaves, max depth = 15, train loss: 0.61154, val loss: 0.60888, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.60079, val loss: 0.59774, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.59070, val loss: 0.58727, in 0.000s 1 tree, 50 leaves, max depth = 15, train loss: 0.58122, val loss: 0.57742, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.57231, val loss: 0.56815, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.56392, val loss: 0.55942, in 0.000s 1 tree, 50 leaves, max depth = 14, train loss: 0.55603, val loss: 0.55121, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.54798, val loss: 0.54375, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.54076, val loss: 0.53621, in 0.000s 1 tree, 51 leaves, max depth = 14, train loss: 0.53396, val loss: 0.52910, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.52670, val loss: 0.52240, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.51986, val loss: 0.51611, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.51341, val loss: 0.51019, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.50748, val loss: 0.50395, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.50151, val loss: 0.49849, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.49605, val loss: 0.49273, in 0.000s 1 tree, 82 leaves, max depth = 13, train loss: 0.49051, val loss: 0.48767, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.48552, val loss: 0.48250, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.48037, val loss: 0.47782, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.47576, val loss: 0.47295, in 0.000s 1 tree, 82 leaves, max depth = 11, train loss: 0.47096, val loss: 0.46861, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.46643, val loss: 0.46451, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.46223, val loss: 0.46001, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.45799, val loss: 0.45620, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.45409, val loss: 0.45202, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.45013, val loss: 0.44846, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.44648, val loss: 0.44460, in 0.000s 1 tree, 82 leaves, max depth = 12, train loss: 0.44277, val loss: 0.44128, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.43940, val loss: 0.43764, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.43592, val loss: 0.43455, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.43263, val loss: 0.43162, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.42953, val loss: 0.42827, in 0.016s 1 tree, 83 leaves, max depth = 11, train loss: 0.42644, val loss: 0.42553, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.42353, val loss: 0.42243, in 0.000s 1 tree, 83 leaves, max depth = 11, train loss: 0.42063, val loss: 0.41986, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.41793, val loss: 0.41692, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.41519, val loss: 0.41451, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.41267, val loss: 0.41175, in 0.000s 1 tree, 84 leaves, max depth = 12, train loss: 0.41009, val loss: 0.40949, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40779, val loss: 0.40697, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.40535, val loss: 0.40486, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40319, val loss: 0.40249, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.40089, val loss: 0.40003, in 0.000s 1 tree, 84 leaves, max depth = 12, train loss: 0.39860, val loss: 0.39807, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39658, val loss: 0.39585, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39467, val loss: 0.39376, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.39250, val loss: 0.39193, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.39040, val loss: 0.38969, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38861, val loss: 0.38772, in 0.016s 1 tree, 80 leaves, max depth = 11, train loss: 0.38656, val loss: 0.38600, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38486, val loss: 0.38413, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.38292, val loss: 0.38252, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.38098, val loss: 0.38047, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.37938, val loss: 0.37871, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.37754, val loss: 0.37720, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37609, val loss: 0.37551, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.37426, val loss: 0.37358, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.37251, val loss: 0.37215, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37114, val loss: 0.37056, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.36944, val loss: 0.36878, in 0.000s 1 tree, 82 leaves, max depth = 12, train loss: 0.36777, val loss: 0.36745, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36648, val loss: 0.36594, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.36488, val loss: 0.36427, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36366, val loss: 0.36282, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.36216, val loss: 0.36126, in 0.000s 1 tree, 83 leaves, max depth = 12, train loss: 0.36055, val loss: 0.36000, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35939, val loss: 0.35861, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.35795, val loss: 0.35711, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.35642, val loss: 0.35591, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35531, val loss: 0.35460, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.35397, val loss: 0.35322, in 0.016s Fit 76 trees in 1.032 s, (4129 total leaves) Time spent computing histograms: 0.350s Time spent finding best splits: 0.070s Time spent applying splits: 0.070s Time spent predicting: 0.000s Trial 23, Fold 1: Log loss = 0.35838270196769895, Average precision = 0.946139576660052, ROC-AUC = 0.9448678861788617, Elapsed Time = 1.0405602000009821 seconds Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 23, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 16, train loss: 0.67725, val loss: 0.67651, in 0.000s 1 tree, 47 leaves, max depth = 14, train loss: 0.66252, val loss: 0.66111, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.64864, val loss: 0.64657, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.63577, val loss: 0.63309, in 0.000s 1 tree, 50 leaves, max depth = 13, train loss: 0.62361, val loss: 0.62031, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.61229, val loss: 0.60843, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.60169, val loss: 0.59729, in 0.000s 1 tree, 48 leaves, max depth = 16, train loss: 0.59175, val loss: 0.58682, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.58228, val loss: 0.57683, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.57350, val loss: 0.56755, in 0.000s 1 tree, 50 leaves, max depth = 13, train loss: 0.56512, val loss: 0.55868, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.55732, val loss: 0.55045, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.54932, val loss: 0.54275, in 0.016s 1 tree, 51 leaves, max depth = 18, train loss: 0.54218, val loss: 0.53519, in 0.000s 1 tree, 51 leaves, max depth = 13, train loss: 0.53534, val loss: 0.52793, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.52812, val loss: 0.52099, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.52133, val loss: 0.51446, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.51492, val loss: 0.50832, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.50904, val loss: 0.50209, in 0.000s 1 tree, 82 leaves, max depth = 14, train loss: 0.50311, val loss: 0.49641, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.49768, val loss: 0.49066, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.49217, val loss: 0.48540, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.48716, val loss: 0.48009, in 0.000s 1 tree, 82 leaves, max depth = 15, train loss: 0.48204, val loss: 0.47520, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.47721, val loss: 0.47059, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.47266, val loss: 0.46577, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.46815, val loss: 0.46149, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.46394, val loss: 0.45701, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.45973, val loss: 0.45303, in 0.016s 1 tree, 53 leaves, max depth = 15, train loss: 0.45583, val loss: 0.44887, in 0.000s 1 tree, 79 leaves, max depth = 15, train loss: 0.45189, val loss: 0.44516, in 0.016s 1 tree, 53 leaves, max depth = 15, train loss: 0.44826, val loss: 0.44128, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.44457, val loss: 0.43780, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.44109, val loss: 0.43450, in 0.016s 1 tree, 53 leaves, max depth = 17, train loss: 0.43776, val loss: 0.43095, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.43449, val loss: 0.42788, in 0.016s 1 tree, 53 leaves, max depth = 17, train loss: 0.43139, val loss: 0.42456, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.42832, val loss: 0.42169, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.42543, val loss: 0.41860, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.42256, val loss: 0.41594, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.41986, val loss: 0.41305, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.41716, val loss: 0.41055, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.41459, val loss: 0.40816, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.41209, val loss: 0.40548, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40985, val loss: 0.40316, in 0.000s 1 tree, 82 leaves, max depth = 12, train loss: 0.40744, val loss: 0.40093, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.40512, val loss: 0.39844, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40304, val loss: 0.39627, in 0.000s 1 tree, 80 leaves, max depth = 13, train loss: 0.40075, val loss: 0.39416, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39878, val loss: 0.39211, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.39662, val loss: 0.39012, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.39451, val loss: 0.38785, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39267, val loss: 0.38594, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.39064, val loss: 0.38408, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38890, val loss: 0.38227, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.38697, val loss: 0.38051, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.38503, val loss: 0.37843, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38339, val loss: 0.37673, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.38157, val loss: 0.37506, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38009, val loss: 0.37351, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.37824, val loss: 0.37152, in 0.000s 1 tree, 82 leaves, max depth = 12, train loss: 0.37650, val loss: 0.36996, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37510, val loss: 0.36850, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.37337, val loss: 0.36664, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37205, val loss: 0.36526, in 0.000s 1 tree, 79 leaves, max depth = 13, train loss: 0.37040, val loss: 0.36379, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.36877, val loss: 0.36204, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36752, val loss: 0.36073, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.36594, val loss: 0.35933, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.36442, val loss: 0.35770, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36322, val loss: 0.35645, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.36179, val loss: 0.35491, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.36028, val loss: 0.35358, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35914, val loss: 0.35239, in 0.000s 1 tree, 52 leaves, max depth = 15, train loss: 0.35775, val loss: 0.35090, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.35632, val loss: 0.34963, in 0.016s Fit 76 trees in 1.142 s, (4173 total leaves) Time spent computing histograms: 0.378s Time spent finding best splits: 0.081s Time spent applying splits: 0.085s Time spent predicting: 0.000s Trial 23, Fold 2: Log loss = 0.3599834861148231, Average precision = 0.9429529922221647, ROC-AUC = 0.9446858836015578, Elapsed Time = 1.1502019999988988 seconds Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 23, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 13, train loss: 0.67733, val loss: 0.67686, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.66257, val loss: 0.66165, in 0.000s 1 tree, 51 leaves, max depth = 13, train loss: 0.64877, val loss: 0.64742, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.63587, val loss: 0.63409, in 0.000s 1 tree, 51 leaves, max depth = 13, train loss: 0.62378, val loss: 0.62160, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.61246, val loss: 0.60989, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.60193, val loss: 0.59899, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.59194, val loss: 0.58865, in 0.000s 1 tree, 47 leaves, max depth = 14, train loss: 0.58266, val loss: 0.57897, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.57383, val loss: 0.56981, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.56561, val loss: 0.56130, in 0.000s 1 tree, 51 leaves, max depth = 13, train loss: 0.55779, val loss: 0.55317, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.54966, val loss: 0.54560, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.54202, val loss: 0.53850, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.53512, val loss: 0.53131, in 0.000s 1 tree, 81 leaves, max depth = 12, train loss: 0.52807, val loss: 0.52478, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.52174, val loss: 0.51817, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.51522, val loss: 0.51214, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.50941, val loss: 0.50605, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.50337, val loss: 0.50047, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.49801, val loss: 0.49483, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.49240, val loss: 0.48967, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.48710, val loss: 0.48481, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.48226, val loss: 0.47971, in 0.000s 1 tree, 80 leaves, max depth = 14, train loss: 0.47733, val loss: 0.47520, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.47285, val loss: 0.47047, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.46825, val loss: 0.46627, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.46411, val loss: 0.46188, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.45981, val loss: 0.45797, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.45574, val loss: 0.45428, in 0.016s 1 tree, 48 leaves, max depth = 16, train loss: 0.45198, val loss: 0.45026, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.44817, val loss: 0.44682, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.44466, val loss: 0.44308, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.44109, val loss: 0.43985, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.43783, val loss: 0.43637, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.43447, val loss: 0.43336, in 0.000s 1 tree, 50 leaves, max depth = 13, train loss: 0.43144, val loss: 0.43011, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.42828, val loss: 0.42728, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.42530, val loss: 0.42462, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.42250, val loss: 0.42160, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.41969, val loss: 0.41911, in 0.000s 1 tree, 52 leaves, max depth = 13, train loss: 0.41707, val loss: 0.41628, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.41442, val loss: 0.41393, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41208, val loss: 0.41178, in 0.000s 1 tree, 81 leaves, max depth = 13, train loss: 0.40958, val loss: 0.40959, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.40717, val loss: 0.40692, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40499, val loss: 0.40491, in 0.000s 1 tree, 81 leaves, max depth = 12, train loss: 0.40262, val loss: 0.40285, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40057, val loss: 0.40096, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.39836, val loss: 0.39851, in 0.000s 1 tree, 81 leaves, max depth = 12, train loss: 0.39614, val loss: 0.39657, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39422, val loss: 0.39480, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39240, val loss: 0.39313, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.39029, val loss: 0.39131, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.38826, val loss: 0.38906, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38656, val loss: 0.38749, in 0.000s 1 tree, 82 leaves, max depth = 13, train loss: 0.38456, val loss: 0.38578, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38295, val loss: 0.38430, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.38104, val loss: 0.38267, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.37917, val loss: 0.38058, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.37765, val loss: 0.37918, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.37585, val loss: 0.37766, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37446, val loss: 0.37638, in 0.000s 1 tree, 49 leaves, max depth = 15, train loss: 0.37268, val loss: 0.37442, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.37096, val loss: 0.37299, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.36965, val loss: 0.37177, in 0.016s 1 tree, 49 leaves, max depth = 15, train loss: 0.36797, val loss: 0.36993, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.36639, val loss: 0.36819, in 0.000s 1 tree, 82 leaves, max depth = 13, train loss: 0.36474, val loss: 0.36681, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36350, val loss: 0.36567, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.36202, val loss: 0.36403, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.36084, val loss: 0.36294, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.35926, val loss: 0.36164, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.35788, val loss: 0.36009, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.35676, val loss: 0.35905, in 0.000s 1 tree, 82 leaves, max depth = 14, train loss: 0.35525, val loss: 0.35782, in 0.016s Fit 76 trees in 1.173 s, (4073 total leaves) Time spent computing histograms: 0.378s Time spent finding best splits: 0.081s Time spent applying splits: 0.084s Time spent predicting: 0.000s Trial 23, Fold 3: Log loss = 0.3552397610452097, Average precision = 0.9484253015879371, ROC-AUC = 0.9490830502210323, Elapsed Time = 1.1743360999989818 seconds Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 23, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 15, train loss: 0.67731, val loss: 0.67637, in 0.000s 1 tree, 48 leaves, max depth = 15, train loss: 0.66252, val loss: 0.66068, in 0.016s 1 tree, 49 leaves, max depth = 15, train loss: 0.64870, val loss: 0.64600, in 0.016s 1 tree, 47 leaves, max depth = 15, train loss: 0.63578, val loss: 0.63222, in 0.000s 1 tree, 48 leaves, max depth = 13, train loss: 0.62368, val loss: 0.61930, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.61233, val loss: 0.60716, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.60169, val loss: 0.59574, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.59170, val loss: 0.58500, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.58232, val loss: 0.57489, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.57350, val loss: 0.56536, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.56521, val loss: 0.55637, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.55740, val loss: 0.54790, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.54941, val loss: 0.54010, in 0.031s 1 tree, 49 leaves, max depth = 15, train loss: 0.54227, val loss: 0.53231, in 0.016s 1 tree, 49 leaves, max depth = 15, train loss: 0.53554, val loss: 0.52497, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.52833, val loss: 0.51795, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.52153, val loss: 0.51133, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.51514, val loss: 0.50510, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.50927, val loss: 0.49874, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.50334, val loss: 0.49296, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.49794, val loss: 0.48709, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.49244, val loss: 0.48174, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.48745, val loss: 0.47631, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.48234, val loss: 0.47134, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.47774, val loss: 0.46631, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.47298, val loss: 0.46170, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.46848, val loss: 0.45736, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.46429, val loss: 0.45272, in 0.000s 1 tree, 79 leaves, max depth = 14, train loss: 0.46009, val loss: 0.44867, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.45619, val loss: 0.44434, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.45228, val loss: 0.44056, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.44866, val loss: 0.43655, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.44499, val loss: 0.43299, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.44162, val loss: 0.42924, in 0.000s 1 tree, 78 leaves, max depth = 13, train loss: 0.43818, val loss: 0.42593, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.43491, val loss: 0.42278, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.43183, val loss: 0.41932, in 0.000s 1 tree, 79 leaves, max depth = 13, train loss: 0.42876, val loss: 0.41638, in 0.031s 1 tree, 50 leaves, max depth = 12, train loss: 0.42588, val loss: 0.41314, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.42301, val loss: 0.41038, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.42032, val loss: 0.40734, in 0.000s 1 tree, 80 leaves, max depth = 14, train loss: 0.41761, val loss: 0.40475, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.41510, val loss: 0.40190, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.41255, val loss: 0.39948, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41031, val loss: 0.39711, in 0.000s 1 tree, 78 leaves, max depth = 12, train loss: 0.40790, val loss: 0.39481, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40580, val loss: 0.39257, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.40353, val loss: 0.38999, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.40126, val loss: 0.38783, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39929, val loss: 0.38574, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.39714, val loss: 0.38372, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39528, val loss: 0.38174, in 0.000s 1 tree, 49 leaves, max depth = 12, train loss: 0.39320, val loss: 0.37938, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39146, val loss: 0.37753, in 0.000s 1 tree, 79 leaves, max depth = 12, train loss: 0.38943, val loss: 0.37562, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38785, val loss: 0.37393, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.38592, val loss: 0.37213, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.38401, val loss: 0.36996, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38251, val loss: 0.36837, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.38068, val loss: 0.36666, in 0.031s 1 tree, 2 leaves, max depth = 1, train loss: 0.37926, val loss: 0.36515, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.37745, val loss: 0.36308, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.37571, val loss: 0.36148, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37437, val loss: 0.36005, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.37270, val loss: 0.35815, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.37104, val loss: 0.35662, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36977, val loss: 0.35526, in 0.016s 1 tree, 54 leaves, max depth = 14, train loss: 0.36817, val loss: 0.35344, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.36659, val loss: 0.35200, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36538, val loss: 0.35071, in 0.000s 1 tree, 51 leaves, max depth = 13, train loss: 0.36390, val loss: 0.34902, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36276, val loss: 0.34780, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.36136, val loss: 0.34620, in 0.000s 1 tree, 80 leaves, max depth = 12, train loss: 0.35984, val loss: 0.34482, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35875, val loss: 0.34366, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.35744, val loss: 0.34216, in 0.000s Fit 76 trees in 1.329 s, (3975 total leaves) Time spent computing histograms: 0.444s Time spent finding best splits: 0.101s Time spent applying splits: 0.101s Time spent predicting: 0.000s Trial 23, Fold 4: Log loss = 0.35819156661274026, Average precision = 0.9483507958808596, ROC-AUC = 0.946384683386148, Elapsed Time = 1.3260851000013645 seconds Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 23, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 47 leaves, max depth = 12, train loss: 0.67714, val loss: 0.67608, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.66220, val loss: 0.66013, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.64837, val loss: 0.64533, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.63529, val loss: 0.63130, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.62304, val loss: 0.61814, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.61156, val loss: 0.60577, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.60078, val loss: 0.59414, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.59075, val loss: 0.58330, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.58124, val loss: 0.57299, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.57230, val loss: 0.56327, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.56395, val loss: 0.55418, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.55602, val loss: 0.54554, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.54801, val loss: 0.53787, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.54082, val loss: 0.53000, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.53397, val loss: 0.52248, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.52676, val loss: 0.51564, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.51996, val loss: 0.50921, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.51390, val loss: 0.50254, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.50760, val loss: 0.49656, in 0.000s 1 tree, 84 leaves, max depth = 16, train loss: 0.50165, val loss: 0.49093, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.49621, val loss: 0.48492, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.49069, val loss: 0.47971, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.48559, val loss: 0.47405, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.48046, val loss: 0.46923, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.47579, val loss: 0.46402, in 0.000s 1 tree, 84 leaves, max depth = 17, train loss: 0.47100, val loss: 0.45954, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.46668, val loss: 0.45471, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.46221, val loss: 0.45054, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.45799, val loss: 0.44660, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.45405, val loss: 0.44218, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.45010, val loss: 0.43852, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.44644, val loss: 0.43440, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.44273, val loss: 0.43100, in 0.000s 1 tree, 48 leaves, max depth = 13, train loss: 0.43935, val loss: 0.42719, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.43588, val loss: 0.42400, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.43258, val loss: 0.42100, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.42946, val loss: 0.41744, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.42636, val loss: 0.41464, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.42345, val loss: 0.41131, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.42053, val loss: 0.40866, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.41781, val loss: 0.40554, in 0.000s 1 tree, 85 leaves, max depth = 13, train loss: 0.41507, val loss: 0.40309, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.41253, val loss: 0.40015, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.40994, val loss: 0.39785, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40770, val loss: 0.39567, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.40525, val loss: 0.39349, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.40292, val loss: 0.39080, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40083, val loss: 0.38877, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.39852, val loss: 0.38674, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39655, val loss: 0.38483, in 0.000s 1 tree, 82 leaves, max depth = 13, train loss: 0.39436, val loss: 0.38290, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.39224, val loss: 0.38044, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39039, val loss: 0.37866, in 0.000s 1 tree, 82 leaves, max depth = 13, train loss: 0.38832, val loss: 0.37684, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38665, val loss: 0.37518, in 0.000s 1 tree, 80 leaves, max depth = 14, train loss: 0.38468, val loss: 0.37348, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.38272, val loss: 0.37119, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38116, val loss: 0.36963, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.37929, val loss: 0.36804, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37780, val loss: 0.36656, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.37598, val loss: 0.36443, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.37420, val loss: 0.36292, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37280, val loss: 0.36152, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.37106, val loss: 0.35952, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36973, val loss: 0.35820, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.36803, val loss: 0.35676, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.36640, val loss: 0.35487, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.36514, val loss: 0.35362, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.36352, val loss: 0.35224, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.36199, val loss: 0.35047, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36080, val loss: 0.34929, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.35937, val loss: 0.34760, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.35780, val loss: 0.34630, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35667, val loss: 0.34517, in 0.000s 1 tree, 50 leaves, max depth = 10, train loss: 0.35533, val loss: 0.34359, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.35383, val loss: 0.34235, in 0.016s Fit 76 trees in 1.236 s, (4137 total leaves) Time spent computing histograms: 0.417s Time spent finding best splits: 0.088s Time spent applying splits: 0.089s Time spent predicting: 0.016s Trial 23, Fold 5: Log loss = 0.3607328935159004, Average precision = 0.946009279757922, ROC-AUC = 0.9442421269717407, Elapsed Time = 1.2442246000009618 seconds
Optimization Progress: 24%|##4 | 24/100 [04:47<15:47, 12.46s/it]
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 24, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 8, train loss: 0.68729, val loss: 0.68711, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.68158, val loss: 0.68122, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.67600, val loss: 0.67547, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.67057, val loss: 0.66986, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.66526, val loss: 0.66438, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.66009, val loss: 0.65904, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.65503, val loss: 0.65382, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.65010, val loss: 0.64872, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.64529, val loss: 0.64374, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.64059, val loss: 0.63888, in 0.000s Fit 10 trees in 0.314 s, (200 total leaves) Time spent computing histograms: 0.038s Time spent finding best splits: 0.004s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 24, Fold 1: Log loss = 0.640623233035379, Average precision = 0.8109263008444648, ROC-AUC = 0.8600171079422435, Elapsed Time = 0.32782589999987977 seconds Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 24, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 8, train loss: 0.68733, val loss: 0.68703, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.68170, val loss: 0.68113, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.67616, val loss: 0.67530, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.67081, val loss: 0.66969, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.66553, val loss: 0.66413, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.66043, val loss: 0.65878, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.65541, val loss: 0.65348, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.65055, val loss: 0.64837, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.64575, val loss: 0.64331, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.64112, val loss: 0.63844, in 0.000s Fit 10 trees in 0.329 s, (199 total leaves) Time spent computing histograms: 0.042s Time spent finding best splits: 0.005s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 24, Fold 2: Log loss = 0.6409561541008428, Average precision = 0.8198208757306849, ROC-AUC = 0.8671791846955682, Elapsed Time = 0.33721589999913704 seconds Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 24, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 7, train loss: 0.68734, val loss: 0.68716, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.68167, val loss: 0.68131, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.67614, val loss: 0.67561, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.67075, val loss: 0.67004, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.66549, val loss: 0.66461, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.66035, val loss: 0.65931, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.65534, val loss: 0.65413, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.65045, val loss: 0.64907, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.64567, val loss: 0.64413, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.64101, val loss: 0.63931, in 0.000s Fit 10 trees in 0.330 s, (200 total leaves) Time spent computing histograms: 0.038s Time spent finding best splits: 0.004s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 24, Fold 3: Log loss = 0.6396197595777118, Average precision = 0.8252803499994151, ROC-AUC = 0.8716421546792383, Elapsed Time = 0.33429789999900095 seconds Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 24, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 19 leaves, max depth = 9, train loss: 0.68737, val loss: 0.68704, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.68173, val loss: 0.68108, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.67624, val loss: 0.67526, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.67087, val loss: 0.66957, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.66564, val loss: 0.66402, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.66053, val loss: 0.65860, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.65555, val loss: 0.65331, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.65065, val loss: 0.64809, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.64590, val loss: 0.64303, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.64124, val loss: 0.63804, in 0.016s Fit 10 trees in 0.329 s, (192 total leaves) Time spent computing histograms: 0.040s Time spent finding best splits: 0.004s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 24, Fold 4: Log loss = 0.640927237290986, Average precision = 0.8185837745206201, ROC-AUC = 0.8648848121442758, Elapsed Time = 0.3400923999997758 seconds Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 24, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 8, train loss: 0.68734, val loss: 0.68696, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.68167, val loss: 0.68091, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.67614, val loss: 0.67502, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.67075, val loss: 0.66926, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.66547, val loss: 0.66359, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.66033, val loss: 0.65810, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.65530, val loss: 0.65269, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.65041, val loss: 0.64745, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.64561, val loss: 0.64228, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.64095, val loss: 0.63728, in 0.016s Fit 10 trees in 0.345 s, (200 total leaves) Time spent computing histograms: 0.041s Time spent finding best splits: 0.005s Time spent applying splits: 0.004s Time spent predicting: 0.000s Trial 24, Fold 5: Log loss = 0.6424195026971156, Average precision = 0.8047005213484206, ROC-AUC = 0.8551868160451852, Elapsed Time = 0.35267949999979464 seconds
Optimization Progress: 25%|##5 | 25/100 [04:55<13:56, 11.15s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 25, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.205 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.66934, val loss: 0.66854, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.65718, val loss: 0.65539, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.63648, val loss: 0.63409, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.61800, val loss: 0.61486, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.60112, val loss: 0.59737, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.58600, val loss: 0.58163, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.57225, val loss: 0.56724, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.55985, val loss: 0.55430, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.55106, val loss: 0.54609, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.53971, val loss: 0.53425, in 0.000s 1 tree, 10 leaves, max depth = 7, train loss: 0.52971, val loss: 0.52376, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.52050, val loss: 0.51412, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.51201, val loss: 0.50520, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.50444, val loss: 0.49722, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.49754, val loss: 0.48992, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.48797, val loss: 0.48101, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.48173, val loss: 0.47441, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.47615, val loss: 0.46844, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.47098, val loss: 0.46285, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.46601, val loss: 0.45755, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.46147, val loss: 0.45268, in 0.000s 1 tree, 28 leaves, max depth = 8, train loss: 0.45596, val loss: 0.44773, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.45228, val loss: 0.44378, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.44856, val loss: 0.43975, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.44366, val loss: 0.43536, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.44050, val loss: 0.43199, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43722, val loss: 0.42867, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.43451, val loss: 0.42578, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.43022, val loss: 0.42200, in 0.016s Fit 29 trees in 0.580 s, (364 total leaves) Time spent computing histograms: 0.133s Time spent finding best splits: 0.010s Time spent applying splits: 0.010s Time spent predicting: 0.000s Trial 25, Fold 1: Log loss = 0.43042724292040524, Average precision = 0.904210098361593, ROC-AUC = 0.9104416997653222, Elapsed Time = 0.5778432999995857 seconds Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 25, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.66933, val loss: 0.66808, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.65699, val loss: 0.65449, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.63639, val loss: 0.63281, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61785, val loss: 0.61333, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.60106, val loss: 0.59560, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.58594, val loss: 0.57960, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.57215, val loss: 0.56503, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.55970, val loss: 0.55184, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.55103, val loss: 0.54364, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.53980, val loss: 0.53169, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.52982, val loss: 0.52101, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.52064, val loss: 0.51121, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.51233, val loss: 0.50229, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.50478, val loss: 0.49415, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.49785, val loss: 0.48671, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.48808, val loss: 0.47735, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.48191, val loss: 0.47069, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.47635, val loss: 0.46469, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.47117, val loss: 0.45907, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.46630, val loss: 0.45375, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.46184, val loss: 0.44887, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.45641, val loss: 0.44392, in 0.017s 1 tree, 18 leaves, max depth = 7, train loss: 0.45268, val loss: 0.43995, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.44903, val loss: 0.43593, in 0.015s 1 tree, 28 leaves, max depth = 8, train loss: 0.44412, val loss: 0.43148, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.44100, val loss: 0.42807, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.43820, val loss: 0.42501, in 0.016s 1 tree, 21 leaves, max depth = 13, train loss: 0.43563, val loss: 0.42215, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.43121, val loss: 0.41822, in 0.016s Fit 29 trees in 0.549 s, (371 total leaves) Time spent computing histograms: 0.131s Time spent finding best splits: 0.010s Time spent applying splits: 0.010s Time spent predicting: 0.000s Trial 25, Fold 2: Log loss = 0.43183056693913974, Average precision = 0.8994185525865448, ROC-AUC = 0.9132483707599013, Elapsed Time = 0.5474708000001556 seconds Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 25, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 7 leaves, max depth = 5, train loss: 0.66943, val loss: 0.66856, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.65706, val loss: 0.65587, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.63667, val loss: 0.63463, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61824, val loss: 0.61550, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.60161, val loss: 0.59821, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.58658, val loss: 0.58263, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.57286, val loss: 0.56833, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.56057, val loss: 0.55544, in 0.000s 1 tree, 28 leaves, max depth = 8, train loss: 0.55180, val loss: 0.54733, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.54066, val loss: 0.53569, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.53074, val loss: 0.52535, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.52161, val loss: 0.51567, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.51327, val loss: 0.50689, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.50575, val loss: 0.49892, in 0.016s 1 tree, 18 leaves, max depth = 12, train loss: 0.49890, val loss: 0.49167, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.48918, val loss: 0.48259, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.48306, val loss: 0.47604, in 0.000s 1 tree, 16 leaves, max depth = 10, train loss: 0.47751, val loss: 0.47022, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.47235, val loss: 0.46476, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.46752, val loss: 0.45967, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.46311, val loss: 0.45504, in 0.016s 1 tree, 28 leaves, max depth = 7, train loss: 0.45761, val loss: 0.45007, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.45397, val loss: 0.44612, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.45035, val loss: 0.44230, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.44540, val loss: 0.43770, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.44231, val loss: 0.43433, in 0.000s 1 tree, 17 leaves, max depth = 5, train loss: 0.43907, val loss: 0.43145, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.43635, val loss: 0.42854, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.43207, val loss: 0.42474, in 0.000s Fit 29 trees in 0.517 s, (390 total leaves) Time spent computing histograms: 0.123s Time spent finding best splits: 0.010s Time spent applying splits: 0.010s Time spent predicting: 0.000s Trial 25, Fold 3: Log loss = 0.4254316361822161, Average precision = 0.910916950452471, ROC-AUC = 0.9208799936916721, Elapsed Time = 0.5182334000000992 seconds Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 25, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.66940, val loss: 0.66796, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.65694, val loss: 0.65464, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.63642, val loss: 0.63287, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.61802, val loss: 0.61333, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.60130, val loss: 0.59547, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.58617, val loss: 0.57925, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.57247, val loss: 0.56456, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.56011, val loss: 0.55124, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.55149, val loss: 0.54270, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.54031, val loss: 0.53052, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.53042, val loss: 0.51977, in 0.016s 1 tree, 14 leaves, max depth = 9, train loss: 0.52123, val loss: 0.50979, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.51286, val loss: 0.50062, in 0.000s 1 tree, 12 leaves, max depth = 8, train loss: 0.50534, val loss: 0.49229, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.49842, val loss: 0.48466, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.48904, val loss: 0.47543, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.48289, val loss: 0.46855, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.47738, val loss: 0.46238, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.47221, val loss: 0.45659, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.46733, val loss: 0.45104, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.46287, val loss: 0.44594, in 0.000s 1 tree, 28 leaves, max depth = 7, train loss: 0.45752, val loss: 0.44083, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45382, val loss: 0.43656, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.45017, val loss: 0.43232, in 0.016s 1 tree, 28 leaves, max depth = 7, train loss: 0.44530, val loss: 0.42758, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.44219, val loss: 0.42396, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43900, val loss: 0.42105, in 0.000s 1 tree, 22 leaves, max depth = 10, train loss: 0.43628, val loss: 0.41782, in 0.016s 1 tree, 28 leaves, max depth = 7, train loss: 0.43211, val loss: 0.41386, in 0.016s Fit 29 trees in 0.518 s, (422 total leaves) Time spent computing histograms: 0.117s Time spent finding best splits: 0.011s Time spent applying splits: 0.011s Time spent predicting: 0.000s Trial 25, Fold 4: Log loss = 0.4316979696405457, Average precision = 0.9031565302826969, ROC-AUC = 0.9134650749539213, Elapsed Time = 0.5205359000010503 seconds Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 25, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 5, train loss: 0.66911, val loss: 0.66737, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.65694, val loss: 0.65387, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.63619, val loss: 0.63160, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.61751, val loss: 0.61156, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.60063, val loss: 0.59337, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.58542, val loss: 0.57687, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.57154, val loss: 0.56182, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.55903, val loss: 0.54818, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.55049, val loss: 0.54039, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.53917, val loss: 0.52793, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.52911, val loss: 0.51685, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.51986, val loss: 0.50663, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.51136, val loss: 0.49722, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50376, val loss: 0.48862, in 0.016s 1 tree, 18 leaves, max depth = 11, train loss: 0.49675, val loss: 0.48080, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.48716, val loss: 0.47176, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.48092, val loss: 0.46469, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.47531, val loss: 0.45830, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.47010, val loss: 0.45236, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.46518, val loss: 0.44670, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.46069, val loss: 0.44149, in 0.000s 1 tree, 28 leaves, max depth = 8, train loss: 0.45537, val loss: 0.43694, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.45163, val loss: 0.43264, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.44794, val loss: 0.42831, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.44315, val loss: 0.42368, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.43999, val loss: 0.41995, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.43715, val loss: 0.41660, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.43454, val loss: 0.41348, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.43022, val loss: 0.40992, in 0.016s Fit 29 trees in 0.549 s, (404 total leaves) Time spent computing histograms: 0.129s Time spent finding best splits: 0.010s Time spent applying splits: 0.010s Time spent predicting: 0.016s Trial 25, Fold 5: Log loss = 0.4376364533477417, Average precision = 0.9000715888354822, ROC-AUC = 0.9088322092785612, Elapsed Time = 0.5473820000006526 seconds
Optimization Progress: 26%|##6 | 26/100 [05:05<13:24, 10.88s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 26, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 190 leaves, max depth = 15, train loss: 0.68518, val loss: 0.68533, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.67727, val loss: 0.67748, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.66946, val loss: 0.66980, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.66197, val loss: 0.66236, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.65457, val loss: 0.65492, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.64780, val loss: 0.64813, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.64114, val loss: 0.64149, in 0.016s 1 tree, 190 leaves, max depth = 16, train loss: 0.63454, val loss: 0.63490, in 0.038s 1 tree, 190 leaves, max depth = 16, train loss: 0.62810, val loss: 0.62848, in 0.009s 1 tree, 190 leaves, max depth = 14, train loss: 0.62143, val loss: 0.62186, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.61512, val loss: 0.61559, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.60919, val loss: 0.60987, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.60318, val loss: 0.60388, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.59720, val loss: 0.59803, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.59128, val loss: 0.59214, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.58601, val loss: 0.58684, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.58044, val loss: 0.58138, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.57510, val loss: 0.57608, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.57009, val loss: 0.57109, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.56530, val loss: 0.56626, in 0.031s Fit 20 trees in 0.783 s, (3800 total leaves) Time spent computing histograms: 0.153s Time spent finding best splits: 0.066s Time spent applying splits: 0.051s Time spent predicting: 0.016s Trial 26, Fold 1: Log loss = 0.569108795906292, Average precision = 0.9202807195831728, ROC-AUC = 0.9315492620314211, Elapsed Time = 0.7984632000006968 seconds Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 26, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.127 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 190 leaves, max depth = 15, train loss: 0.68526, val loss: 0.68526, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.67718, val loss: 0.67722, in 0.031s 1 tree, 190 leaves, max depth = 18, train loss: 0.66990, val loss: 0.66999, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.66234, val loss: 0.66253, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.65538, val loss: 0.65564, in 0.031s 1 tree, 190 leaves, max depth = 17, train loss: 0.64807, val loss: 0.64842, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.64096, val loss: 0.64137, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.63448, val loss: 0.63492, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.62772, val loss: 0.62822, in 0.016s 1 tree, 190 leaves, max depth = 16, train loss: 0.62110, val loss: 0.62174, in 0.031s 1 tree, 190 leaves, max depth = 19, train loss: 0.61505, val loss: 0.61569, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.60889, val loss: 0.60957, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.60289, val loss: 0.60361, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.59698, val loss: 0.59780, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.59112, val loss: 0.59202, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.58541, val loss: 0.58637, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.58010, val loss: 0.58107, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.57464, val loss: 0.57569, in 0.016s 1 tree, 190 leaves, max depth = 19, train loss: 0.56974, val loss: 0.57081, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.56483, val loss: 0.56595, in 0.031s Fit 20 trees in 0.846 s, (3800 total leaves) Time spent computing histograms: 0.177s Time spent finding best splits: 0.074s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 26, Fold 2: Log loss = 0.5681891539256246, Average precision = 0.9177697630994953, ROC-AUC = 0.9322516700602529, Elapsed Time = 0.8451671000002534 seconds Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 26, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 190 leaves, max depth = 13, train loss: 0.68511, val loss: 0.68519, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.67719, val loss: 0.67737, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.66938, val loss: 0.66965, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.66185, val loss: 0.66224, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.65452, val loss: 0.65501, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.64778, val loss: 0.64843, in 0.031s 1 tree, 190 leaves, max depth = 13, train loss: 0.64098, val loss: 0.64179, in 0.031s 1 tree, 190 leaves, max depth = 13, train loss: 0.63436, val loss: 0.63532, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.62801, val loss: 0.62913, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.62142, val loss: 0.62264, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.61500, val loss: 0.61634, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.60882, val loss: 0.61026, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.60271, val loss: 0.60427, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.59673, val loss: 0.59839, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.59090, val loss: 0.59265, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.58554, val loss: 0.58744, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.57998, val loss: 0.58200, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.57462, val loss: 0.57673, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.56961, val loss: 0.57186, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.56472, val loss: 0.56711, in 0.016s Fit 20 trees in 0.860 s, (3800 total leaves) Time spent computing histograms: 0.176s Time spent finding best splits: 0.073s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 26, Fold 3: Log loss = 0.5659776112241304, Average precision = 0.921698723740934, ROC-AUC = 0.9365434425894001, Elapsed Time = 0.8646678000004613 seconds Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 26, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 190 leaves, max depth = 14, train loss: 0.68512, val loss: 0.68504, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.67723, val loss: 0.67691, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.66944, val loss: 0.66890, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.66192, val loss: 0.66114, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.65462, val loss: 0.65365, in 0.016s 1 tree, 190 leaves, max depth = 17, train loss: 0.64785, val loss: 0.64666, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.64082, val loss: 0.63943, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.63409, val loss: 0.63252, in 0.031s 1 tree, 190 leaves, max depth = 19, train loss: 0.62780, val loss: 0.62608, in 0.016s 1 tree, 190 leaves, max depth = 14, train loss: 0.62127, val loss: 0.61938, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.61495, val loss: 0.61284, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.60908, val loss: 0.60697, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.60305, val loss: 0.60073, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.59707, val loss: 0.59462, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.59164, val loss: 0.58903, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.58632, val loss: 0.58350, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.58073, val loss: 0.57779, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.57537, val loss: 0.57224, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.57003, val loss: 0.56679, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.56485, val loss: 0.56147, in 0.031s Fit 20 trees in 0.861 s, (3800 total leaves) Time spent computing histograms: 0.170s Time spent finding best splits: 0.073s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 26, Fold 4: Log loss = 0.5667484174551578, Average precision = 0.9241353906034142, ROC-AUC = 0.9350950246905633, Elapsed Time = 0.8688241999989259 seconds Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 26, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 190 leaves, max depth = 14, train loss: 0.68510, val loss: 0.68483, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.67701, val loss: 0.67665, in 0.031s 1 tree, 190 leaves, max depth = 17, train loss: 0.66914, val loss: 0.66863, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.66156, val loss: 0.66084, in 0.016s 1 tree, 190 leaves, max depth = 16, train loss: 0.65417, val loss: 0.65324, in 0.031s 1 tree, 190 leaves, max depth = 17, train loss: 0.64732, val loss: 0.64616, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.64065, val loss: 0.63930, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.63371, val loss: 0.63230, in 0.016s 1 tree, 190 leaves, max depth = 16, train loss: 0.62702, val loss: 0.62541, in 0.031s 1 tree, 190 leaves, max depth = 17, train loss: 0.62053, val loss: 0.61880, in 0.016s 1 tree, 190 leaves, max depth = 16, train loss: 0.61406, val loss: 0.61214, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.60809, val loss: 0.60622, in 0.031s 1 tree, 190 leaves, max depth = 15, train loss: 0.60205, val loss: 0.60012, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.59603, val loss: 0.59393, in 0.016s 1 tree, 190 leaves, max depth = 17, train loss: 0.59056, val loss: 0.58826, in 0.031s 1 tree, 190 leaves, max depth = 14, train loss: 0.58512, val loss: 0.58288, in 0.016s 1 tree, 190 leaves, max depth = 15, train loss: 0.57962, val loss: 0.57732, in 0.016s 1 tree, 190 leaves, max depth = 16, train loss: 0.57422, val loss: 0.57177, in 0.031s 1 tree, 190 leaves, max depth = 17, train loss: 0.56910, val loss: 0.56652, in 0.031s 1 tree, 190 leaves, max depth = 18, train loss: 0.56424, val loss: 0.56147, in 0.016s Fit 20 trees in 0.845 s, (3800 total leaves) Time spent computing histograms: 0.172s Time spent finding best splits: 0.074s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 26, Fold 5: Log loss = 0.5685353232718314, Average precision = 0.9194077007754793, ROC-AUC = 0.9328047117403341, Elapsed Time = 0.863473299999896 seconds
Optimization Progress: 27%|##7 | 27/100 [05:16<13:20, 10.96s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 27, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 39 leaves, max depth = 9, train loss: 0.66511, val loss: 0.66498, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.63941, val loss: 0.63945, in 0.016s 1 tree, 55 leaves, max depth = 9, train loss: 0.61638, val loss: 0.61627, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.59531, val loss: 0.59502, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.57582, val loss: 0.57567, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.55789, val loss: 0.55776, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.54232, val loss: 0.54228, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.52719, val loss: 0.52691, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.51281, val loss: 0.51246, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.49997, val loss: 0.49944, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.48764, val loss: 0.48698, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.47663, val loss: 0.47582, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.46598, val loss: 0.46512, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.45610, val loss: 0.45520, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.44700, val loss: 0.44608, in 0.032s 1 tree, 43 leaves, max depth = 9, train loss: 0.43879, val loss: 0.43762, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.43107, val loss: 0.42985, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.41904, val loss: 0.41825, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.41213, val loss: 0.41119, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.40570, val loss: 0.40462, in 0.000s 1 tree, 48 leaves, max depth = 10, train loss: 0.39540, val loss: 0.39482, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.38581, val loss: 0.38569, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.37722, val loss: 0.37743, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.37196, val loss: 0.37233, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.36713, val loss: 0.36730, in 0.016s 1 tree, 23 leaves, max depth = 10, train loss: 0.35995, val loss: 0.36020, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.35337, val loss: 0.35368, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.34821, val loss: 0.34851, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.34237, val loss: 0.34335, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.33768, val loss: 0.33866, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.33225, val loss: 0.33329, in 0.000s 1 tree, 55 leaves, max depth = 10, train loss: 0.32873, val loss: 0.33000, in 0.031s 1 tree, 38 leaves, max depth = 10, train loss: 0.32390, val loss: 0.32579, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.31994, val loss: 0.32182, in 0.000s 1 tree, 23 leaves, max depth = 9, train loss: 0.31547, val loss: 0.31741, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.31134, val loss: 0.31386, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.30744, val loss: 0.31001, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.30380, val loss: 0.30691, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.30036, val loss: 0.30352, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.29713, val loss: 0.30080, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.29484, val loss: 0.29845, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.29186, val loss: 0.29550, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.28905, val loss: 0.29316, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.28645, val loss: 0.29055, in 0.000s 1 tree, 63 leaves, max depth = 10, train loss: 0.28350, val loss: 0.28762, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.28107, val loss: 0.28564, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.27881, val loss: 0.28335, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.27621, val loss: 0.28080, in 0.016s Fit 48 trees in 1.018 s, (2200 total leaves) Time spent computing histograms: 0.277s Time spent finding best splits: 0.044s Time spent applying splits: 0.041s Time spent predicting: 0.016s Trial 27, Fold 1: Log loss = 0.2806973281608503, Average precision = 0.9612056199159554, ROC-AUC = 0.9552867932779856, Elapsed Time = 1.0278072000000975 seconds Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 27, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 39 leaves, max depth = 10, train loss: 0.66495, val loss: 0.66419, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.63895, val loss: 0.63762, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.61502, val loss: 0.61328, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.59406, val loss: 0.59219, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.57545, val loss: 0.57337, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.55696, val loss: 0.55455, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.54118, val loss: 0.53836, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.52571, val loss: 0.52277, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.51120, val loss: 0.50800, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.49886, val loss: 0.49557, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.48638, val loss: 0.48286, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.47557, val loss: 0.47196, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.46478, val loss: 0.46098, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.45477, val loss: 0.45074, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.44568, val loss: 0.44156, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.43731, val loss: 0.43298, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.42975, val loss: 0.42539, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.41788, val loss: 0.41377, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.41092, val loss: 0.40674, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.40447, val loss: 0.40022, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.39428, val loss: 0.39026, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.38482, val loss: 0.38102, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.37934, val loss: 0.37558, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.37097, val loss: 0.36741, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.36624, val loss: 0.36260, in 0.016s 1 tree, 23 leaves, max depth = 11, train loss: 0.35916, val loss: 0.35565, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.35384, val loss: 0.35042, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.34750, val loss: 0.34458, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.34263, val loss: 0.33979, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.33685, val loss: 0.33417, in 0.000s 1 tree, 40 leaves, max depth = 12, train loss: 0.33148, val loss: 0.32923, in 0.016s 1 tree, 23 leaves, max depth = 11, train loss: 0.32643, val loss: 0.32427, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.32173, val loss: 0.31996, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.31753, val loss: 0.31593, in 0.016s 1 tree, 23 leaves, max depth = 10, train loss: 0.31325, val loss: 0.31169, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.30952, val loss: 0.30805, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.30569, val loss: 0.30431, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.30215, val loss: 0.30095, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.29900, val loss: 0.29791, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.29591, val loss: 0.29498, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.29248, val loss: 0.29189, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.28934, val loss: 0.28885, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.28667, val loss: 0.28631, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.28403, val loss: 0.28385, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.28125, val loss: 0.28118, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.27851, val loss: 0.27870, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.27597, val loss: 0.27644, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.27364, val loss: 0.27416, in 0.016s Fit 48 trees in 1.096 s, (2224 total leaves) Time spent computing histograms: 0.310s Time spent finding best splits: 0.046s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 27, Fold 2: Log loss = 0.2768179417669449, Average precision = 0.9623801790052878, ROC-AUC = 0.9598748505663682, Elapsed Time = 1.1042353000011644 seconds Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 27, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 40 leaves, max depth = 10, train loss: 0.66514, val loss: 0.66509, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.63945, val loss: 0.63947, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.61602, val loss: 0.61600, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.59528, val loss: 0.59509, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.57657, val loss: 0.57649, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.55824, val loss: 0.55830, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.54254, val loss: 0.54254, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.52716, val loss: 0.52721, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.51374, val loss: 0.51390, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.50110, val loss: 0.50136, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.48866, val loss: 0.48897, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.47715, val loss: 0.47752, in 0.031s 1 tree, 57 leaves, max depth = 11, train loss: 0.46669, val loss: 0.46713, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.45699, val loss: 0.45748, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.44802, val loss: 0.44859, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.43970, val loss: 0.44031, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.43207, val loss: 0.43275, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.42001, val loss: 0.42170, in 0.031s 1 tree, 62 leaves, max depth = 12, train loss: 0.41307, val loss: 0.41475, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.40660, val loss: 0.40830, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.39627, val loss: 0.39888, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.38672, val loss: 0.39025, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.37824, val loss: 0.38257, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.37214, val loss: 0.37624, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.36655, val loss: 0.37041, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.35934, val loss: 0.36380, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.35271, val loss: 0.35778, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.34769, val loss: 0.35258, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.34171, val loss: 0.34754, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.33727, val loss: 0.34298, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.33182, val loss: 0.33846, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.32768, val loss: 0.33416, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.32383, val loss: 0.33018, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.31898, val loss: 0.32580, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.31447, val loss: 0.32207, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.31016, val loss: 0.31819, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.30682, val loss: 0.31469, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.30298, val loss: 0.31151, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.30046, val loss: 0.30906, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.29644, val loss: 0.30545, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.29354, val loss: 0.30230, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.28993, val loss: 0.29909, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.28801, val loss: 0.29723, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.28497, val loss: 0.29486, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.28217, val loss: 0.29239, in 0.000s 1 tree, 62 leaves, max depth = 12, train loss: 0.27970, val loss: 0.28969, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.27701, val loss: 0.28729, in 0.031s 1 tree, 23 leaves, max depth = 9, train loss: 0.27468, val loss: 0.28520, in 0.016s Fit 48 trees in 1.142 s, (2236 total leaves) Time spent computing histograms: 0.324s Time spent finding best splits: 0.047s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 27, Fold 3: Log loss = 0.27394828088236733, Average precision = 0.9627584780797169, ROC-AUC = 0.9596843368069268, Elapsed Time = 1.136641499999314 seconds Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 27, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 9, train loss: 0.66513, val loss: 0.66404, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.63931, val loss: 0.63718, in 0.016s 1 tree, 57 leaves, max depth = 9, train loss: 0.61626, val loss: 0.61303, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.59551, val loss: 0.59139, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.57706, val loss: 0.57190, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.55892, val loss: 0.55295, in 0.031s 1 tree, 54 leaves, max depth = 11, train loss: 0.54313, val loss: 0.53670, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.52778, val loss: 0.52044, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.51330, val loss: 0.50525, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.50104, val loss: 0.49215, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.48854, val loss: 0.47900, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.47777, val loss: 0.46763, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.46696, val loss: 0.45624, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.45693, val loss: 0.44565, in 0.031s 1 tree, 57 leaves, max depth = 10, train loss: 0.44765, val loss: 0.43582, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.43938, val loss: 0.42702, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.43185, val loss: 0.41905, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.42000, val loss: 0.40704, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.41309, val loss: 0.39959, in 0.016s 1 tree, 62 leaves, max depth = 10, train loss: 0.40662, val loss: 0.39261, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.39644, val loss: 0.38234, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.38709, val loss: 0.37294, in 0.016s 1 tree, 62 leaves, max depth = 10, train loss: 0.38164, val loss: 0.36704, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.37324, val loss: 0.35855, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.36749, val loss: 0.35263, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.36039, val loss: 0.34528, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.35376, val loss: 0.33901, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.34866, val loss: 0.33389, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.34269, val loss: 0.32769, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.33802, val loss: 0.32293, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.33254, val loss: 0.31768, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.32748, val loss: 0.31242, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.32327, val loss: 0.30810, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.31875, val loss: 0.30341, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.31457, val loss: 0.29905, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.31091, val loss: 0.29533, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.30683, val loss: 0.29150, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.30299, val loss: 0.28789, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.29944, val loss: 0.28461, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.29619, val loss: 0.28120, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.29300, val loss: 0.27791, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.29010, val loss: 0.27507, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.28737, val loss: 0.27237, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.28451, val loss: 0.26970, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.28185, val loss: 0.26686, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.27940, val loss: 0.26422, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.27746, val loss: 0.26223, in 0.016s 1 tree, 23 leaves, max depth = 10, train loss: 0.27527, val loss: 0.25988, in 0.000s Fit 48 trees in 1.143 s, (2224 total leaves) Time spent computing histograms: 0.302s Time spent finding best splits: 0.046s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 27, Fold 4: Log loss = 0.274923146793898, Average precision = 0.9643716512099001, ROC-AUC = 0.9600594224721084, Elapsed Time = 1.1375859999989189 seconds Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 27, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 38 leaves, max depth = 10, train loss: 0.66504, val loss: 0.66373, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.63879, val loss: 0.63634, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.61501, val loss: 0.61152, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.59374, val loss: 0.58917, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.57486, val loss: 0.56936, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.55654, val loss: 0.55020, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.54081, val loss: 0.53366, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.52516, val loss: 0.51733, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.51192, val loss: 0.50337, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.49945, val loss: 0.49027, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.48694, val loss: 0.47720, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.47628, val loss: 0.46601, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.46569, val loss: 0.45490, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.45599, val loss: 0.44476, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.44689, val loss: 0.43521, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.43855, val loss: 0.42632, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.43105, val loss: 0.41849, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.41910, val loss: 0.40661, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.41211, val loss: 0.39926, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.40570, val loss: 0.39254, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.39540, val loss: 0.38234, in 0.031s 1 tree, 45 leaves, max depth = 11, train loss: 0.38592, val loss: 0.37299, in 0.016s 1 tree, 59 leaves, max depth = 9, train loss: 0.38045, val loss: 0.36724, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.37506, val loss: 0.36171, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.36999, val loss: 0.35647, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.36247, val loss: 0.34881, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.35543, val loss: 0.34243, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.34889, val loss: 0.33576, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.34388, val loss: 0.33075, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.33918, val loss: 0.32606, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.33317, val loss: 0.32061, in 0.016s 1 tree, 53 leaves, max depth = 15, train loss: 0.32956, val loss: 0.31710, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.32551, val loss: 0.31304, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.32003, val loss: 0.30792, in 0.031s 1 tree, 21 leaves, max depth = 8, train loss: 0.31529, val loss: 0.30300, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.31051, val loss: 0.29842, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.30690, val loss: 0.29472, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.30294, val loss: 0.29072, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.29901, val loss: 0.28733, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.29648, val loss: 0.28483, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.29339, val loss: 0.28166, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.29009, val loss: 0.27823, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.28729, val loss: 0.27552, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.28406, val loss: 0.27276, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.28108, val loss: 0.27013, in 0.000s 1 tree, 22 leaves, max depth = 10, train loss: 0.27839, val loss: 0.26740, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.27588, val loss: 0.26477, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.27335, val loss: 0.26259, in 0.016s Fit 48 trees in 1.110 s, (2258 total leaves) Time spent computing histograms: 0.307s Time spent finding best splits: 0.046s Time spent applying splits: 0.043s Time spent predicting: 0.000s Trial 27, Fold 5: Log loss = 0.28149502590144165, Average precision = 0.9592432659407226, ROC-AUC = 0.9550485108596697, Elapsed Time = 1.121284700000615 seconds
Optimization Progress: 28%|##8 | 28/100 [05:28<13:31, 11.27s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 28, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.67338, val loss: 0.67336, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.65449, val loss: 0.65448, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.63710, val loss: 0.63698, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.62067, val loss: 0.62051, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.60563, val loss: 0.60531, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59171, val loss: 0.59136, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.57880, val loss: 0.57822, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56571, val loss: 0.56515, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.55338, val loss: 0.55287, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54237, val loss: 0.54183, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.53133, val loss: 0.53078, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.52150, val loss: 0.52093, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.51158, val loss: 0.51102, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.50201, val loss: 0.50144, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.49341, val loss: 0.49268, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.48497, val loss: 0.48428, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.47679, val loss: 0.47611, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.46903, val loss: 0.46837, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.46179, val loss: 0.46101, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.45478, val loss: 0.45405, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.44543, val loss: 0.44496, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.43901, val loss: 0.43859, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.43291, val loss: 0.43252, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.42443, val loss: 0.42442, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.41881, val loss: 0.41886, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.41360, val loss: 0.41356, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.40851, val loss: 0.40845, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40112, val loss: 0.40146, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.39644, val loss: 0.39682, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.38976, val loss: 0.39040, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.38552, val loss: 0.38629, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37917, val loss: 0.38033, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37516, val loss: 0.37642, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.37159, val loss: 0.37289, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.36586, val loss: 0.36754, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.36239, val loss: 0.36422, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.35736, val loss: 0.35930, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.35262, val loss: 0.35466, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.34816, val loss: 0.35029, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.34451, val loss: 0.34675, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.34039, val loss: 0.34272, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.33698, val loss: 0.33941, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.33276, val loss: 0.33552, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.32913, val loss: 0.33198, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.32600, val loss: 0.32898, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.32230, val loss: 0.32568, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.31880, val loss: 0.32257, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31548, val loss: 0.31964, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.31234, val loss: 0.31688, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.30943, val loss: 0.31405, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.30667, val loss: 0.31138, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30385, val loss: 0.30888, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30118, val loss: 0.30652, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29895, val loss: 0.30443, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29691, val loss: 0.30242, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.29457, val loss: 0.30015, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29205, val loss: 0.29757, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28975, val loss: 0.29537, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28747, val loss: 0.29340, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28530, val loss: 0.29149, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.28306, val loss: 0.28918, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28110, val loss: 0.28727, in 0.016s 1 tree, 35 leaves, max depth = 15, train loss: 0.27912, val loss: 0.28553, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.27732, val loss: 0.28381, in 0.000s Fit 64 trees in 1.283 s, (2240 total leaves) Time spent computing histograms: 0.388s Time spent finding best splits: 0.073s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 28, Fold 1: Log loss = 0.28464131570100243, Average precision = 0.9620374566234541, ROC-AUC = 0.9565089491834071, Elapsed Time = 1.2902052999997977 seconds Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 28, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 9, train loss: 0.67318, val loss: 0.67290, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65421, val loss: 0.65369, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.63651, val loss: 0.63571, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62079, val loss: 0.61980, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.60512, val loss: 0.60407, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59126, val loss: 0.59005, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.57844, val loss: 0.57718, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56506, val loss: 0.56373, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.55252, val loss: 0.55104, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.54155, val loss: 0.53993, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.53026, val loss: 0.52859, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52048, val loss: 0.51868, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.51040, val loss: 0.50853, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50079, val loss: 0.49885, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49169, val loss: 0.48968, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.48383, val loss: 0.48172, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47560, val loss: 0.47341, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46781, val loss: 0.46563, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46039, val loss: 0.45815, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45335, val loss: 0.45106, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.44401, val loss: 0.44186, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.43757, val loss: 0.43541, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43146, val loss: 0.42930, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.42575, val loss: 0.42364, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42032, val loss: 0.41827, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.41237, val loss: 0.41050, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40727, val loss: 0.40542, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.39996, val loss: 0.39828, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39527, val loss: 0.39362, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39080, val loss: 0.38920, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.38418, val loss: 0.38280, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37805, val loss: 0.37692, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.37439, val loss: 0.37323, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37067, val loss: 0.36954, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.36700, val loss: 0.36584, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.36146, val loss: 0.36046, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.35623, val loss: 0.35538, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.35154, val loss: 0.35082, in 0.031s 1 tree, 35 leaves, max depth = 14, train loss: 0.34711, val loss: 0.34654, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.34347, val loss: 0.34304, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.33939, val loss: 0.33910, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.33589, val loss: 0.33578, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.33188, val loss: 0.33200, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.32869, val loss: 0.32893, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.32482, val loss: 0.32525, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.32126, val loss: 0.32193, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31788, val loss: 0.31879, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31473, val loss: 0.31573, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31163, val loss: 0.31286, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.30876, val loss: 0.31008, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30591, val loss: 0.30742, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.30328, val loss: 0.30489, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.30056, val loss: 0.30236, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29848, val loss: 0.30039, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.29593, val loss: 0.29804, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29344, val loss: 0.29574, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29118, val loss: 0.29358, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.28888, val loss: 0.29147, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28680, val loss: 0.28948, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.28452, val loss: 0.28740, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.28245, val loss: 0.28552, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28048, val loss: 0.28369, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.27865, val loss: 0.28196, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.27683, val loss: 0.28030, in 0.016s Fit 64 trees in 1.346 s, (2240 total leaves) Time spent computing histograms: 0.406s Time spent finding best splits: 0.076s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 28, Fold 2: Log loss = 0.2808506028525122, Average precision = 0.9628806869606636, ROC-AUC = 0.959936611915054, Elapsed Time = 1.3568864999997459 seconds Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 28, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 9, train loss: 0.67335, val loss: 0.67322, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65453, val loss: 0.65434, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.63715, val loss: 0.63693, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62067, val loss: 0.62039, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.60562, val loss: 0.60540, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59174, val loss: 0.59162, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.57887, val loss: 0.57886, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.56579, val loss: 0.56578, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.55348, val loss: 0.55346, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54228, val loss: 0.54237, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.53115, val loss: 0.53122, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.52114, val loss: 0.52132, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.51106, val loss: 0.51132, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.50152, val loss: 0.50178, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.49273, val loss: 0.49296, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.48417, val loss: 0.48443, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.47606, val loss: 0.47637, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46836, val loss: 0.46871, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.46114, val loss: 0.46155, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.45418, val loss: 0.45461, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44479, val loss: 0.44593, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.43843, val loss: 0.43961, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.43237, val loss: 0.43361, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42387, val loss: 0.42581, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.41833, val loss: 0.42038, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.41314, val loss: 0.41534, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40556, val loss: 0.40838, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.40102, val loss: 0.40397, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.39398, val loss: 0.39757, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.38959, val loss: 0.39324, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.38312, val loss: 0.38740, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.37916, val loss: 0.38347, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.37314, val loss: 0.37806, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.36928, val loss: 0.37430, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.36558, val loss: 0.37072, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.36040, val loss: 0.36602, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.35554, val loss: 0.36158, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.35094, val loss: 0.35742, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.34637, val loss: 0.35360, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.34272, val loss: 0.34984, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.33865, val loss: 0.34613, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.33525, val loss: 0.34265, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.33123, val loss: 0.33931, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.32805, val loss: 0.33601, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.32502, val loss: 0.33286, in 0.000s 1 tree, 35 leaves, max depth = 8, train loss: 0.32212, val loss: 0.32984, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31851, val loss: 0.32691, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.31520, val loss: 0.32396, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.31189, val loss: 0.32129, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30888, val loss: 0.31861, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.30586, val loss: 0.31613, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30311, val loss: 0.31366, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.30030, val loss: 0.31132, in 0.016s 1 tree, 35 leaves, max depth = 18, train loss: 0.29782, val loss: 0.30907, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.29523, val loss: 0.30696, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.29294, val loss: 0.30489, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29063, val loss: 0.30252, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.28842, val loss: 0.30019, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.28612, val loss: 0.29834, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.28394, val loss: 0.29656, in 0.016s 1 tree, 35 leaves, max depth = 15, train loss: 0.28198, val loss: 0.29480, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.27996, val loss: 0.29315, in 0.016s 1 tree, 35 leaves, max depth = 15, train loss: 0.27816, val loss: 0.29153, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.27609, val loss: 0.28921, in 0.016s Fit 64 trees in 1.408 s, (2240 total leaves) Time spent computing histograms: 0.420s Time spent finding best splits: 0.080s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 28, Fold 3: Log loss = 0.2772330542065684, Average precision = 0.9641706060782602, ROC-AUC = 0.96079649562751, Elapsed Time = 1.4219327000009798 seconds Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 28, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 9, train loss: 0.67344, val loss: 0.67260, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65470, val loss: 0.65308, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.63721, val loss: 0.63482, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62083, val loss: 0.61772, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.60547, val loss: 0.60166, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.59105, val loss: 0.58656, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.57827, val loss: 0.57307, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56518, val loss: 0.55936, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.55289, val loss: 0.54650, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.54198, val loss: 0.53512, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.53099, val loss: 0.52358, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.52104, val loss: 0.51329, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.51112, val loss: 0.50285, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50178, val loss: 0.49304, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.49299, val loss: 0.48372, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.48455, val loss: 0.47481, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47658, val loss: 0.46648, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.46896, val loss: 0.45847, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.46185, val loss: 0.45095, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45498, val loss: 0.44370, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.44575, val loss: 0.43430, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43946, val loss: 0.42765, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43348, val loss: 0.42132, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.42512, val loss: 0.41287, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.41965, val loss: 0.40707, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.41457, val loss: 0.40162, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.40962, val loss: 0.39639, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.40227, val loss: 0.38904, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.39770, val loss: 0.38417, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.39105, val loss: 0.37742, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.38459, val loss: 0.37089, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.38056, val loss: 0.36662, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.37459, val loss: 0.36057, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37104, val loss: 0.35676, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.36743, val loss: 0.35294, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.36205, val loss: 0.34758, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.35725, val loss: 0.34257, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.35270, val loss: 0.33784, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.34900, val loss: 0.33410, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.34479, val loss: 0.32973, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.34133, val loss: 0.32624, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.33731, val loss: 0.32252, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.33357, val loss: 0.31862, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.33040, val loss: 0.31543, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.32737, val loss: 0.31237, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.32447, val loss: 0.30944, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.32101, val loss: 0.30625, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31779, val loss: 0.30292, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.31463, val loss: 0.30003, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.31170, val loss: 0.29695, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.30878, val loss: 0.29425, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30610, val loss: 0.29146, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.30342, val loss: 0.28900, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30096, val loss: 0.28642, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29844, val loss: 0.28373, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.29603, val loss: 0.28147, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29368, val loss: 0.27894, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29143, val loss: 0.27652, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28923, val loss: 0.27448, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.28718, val loss: 0.27241, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28516, val loss: 0.27032, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28317, val loss: 0.26852, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28131, val loss: 0.26657, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.27948, val loss: 0.26491, in 0.016s Fit 64 trees in 1.361 s, (2240 total leaves) Time spent computing histograms: 0.400s Time spent finding best splits: 0.075s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 28, Fold 4: Log loss = 0.28037042522834454, Average precision = 0.9633028006312216, ROC-AUC = 0.959027792680339, Elapsed Time = 1.3770490999995673 seconds Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 28, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 9, train loss: 0.67321, val loss: 0.67246, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65418, val loss: 0.65279, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.63649, val loss: 0.63452, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.61979, val loss: 0.61728, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.60436, val loss: 0.60127, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.59016, val loss: 0.58647, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.57704, val loss: 0.57269, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.56379, val loss: 0.55892, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.55130, val loss: 0.54598, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.54029, val loss: 0.53455, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.52912, val loss: 0.52297, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.51929, val loss: 0.51277, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50935, val loss: 0.50248, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.49985, val loss: 0.49262, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.49115, val loss: 0.48341, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.48261, val loss: 0.47454, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.47451, val loss: 0.46617, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.46682, val loss: 0.45823, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.45966, val loss: 0.45079, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.45272, val loss: 0.44362, in 0.000s 1 tree, 35 leaves, max depth = 8, train loss: 0.44343, val loss: 0.43433, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.43708, val loss: 0.42779, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.43104, val loss: 0.42157, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.42260, val loss: 0.41323, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.41706, val loss: 0.40753, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.41194, val loss: 0.40224, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.40694, val loss: 0.39717, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.39956, val loss: 0.38992, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.39492, val loss: 0.38517, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.38826, val loss: 0.37860, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.38404, val loss: 0.37429, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37795, val loss: 0.36815, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37403, val loss: 0.36422, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.36833, val loss: 0.35861, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.36466, val loss: 0.35495, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.35925, val loss: 0.34973, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.35406, val loss: 0.34471, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.34920, val loss: 0.33996, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.34601, val loss: 0.33673, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.34178, val loss: 0.33236, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.33825, val loss: 0.32891, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.33415, val loss: 0.32520, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.33034, val loss: 0.32137, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.32711, val loss: 0.31823, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.32398, val loss: 0.31516, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.32027, val loss: 0.31158, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31679, val loss: 0.30846, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31363, val loss: 0.30530, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31045, val loss: 0.30245, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30756, val loss: 0.29956, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.30477, val loss: 0.29668, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30190, val loss: 0.29414, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.29920, val loss: 0.29170, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.29671, val loss: 0.28919, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.29422, val loss: 0.28685, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.29176, val loss: 0.28466, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.28952, val loss: 0.28240, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.28716, val loss: 0.27998, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.28520, val loss: 0.27822, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28301, val loss: 0.27628, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28104, val loss: 0.27423, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.27902, val loss: 0.27249, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.27697, val loss: 0.27057, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.27509, val loss: 0.26888, in 0.016s Fit 64 trees in 1.424 s, (2240 total leaves) Time spent computing histograms: 0.441s Time spent finding best splits: 0.080s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 28, Fold 5: Log loss = 0.2856709964198882, Average precision = 0.9600743059726313, ROC-AUC = 0.9556647529866413, Elapsed Time = 1.4378190000006725 seconds
Optimization Progress: 29%|##9 | 29/100 [05:42<14:05, 11.91s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 29, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 13 leaves, max depth = 5, train loss: 0.68812, val loss: 0.68797, in 0.015s 1 tree, 13 leaves, max depth = 5, train loss: 0.68339, val loss: 0.68309, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.67875, val loss: 0.67831, in 0.000s 1 tree, 16 leaves, max depth = 9, train loss: 0.67425, val loss: 0.67371, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.66979, val loss: 0.66911, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.66543, val loss: 0.66461, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.66114, val loss: 0.66019, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.65695, val loss: 0.65586, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.65283, val loss: 0.65162, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.64880, val loss: 0.64746, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.64484, val loss: 0.64337, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.64096, val loss: 0.63937, in 0.000s 1 tree, 15 leaves, max depth = 9, train loss: 0.63721, val loss: 0.63550, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.63348, val loss: 0.63164, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.62982, val loss: 0.62786, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.62627, val loss: 0.62421, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.62274, val loss: 0.62056, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.61929, val loss: 0.61699, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.61596, val loss: 0.61353, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.61263, val loss: 0.61008, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.60936, val loss: 0.60670, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.60616, val loss: 0.60338, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.60301, val loss: 0.60012, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.59992, val loss: 0.59691, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.59692, val loss: 0.59383, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.59395, val loss: 0.59074, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.59106, val loss: 0.58777, in 0.000s 1 tree, 23 leaves, max depth = 10, train loss: 0.58825, val loss: 0.58484, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.58543, val loss: 0.58192, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.58266, val loss: 0.57904, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.57994, val loss: 0.57622, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.57728, val loss: 0.57345, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.57465, val loss: 0.57073, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.57214, val loss: 0.56809, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.56961, val loss: 0.56546, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.56713, val loss: 0.56288, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.56469, val loss: 0.56034, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.56229, val loss: 0.55785, in 0.000s 1 tree, 22 leaves, max depth = 10, train loss: 0.55997, val loss: 0.55545, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.55769, val loss: 0.55310, in 0.000s Fit 40 trees in 0.517 s, (642 total leaves) Time spent computing histograms: 0.152s Time spent finding best splits: 0.016s Time spent applying splits: 0.012s Time spent predicting: 0.000s Trial 29, Fold 1: Log loss = 0.5583189356982935, Average precision = 0.8176059446619469, ROC-AUC = 0.8618765922006664, Elapsed Time = 0.5281728000009025 seconds Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 29, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 16 leaves, max depth = 8, train loss: 0.68832, val loss: 0.68809, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.68365, val loss: 0.68321, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.67907, val loss: 0.67842, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.67459, val loss: 0.67373, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.67021, val loss: 0.66913, in 0.000s 1 tree, 21 leaves, max depth = 12, train loss: 0.66592, val loss: 0.66463, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.66169, val loss: 0.66021, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.65757, val loss: 0.65587, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.65351, val loss: 0.65162, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.64954, val loss: 0.64745, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.64564, val loss: 0.64336, in 0.000s 1 tree, 21 leaves, max depth = 12, train loss: 0.64182, val loss: 0.63935, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.63808, val loss: 0.63541, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.63439, val loss: 0.63155, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.63075, val loss: 0.62769, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.62721, val loss: 0.62397, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.62374, val loss: 0.62032, in 0.000s 1 tree, 21 leaves, max depth = 12, train loss: 0.62034, val loss: 0.61674, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.61700, val loss: 0.61322, in 0.000s 1 tree, 21 leaves, max depth = 12, train loss: 0.61372, val loss: 0.60977, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.61050, val loss: 0.60638, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.60733, val loss: 0.60305, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.60423, val loss: 0.59978, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.60115, val loss: 0.59651, in 0.000s 1 tree, 21 leaves, max depth = 12, train loss: 0.59816, val loss: 0.59336, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.59524, val loss: 0.59027, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.59235, val loss: 0.58723, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.58953, val loss: 0.58425, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.58671, val loss: 0.58126, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.58394, val loss: 0.57832, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.58122, val loss: 0.57544, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.57859, val loss: 0.57266, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.57597, val loss: 0.56988, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.57344, val loss: 0.56720, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.57091, val loss: 0.56452, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.56843, val loss: 0.56188, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.56599, val loss: 0.55928, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.56359, val loss: 0.55673, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.56128, val loss: 0.55428, in 0.000s 1 tree, 49 leaves, max depth = 10, train loss: 0.55885, val loss: 0.55194, in 0.016s Fit 40 trees in 0.548 s, (802 total leaves) Time spent computing histograms: 0.157s Time spent finding best splits: 0.020s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 29, Fold 2: Log loss = 0.558383755264408, Average precision = 0.8717239998038205, ROC-AUC = 0.8998013166244859, Elapsed Time = 0.5486139999993611 seconds Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 29, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 18 leaves, max depth = 8, train loss: 0.68828, val loss: 0.68813, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.68366, val loss: 0.68335, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.67913, val loss: 0.67867, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.67469, val loss: 0.67409, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.67027, val loss: 0.66953, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.66595, val loss: 0.66506, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.66170, val loss: 0.66068, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.65754, val loss: 0.65638, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.65347, val loss: 0.65217, in 0.000s 1 tree, 23 leaves, max depth = 11, train loss: 0.64953, val loss: 0.64809, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.64561, val loss: 0.64404, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.64176, val loss: 0.64006, in 0.016s 1 tree, 23 leaves, max depth = 11, train loss: 0.63804, val loss: 0.63621, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.63434, val loss: 0.63239, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.63071, val loss: 0.62863, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.62721, val loss: 0.62501, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.62372, val loss: 0.62139, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.62029, val loss: 0.61784, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.61698, val loss: 0.61441, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61368, val loss: 0.61099, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.61044, val loss: 0.60763, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.60726, val loss: 0.60434, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.60414, val loss: 0.60110, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.60107, val loss: 0.59793, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.59811, val loss: 0.59485, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.59516, val loss: 0.59179, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.59231, val loss: 0.58883, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.58951, val loss: 0.58592, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.58671, val loss: 0.58302, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.58397, val loss: 0.58017, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.58128, val loss: 0.57737, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.57863, val loss: 0.57463, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.57603, val loss: 0.57193, in 0.000s 1 tree, 28 leaves, max depth = 13, train loss: 0.57352, val loss: 0.56931, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.57102, val loss: 0.56671, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.56856, val loss: 0.56415, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.56614, val loss: 0.56164, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.56377, val loss: 0.55917, in 0.016s 1 tree, 28 leaves, max depth = 13, train loss: 0.56147, val loss: 0.55678, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.55905, val loss: 0.55452, in 0.016s Fit 40 trees in 0.612 s, (784 total leaves) Time spent computing histograms: 0.175s Time spent finding best splits: 0.022s Time spent applying splits: 0.017s Time spent predicting: 0.000s Trial 29, Fold 3: Log loss = 0.5556490839538677, Average precision = 0.8730575981840494, ROC-AUC = 0.9010974714921364, Elapsed Time = 0.614298399999825 seconds Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 29, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 10 leaves, max depth = 5, train loss: 0.68826, val loss: 0.68798, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.68357, val loss: 0.68301, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.67898, val loss: 0.67814, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.67451, val loss: 0.67343, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.67010, val loss: 0.66875, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.66577, val loss: 0.66416, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.66154, val loss: 0.65965, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.65738, val loss: 0.65524, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.65331, val loss: 0.65091, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.64932, val loss: 0.64666, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.64540, val loss: 0.64249, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.64157, val loss: 0.63840, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.63785, val loss: 0.63444, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.63415, val loss: 0.63050, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.63053, val loss: 0.62663, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.62700, val loss: 0.62288, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.62351, val loss: 0.61915, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.62009, val loss: 0.61550, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.61677, val loss: 0.61195, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.61348, val loss: 0.60842, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.61025, val loss: 0.60496, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.60707, val loss: 0.60155, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.60396, val loss: 0.59821, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.60090, val loss: 0.59493, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.59792, val loss: 0.59175, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.59498, val loss: 0.58858, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.59211, val loss: 0.58551, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.58930, val loss: 0.58249, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.58651, val loss: 0.57949, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58378, val loss: 0.57654, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.58109, val loss: 0.57364, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.57845, val loss: 0.57078, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.57586, val loss: 0.56798, in 0.016s 1 tree, 27 leaves, max depth = 15, train loss: 0.57335, val loss: 0.56527, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.57085, val loss: 0.56256, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.56840, val loss: 0.55990, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.56599, val loss: 0.55729, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.56362, val loss: 0.55472, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.56131, val loss: 0.55223, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.55904, val loss: 0.54978, in 0.000s Fit 40 trees in 0.579 s, (608 total leaves) Time spent computing histograms: 0.166s Time spent finding best splits: 0.019s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 29, Fold 4: Log loss = 0.557560007433454, Average precision = 0.8233037899458493, ROC-AUC = 0.8688659568278767, Elapsed Time = 0.5799826999991637 seconds Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 29, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 14 leaves, max depth = 6, train loss: 0.68817, val loss: 0.68786, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.68343, val loss: 0.68281, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.67879, val loss: 0.67787, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.67429, val loss: 0.67308, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.66983, val loss: 0.66832, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.66546, val loss: 0.66366, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.66118, val loss: 0.65909, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.65698, val loss: 0.65460, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.65287, val loss: 0.65020, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.64883, val loss: 0.64589, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.64487, val loss: 0.64165, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.64099, val loss: 0.63749, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.63723, val loss: 0.63347, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.63349, val loss: 0.62947, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.62983, val loss: 0.62554, in 0.000s 1 tree, 23 leaves, max depth = 13, train loss: 0.62627, val loss: 0.62173, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.62275, val loss: 0.61794, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.61929, val loss: 0.61422, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.61593, val loss: 0.61062, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.61260, val loss: 0.60703, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.60933, val loss: 0.60351, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.60612, val loss: 0.60006, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.60297, val loss: 0.59666, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.59988, val loss: 0.59333, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59688, val loss: 0.59010, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.59390, val loss: 0.58688, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59101, val loss: 0.58376, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.58817, val loss: 0.58070, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.58535, val loss: 0.57764, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.58258, val loss: 0.57464, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.57986, val loss: 0.57170, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.57719, val loss: 0.56880, in 0.000s 1 tree, 15 leaves, max depth = 7, train loss: 0.57457, val loss: 0.56595, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.57203, val loss: 0.56320, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.56950, val loss: 0.56045, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.56702, val loss: 0.55775, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.56458, val loss: 0.55509, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.56218, val loss: 0.55248, in 0.016s 1 tree, 26 leaves, max depth = 12, train loss: 0.55986, val loss: 0.54996, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.55757, val loss: 0.54748, in 0.016s Fit 40 trees in 0.565 s, (688 total leaves) Time spent computing histograms: 0.165s Time spent finding best splits: 0.021s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 29, Fold 5: Log loss = 0.5614857329703491, Average precision = 0.816707956377658, ROC-AUC = 0.8589935157832154, Elapsed Time = 0.5744513999998162 seconds
Optimization Progress: 30%|### | 30/100 [05:51<12:56, 11.09s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 30, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.155 s 0.040 GB of training data: 0.003 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 79 leaves, max depth = 17, train loss: 0.68638, val loss: 0.68620, in 0.016s 1 tree, 84 leaves, max depth = 19, train loss: 0.67984, val loss: 0.67947, in 0.000s 1 tree, 84 leaves, max depth = 19, train loss: 0.67349, val loss: 0.67292, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.66737, val loss: 0.66667, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.66144, val loss: 0.66057, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.65567, val loss: 0.65464, in 0.000s 1 tree, 80 leaves, max depth = 18, train loss: 0.65010, val loss: 0.64890, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.64467, val loss: 0.64335, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.63935, val loss: 0.63787, in 0.000s 1 tree, 84 leaves, max depth = 18, train loss: 0.63410, val loss: 0.63244, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.62907, val loss: 0.62724, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.62415, val loss: 0.62220, in 0.000s 1 tree, 84 leaves, max depth = 17, train loss: 0.61937, val loss: 0.61727, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.61464, val loss: 0.61238, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.61010, val loss: 0.60770, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.60569, val loss: 0.60314, in 0.000s 1 tree, 84 leaves, max depth = 16, train loss: 0.60137, val loss: 0.59871, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.59716, val loss: 0.59439, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.59310, val loss: 0.59023, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.58904, val loss: 0.58602, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.58509, val loss: 0.58191, in 0.000s 1 tree, 84 leaves, max depth = 18, train loss: 0.58123, val loss: 0.57791, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.57747, val loss: 0.57401, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.57381, val loss: 0.57020, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.57029, val loss: 0.56659, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.56686, val loss: 0.56306, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.56352, val loss: 0.55958, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.56027, val loss: 0.55619, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.55711, val loss: 0.55292, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.55380, val loss: 0.54983, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.55075, val loss: 0.54668, in 0.000s 1 tree, 85 leaves, max depth = 14, train loss: 0.54777, val loss: 0.54356, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.54479, val loss: 0.54045, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.54164, val loss: 0.53752, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.53876, val loss: 0.53452, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.53601, val loss: 0.53167, in 0.000s 1 tree, 84 leaves, max depth = 18, train loss: 0.53327, val loss: 0.52881, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.53027, val loss: 0.52603, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.52768, val loss: 0.52335, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.52515, val loss: 0.52070, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.52268, val loss: 0.51815, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.51982, val loss: 0.51550, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.51743, val loss: 0.51304, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.51466, val loss: 0.51048, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.51236, val loss: 0.50806, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.50968, val loss: 0.50558, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.50706, val loss: 0.50316, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.50481, val loss: 0.50079, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.50227, val loss: 0.49845, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.49979, val loss: 0.49617, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.49769, val loss: 0.49395, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.49528, val loss: 0.49174, in 0.016s [53/100] 1 tree, 128 leaves, max depth = 15, train loss: 0.49294, val loss: 0.48958, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.49087, val loss: 0.48740, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.48860, val loss: 0.48531, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.48637, val loss: 0.48328, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.48420, val loss: 0.48129, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.48229, val loss: 0.47926, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.48018, val loss: 0.47733, in 0.016s 1 tree, 83 leaves, max depth = 19, train loss: 0.47833, val loss: 0.47542, in 0.000s 1 tree, 85 leaves, max depth = 13, train loss: 0.47652, val loss: 0.47349, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.47448, val loss: 0.47164, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.47249, val loss: 0.46982, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.47070, val loss: 0.46796, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46877, val loss: 0.46620, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.46709, val loss: 0.46445, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.46544, val loss: 0.46273, in 0.016s 1 tree, 83 leaves, max depth = 19, train loss: 0.46384, val loss: 0.46107, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46198, val loss: 0.45938, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46017, val loss: 0.45774, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.45839, val loss: 0.45613, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.45686, val loss: 0.45453, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.45532, val loss: 0.45292, in 0.000s 1 tree, 80 leaves, max depth = 18, train loss: 0.45386, val loss: 0.45138, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.45243, val loss: 0.44987, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.45102, val loss: 0.44836, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.44961, val loss: 0.44685, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.44822, val loss: 0.44541, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.44692, val loss: 0.44404, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.44561, val loss: 0.44263, in 0.000s 1 tree, 84 leaves, max depth = 13, train loss: 0.44432, val loss: 0.44125, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.44266, val loss: 0.43975, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.44141, val loss: 0.43841, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.43979, val loss: 0.43696, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.43821, val loss: 0.43554, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.43701, val loss: 0.43424, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.43547, val loss: 0.43286, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.43431, val loss: 0.43161, in 0.000s 1 tree, 84 leaves, max depth = 13, train loss: 0.43318, val loss: 0.43038, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.43168, val loss: 0.42904, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.43058, val loss: 0.42785, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.42912, val loss: 0.42655, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42810, val loss: 0.42543, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.42706, val loss: 0.42432, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42607, val loss: 0.42323, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42511, val loss: 0.42216, in 0.000s 1 tree, 84 leaves, max depth = 17, train loss: 0.42408, val loss: 0.42105, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42315, val loss: 0.42002, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.42173, val loss: 0.41877, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.42074, val loss: 0.41770, in 0.016s Fit 100 trees in 1.485 s, (9287 total leaves) Time spent computing histograms: 0.510s Time spent finding best splits: 0.125s Time spent applying splits: 0.152s Time spent predicting: 0.000s Trial 30, Fold 1: Log loss = 0.4232796007217628, Average precision = 0.9459918060041705, ROC-AUC = 0.9419506190762966, Elapsed Time = 1.4879868999996688 seconds Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 30, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 21, train loss: 0.68647, val loss: 0.68619, in 0.000s 1 tree, 81 leaves, max depth = 18, train loss: 0.68003, val loss: 0.67948, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.67370, val loss: 0.67283, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.66763, val loss: 0.66652, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.66171, val loss: 0.66034, in 0.000s 1 tree, 82 leaves, max depth = 18, train loss: 0.65596, val loss: 0.65432, in 0.016s 1 tree, 78 leaves, max depth = 21, train loss: 0.65037, val loss: 0.64849, in 0.016s 1 tree, 83 leaves, max depth = 20, train loss: 0.64493, val loss: 0.64280, in 0.000s 1 tree, 85 leaves, max depth = 11, train loss: 0.63960, val loss: 0.63719, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.63437, val loss: 0.63169, in 0.016s 1 tree, 81 leaves, max depth = 18, train loss: 0.62934, val loss: 0.62642, in 0.016s 1 tree, 84 leaves, max depth = 19, train loss: 0.62443, val loss: 0.62127, in 0.000s 1 tree, 85 leaves, max depth = 11, train loss: 0.61964, val loss: 0.61623, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.61493, val loss: 0.61126, in 0.016s 1 tree, 82 leaves, max depth = 16, train loss: 0.61042, val loss: 0.60655, in 0.000s 1 tree, 85 leaves, max depth = 11, train loss: 0.60600, val loss: 0.60188, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.60170, val loss: 0.59737, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.59752, val loss: 0.59298, in 0.000s 1 tree, 83 leaves, max depth = 23, train loss: 0.59345, val loss: 0.58871, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.58941, val loss: 0.58443, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.58548, val loss: 0.58027, in 0.000s 1 tree, 85 leaves, max depth = 15, train loss: 0.58164, val loss: 0.57621, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.57790, val loss: 0.57225, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.57425, val loss: 0.56839, in 0.016s 1 tree, 82 leaves, max depth = 16, train loss: 0.57075, val loss: 0.56470, in 0.000s 1 tree, 82 leaves, max depth = 16, train loss: 0.56734, val loss: 0.56111, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.56401, val loss: 0.55761, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.56077, val loss: 0.55419, in 0.000s 1 tree, 128 leaves, max depth = 16, train loss: 0.55742, val loss: 0.55097, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.55428, val loss: 0.54764, in 0.016s 1 tree, 78 leaves, max depth = 18, train loss: 0.55122, val loss: 0.54443, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.54824, val loss: 0.54129, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.54528, val loss: 0.53814, in 0.000s 1 tree, 128 leaves, max depth = 17, train loss: 0.54213, val loss: 0.53511, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.53927, val loss: 0.53206, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.53652, val loss: 0.52915, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.53379, val loss: 0.52625, in 0.000s 1 tree, 127 leaves, max depth = 16, train loss: 0.53079, val loss: 0.52336, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.52821, val loss: 0.52062, in 0.016s 1 tree, 82 leaves, max depth = 16, train loss: 0.52569, val loss: 0.51796, in 0.000s 1 tree, 85 leaves, max depth = 18, train loss: 0.52322, val loss: 0.51534, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.52036, val loss: 0.51259, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.51799, val loss: 0.51009, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.51522, val loss: 0.50743, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.51293, val loss: 0.50500, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.51025, val loss: 0.50243, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.50762, val loss: 0.49992, in 0.000s 1 tree, 85 leaves, max depth = 16, train loss: 0.50539, val loss: 0.49752, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.50285, val loss: 0.49509, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.50037, val loss: 0.49272, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.49828, val loss: 0.49050, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.49587, val loss: 0.48820, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.49352, val loss: 0.48596, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.49147, val loss: 0.48376, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.48920, val loss: 0.48159, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.48697, val loss: 0.47947, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.48480, val loss: 0.47740, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.48290, val loss: 0.47538, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.48079, val loss: 0.47337, in 0.000s 1 tree, 83 leaves, max depth = 26, train loss: 0.47895, val loss: 0.47141, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.47715, val loss: 0.46949, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.47511, val loss: 0.46756, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.47312, val loss: 0.46567, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.47138, val loss: 0.46379, in 0.016s 1 tree, 127 leaves, max depth = 18, train loss: 0.46945, val loss: 0.46196, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.46778, val loss: 0.46017, in 0.000s 1 tree, 82 leaves, max depth = 14, train loss: 0.46614, val loss: 0.45843, in 0.016s 1 tree, 83 leaves, max depth = 26, train loss: 0.46455, val loss: 0.45673, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46269, val loss: 0.45496, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46087, val loss: 0.45324, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.45909, val loss: 0.45156, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.45756, val loss: 0.44990, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.45607, val loss: 0.44831, in 0.000s 1 tree, 79 leaves, max depth = 19, train loss: 0.45462, val loss: 0.44677, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.45320, val loss: 0.44525, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.45181, val loss: 0.44377, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.45042, val loss: 0.44226, in 0.000s 1 tree, 78 leaves, max depth = 19, train loss: 0.44909, val loss: 0.44086, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.44780, val loss: 0.43948, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.44650, val loss: 0.43806, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.44523, val loss: 0.43668, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.44356, val loss: 0.43511, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.44233, val loss: 0.43377, in 0.004s 1 tree, 128 leaves, max depth = 16, train loss: 0.44070, val loss: 0.43223, in 0.013s 1 tree, 128 leaves, max depth = 16, train loss: 0.43911, val loss: 0.43074, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.43793, val loss: 0.42945, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.43638, val loss: 0.42799, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.43523, val loss: 0.42674, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.43411, val loss: 0.42552, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.43261, val loss: 0.42411, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.43152, val loss: 0.42292, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.43005, val loss: 0.42154, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42906, val loss: 0.42052, in 0.000s 1 tree, 83 leaves, max depth = 22, train loss: 0.42804, val loss: 0.41942, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42708, val loss: 0.41842, in 0.000s 1 tree, 83 leaves, max depth = 22, train loss: 0.42609, val loss: 0.41735, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.42509, val loss: 0.41626, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42416, val loss: 0.41529, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.42274, val loss: 0.41397, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.42178, val loss: 0.41294, in 0.016s Fit 100 trees in 1.534 s, (9315 total leaves) Time spent computing histograms: 0.519s Time spent finding best splits: 0.128s Time spent applying splits: 0.154s Time spent predicting: 0.016s Trial 30, Fold 2: Log loss = 0.42380552050116543, Average precision = 0.9431906991089813, ROC-AUC = 0.9425675603903075, Elapsed Time = 1.5441061000001355 seconds Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 30, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 14, train loss: 0.68646, val loss: 0.68625, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.68007, val loss: 0.67968, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.67377, val loss: 0.67320, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.66771, val loss: 0.66692, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.66183, val loss: 0.66087, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.65612, val loss: 0.65498, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.65059, val loss: 0.64926, in 0.016s 1 tree, 80 leaves, max depth = 18, train loss: 0.64520, val loss: 0.64369, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.63991, val loss: 0.63818, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.63471, val loss: 0.63282, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.62971, val loss: 0.62767, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.62483, val loss: 0.62260, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.62008, val loss: 0.61765, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.61540, val loss: 0.61282, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.61094, val loss: 0.60819, in 0.016s 1 tree, 83 leaves, max depth = 18, train loss: 0.60657, val loss: 0.60367, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.60231, val loss: 0.59927, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.59815, val loss: 0.59497, in 0.000s 1 tree, 80 leaves, max depth = 19, train loss: 0.59411, val loss: 0.59077, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.59009, val loss: 0.58662, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.58617, val loss: 0.58258, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.58235, val loss: 0.57863, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.57863, val loss: 0.57479, in 0.000s 1 tree, 83 leaves, max depth = 14, train loss: 0.57500, val loss: 0.57103, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.57152, val loss: 0.56743, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.56813, val loss: 0.56392, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.56482, val loss: 0.56050, in 0.000s 1 tree, 84 leaves, max depth = 13, train loss: 0.56160, val loss: 0.55716, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.55818, val loss: 0.55399, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.55485, val loss: 0.55090, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.55179, val loss: 0.54770, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.54879, val loss: 0.54459, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.54580, val loss: 0.54149, in 0.000s 1 tree, 125 leaves, max depth = 15, train loss: 0.54264, val loss: 0.53856, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.53976, val loss: 0.53557, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.53699, val loss: 0.53265, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.53425, val loss: 0.52980, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.53124, val loss: 0.52702, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.52864, val loss: 0.52432, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.52611, val loss: 0.52168, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.52363, val loss: 0.51907, in 0.000s 1 tree, 124 leaves, max depth = 15, train loss: 0.52076, val loss: 0.51641, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.51837, val loss: 0.51393, in 0.000s 1 tree, 125 leaves, max depth = 15, train loss: 0.51559, val loss: 0.51136, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.51329, val loss: 0.50896, in 0.000s 1 tree, 125 leaves, max depth = 15, train loss: 0.51059, val loss: 0.50647, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.50796, val loss: 0.50404, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.50571, val loss: 0.50169, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.50316, val loss: 0.49934, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.50067, val loss: 0.49705, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.49857, val loss: 0.49485, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.49615, val loss: 0.49263, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.49380, val loss: 0.49046, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.49173, val loss: 0.48830, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.48945, val loss: 0.48620, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.48722, val loss: 0.48416, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.48504, val loss: 0.48216, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.48312, val loss: 0.48015, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.48100, val loss: 0.47821, in 0.016s 1 tree, 80 leaves, max depth = 23, train loss: 0.47915, val loss: 0.47624, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.47734, val loss: 0.47434, in 0.000s 1 tree, 124 leaves, max depth = 15, train loss: 0.47530, val loss: 0.47247, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.47330, val loss: 0.47065, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.47155, val loss: 0.46877, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.46961, val loss: 0.46700, in 0.031s 1 tree, 84 leaves, max depth = 14, train loss: 0.46793, val loss: 0.46522, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.46628, val loss: 0.46349, in 0.016s 1 tree, 80 leaves, max depth = 23, train loss: 0.46468, val loss: 0.46178, in 0.000s 1 tree, 125 leaves, max depth = 14, train loss: 0.46281, val loss: 0.46008, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.46099, val loss: 0.45842, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.45920, val loss: 0.45680, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.45766, val loss: 0.45513, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.45617, val loss: 0.45355, in 0.016s 1 tree, 83 leaves, max depth = 18, train loss: 0.45471, val loss: 0.45200, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.45328, val loss: 0.45049, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.45189, val loss: 0.44901, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.45048, val loss: 0.44753, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.44910, val loss: 0.44604, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.44780, val loss: 0.44466, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.44649, val loss: 0.44327, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.44521, val loss: 0.44192, in 0.000s 1 tree, 125 leaves, max depth = 15, train loss: 0.44353, val loss: 0.44040, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.44229, val loss: 0.43908, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.44066, val loss: 0.43761, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.43907, val loss: 0.43617, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.43787, val loss: 0.43490, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.43632, val loss: 0.43350, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.43517, val loss: 0.43227, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.43404, val loss: 0.43107, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.43253, val loss: 0.42971, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.43143, val loss: 0.42854, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.42996, val loss: 0.42722, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42894, val loss: 0.42627, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.42789, val loss: 0.42515, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.42690, val loss: 0.42423, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42592, val loss: 0.42333, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.42491, val loss: 0.42225, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42397, val loss: 0.42138, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.42254, val loss: 0.42010, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.42157, val loss: 0.41906, in 0.000s Fit 100 trees in 1.674 s, (9225 total leaves) Time spent computing histograms: 0.570s Time spent finding best splits: 0.144s Time spent applying splits: 0.172s Time spent predicting: 0.031s Trial 30, Fold 3: Log loss = 0.4193375609257467, Average precision = 0.9465947262409895, ROC-AUC = 0.945489996966224, Elapsed Time = 1.6884934999998222 seconds Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 30, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 79 leaves, max depth = 13, train loss: 0.68647, val loss: 0.68614, in 0.016s 1 tree, 81 leaves, max depth = 19, train loss: 0.68000, val loss: 0.67930, in 0.016s 1 tree, 81 leaves, max depth = 19, train loss: 0.67370, val loss: 0.67264, in 0.000s 1 tree, 81 leaves, max depth = 16, train loss: 0.66762, val loss: 0.66623, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.66171, val loss: 0.66000, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.65597, val loss: 0.65394, in 0.000s 1 tree, 82 leaves, max depth = 13, train loss: 0.65042, val loss: 0.64808, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.64503, val loss: 0.64239, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.63973, val loss: 0.63678, in 0.000s 1 tree, 84 leaves, max depth = 20, train loss: 0.63453, val loss: 0.63125, in 0.016s 1 tree, 82 leaves, max depth = 19, train loss: 0.62953, val loss: 0.62593, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.62464, val loss: 0.62074, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.61988, val loss: 0.61569, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.61519, val loss: 0.61069, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.61068, val loss: 0.60590, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.60632, val loss: 0.60125, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.60202, val loss: 0.59668, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.59784, val loss: 0.59223, in 0.000s 1 tree, 80 leaves, max depth = 13, train loss: 0.59380, val loss: 0.58793, in 0.016s 1 tree, 83 leaves, max depth = 20, train loss: 0.58979, val loss: 0.58363, in 0.016s 1 tree, 83 leaves, max depth = 20, train loss: 0.58587, val loss: 0.57943, in 0.000s 1 tree, 83 leaves, max depth = 20, train loss: 0.58206, val loss: 0.57533, in 0.016s 1 tree, 83 leaves, max depth = 20, train loss: 0.57834, val loss: 0.57133, in 0.016s 1 tree, 83 leaves, max depth = 20, train loss: 0.57472, val loss: 0.56742, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.57122, val loss: 0.56368, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.56781, val loss: 0.56002, in 0.016s 1 tree, 83 leaves, max depth = 19, train loss: 0.56449, val loss: 0.55644, in 0.016s 1 tree, 83 leaves, max depth = 19, train loss: 0.56126, val loss: 0.55294, in 0.000s 1 tree, 128 leaves, max depth = 13, train loss: 0.55781, val loss: 0.54959, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.55468, val loss: 0.54622, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.55164, val loss: 0.54296, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.54868, val loss: 0.53974, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.54573, val loss: 0.53654, in 0.016s 1 tree, 128 leaves, max depth = 14, train loss: 0.54249, val loss: 0.53340, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.53966, val loss: 0.53031, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.53692, val loss: 0.52736, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.53422, val loss: 0.52441, in 0.000s 1 tree, 128 leaves, max depth = 14, train loss: 0.53114, val loss: 0.52142, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.52856, val loss: 0.51863, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.52605, val loss: 0.51589, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.52360, val loss: 0.51323, in 0.000s [42/100] 1 tree, 128 leaves, max depth = 16, train loss: 0.52066, val loss: 0.51038, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.51829, val loss: 0.50781, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.51544, val loss: 0.50505, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.51316, val loss: 0.50254, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.51040, val loss: 0.49987, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.50770, val loss: 0.49726, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.50549, val loss: 0.49483, in 0.000s 1 tree, 128 leaves, max depth = 16, train loss: 0.50288, val loss: 0.49230, in 0.031s 1 tree, 128 leaves, max depth = 16, train loss: 0.50032, val loss: 0.48983, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.49824, val loss: 0.48754, in 0.000s 1 tree, 128 leaves, max depth = 16, train loss: 0.49576, val loss: 0.48514, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.49334, val loss: 0.48281, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.49132, val loss: 0.48058, in 0.000s 1 tree, 128 leaves, max depth = 14, train loss: 0.48898, val loss: 0.47831, in 0.016s 1 tree, 128 leaves, max depth = 14, train loss: 0.48668, val loss: 0.47610, in 0.016s 1 tree, 128 leaves, max depth = 14, train loss: 0.48445, val loss: 0.47393, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.48255, val loss: 0.47184, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.48038, val loss: 0.46974, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.47857, val loss: 0.46776, in 0.016s 1 tree, 83 leaves, max depth = 18, train loss: 0.47678, val loss: 0.46578, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.47467, val loss: 0.46375, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.47262, val loss: 0.46177, in 0.031s 1 tree, 83 leaves, max depth = 12, train loss: 0.47085, val loss: 0.45984, in 0.000s 1 tree, 128 leaves, max depth = 17, train loss: 0.46886, val loss: 0.45791, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.46720, val loss: 0.45609, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.46558, val loss: 0.45430, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.46394, val loss: 0.45250, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46202, val loss: 0.45066, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46015, val loss: 0.44886, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.45832, val loss: 0.44709, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.45682, val loss: 0.44543, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.45529, val loss: 0.44374, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.45386, val loss: 0.44214, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.45246, val loss: 0.44058, in 0.031s 1 tree, 83 leaves, max depth = 15, train loss: 0.45108, val loss: 0.43904, in 0.031s 1 tree, 84 leaves, max depth = 17, train loss: 0.44971, val loss: 0.43750, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.44834, val loss: 0.43599, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.44707, val loss: 0.43457, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.44579, val loss: 0.43312, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.44454, val loss: 0.43171, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.44283, val loss: 0.43007, in 0.031s 1 tree, 84 leaves, max depth = 17, train loss: 0.44162, val loss: 0.42870, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.43995, val loss: 0.42710, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.43832, val loss: 0.42554, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.43716, val loss: 0.42422, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.43556, val loss: 0.42270, in 0.031s 1 tree, 84 leaves, max depth = 17, train loss: 0.43444, val loss: 0.42142, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.43334, val loss: 0.42017, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.43179, val loss: 0.41869, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.43073, val loss: 0.41748, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.42922, val loss: 0.41604, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42823, val loss: 0.41498, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.42721, val loss: 0.41382, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42625, val loss: 0.41279, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42531, val loss: 0.41178, in 0.000s 1 tree, 84 leaves, max depth = 17, train loss: 0.42432, val loss: 0.41065, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42340, val loss: 0.40967, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.42194, val loss: 0.40828, in 0.016s 1 tree, 84 leaves, max depth = 17, train loss: 0.42098, val loss: 0.40718, in 0.000s Fit 100 trees in 1.720 s, (9210 total leaves) Time spent computing histograms: 0.582s Time spent finding best splits: 0.158s Time spent applying splits: 0.190s Time spent predicting: 0.031s Trial 30, Fold 4: Log loss = 0.4216082511273579, Average precision = 0.9466523855487519, ROC-AUC = 0.9441516091189367, Elapsed Time = 1.738201800000752 seconds Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 30, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 83 leaves, max depth = 14, train loss: 0.68635, val loss: 0.68594, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.67980, val loss: 0.67897, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.67344, val loss: 0.67218, in 0.016s 1 tree, 81 leaves, max depth = 18, train loss: 0.66733, val loss: 0.66569, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.66137, val loss: 0.65935, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.65558, val loss: 0.65317, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.64995, val loss: 0.64719, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.64448, val loss: 0.64137, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.63913, val loss: 0.63565, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.63387, val loss: 0.63001, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.62881, val loss: 0.62459, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.62387, val loss: 0.61928, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.61908, val loss: 0.61417, in 0.000s 1 tree, 84 leaves, max depth = 16, train loss: 0.61434, val loss: 0.60908, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.60979, val loss: 0.60422, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.60536, val loss: 0.59949, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.60103, val loss: 0.59480, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.59681, val loss: 0.59024, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.59271, val loss: 0.58584, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.58865, val loss: 0.58145, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.58468, val loss: 0.57716, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.58082, val loss: 0.57298, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.57706, val loss: 0.56890, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.57339, val loss: 0.56492, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.56986, val loss: 0.56110, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.56641, val loss: 0.55735, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.56305, val loss: 0.55371, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.55977, val loss: 0.55015, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.55658, val loss: 0.54671, in 0.000s 1 tree, 128 leaves, max depth = 20, train loss: 0.55323, val loss: 0.54354, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.55016, val loss: 0.54019, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.54715, val loss: 0.53692, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.54417, val loss: 0.53365, in 0.016s 1 tree, 128 leaves, max depth = 20, train loss: 0.54098, val loss: 0.53066, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.53810, val loss: 0.52750, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.53533, val loss: 0.52447, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.53259, val loss: 0.52146, in 0.000s 1 tree, 128 leaves, max depth = 18, train loss: 0.52956, val loss: 0.51862, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.52695, val loss: 0.51575, in 0.000s 1 tree, 82 leaves, max depth = 17, train loss: 0.52441, val loss: 0.51297, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.52193, val loss: 0.51023, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.51904, val loss: 0.50753, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.51664, val loss: 0.50489, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.51384, val loss: 0.50227, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.51153, val loss: 0.49973, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.50881, val loss: 0.49719, in 0.000s 1 tree, 128 leaves, max depth = 19, train loss: 0.50616, val loss: 0.49473, in 0.031s 1 tree, 84 leaves, max depth = 15, train loss: 0.50391, val loss: 0.49224, in 0.000s 1 tree, 128 leaves, max depth = 20, train loss: 0.50134, val loss: 0.48985, in 0.016s 1 tree, 128 leaves, max depth = 20, train loss: 0.49883, val loss: 0.48752, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.49672, val loss: 0.48519, in 0.016s 1 tree, 128 leaves, max depth = 21, train loss: 0.49428, val loss: 0.48293, in 0.016s 1 tree, 128 leaves, max depth = 21, train loss: 0.49190, val loss: 0.48073, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.48984, val loss: 0.47844, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.48753, val loss: 0.47631, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.48528, val loss: 0.47422, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.48308, val loss: 0.47219, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.48116, val loss: 0.47006, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.47902, val loss: 0.46809, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.47717, val loss: 0.46605, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.47535, val loss: 0.46403, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.47328, val loss: 0.46213, in 0.000s 1 tree, 128 leaves, max depth = 19, train loss: 0.47127, val loss: 0.46028, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.46952, val loss: 0.45833, in 0.016s 1 tree, 128 leaves, max depth = 19, train loss: 0.46756, val loss: 0.45654, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.46587, val loss: 0.45465, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.46422, val loss: 0.45281, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.46262, val loss: 0.45101, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46073, val loss: 0.44929, in 0.031s 1 tree, 128 leaves, max depth = 16, train loss: 0.45889, val loss: 0.44761, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.45708, val loss: 0.44596, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.45555, val loss: 0.44424, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.45405, val loss: 0.44255, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.45259, val loss: 0.44093, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.45117, val loss: 0.43933, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.44977, val loss: 0.43775, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.44837, val loss: 0.43617, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.44705, val loss: 0.43467, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.44575, val loss: 0.43320, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.44444, val loss: 0.43171, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.44316, val loss: 0.43026, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.44147, val loss: 0.42873, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.44023, val loss: 0.42732, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.43858, val loss: 0.42583, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.43696, val loss: 0.42438, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.43577, val loss: 0.42303, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.43419, val loss: 0.42161, in 0.031s 1 tree, 84 leaves, max depth = 15, train loss: 0.43304, val loss: 0.42030, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.43192, val loss: 0.41901, in 0.000s 1 tree, 128 leaves, max depth = 18, train loss: 0.43038, val loss: 0.41764, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.42929, val loss: 0.41639, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.42780, val loss: 0.41505, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42680, val loss: 0.41409, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.42578, val loss: 0.41293, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42481, val loss: 0.41200, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.42386, val loss: 0.41108, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.42284, val loss: 0.40991, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.42185, val loss: 0.40877, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.42040, val loss: 0.40749, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.41944, val loss: 0.40637, in 0.016s Fit 100 trees in 1.626 s, (9266 total leaves) Time spent computing histograms: 0.551s Time spent finding best splits: 0.141s Time spent applying splits: 0.168s Time spent predicting: 0.000s Trial 30, Fold 5: Log loss = 0.427324505663281, Average precision = 0.9425507706044726, ROC-AUC = 0.9406792635118816, Elapsed Time = 1.635113199999978 seconds
Optimization Progress: 31%|###1 | 31/100 [06:06<14:02, 12.21s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 31, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 194 leaves, max depth = 18, train loss: 0.68167, val loss: 0.68189, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.67091, val loss: 0.67120, in 0.031s 1 tree, 63 leaves, max depth = 10, train loss: 0.66041, val loss: 0.66056, in 0.016s 1 tree, 183 leaves, max depth = 15, train loss: 0.64992, val loss: 0.65031, in 0.031s 1 tree, 64 leaves, max depth = 13, train loss: 0.64028, val loss: 0.64060, in 0.016s 1 tree, 184 leaves, max depth = 16, train loss: 0.63057, val loss: 0.63109, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.62149, val loss: 0.62184, in 0.016s 1 tree, 113 leaves, max depth = 15, train loss: 0.61202, val loss: 0.61229, in 0.016s 1 tree, 116 leaves, max depth = 16, train loss: 0.60307, val loss: 0.60325, in 0.031s 1 tree, 207 leaves, max depth = 15, train loss: 0.59509, val loss: 0.59550, in 0.016s 1 tree, 213 leaves, max depth = 16, train loss: 0.58714, val loss: 0.58771, in 0.031s 1 tree, 116 leaves, max depth = 16, train loss: 0.57905, val loss: 0.57950, in 0.031s 1 tree, 119 leaves, max depth = 15, train loss: 0.57112, val loss: 0.57159, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.56406, val loss: 0.56447, in 0.031s 1 tree, 122 leaves, max depth = 20, train loss: 0.55662, val loss: 0.55708, in 0.016s 1 tree, 110 leaves, max depth = 16, train loss: 0.55005, val loss: 0.55044, in 0.016s 1 tree, 203 leaves, max depth = 17, train loss: 0.54366, val loss: 0.54406, in 0.031s 1 tree, 213 leaves, max depth = 13, train loss: 0.53740, val loss: 0.53798, in 0.016s 1 tree, 147 leaves, max depth = 17, train loss: 0.53085, val loss: 0.53154, in 0.031s 1 tree, 129 leaves, max depth = 15, train loss: 0.52507, val loss: 0.52568, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.51936, val loss: 0.51989, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.51393, val loss: 0.51441, in 0.031s 1 tree, 213 leaves, max depth = 16, train loss: 0.50856, val loss: 0.50915, in 0.016s 1 tree, 213 leaves, max depth = 14, train loss: 0.50345, val loss: 0.50418, in 0.031s 1 tree, 186 leaves, max depth = 17, train loss: 0.49822, val loss: 0.49891, in 0.031s 1 tree, 129 leaves, max depth = 14, train loss: 0.49343, val loss: 0.49405, in 0.016s 1 tree, 213 leaves, max depth = 18, train loss: 0.48866, val loss: 0.48948, in 0.031s 1 tree, 213 leaves, max depth = 16, train loss: 0.48393, val loss: 0.48494, in 0.031s 1 tree, 131 leaves, max depth = 15, train loss: 0.47906, val loss: 0.48000, in 0.031s 1 tree, 124 leaves, max depth = 17, train loss: 0.47431, val loss: 0.47516, in 0.016s 1 tree, 213 leaves, max depth = 16, train loss: 0.47004, val loss: 0.47111, in 0.031s 1 tree, 207 leaves, max depth = 17, train loss: 0.46604, val loss: 0.46727, in 0.016s 1 tree, 189 leaves, max depth = 18, train loss: 0.46204, val loss: 0.46321, in 0.031s 1 tree, 192 leaves, max depth = 15, train loss: 0.45788, val loss: 0.45918, in 0.031s 1 tree, 132 leaves, max depth = 17, train loss: 0.45365, val loss: 0.45499, in 0.016s 1 tree, 134 leaves, max depth = 15, train loss: 0.44950, val loss: 0.45086, in 0.031s 1 tree, 139 leaves, max depth = 14, train loss: 0.44561, val loss: 0.44686, in 0.016s 1 tree, 149 leaves, max depth = 17, train loss: 0.44168, val loss: 0.44284, in 0.031s 1 tree, 213 leaves, max depth = 17, train loss: 0.43817, val loss: 0.43953, in 0.031s 1 tree, 138 leaves, max depth = 15, train loss: 0.43452, val loss: 0.43588, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.42957, val loss: 0.43108, in 0.016s 1 tree, 144 leaves, max depth = 14, train loss: 0.42597, val loss: 0.42756, in 0.031s 1 tree, 139 leaves, max depth = 18, train loss: 0.42270, val loss: 0.42427, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.41806, val loss: 0.41979, in 0.016s 1 tree, 213 leaves, max depth = 16, train loss: 0.41507, val loss: 0.41698, in 0.016s 1 tree, 150 leaves, max depth = 14, train loss: 0.41205, val loss: 0.41388, in 0.031s 1 tree, 177 leaves, max depth = 19, train loss: 0.40905, val loss: 0.41080, in 0.016s 1 tree, 136 leaves, max depth = 15, train loss: 0.40599, val loss: 0.40782, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.40165, val loss: 0.40362, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.39753, val loss: 0.39963, in 0.016s 1 tree, 26 leaves, max depth = 8, train loss: 0.39337, val loss: 0.39562, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.38953, val loss: 0.39189, in 0.016s 1 tree, 10 leaves, max depth = 4, train loss: 0.38583, val loss: 0.38828, in 0.016s 1 tree, 154 leaves, max depth = 13, train loss: 0.38284, val loss: 0.38542, in 0.016s 1 tree, 146 leaves, max depth = 15, train loss: 0.37996, val loss: 0.38256, in 0.031s 1 tree, 156 leaves, max depth = 14, train loss: 0.37713, val loss: 0.37985, in 0.031s 1 tree, 149 leaves, max depth = 24, train loss: 0.37506, val loss: 0.37779, in 0.016s 1 tree, 141 leaves, max depth = 14, train loss: 0.37237, val loss: 0.37519, in 0.031s 1 tree, 158 leaves, max depth = 15, train loss: 0.36973, val loss: 0.37252, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.36751, val loss: 0.37024, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36438, val loss: 0.36720, in 0.016s 1 tree, 135 leaves, max depth = 17, train loss: 0.36192, val loss: 0.36478, in 0.016s 1 tree, 212 leaves, max depth = 26, train loss: 0.36005, val loss: 0.36300, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.35669, val loss: 0.36001, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.35362, val loss: 0.35705, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.35050, val loss: 0.35410, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.34780, val loss: 0.35166, in 0.016s 1 tree, 153 leaves, max depth = 17, train loss: 0.34584, val loss: 0.34981, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.34292, val loss: 0.34710, in 0.016s 1 tree, 165 leaves, max depth = 16, train loss: 0.34093, val loss: 0.34519, in 0.016s Fit 70 trees in 1.845 s, (9063 total leaves) Time spent computing histograms: 0.544s Time spent finding best splits: 0.175s Time spent applying splits: 0.138s Time spent predicting: 0.016s Trial 31, Fold 1: Log loss = 0.35161783356055243, Average precision = 0.9569503128917286, ROC-AUC = 0.9514637193329605, Elapsed Time = 1.8531702999989648 seconds Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 31, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 156 leaves, max depth = 14, train loss: 0.68167, val loss: 0.68164, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.67017, val loss: 0.67009, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.65906, val loss: 0.65895, in 0.031s 1 tree, 83 leaves, max depth = 16, train loss: 0.64946, val loss: 0.64902, in 0.016s 1 tree, 135 leaves, max depth = 16, train loss: 0.63915, val loss: 0.63863, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.62900, val loss: 0.62839, in 0.031s 1 tree, 109 leaves, max depth = 15, train loss: 0.61920, val loss: 0.61853, in 0.016s 1 tree, 110 leaves, max depth = 14, train loss: 0.61057, val loss: 0.60996, in 0.031s 1 tree, 113 leaves, max depth = 14, train loss: 0.60152, val loss: 0.60081, in 0.016s 1 tree, 111 leaves, max depth = 17, train loss: 0.59280, val loss: 0.59206, in 0.031s 1 tree, 213 leaves, max depth = 14, train loss: 0.58480, val loss: 0.58415, in 0.016s 1 tree, 119 leaves, max depth = 15, train loss: 0.57657, val loss: 0.57586, in 0.031s 1 tree, 116 leaves, max depth = 19, train loss: 0.56919, val loss: 0.56844, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.56225, val loss: 0.56141, in 0.016s 1 tree, 136 leaves, max depth = 15, train loss: 0.55494, val loss: 0.55407, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.54787, val loss: 0.54709, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.54137, val loss: 0.54066, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.53532, val loss: 0.53460, in 0.016s 1 tree, 147 leaves, max depth = 16, train loss: 0.52887, val loss: 0.52824, in 0.016s 1 tree, 213 leaves, max depth = 17, train loss: 0.52287, val loss: 0.52229, in 0.031s 1 tree, 131 leaves, max depth = 17, train loss: 0.51669, val loss: 0.51607, in 0.016s 1 tree, 179 leaves, max depth = 18, train loss: 0.51117, val loss: 0.51065, in 0.031s 1 tree, 165 leaves, max depth = 23, train loss: 0.50549, val loss: 0.50504, in 0.016s 1 tree, 164 leaves, max depth = 17, train loss: 0.49986, val loss: 0.49940, in 0.031s 1 tree, 205 leaves, max depth = 16, train loss: 0.49458, val loss: 0.49429, in 0.031s 1 tree, 144 leaves, max depth = 24, train loss: 0.48966, val loss: 0.48944, in 0.016s 1 tree, 120 leaves, max depth = 13, train loss: 0.48492, val loss: 0.48473, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.48033, val loss: 0.48016, in 0.031s 1 tree, 140 leaves, max depth = 20, train loss: 0.47556, val loss: 0.47533, in 0.016s 1 tree, 139 leaves, max depth = 15, train loss: 0.47076, val loss: 0.47054, in 0.031s 1 tree, 170 leaves, max depth = 18, train loss: 0.46625, val loss: 0.46606, in 0.016s 1 tree, 152 leaves, max depth = 16, train loss: 0.46232, val loss: 0.46215, in 0.031s 1 tree, 140 leaves, max depth = 16, train loss: 0.45790, val loss: 0.45778, in 0.016s 1 tree, 172 leaves, max depth = 18, train loss: 0.45396, val loss: 0.45387, in 0.031s 1 tree, 170 leaves, max depth = 20, train loss: 0.44991, val loss: 0.44986, in 0.031s 1 tree, 174 leaves, max depth = 20, train loss: 0.44585, val loss: 0.44585, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.44218, val loss: 0.44208, in 0.031s 1 tree, 157 leaves, max depth = 16, train loss: 0.43852, val loss: 0.43837, in 0.016s 1 tree, 181 leaves, max depth = 15, train loss: 0.43483, val loss: 0.43477, in 0.031s 1 tree, 133 leaves, max depth = 17, train loss: 0.43133, val loss: 0.43129, in 0.016s 1 tree, 138 leaves, max depth = 16, train loss: 0.42795, val loss: 0.42796, in 0.031s 1 tree, 178 leaves, max depth = 17, train loss: 0.42456, val loss: 0.42472, in 0.031s 1 tree, 10 leaves, max depth = 4, train loss: 0.41969, val loss: 0.41994, in 0.016s 1 tree, 173 leaves, max depth = 16, train loss: 0.41655, val loss: 0.41681, in 0.016s 1 tree, 168 leaves, max depth = 22, train loss: 0.41377, val loss: 0.41413, in 0.031s 1 tree, 213 leaves, max depth = 15, train loss: 0.41083, val loss: 0.41126, in 0.031s 1 tree, 24 leaves, max depth = 8, train loss: 0.40623, val loss: 0.40674, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.40344, val loss: 0.40404, in 0.031s 1 tree, 207 leaves, max depth = 21, train loss: 0.40118, val loss: 0.40183, in 0.031s 1 tree, 33 leaves, max depth = 10, train loss: 0.39714, val loss: 0.39786, in 0.016s 1 tree, 116 leaves, max depth = 16, train loss: 0.39449, val loss: 0.39529, in 0.016s 1 tree, 163 leaves, max depth = 17, train loss: 0.39174, val loss: 0.39261, in 0.031s 1 tree, 213 leaves, max depth = 17, train loss: 0.38935, val loss: 0.39037, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.38534, val loss: 0.38647, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.38164, val loss: 0.38279, in 0.031s 1 tree, 185 leaves, max depth = 22, train loss: 0.37942, val loss: 0.38069, in 0.016s 1 tree, 197 leaves, max depth = 20, train loss: 0.37727, val loss: 0.37867, in 0.031s 1 tree, 14 leaves, max depth = 5, train loss: 0.37383, val loss: 0.37531, in 0.016s 1 tree, 146 leaves, max depth = 18, train loss: 0.37145, val loss: 0.37300, in 0.031s 1 tree, 168 leaves, max depth = 18, train loss: 0.36886, val loss: 0.37053, in 0.031s 1 tree, 11 leaves, max depth = 5, train loss: 0.36558, val loss: 0.36731, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.36229, val loss: 0.36417, in 0.016s 1 tree, 171 leaves, max depth = 17, train loss: 0.35981, val loss: 0.36182, in 0.031s 1 tree, 22 leaves, max depth = 9, train loss: 0.35683, val loss: 0.35902, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.35391, val loss: 0.35617, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.35120, val loss: 0.35358, in 0.016s 1 tree, 166 leaves, max depth = 18, train loss: 0.34892, val loss: 0.35144, in 0.031s 1 tree, 14 leaves, max depth = 6, train loss: 0.34637, val loss: 0.34889, in 0.000s 1 tree, 171 leaves, max depth = 15, train loss: 0.34409, val loss: 0.34679, in 0.031s 1 tree, 176 leaves, max depth = 16, train loss: 0.34186, val loss: 0.34475, in 0.031s Fit 70 trees in 2.017 s, (8807 total leaves) Time spent computing histograms: 0.589s Time spent finding best splits: 0.188s Time spent applying splits: 0.147s Time spent predicting: 0.031s Trial 31, Fold 2: Log loss = 0.3499749363102381, Average precision = 0.9564690837470304, ROC-AUC = 0.9537399152215471, Elapsed Time = 2.0252207000012277 seconds Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 31, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 161 leaves, max depth = 15, train loss: 0.68177, val loss: 0.68167, in 0.016s 1 tree, 97 leaves, max depth = 13, train loss: 0.67085, val loss: 0.67080, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.66039, val loss: 0.66035, in 0.016s 1 tree, 94 leaves, max depth = 13, train loss: 0.65019, val loss: 0.65022, in 0.016s 1 tree, 200 leaves, max depth = 15, train loss: 0.64067, val loss: 0.64094, in 0.031s 1 tree, 136 leaves, max depth = 18, train loss: 0.63065, val loss: 0.63098, in 0.031s 1 tree, 213 leaves, max depth = 18, train loss: 0.62170, val loss: 0.62227, in 0.016s 1 tree, 160 leaves, max depth = 17, train loss: 0.61269, val loss: 0.61324, in 0.031s 1 tree, 89 leaves, max depth = 13, train loss: 0.60408, val loss: 0.60463, in 0.016s 1 tree, 185 leaves, max depth = 18, train loss: 0.59600, val loss: 0.59648, in 0.016s 1 tree, 213 leaves, max depth = 16, train loss: 0.58819, val loss: 0.58875, in 0.031s 1 tree, 120 leaves, max depth = 18, train loss: 0.58004, val loss: 0.58067, in 0.031s 1 tree, 116 leaves, max depth = 14, train loss: 0.57219, val loss: 0.57286, in 0.016s 1 tree, 171 leaves, max depth = 17, train loss: 0.56493, val loss: 0.56560, in 0.031s 1 tree, 213 leaves, max depth = 17, train loss: 0.55823, val loss: 0.55903, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.55096, val loss: 0.55189, in 0.031s 1 tree, 134 leaves, max depth = 20, train loss: 0.54443, val loss: 0.54548, in 0.031s 1 tree, 88 leaves, max depth = 11, train loss: 0.53797, val loss: 0.53920, in 0.016s 1 tree, 89 leaves, max depth = 11, train loss: 0.53190, val loss: 0.53319, in 0.016s 1 tree, 116 leaves, max depth = 16, train loss: 0.52556, val loss: 0.52692, in 0.031s 1 tree, 116 leaves, max depth = 14, train loss: 0.51945, val loss: 0.52091, in 0.031s 1 tree, 183 leaves, max depth = 16, train loss: 0.51390, val loss: 0.51539, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.50851, val loss: 0.51013, in 0.031s 1 tree, 208 leaves, max depth = 17, train loss: 0.50320, val loss: 0.50482, in 0.031s 1 tree, 213 leaves, max depth = 17, train loss: 0.49807, val loss: 0.49980, in 0.016s 1 tree, 151 leaves, max depth = 15, train loss: 0.49266, val loss: 0.49453, in 0.031s 1 tree, 109 leaves, max depth = 15, train loss: 0.48755, val loss: 0.48950, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.48325, val loss: 0.48518, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.47842, val loss: 0.48042, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.47366, val loss: 0.47572, in 0.016s 1 tree, 213 leaves, max depth = 17, train loss: 0.46953, val loss: 0.47170, in 0.031s 1 tree, 86 leaves, max depth = 13, train loss: 0.46542, val loss: 0.46762, in 0.031s 1 tree, 14 leaves, max depth = 7, train loss: 0.45968, val loss: 0.46218, in 0.016s 1 tree, 137 leaves, max depth = 17, train loss: 0.45558, val loss: 0.45813, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.45029, val loss: 0.45313, in 0.016s 1 tree, 163 leaves, max depth = 20, train loss: 0.44659, val loss: 0.44956, in 0.031s 1 tree, 42 leaves, max depth = 10, train loss: 0.44139, val loss: 0.44469, in 0.016s 1 tree, 158 leaves, max depth = 20, train loss: 0.43745, val loss: 0.44083, in 0.016s 1 tree, 139 leaves, max depth = 17, train loss: 0.43400, val loss: 0.43749, in 0.031s 1 tree, 141 leaves, max depth = 16, train loss: 0.43042, val loss: 0.43411, in 0.016s 1 tree, 134 leaves, max depth = 18, train loss: 0.42690, val loss: 0.43064, in 0.031s 1 tree, 18 leaves, max depth = 7, train loss: 0.42242, val loss: 0.42651, in 0.016s 1 tree, 146 leaves, max depth = 17, train loss: 0.41913, val loss: 0.42332, in 0.031s 1 tree, 136 leaves, max depth = 16, train loss: 0.41585, val loss: 0.42009, in 0.016s 1 tree, 155 leaves, max depth = 18, train loss: 0.41270, val loss: 0.41711, in 0.031s 1 tree, 169 leaves, max depth = 19, train loss: 0.40979, val loss: 0.41430, in 0.031s 1 tree, 26 leaves, max depth = 7, train loss: 0.40553, val loss: 0.41039, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.40282, val loss: 0.40772, in 0.016s 1 tree, 195 leaves, max depth = 24, train loss: 0.40022, val loss: 0.40525, in 0.031s 1 tree, 12 leaves, max depth = 6, train loss: 0.39627, val loss: 0.40160, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.39358, val loss: 0.39904, in 0.016s 1 tree, 155 leaves, max depth = 18, train loss: 0.39078, val loss: 0.39639, in 0.031s 1 tree, 176 leaves, max depth = 18, train loss: 0.38812, val loss: 0.39390, in 0.031s 1 tree, 116 leaves, max depth = 12, train loss: 0.38523, val loss: 0.39097, in 0.016s 1 tree, 177 leaves, max depth = 18, train loss: 0.38296, val loss: 0.38891, in 0.031s 1 tree, 132 leaves, max depth = 12, train loss: 0.38017, val loss: 0.38610, in 0.031s 1 tree, 18 leaves, max depth = 8, train loss: 0.37670, val loss: 0.38291, in 0.016s 1 tree, 157 leaves, max depth = 20, train loss: 0.37439, val loss: 0.38079, in 0.031s 1 tree, 97 leaves, max depth = 14, train loss: 0.37088, val loss: 0.37779, in 0.016s 1 tree, 169 leaves, max depth = 14, train loss: 0.36827, val loss: 0.37520, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.36492, val loss: 0.37235, in 0.031s 1 tree, 113 leaves, max depth = 13, train loss: 0.36270, val loss: 0.37017, in 0.016s 1 tree, 141 leaves, max depth = 18, train loss: 0.36055, val loss: 0.36812, in 0.031s 1 tree, 132 leaves, max depth = 16, train loss: 0.35822, val loss: 0.36574, in 0.031s 1 tree, 16 leaves, max depth = 5, train loss: 0.35520, val loss: 0.36296, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.35235, val loss: 0.36048, in 0.000s 1 tree, 134 leaves, max depth = 13, train loss: 0.35010, val loss: 0.35817, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.34749, val loss: 0.35569, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.34454, val loss: 0.35304, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.34191, val loss: 0.35063, in 0.016s Fit 70 trees in 2.002 s, (8348 total leaves) Time spent computing histograms: 0.591s Time spent finding best splits: 0.183s Time spent applying splits: 0.143s Time spent predicting: 0.000s Trial 31, Fold 3: Log loss = 0.346050974179654, Average precision = 0.9593904984587104, ROC-AUC = 0.9552786649699992, Elapsed Time = 2.0108632999999827 seconds Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 31, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 169 leaves, max depth = 15, train loss: 0.68174, val loss: 0.68155, in 0.016s 1 tree, 137 leaves, max depth = 16, train loss: 0.67022, val loss: 0.66958, in 0.031s 1 tree, 207 leaves, max depth = 17, train loss: 0.65982, val loss: 0.65903, in 0.031s 1 tree, 113 leaves, max depth = 13, train loss: 0.64919, val loss: 0.64805, in 0.016s 1 tree, 180 leaves, max depth = 15, train loss: 0.63929, val loss: 0.63789, in 0.032s 1 tree, 93 leaves, max depth = 20, train loss: 0.63000, val loss: 0.62821, in 0.016s 1 tree, 127 leaves, max depth = 20, train loss: 0.62052, val loss: 0.61838, in 0.031s 1 tree, 107 leaves, max depth = 18, train loss: 0.61204, val loss: 0.60955, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.60386, val loss: 0.60093, in 0.016s 1 tree, 148 leaves, max depth = 17, train loss: 0.59577, val loss: 0.59254, in 0.031s 1 tree, 110 leaves, max depth = 16, train loss: 0.58718, val loss: 0.58365, in 0.016s 1 tree, 135 leaves, max depth = 20, train loss: 0.57895, val loss: 0.57517, in 0.031s 1 tree, 130 leaves, max depth = 14, train loss: 0.57093, val loss: 0.56691, in 0.016s 1 tree, 185 leaves, max depth = 17, train loss: 0.56379, val loss: 0.55948, in 0.031s 1 tree, 133 leaves, max depth = 19, train loss: 0.55711, val loss: 0.55255, in 0.016s 1 tree, 132 leaves, max depth = 15, train loss: 0.55014, val loss: 0.54533, in 0.031s 1 tree, 181 leaves, max depth = 22, train loss: 0.54370, val loss: 0.53868, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.53749, val loss: 0.53225, in 0.031s 1 tree, 135 leaves, max depth = 19, train loss: 0.53143, val loss: 0.52598, in 0.031s 1 tree, 213 leaves, max depth = 18, train loss: 0.52564, val loss: 0.52013, in 0.031s 1 tree, 126 leaves, max depth = 17, train loss: 0.51993, val loss: 0.51418, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.51396, val loss: 0.50797, in 0.031s 1 tree, 183 leaves, max depth = 15, train loss: 0.50855, val loss: 0.50231, in 0.016s 1 tree, 213 leaves, max depth = 18, train loss: 0.50321, val loss: 0.49709, in 0.031s 1 tree, 123 leaves, max depth = 19, train loss: 0.49842, val loss: 0.49208, in 0.031s 1 tree, 124 leaves, max depth = 16, train loss: 0.49303, val loss: 0.48645, in 0.016s 1 tree, 130 leaves, max depth = 13, train loss: 0.48798, val loss: 0.48118, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.48171, val loss: 0.47475, in 0.016s 1 tree, 122 leaves, max depth = 19, train loss: 0.47736, val loss: 0.47018, in 0.031s 1 tree, 144 leaves, max depth = 18, train loss: 0.47252, val loss: 0.46525, in 0.016s 1 tree, 128 leaves, max depth = 22, train loss: 0.46833, val loss: 0.46088, in 0.031s 1 tree, 19 leaves, max depth = 10, train loss: 0.46291, val loss: 0.45528, in 0.016s 1 tree, 136 leaves, max depth = 13, train loss: 0.45842, val loss: 0.45070, in 0.031s 1 tree, 178 leaves, max depth = 17, train loss: 0.45435, val loss: 0.44652, in 0.031s 1 tree, 168 leaves, max depth = 15, train loss: 0.45007, val loss: 0.44218, in 0.031s 1 tree, 172 leaves, max depth = 15, train loss: 0.44592, val loss: 0.43795, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.44232, val loss: 0.43411, in 0.031s 1 tree, 139 leaves, max depth = 20, train loss: 0.43858, val loss: 0.43020, in 0.016s 1 tree, 140 leaves, max depth = 17, train loss: 0.43499, val loss: 0.42641, in 0.031s 1 tree, 138 leaves, max depth = 20, train loss: 0.43146, val loss: 0.42272, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.42669, val loss: 0.41779, in 0.016s 1 tree, 165 leaves, max depth = 15, train loss: 0.42320, val loss: 0.41421, in 0.031s 1 tree, 14 leaves, max depth = 6, train loss: 0.41871, val loss: 0.40958, in 0.016s 1 tree, 213 leaves, max depth = 18, train loss: 0.41556, val loss: 0.40652, in 0.031s 1 tree, 154 leaves, max depth = 15, train loss: 0.41238, val loss: 0.40323, in 0.031s 1 tree, 167 leaves, max depth = 17, train loss: 0.40931, val loss: 0.40011, in 0.016s 1 tree, 187 leaves, max depth = 18, train loss: 0.40632, val loss: 0.39700, in 0.031s 1 tree, 171 leaves, max depth = 17, train loss: 0.40345, val loss: 0.39399, in 0.031s 1 tree, 148 leaves, max depth = 15, train loss: 0.40062, val loss: 0.39103, in 0.031s 1 tree, 27 leaves, max depth = 8, train loss: 0.39660, val loss: 0.38688, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.39275, val loss: 0.38293, in 0.016s 1 tree, 167 leaves, max depth = 15, train loss: 0.39012, val loss: 0.38023, in 0.016s 1 tree, 119 leaves, max depth = 14, train loss: 0.38721, val loss: 0.37743, in 0.016s 1 tree, 155 leaves, max depth = 17, train loss: 0.38456, val loss: 0.37475, in 0.031s 1 tree, 207 leaves, max depth = 17, train loss: 0.38195, val loss: 0.37209, in 0.031s 1 tree, 200 leaves, max depth = 19, train loss: 0.37940, val loss: 0.36954, in 0.031s 1 tree, 150 leaves, max depth = 15, train loss: 0.37709, val loss: 0.36715, in 0.016s 1 tree, 156 leaves, max depth = 15, train loss: 0.37439, val loss: 0.36460, in 0.047s 1 tree, 20 leaves, max depth = 7, train loss: 0.37126, val loss: 0.36125, in 0.000s 1 tree, 142 leaves, max depth = 18, train loss: 0.36870, val loss: 0.35876, in 0.031s 1 tree, 21 leaves, max depth = 10, train loss: 0.36553, val loss: 0.35574, in 0.016s 1 tree, 142 leaves, max depth = 13, train loss: 0.36306, val loss: 0.35335, in 0.016s 1 tree, 129 leaves, max depth = 13, train loss: 0.36075, val loss: 0.35108, in 0.031s 1 tree, 21 leaves, max depth = 8, train loss: 0.35775, val loss: 0.34794, in 0.016s 1 tree, 211 leaves, max depth = 17, train loss: 0.35582, val loss: 0.34606, in 0.031s 1 tree, 171 leaves, max depth = 16, train loss: 0.35351, val loss: 0.34382, in 0.031s 1 tree, 19 leaves, max depth = 10, train loss: 0.35065, val loss: 0.34083, in 0.016s 1 tree, 153 leaves, max depth = 14, train loss: 0.34850, val loss: 0.33873, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.34575, val loss: 0.33587, in 0.016s 1 tree, 152 leaves, max depth = 15, train loss: 0.34365, val loss: 0.33382, in 0.031s Fit 70 trees in 2.033 s, (9027 total leaves) Time spent computing histograms: 0.594s Time spent finding best splits: 0.194s Time spent applying splits: 0.153s Time spent predicting: 0.016s Trial 31, Fold 4: Log loss = 0.3492675491284612, Average precision = 0.9588931444376244, ROC-AUC = 0.9539327560677268, Elapsed Time = 2.041462600000159 seconds Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 31, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 176 leaves, max depth = 16, train loss: 0.68165, val loss: 0.68129, in 0.016s 1 tree, 190 leaves, max depth = 19, train loss: 0.67054, val loss: 0.66977, in 0.031s 1 tree, 213 leaves, max depth = 14, train loss: 0.65976, val loss: 0.65893, in 0.031s 1 tree, 119 leaves, max depth = 14, train loss: 0.64884, val loss: 0.64774, in 0.016s 1 tree, 122 leaves, max depth = 16, train loss: 0.63859, val loss: 0.63713, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.62920, val loss: 0.62738, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.62012, val loss: 0.61796, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.61083, val loss: 0.60839, in 0.031s 1 tree, 106 leaves, max depth = 15, train loss: 0.60194, val loss: 0.59918, in 0.016s 1 tree, 213 leaves, max depth = 15, train loss: 0.59378, val loss: 0.59103, in 0.031s 1 tree, 62 leaves, max depth = 16, train loss: 0.58628, val loss: 0.58335, in 0.016s 1 tree, 184 leaves, max depth = 16, train loss: 0.57871, val loss: 0.57548, in 0.031s 1 tree, 119 leaves, max depth = 14, train loss: 0.57071, val loss: 0.56729, in 0.016s 1 tree, 182 leaves, max depth = 15, train loss: 0.56330, val loss: 0.55971, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.55579, val loss: 0.55204, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.54856, val loss: 0.54462, in 0.016s 1 tree, 135 leaves, max depth = 16, train loss: 0.54151, val loss: 0.53752, in 0.016s 1 tree, 138 leaves, max depth = 14, train loss: 0.53472, val loss: 0.53058, in 0.031s 1 tree, 104 leaves, max depth = 13, train loss: 0.52860, val loss: 0.52431, in 0.016s 1 tree, 136 leaves, max depth = 14, train loss: 0.52227, val loss: 0.51788, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.51659, val loss: 0.51205, in 0.031s 1 tree, 138 leaves, max depth = 18, train loss: 0.51082, val loss: 0.50619, in 0.031s 1 tree, 213 leaves, max depth = 18, train loss: 0.50537, val loss: 0.50078, in 0.016s 1 tree, 112 leaves, max depth = 16, train loss: 0.49974, val loss: 0.49502, in 0.031s 1 tree, 194 leaves, max depth = 17, train loss: 0.49442, val loss: 0.48963, in 0.016s 1 tree, 106 leaves, max depth = 21, train loss: 0.48950, val loss: 0.48461, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.48323, val loss: 0.47817, in 0.016s 1 tree, 166 leaves, max depth = 19, train loss: 0.47858, val loss: 0.47341, in 0.016s 1 tree, 142 leaves, max depth = 17, train loss: 0.47362, val loss: 0.46843, in 0.031s 1 tree, 127 leaves, max depth = 16, train loss: 0.46888, val loss: 0.46360, in 0.031s 1 tree, 129 leaves, max depth = 16, train loss: 0.46465, val loss: 0.45927, in 0.016s 1 tree, 162 leaves, max depth = 14, train loss: 0.46013, val loss: 0.45471, in 0.031s 1 tree, 18 leaves, max depth = 8, train loss: 0.45465, val loss: 0.44918, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.44922, val loss: 0.44369, in 0.016s 1 tree, 146 leaves, max depth = 16, train loss: 0.44520, val loss: 0.43961, in 0.031s 1 tree, 136 leaves, max depth = 15, train loss: 0.44127, val loss: 0.43565, in 0.016s 1 tree, 213 leaves, max depth = 16, train loss: 0.43770, val loss: 0.43210, in 0.031s 1 tree, 118 leaves, max depth = 13, train loss: 0.43387, val loss: 0.42825, in 0.031s 1 tree, 139 leaves, max depth = 15, train loss: 0.43023, val loss: 0.42451, in 0.016s 1 tree, 212 leaves, max depth = 19, train loss: 0.42679, val loss: 0.42103, in 0.031s 1 tree, 154 leaves, max depth = 15, train loss: 0.42332, val loss: 0.41760, in 0.031s 1 tree, 213 leaves, max depth = 16, train loss: 0.42023, val loss: 0.41459, in 0.031s 1 tree, 139 leaves, max depth = 14, train loss: 0.41682, val loss: 0.41118, in 0.016s 1 tree, 171 leaves, max depth = 18, train loss: 0.41369, val loss: 0.40794, in 0.031s 1 tree, 134 leaves, max depth = 16, train loss: 0.41065, val loss: 0.40492, in 0.016s 1 tree, 118 leaves, max depth = 15, train loss: 0.40759, val loss: 0.40188, in 0.031s 1 tree, 165 leaves, max depth = 18, train loss: 0.40464, val loss: 0.39894, in 0.031s 1 tree, 163 leaves, max depth = 14, train loss: 0.40162, val loss: 0.39604, in 0.031s 1 tree, 204 leaves, max depth = 16, train loss: 0.39872, val loss: 0.39324, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.39462, val loss: 0.38914, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39068, val loss: 0.38528, in 0.016s 1 tree, 213 leaves, max depth = 19, train loss: 0.38826, val loss: 0.38304, in 0.031s 1 tree, 196 leaves, max depth = 15, train loss: 0.38574, val loss: 0.38052, in 0.031s 1 tree, 11 leaves, max depth = 5, train loss: 0.38341, val loss: 0.37812, in 0.016s 1 tree, 189 leaves, max depth = 20, train loss: 0.38081, val loss: 0.37563, in 0.031s 1 tree, 213 leaves, max depth = 18, train loss: 0.37858, val loss: 0.37359, in 0.031s 1 tree, 213 leaves, max depth = 21, train loss: 0.37639, val loss: 0.37148, in 0.031s 1 tree, 173 leaves, max depth = 16, train loss: 0.37401, val loss: 0.36919, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.37053, val loss: 0.36567, in 0.016s 1 tree, 213 leaves, max depth = 20, train loss: 0.36839, val loss: 0.36367, in 0.031s 1 tree, 19 leaves, max depth = 7, train loss: 0.36511, val loss: 0.36030, in 0.016s 1 tree, 168 leaves, max depth = 19, train loss: 0.36262, val loss: 0.35791, in 0.031s 1 tree, 11 leaves, max depth = 5, train loss: 0.35948, val loss: 0.35474, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.35658, val loss: 0.35204, in 0.016s 1 tree, 137 leaves, max depth = 13, train loss: 0.35419, val loss: 0.34974, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.35128, val loss: 0.34681, in 0.016s 1 tree, 158 leaves, max depth = 15, train loss: 0.34899, val loss: 0.34466, in 0.031s 1 tree, 148 leaves, max depth = 16, train loss: 0.34719, val loss: 0.34290, in 0.016s 1 tree, 171 leaves, max depth = 17, train loss: 0.34529, val loss: 0.34113, in 0.031s 1 tree, 109 leaves, max depth = 12, train loss: 0.34322, val loss: 0.33928, in 0.016s Fit 70 trees in 2.033 s, (9185 total leaves) Time spent computing histograms: 0.593s Time spent finding best splits: 0.195s Time spent applying splits: 0.156s Time spent predicting: 0.000s Trial 31, Fold 5: Log loss = 0.3565390565096313, Average precision = 0.9549716549634287, ROC-AUC = 0.9506515801794772, Elapsed Time = 2.0520335999990493 seconds
Optimization Progress: 32%|###2 | 32/100 [06:23<15:39, 13.81s/it]
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 32, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 9, train loss: 0.67187, val loss: 0.67116, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.65255, val loss: 0.65121, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.63460, val loss: 0.63274, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.61833, val loss: 0.61577, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.60334, val loss: 0.60025, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.58961, val loss: 0.58601, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.57720, val loss: 0.57310, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.56586, val loss: 0.56125, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.55519, val loss: 0.55013, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.54514, val loss: 0.53959, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.53623, val loss: 0.53019, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.52809, val loss: 0.52163, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.52038, val loss: 0.51355, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.51336, val loss: 0.50612, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.50678, val loss: 0.49920, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.49755, val loss: 0.49064, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.49169, val loss: 0.48450, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.48629, val loss: 0.47883, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.48124, val loss: 0.47337, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.47644, val loss: 0.46821, in 0.000s 1 tree, 63 leaves, max depth = 13, train loss: 0.47118, val loss: 0.46335, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.46580, val loss: 0.45842, in 0.000s 1 tree, 26 leaves, max depth = 8, train loss: 0.46162, val loss: 0.45390, in 0.016s 1 tree, 26 leaves, max depth = 8, train loss: 0.45776, val loss: 0.44972, in 0.000s 1 tree, 63 leaves, max depth = 14, train loss: 0.45326, val loss: 0.44560, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.44992, val loss: 0.44191, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.44672, val loss: 0.43842, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.44379, val loss: 0.43530, in 0.000s 1 tree, 61 leaves, max depth = 9, train loss: 0.43932, val loss: 0.43129, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.43299, val loss: 0.42560, in 0.016s 1 tree, 61 leaves, max depth = 9, train loss: 0.42917, val loss: 0.42221, in 0.000s 1 tree, 58 leaves, max depth = 17, train loss: 0.42631, val loss: 0.41937, in 0.016s 1 tree, 58 leaves, max depth = 17, train loss: 0.42367, val loss: 0.41675, in 0.000s 1 tree, 31 leaves, max depth = 16, train loss: 0.42125, val loss: 0.41417, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.41881, val loss: 0.41149, in 0.000s 1 tree, 60 leaves, max depth = 15, train loss: 0.41638, val loss: 0.40911, in 0.016s 1 tree, 54 leaves, max depth = 9, train loss: 0.41384, val loss: 0.40689, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.40898, val loss: 0.40257, in 0.000s 1 tree, 28 leaves, max depth = 8, train loss: 0.40685, val loss: 0.40031, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.40402, val loss: 0.39787, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40128, val loss: 0.39485, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.39936, val loss: 0.39273, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.39737, val loss: 0.39082, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.39322, val loss: 0.38718, in 0.000s 1 tree, 60 leaves, max depth = 10, train loss: 0.39088, val loss: 0.38522, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.38874, val loss: 0.38344, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.38518, val loss: 0.38035, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.38353, val loss: 0.37858, in 0.016s 1 tree, 62 leaves, max depth = 10, train loss: 0.38172, val loss: 0.37710, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.37855, val loss: 0.37436, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.37715, val loss: 0.37302, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.37430, val loss: 0.37058, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.37167, val loss: 0.36833, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.37022, val loss: 0.36676, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.36888, val loss: 0.36531, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.36644, val loss: 0.36324, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.36513, val loss: 0.36177, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.36400, val loss: 0.36057, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.36172, val loss: 0.35865, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35928, val loss: 0.35595, in 0.000s 1 tree, 64 leaves, max depth = 12, train loss: 0.35791, val loss: 0.35487, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.35588, val loss: 0.35319, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.35480, val loss: 0.35191, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.35386, val loss: 0.35090, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.35292, val loss: 0.34974, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.35165, val loss: 0.34863, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34939, val loss: 0.34612, in 0.016s 1 tree, 63 leaves, max depth = 13, train loss: 0.34820, val loss: 0.34523, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.34643, val loss: 0.34380, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.34480, val loss: 0.34249, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.34385, val loss: 0.34134, in 0.000s 1 tree, 37 leaves, max depth = 15, train loss: 0.34299, val loss: 0.34031, in 0.016s Fit 72 trees in 0.845 s, (2787 total leaves) Time spent computing histograms: 0.299s Time spent finding best splits: 0.056s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 32, Fold 1: Log loss = 0.3466410806136022, Average precision = 0.9522885844063388, ROC-AUC = 0.947052009100338, Elapsed Time = 0.8517611000006582 seconds Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 32, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 9, train loss: 0.67195, val loss: 0.67090, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.65251, val loss: 0.65046, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.63462, val loss: 0.63170, in 0.000s 1 tree, 9 leaves, max depth = 7, train loss: 0.61829, val loss: 0.61452, in 0.016s 1 tree, 29 leaves, max depth = 14, train loss: 0.60349, val loss: 0.59889, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.58987, val loss: 0.58451, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.57741, val loss: 0.57134, in 0.000s 1 tree, 29 leaves, max depth = 14, train loss: 0.56599, val loss: 0.55921, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.55534, val loss: 0.54785, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.54537, val loss: 0.53725, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.53644, val loss: 0.52773, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.52825, val loss: 0.51896, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.52068, val loss: 0.51085, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.51367, val loss: 0.50333, in 0.000s 1 tree, 30 leaves, max depth = 13, train loss: 0.50725, val loss: 0.49636, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.49811, val loss: 0.48760, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.49227, val loss: 0.48133, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.48688, val loss: 0.47553, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.48184, val loss: 0.47009, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.47708, val loss: 0.46493, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.47181, val loss: 0.46008, in 0.016s 1 tree, 63 leaves, max depth = 9, train loss: 0.46650, val loss: 0.45517, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.46235, val loss: 0.45066, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.45851, val loss: 0.44649, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.45401, val loss: 0.44238, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.45074, val loss: 0.43901, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.44754, val loss: 0.43551, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.44461, val loss: 0.43228, in 0.016s 1 tree, 63 leaves, max depth = 9, train loss: 0.44021, val loss: 0.42827, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.43396, val loss: 0.42236, in 0.000s 1 tree, 63 leaves, max depth = 9, train loss: 0.43021, val loss: 0.41898, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.42724, val loss: 0.41645, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.42451, val loss: 0.41414, in 0.000s 1 tree, 58 leaves, max depth = 12, train loss: 0.42199, val loss: 0.41203, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41942, val loss: 0.40920, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.41711, val loss: 0.40728, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.41491, val loss: 0.40482, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.41009, val loss: 0.40030, in 0.000s 1 tree, 30 leaves, max depth = 14, train loss: 0.40812, val loss: 0.39809, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.40527, val loss: 0.39559, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40261, val loss: 0.39282, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.40070, val loss: 0.39071, in 0.000s 1 tree, 59 leaves, max depth = 10, train loss: 0.39874, val loss: 0.38914, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.39461, val loss: 0.38528, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.39214, val loss: 0.38315, in 0.000s 1 tree, 62 leaves, max depth = 9, train loss: 0.38989, val loss: 0.38123, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.38640, val loss: 0.37797, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.38474, val loss: 0.37614, in 0.000s 1 tree, 62 leaves, max depth = 9, train loss: 0.38283, val loss: 0.37455, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.37973, val loss: 0.37167, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.37834, val loss: 0.37062, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.37555, val loss: 0.36804, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.37298, val loss: 0.36567, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.37162, val loss: 0.36413, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.37026, val loss: 0.36261, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.36787, val loss: 0.36041, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.36656, val loss: 0.35893, in 0.000s 1 tree, 31 leaves, max depth = 15, train loss: 0.36549, val loss: 0.35772, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.36325, val loss: 0.35566, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.36089, val loss: 0.35320, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.35951, val loss: 0.35211, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.35752, val loss: 0.35030, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.35647, val loss: 0.34905, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.35554, val loss: 0.34810, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.35462, val loss: 0.34703, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.35324, val loss: 0.34594, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35105, val loss: 0.34365, in 0.000s 1 tree, 64 leaves, max depth = 11, train loss: 0.34986, val loss: 0.34274, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.34813, val loss: 0.34118, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.34654, val loss: 0.33974, in 0.000s 1 tree, 37 leaves, max depth = 13, train loss: 0.34561, val loss: 0.33865, in 0.016s 1 tree, 36 leaves, max depth = 15, train loss: 0.34475, val loss: 0.33761, in 0.000s Fit 72 trees in 0.892 s, (2879 total leaves) Time spent computing histograms: 0.315s Time spent finding best splits: 0.063s Time spent applying splits: 0.053s Time spent predicting: 0.000s Trial 32, Fold 2: Log loss = 0.34737620853550505, Average precision = 0.949150803587019, ROC-AUC = 0.94730262241418, Elapsed Time = 0.9006196000009368 seconds Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 32, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 10, train loss: 0.67201, val loss: 0.67129, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.65277, val loss: 0.65134, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.63501, val loss: 0.63285, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.61876, val loss: 0.61601, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.60411, val loss: 0.60073, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.59060, val loss: 0.58675, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.57827, val loss: 0.57386, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.56697, val loss: 0.56202, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.55639, val loss: 0.55091, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.54644, val loss: 0.54056, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.53757, val loss: 0.53134, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.52948, val loss: 0.52279, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.52185, val loss: 0.51472, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.51490, val loss: 0.50738, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.50840, val loss: 0.50047, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.49925, val loss: 0.49204, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.49347, val loss: 0.48588, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.48814, val loss: 0.48018, in 0.000s 1 tree, 9 leaves, max depth = 7, train loss: 0.48310, val loss: 0.47489, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.47837, val loss: 0.46992, in 0.000s 1 tree, 61 leaves, max depth = 12, train loss: 0.47308, val loss: 0.46496, in 0.016s 1 tree, 61 leaves, max depth = 9, train loss: 0.46770, val loss: 0.46007, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.46358, val loss: 0.45572, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.45979, val loss: 0.45170, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.45526, val loss: 0.44748, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.45188, val loss: 0.44438, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.44870, val loss: 0.44103, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.44581, val loss: 0.43789, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.44135, val loss: 0.43389, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.43511, val loss: 0.42824, in 0.016s 1 tree, 62 leaves, max depth = 9, train loss: 0.43131, val loss: 0.42487, in 0.016s 1 tree, 59 leaves, max depth = 16, train loss: 0.42832, val loss: 0.42231, in 0.000s 1 tree, 60 leaves, max depth = 16, train loss: 0.42558, val loss: 0.41996, in 0.016s 1 tree, 60 leaves, max depth = 16, train loss: 0.42305, val loss: 0.41781, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.42052, val loss: 0.41509, in 0.016s 1 tree, 60 leaves, max depth = 15, train loss: 0.41819, val loss: 0.41314, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.41587, val loss: 0.41054, in 0.000s 1 tree, 52 leaves, max depth = 13, train loss: 0.41107, val loss: 0.40626, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.40899, val loss: 0.40391, in 0.016s 1 tree, 62 leaves, max depth = 9, train loss: 0.40614, val loss: 0.40146, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40338, val loss: 0.39892, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.40150, val loss: 0.39688, in 0.000s 1 tree, 60 leaves, max depth = 14, train loss: 0.39954, val loss: 0.39530, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.39542, val loss: 0.39167, in 0.000s 1 tree, 60 leaves, max depth = 10, train loss: 0.39298, val loss: 0.38946, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.39072, val loss: 0.38758, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.38723, val loss: 0.38455, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.38563, val loss: 0.38276, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.38372, val loss: 0.38120, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.38061, val loss: 0.37852, in 0.000s 1 tree, 60 leaves, max depth = 14, train loss: 0.37920, val loss: 0.37746, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.37640, val loss: 0.37507, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.37381, val loss: 0.37287, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.37240, val loss: 0.37127, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.37110, val loss: 0.36980, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.36868, val loss: 0.36776, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.36739, val loss: 0.36633, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.36632, val loss: 0.36513, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.36405, val loss: 0.36324, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36161, val loss: 0.36096, in 0.000s 1 tree, 60 leaves, max depth = 11, train loss: 0.36025, val loss: 0.35980, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.35823, val loss: 0.35815, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.35714, val loss: 0.35693, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.35626, val loss: 0.35602, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.35535, val loss: 0.35475, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.35401, val loss: 0.35372, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35175, val loss: 0.35162, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.35060, val loss: 0.35065, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.34884, val loss: 0.34924, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.34722, val loss: 0.34795, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.34630, val loss: 0.34666, in 0.000s 1 tree, 37 leaves, max depth = 15, train loss: 0.34545, val loss: 0.34546, in 0.016s Fit 72 trees in 0.924 s, (2769 total leaves) Time spent computing histograms: 0.318s Time spent finding best splits: 0.063s Time spent applying splits: 0.052s Time spent predicting: 0.016s Trial 32, Fold 3: Log loss = 0.34113213388950736, Average precision = 0.9536000701204155, ROC-AUC = 0.9504115311178742, Elapsed Time = 0.9310433000009652 seconds Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 32, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 9, train loss: 0.67201, val loss: 0.67076, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.65271, val loss: 0.65035, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.63491, val loss: 0.63146, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.61867, val loss: 0.61423, in 0.000s 1 tree, 26 leaves, max depth = 11, train loss: 0.60396, val loss: 0.59852, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.59042, val loss: 0.58401, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.57803, val loss: 0.57073, in 0.000s 1 tree, 26 leaves, max depth = 11, train loss: 0.56668, val loss: 0.55848, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.55607, val loss: 0.54701, in 0.000s 1 tree, 26 leaves, max depth = 12, train loss: 0.54615, val loss: 0.53620, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.53726, val loss: 0.52650, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.52912, val loss: 0.51758, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.52146, val loss: 0.50918, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.51449, val loss: 0.50143, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.50795, val loss: 0.49420, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.49881, val loss: 0.48525, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.49300, val loss: 0.47880, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.48765, val loss: 0.47282, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.48262, val loss: 0.46722, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.47791, val loss: 0.46188, in 0.016s 1 tree, 64 leaves, max depth = 11, train loss: 0.47270, val loss: 0.45673, in 0.000s 1 tree, 60 leaves, max depth = 8, train loss: 0.46742, val loss: 0.45163, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.46333, val loss: 0.44695, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.45955, val loss: 0.44261, in 0.000s 1 tree, 64 leaves, max depth = 11, train loss: 0.45509, val loss: 0.43822, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.45184, val loss: 0.43476, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.44865, val loss: 0.43108, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.44572, val loss: 0.42770, in 0.000s 1 tree, 63 leaves, max depth = 10, train loss: 0.44134, val loss: 0.42355, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.43509, val loss: 0.41754, in 0.016s 1 tree, 63 leaves, max depth = 10, train loss: 0.43136, val loss: 0.41401, in 0.000s 1 tree, 58 leaves, max depth = 17, train loss: 0.42841, val loss: 0.41103, in 0.016s 1 tree, 58 leaves, max depth = 17, train loss: 0.42569, val loss: 0.40828, in 0.016s 1 tree, 58 leaves, max depth = 17, train loss: 0.42319, val loss: 0.40574, in 0.000s 1 tree, 27 leaves, max depth = 8, train loss: 0.42068, val loss: 0.40279, in 0.016s 1 tree, 58 leaves, max depth = 16, train loss: 0.41838, val loss: 0.40045, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.41600, val loss: 0.39770, in 0.000s 1 tree, 50 leaves, max depth = 10, train loss: 0.41120, val loss: 0.39313, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.40907, val loss: 0.39064, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.40628, val loss: 0.38806, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40363, val loss: 0.38521, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.40177, val loss: 0.38298, in 0.000s 1 tree, 58 leaves, max depth = 15, train loss: 0.39983, val loss: 0.38104, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.39574, val loss: 0.37716, in 0.016s 1 tree, 64 leaves, max depth = 13, train loss: 0.39332, val loss: 0.37487, in 0.000s 1 tree, 61 leaves, max depth = 10, train loss: 0.39111, val loss: 0.37286, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.38763, val loss: 0.36958, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.38602, val loss: 0.36766, in 0.000s 1 tree, 61 leaves, max depth = 9, train loss: 0.38414, val loss: 0.36597, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.38105, val loss: 0.36307, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.37961, val loss: 0.36136, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.37675, val loss: 0.35866, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.37410, val loss: 0.35618, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.37278, val loss: 0.35459, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.37156, val loss: 0.35312, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.36910, val loss: 0.35082, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.36789, val loss: 0.34940, in 0.000s 1 tree, 28 leaves, max depth = 8, train loss: 0.36673, val loss: 0.34846, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.36450, val loss: 0.34639, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.36215, val loss: 0.34383, in 0.016s 1 tree, 65 leaves, max depth = 15, train loss: 0.36077, val loss: 0.34259, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.35880, val loss: 0.34075, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.35771, val loss: 0.33948, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.35681, val loss: 0.33855, in 0.000s 1 tree, 37 leaves, max depth = 14, train loss: 0.35589, val loss: 0.33752, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.35448, val loss: 0.33617, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35230, val loss: 0.33380, in 0.016s 1 tree, 65 leaves, max depth = 15, train loss: 0.35113, val loss: 0.33276, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.34942, val loss: 0.33119, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.34784, val loss: 0.32974, in 0.016s 1 tree, 36 leaves, max depth = 14, train loss: 0.34690, val loss: 0.32871, in 0.016s 1 tree, 38 leaves, max depth = 17, train loss: 0.34603, val loss: 0.32777, in 0.000s Fit 72 trees in 0.955 s, (2725 total leaves) Time spent computing histograms: 0.337s Time spent finding best splits: 0.065s Time spent applying splits: 0.055s Time spent predicting: 0.000s Trial 32, Fold 4: Log loss = 0.3466575263630329, Average precision = 0.9518809856724947, ROC-AUC = 0.9474566246056781, Elapsed Time = 0.9676071000012598 seconds Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 32, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 10, train loss: 0.67170, val loss: 0.67020, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.65213, val loss: 0.64928, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.63415, val loss: 0.63004, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.61770, val loss: 0.61238, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.60282, val loss: 0.59636, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.58913, val loss: 0.58154, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.57659, val loss: 0.56794, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.56511, val loss: 0.55546, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.55441, val loss: 0.54380, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.54442, val loss: 0.53282, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.53543, val loss: 0.52290, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.52719, val loss: 0.51381, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.51959, val loss: 0.50540, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.51254, val loss: 0.49748, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.50595, val loss: 0.49013, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.49695, val loss: 0.48163, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.49108, val loss: 0.47504, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.48566, val loss: 0.46892, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.48057, val loss: 0.46315, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.47581, val loss: 0.45770, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.47142, val loss: 0.45264, in 0.016s 1 tree, 63 leaves, max depth = 8, train loss: 0.46604, val loss: 0.44799, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.46211, val loss: 0.44343, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.45847, val loss: 0.43919, in 0.016s 1 tree, 64 leaves, max depth = 15, train loss: 0.45379, val loss: 0.43466, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.45053, val loss: 0.43152, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.44747, val loss: 0.42791, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.44405, val loss: 0.42437, in 0.016s 1 tree, 63 leaves, max depth = 8, train loss: 0.43972, val loss: 0.42076, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.43350, val loss: 0.41510, in 0.016s 1 tree, 63 leaves, max depth = 8, train loss: 0.42981, val loss: 0.41209, in 0.000s 1 tree, 60 leaves, max depth = 21, train loss: 0.42690, val loss: 0.40919, in 0.016s 1 tree, 60 leaves, max depth = 21, train loss: 0.42422, val loss: 0.40651, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.42175, val loss: 0.40359, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.41935, val loss: 0.40072, in 0.000s 1 tree, 59 leaves, max depth = 19, train loss: 0.41688, val loss: 0.39827, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.41479, val loss: 0.39576, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.40986, val loss: 0.39136, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.40785, val loss: 0.38893, in 0.000s 1 tree, 64 leaves, max depth = 11, train loss: 0.40498, val loss: 0.38629, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40232, val loss: 0.38374, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.40036, val loss: 0.38221, in 0.000s 1 tree, 61 leaves, max depth = 18, train loss: 0.39835, val loss: 0.38024, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.39425, val loss: 0.37663, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.39186, val loss: 0.37446, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.38963, val loss: 0.37280, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.38615, val loss: 0.36977, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.38452, val loss: 0.36781, in 0.000s 1 tree, 63 leaves, max depth = 8, train loss: 0.38264, val loss: 0.36645, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.37955, val loss: 0.36378, in 0.016s 1 tree, 61 leaves, max depth = 20, train loss: 0.37812, val loss: 0.36237, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.37535, val loss: 0.35999, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.37279, val loss: 0.35780, in 0.016s 1 tree, 28 leaves, max depth = 14, train loss: 0.37141, val loss: 0.35609, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.37008, val loss: 0.35444, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.36768, val loss: 0.35241, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.36638, val loss: 0.35081, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.36525, val loss: 0.34937, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.36301, val loss: 0.34749, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36063, val loss: 0.34523, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.35929, val loss: 0.34409, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.35730, val loss: 0.34245, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.35617, val loss: 0.34121, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.35514, val loss: 0.34006, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.35422, val loss: 0.33889, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.35284, val loss: 0.33775, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35065, val loss: 0.33565, in 0.000s 1 tree, 65 leaves, max depth = 11, train loss: 0.34947, val loss: 0.33469, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.34772, val loss: 0.33327, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.34611, val loss: 0.33197, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.34516, val loss: 0.33092, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.34438, val loss: 0.33003, in 0.000s Fit 72 trees in 1.127 s, (2802 total leaves) Time spent computing histograms: 0.405s Time spent finding best splits: 0.086s Time spent applying splits: 0.070s Time spent predicting: 0.000s Trial 32, Fold 5: Log loss = 0.35100856288112636, Average precision = 0.950646473005743, ROC-AUC = 0.9457785704996006, Elapsed Time = 1.1419028999989678 seconds
Optimization Progress: 33%|###3 | 33/100 [06:35<14:38, 13.11s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 33, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.015 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 12, train loss: 0.67757, val loss: 0.67715, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.66302, val loss: 0.66218, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.64939, val loss: 0.64816, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.63662, val loss: 0.63501, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.62465, val loss: 0.62267, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.61341, val loss: 0.61107, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.60286, val loss: 0.60017, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.59293, val loss: 0.58991, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.58360, val loss: 0.58024, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.57481, val loss: 0.57113, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.56654, val loss: 0.56255, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.55874, val loss: 0.55446, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.55145, val loss: 0.54680, in 0.000s Fit 13 trees in 0.361 s, (377 total leaves) Time spent computing histograms: 0.052s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 33, Fold 1: Log loss = 0.5523366704044703, Average precision = 0.8140478710455946, ROC-AUC = 0.8619023571739245, Elapsed Time = 0.36222819999966305 seconds Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 33, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 9, train loss: 0.67775, val loss: 0.67702, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.66336, val loss: 0.66191, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.64990, val loss: 0.64776, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.63728, val loss: 0.63449, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.62545, val loss: 0.62202, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.61434, val loss: 0.61036, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.60390, val loss: 0.59934, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.59408, val loss: 0.58902, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.58485, val loss: 0.57923, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.57615, val loss: 0.57007, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.56796, val loss: 0.56144, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.56010, val loss: 0.55310, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.55226, val loss: 0.54561, in 0.000s Fit 13 trees in 0.377 s, (407 total leaves) Time spent computing histograms: 0.049s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 33, Fold 2: Log loss = 0.5521814942210168, Average precision = 0.8878644760545134, ROC-AUC = 0.9063182770192243, Elapsed Time = 0.3772525999993377 seconds Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 33, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 11, train loss: 0.67789, val loss: 0.67739, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.66362, val loss: 0.66265, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.65013, val loss: 0.64872, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.63762, val loss: 0.63576, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.62575, val loss: 0.62348, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.61473, val loss: 0.61205, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.60425, val loss: 0.60119, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.59452, val loss: 0.59107, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.58525, val loss: 0.58145, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.57663, val loss: 0.57247, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.56840, val loss: 0.56391, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.56075, val loss: 0.55593, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.55293, val loss: 0.54866, in 0.016s Fit 13 trees in 0.377 s, (357 total leaves) Time spent computing histograms: 0.053s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 33, Fold 3: Log loss = 0.5495966806223601, Average precision = 0.8902275823415812, ROC-AUC = 0.9079231666120907, Elapsed Time = 0.3846448000003875 seconds Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 33, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 12, train loss: 0.67773, val loss: 0.67684, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.66332, val loss: 0.66157, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.64984, val loss: 0.64725, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.63721, val loss: 0.63381, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.62536, val loss: 0.62117, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.61424, val loss: 0.60929, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.60379, val loss: 0.59810, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.59403, val loss: 0.58762, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.58478, val loss: 0.57768, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.57613, val loss: 0.56836, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.56792, val loss: 0.55950, in 0.016s 1 tree, 33 leaves, max depth = 13, train loss: 0.56024, val loss: 0.55118, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.55295, val loss: 0.54326, in 0.016s Fit 13 trees in 0.376 s, (378 total leaves) Time spent computing histograms: 0.051s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 33, Fold 4: Log loss = 0.5517180277152086, Average precision = 0.8204335615774272, ROC-AUC = 0.8677278482370867, Elapsed Time = 0.38067810000029567 seconds Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 33, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 10, train loss: 0.67760, val loss: 0.67656, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.66306, val loss: 0.66102, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.64952, val loss: 0.64655, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.63675, val loss: 0.63285, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.62485, val loss: 0.62008, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.61360, val loss: 0.60796, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.60309, val loss: 0.59664, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.59314, val loss: 0.58587, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.58385, val loss: 0.57582, in 0.000s 1 tree, 26 leaves, max depth = 12, train loss: 0.57510, val loss: 0.56633, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.56678, val loss: 0.55726, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.55901, val loss: 0.54880, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.55133, val loss: 0.54145, in 0.016s Fit 13 trees in 0.377 s, (378 total leaves) Time spent computing histograms: 0.050s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 33, Fold 5: Log loss = 0.5551145966827552, Average precision = 0.8855353230294833, ROC-AUC = 0.9000196941827843, Elapsed Time = 0.3723337999999785 seconds
Optimization Progress: 34%|###4 | 34/100 [06:44<12:59, 11.81s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 34, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.141 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 16, train loss: 0.65572, val loss: 0.65450, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.62421, val loss: 0.62186, in 0.016s 1 tree, 68 leaves, max depth = 19, train loss: 0.59754, val loss: 0.59454, in 0.016s 1 tree, 68 leaves, max depth = 19, train loss: 0.57469, val loss: 0.57110, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.55484, val loss: 0.55033, in 0.016s 1 tree, 107 leaves, max depth = 18, train loss: 0.53642, val loss: 0.53322, in 0.000s 1 tree, 67 leaves, max depth = 18, train loss: 0.52055, val loss: 0.51683, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.50540, val loss: 0.50285, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.49249, val loss: 0.48932, in 0.000s 1 tree, 107 leaves, max depth = 18, train loss: 0.47987, val loss: 0.47776, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.46924, val loss: 0.46663, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.45859, val loss: 0.45695, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.44930, val loss: 0.44856, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.44082, val loss: 0.43960, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.43316, val loss: 0.43122, in 0.000s 1 tree, 107 leaves, max depth = 13, train loss: 0.42538, val loss: 0.42429, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.41903, val loss: 0.41746, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.41233, val loss: 0.41156, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.40685, val loss: 0.40550, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.40102, val loss: 0.40048, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.39630, val loss: 0.39522, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.39119, val loss: 0.39094, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.38623, val loss: 0.38562, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.38193, val loss: 0.38099, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.37791, val loss: 0.37649, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.37343, val loss: 0.37285, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.36951, val loss: 0.36970, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.36598, val loss: 0.36571, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.36221, val loss: 0.36159, in 0.000s 1 tree, 106 leaves, max depth = 14, train loss: 0.35873, val loss: 0.35888, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.35557, val loss: 0.35549, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.35248, val loss: 0.35198, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34978, val loss: 0.34889, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.34705, val loss: 0.34604, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.34384, val loss: 0.34366, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.34142, val loss: 0.34082, in 0.016s 1 tree, 86 leaves, max depth = 17, train loss: 0.33886, val loss: 0.33801, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.33596, val loss: 0.33592, in 0.000s 1 tree, 86 leaves, max depth = 15, train loss: 0.33371, val loss: 0.33340, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33152, val loss: 0.33086, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.32959, val loss: 0.32860, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.32690, val loss: 0.32672, in 0.000s 1 tree, 85 leaves, max depth = 14, train loss: 0.32465, val loss: 0.32419, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32288, val loss: 0.32207, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.32097, val loss: 0.32013, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.31851, val loss: 0.31845, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.31671, val loss: 0.31651, in 0.016s [48/85] 1 tree, 107 leaves, max depth = 14, train loss: 0.31454, val loss: 0.31507, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.31287, val loss: 0.31308, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.31129, val loss: 0.31145, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.30933, val loss: 0.31021, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30780, val loss: 0.30834, in 0.000s 1 tree, 86 leaves, max depth = 15, train loss: 0.30623, val loss: 0.30665, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.30443, val loss: 0.30553, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.30294, val loss: 0.30382, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.30152, val loss: 0.30207, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.29989, val loss: 0.30112, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29861, val loss: 0.29952, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.29723, val loss: 0.29826, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.29609, val loss: 0.29684, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.29481, val loss: 0.29542, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.29323, val loss: 0.29450, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.29213, val loss: 0.29350, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29104, val loss: 0.29212, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.28966, val loss: 0.29138, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28868, val loss: 0.29012, in 0.000s 1 tree, 85 leaves, max depth = 19, train loss: 0.28753, val loss: 0.28883, in 0.016s 1 tree, 132 leaves, max depth = 24, train loss: 0.28595, val loss: 0.28798, in 0.016s 1 tree, 64 leaves, max depth = 15, train loss: 0.28497, val loss: 0.28696, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28404, val loss: 0.28575, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.28243, val loss: 0.28485, in 0.016s 1 tree, 140 leaves, max depth = 20, train loss: 0.28082, val loss: 0.28362, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27996, val loss: 0.28251, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.27896, val loss: 0.28156, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27753, val loss: 0.28080, in 0.016s 1 tree, 131 leaves, max depth = 24, train loss: 0.27623, val loss: 0.28017, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27541, val loss: 0.27910, in 0.000s 1 tree, 40 leaves, max depth = 10, train loss: 0.27456, val loss: 0.27836, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.27383, val loss: 0.27738, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.27293, val loss: 0.27635, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.27138, val loss: 0.27547, in 0.000s 1 tree, 140 leaves, max depth = 17, train loss: 0.27018, val loss: 0.27490, in 0.016s 1 tree, 140 leaves, max depth = 17, train loss: 0.26887, val loss: 0.27393, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.26804, val loss: 0.27291, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.26732, val loss: 0.27195, in 0.000s Fit 85 trees in 1.064 s, (5840 total leaves) Time spent computing histograms: 0.358s Time spent finding best splits: 0.077s Time spent applying splits: 0.088s Time spent predicting: 0.000s Trial 34, Fold 1: Log loss = 0.2807773485256574, Average precision = 0.9522178452405305, ROC-AUC = 0.9524693001008493, Elapsed Time = 1.0698346000008314 seconds Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 34, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 15, train loss: 0.65638, val loss: 0.65475, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.62534, val loss: 0.62225, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.59839, val loss: 0.59387, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.57553, val loss: 0.56976, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.55565, val loss: 0.54871, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.53740, val loss: 0.53119, in 0.016s 1 tree, 67 leaves, max depth = 20, train loss: 0.52158, val loss: 0.51443, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.50660, val loss: 0.50012, in 0.000s 1 tree, 68 leaves, max depth = 12, train loss: 0.49338, val loss: 0.48599, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.48096, val loss: 0.47419, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.47029, val loss: 0.46280, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.45981, val loss: 0.45289, in 0.016s 1 tree, 68 leaves, max depth = 17, train loss: 0.45088, val loss: 0.44338, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.44292, val loss: 0.43475, in 0.016s 1 tree, 68 leaves, max depth = 18, train loss: 0.43616, val loss: 0.42749, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.42762, val loss: 0.41952, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.42217, val loss: 0.41385, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.41477, val loss: 0.40700, in 0.000s 1 tree, 68 leaves, max depth = 14, train loss: 0.40943, val loss: 0.40129, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.40301, val loss: 0.39541, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.39738, val loss: 0.39024, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.39249, val loss: 0.38583, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.38798, val loss: 0.38112, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.38358, val loss: 0.37632, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.37970, val loss: 0.37226, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.37539, val loss: 0.36839, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.37166, val loss: 0.36510, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36824, val loss: 0.36151, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.36446, val loss: 0.35744, in 0.000s 1 tree, 107 leaves, max depth = 17, train loss: 0.36117, val loss: 0.35458, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.35795, val loss: 0.35107, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35495, val loss: 0.34791, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35233, val loss: 0.34513, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.34955, val loss: 0.34213, in 0.000s 1 tree, 107 leaves, max depth = 18, train loss: 0.34648, val loss: 0.33949, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34413, val loss: 0.33700, in 0.016s 1 tree, 86 leaves, max depth = 16, train loss: 0.34166, val loss: 0.33427, in 0.000s 1 tree, 107 leaves, max depth = 18, train loss: 0.33894, val loss: 0.33197, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.33661, val loss: 0.32957, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33448, val loss: 0.32730, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.33225, val loss: 0.32487, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33036, val loss: 0.32284, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.32843, val loss: 0.32074, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.32585, val loss: 0.31858, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32412, val loss: 0.31673, in 0.000s 1 tree, 107 leaves, max depth = 18, train loss: 0.32184, val loss: 0.31484, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.32009, val loss: 0.31302, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.31808, val loss: 0.31138, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.31646, val loss: 0.30964, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.31474, val loss: 0.30775, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.31286, val loss: 0.30621, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.31137, val loss: 0.30461, in 0.016s 1 tree, 86 leaves, max depth = 16, train loss: 0.30985, val loss: 0.30298, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.30814, val loss: 0.30158, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.30675, val loss: 0.30017, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30537, val loss: 0.29869, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.30406, val loss: 0.29725, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30283, val loss: 0.29591, in 0.000s 1 tree, 107 leaves, max depth = 17, train loss: 0.30122, val loss: 0.29464, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30010, val loss: 0.29341, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.29813, val loss: 0.29203, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.29687, val loss: 0.29064, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.29578, val loss: 0.28960, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.29473, val loss: 0.28845, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.29333, val loss: 0.28733, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29238, val loss: 0.28628, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.29129, val loss: 0.28509, in 0.016s 1 tree, 131 leaves, max depth = 21, train loss: 0.28970, val loss: 0.28436, in 0.016s 1 tree, 65 leaves, max depth = 18, train loss: 0.28867, val loss: 0.28342, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.28777, val loss: 0.28242, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.28655, val loss: 0.28151, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.28494, val loss: 0.28081, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28409, val loss: 0.27987, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.28304, val loss: 0.27882, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.28152, val loss: 0.27780, in 0.016s 1 tree, 86 leaves, max depth = 21, train loss: 0.28056, val loss: 0.27673, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27976, val loss: 0.27584, in 0.000s 1 tree, 140 leaves, max depth = 17, train loss: 0.27823, val loss: 0.27522, in 0.016s 1 tree, 131 leaves, max depth = 21, train loss: 0.27700, val loss: 0.27475, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27626, val loss: 0.27392, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.27544, val loss: 0.27315, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.27406, val loss: 0.27262, in 0.016s 1 tree, 86 leaves, max depth = 21, train loss: 0.27322, val loss: 0.27167, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27199, val loss: 0.27124, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27128, val loss: 0.27043, in 0.000s Fit 85 trees in 1.126 s, (5776 total leaves) Time spent computing histograms: 0.381s Time spent finding best splits: 0.081s Time spent applying splits: 0.093s Time spent predicting: 0.000s Trial 34, Fold 2: Log loss = 0.2800543717383729, Average precision = 0.9508008089697524, ROC-AUC = 0.9531672190791882, Elapsed Time = 1.1384725000007165 seconds Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 34, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 13, train loss: 0.65601, val loss: 0.65491, in 0.000s 1 tree, 65 leaves, max depth = 13, train loss: 0.62471, val loss: 0.62261, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.59805, val loss: 0.59502, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.57547, val loss: 0.57137, in 0.000s 1 tree, 66 leaves, max depth = 13, train loss: 0.55579, val loss: 0.55095, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.53741, val loss: 0.53400, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.52169, val loss: 0.51748, in 0.000s 1 tree, 106 leaves, max depth = 13, train loss: 0.50658, val loss: 0.50363, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.49376, val loss: 0.49006, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.48118, val loss: 0.47860, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.47049, val loss: 0.46743, in 0.000s 1 tree, 106 leaves, max depth = 14, train loss: 0.45989, val loss: 0.45783, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.45098, val loss: 0.44844, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.44313, val loss: 0.44007, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.43639, val loss: 0.43281, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.42773, val loss: 0.42502, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.42206, val loss: 0.41978, in 0.000s 1 tree, 106 leaves, max depth = 12, train loss: 0.41456, val loss: 0.41311, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40969, val loss: 0.40860, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.40315, val loss: 0.40285, in 0.000s 1 tree, 106 leaves, max depth = 12, train loss: 0.39742, val loss: 0.39788, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.39241, val loss: 0.39358, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.38754, val loss: 0.38822, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.38330, val loss: 0.38357, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.37922, val loss: 0.37978, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.37483, val loss: 0.37605, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.37099, val loss: 0.37285, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.36740, val loss: 0.36951, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.36374, val loss: 0.36541, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.36033, val loss: 0.36262, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.35724, val loss: 0.35919, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35410, val loss: 0.35626, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.35135, val loss: 0.35370, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34895, val loss: 0.35144, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.34628, val loss: 0.34854, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.34308, val loss: 0.34598, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.34066, val loss: 0.34293, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.33783, val loss: 0.34070, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.33553, val loss: 0.33832, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33330, val loss: 0.33623, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.33133, val loss: 0.33438, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.32915, val loss: 0.33179, in 0.000s 1 tree, 83 leaves, max depth = 14, train loss: 0.32729, val loss: 0.32954, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.32460, val loss: 0.32744, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32278, val loss: 0.32572, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.32039, val loss: 0.32389, in 0.016s [47/85] 1 tree, 67 leaves, max depth = 13, train loss: 0.31874, val loss: 0.32211, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.31662, val loss: 0.32052, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.31492, val loss: 0.31893, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.31326, val loss: 0.31689, in 0.000s 1 tree, 106 leaves, max depth = 15, train loss: 0.31133, val loss: 0.31547, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30977, val loss: 0.31399, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.30838, val loss: 0.31250, in 0.000s 1 tree, 106 leaves, max depth = 15, train loss: 0.30662, val loss: 0.31123, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.30519, val loss: 0.30943, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30374, val loss: 0.30805, in 0.000s 1 tree, 106 leaves, max depth = 13, train loss: 0.30213, val loss: 0.30692, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30083, val loss: 0.30569, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.29952, val loss: 0.30404, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.29835, val loss: 0.30292, in 0.016s 1 tree, 129 leaves, max depth = 19, train loss: 0.29654, val loss: 0.30210, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.29534, val loss: 0.30085, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.29392, val loss: 0.29988, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.29281, val loss: 0.29883, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.29169, val loss: 0.29740, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.29069, val loss: 0.29644, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.28934, val loss: 0.29554, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28843, val loss: 0.29467, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.28739, val loss: 0.29361, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.28617, val loss: 0.29282, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.28518, val loss: 0.29153, in 0.016s 1 tree, 130 leaves, max depth = 20, train loss: 0.28374, val loss: 0.29104, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28285, val loss: 0.29020, in 0.000s 1 tree, 40 leaves, max depth = 10, train loss: 0.28192, val loss: 0.28939, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28112, val loss: 0.28862, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.27949, val loss: 0.28740, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.27847, val loss: 0.28630, in 0.000s 1 tree, 140 leaves, max depth = 15, train loss: 0.27708, val loss: 0.28525, in 0.031s 1 tree, 4 leaves, max depth = 3, train loss: 0.27632, val loss: 0.28451, in 0.000s 1 tree, 140 leaves, max depth = 17, train loss: 0.27491, val loss: 0.28359, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.27401, val loss: 0.28259, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.27277, val loss: 0.28166, in 0.016s 1 tree, 131 leaves, max depth = 18, train loss: 0.27160, val loss: 0.28135, in 0.016s 1 tree, 83 leaves, max depth = 21, train loss: 0.27085, val loss: 0.28032, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.27010, val loss: 0.27961, in 0.016s Fit 85 trees in 1.189 s, (5765 total leaves) Time spent computing histograms: 0.402s Time spent finding best splits: 0.084s Time spent applying splits: 0.096s Time spent predicting: 0.000s Trial 34, Fold 3: Log loss = 0.2744742454659397, Average precision = 0.9557721692213328, ROC-AUC = 0.9553627860417409, Elapsed Time = 1.1863267999997333 seconds Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 34, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 67 leaves, max depth = 13, train loss: 0.65633, val loss: 0.65431, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.62527, val loss: 0.62136, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.59856, val loss: 0.59275, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.57590, val loss: 0.56845, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.55621, val loss: 0.54711, in 0.000s 1 tree, 101 leaves, max depth = 14, train loss: 0.53808, val loss: 0.52926, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.52227, val loss: 0.51210, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.50739, val loss: 0.49748, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.49449, val loss: 0.48334, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.48211, val loss: 0.47120, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.47129, val loss: 0.45916, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.46088, val loss: 0.44898, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.45197, val loss: 0.43904, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.44410, val loss: 0.43012, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.43733, val loss: 0.42244, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.42888, val loss: 0.41423, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.42340, val loss: 0.40837, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.41607, val loss: 0.40125, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.41067, val loss: 0.39502, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.40432, val loss: 0.38885, in 0.000s 1 tree, 104 leaves, max depth = 13, train loss: 0.39876, val loss: 0.38346, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.39390, val loss: 0.37877, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.38935, val loss: 0.37387, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.38507, val loss: 0.36895, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.38117, val loss: 0.36474, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.37690, val loss: 0.36070, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.37317, val loss: 0.35713, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36973, val loss: 0.35340, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.36603, val loss: 0.34919, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.36274, val loss: 0.34606, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.35961, val loss: 0.34245, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35659, val loss: 0.33918, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.35395, val loss: 0.33632, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.35126, val loss: 0.33324, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.34821, val loss: 0.33045, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34585, val loss: 0.32788, in 0.000s 1 tree, 86 leaves, max depth = 17, train loss: 0.34338, val loss: 0.32537, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.34062, val loss: 0.32287, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.33836, val loss: 0.32036, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33622, val loss: 0.31803, in 0.000s 1 tree, 85 leaves, max depth = 15, train loss: 0.33404, val loss: 0.31583, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33214, val loss: 0.31374, in 0.000s 1 tree, 85 leaves, max depth = 15, train loss: 0.33025, val loss: 0.31185, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.32763, val loss: 0.30951, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32590, val loss: 0.30762, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.32355, val loss: 0.30553, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.32190, val loss: 0.30399, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.31988, val loss: 0.30222, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.31825, val loss: 0.30042, in 0.016s 1 tree, 85 leaves, max depth = 18, train loss: 0.31654, val loss: 0.29874, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.31507, val loss: 0.29701, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.31360, val loss: 0.29541, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.31167, val loss: 0.29378, in 0.000s 1 tree, 86 leaves, max depth = 15, train loss: 0.31021, val loss: 0.29233, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.30851, val loss: 0.29084, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30712, val loss: 0.28930, in 0.000s 1 tree, 85 leaves, max depth = 18, train loss: 0.30580, val loss: 0.28806, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30456, val loss: 0.28669, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.30295, val loss: 0.28536, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.30182, val loss: 0.28411, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.30057, val loss: 0.28289, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.29856, val loss: 0.28111, in 0.016s 1 tree, 85 leaves, max depth = 18, train loss: 0.29740, val loss: 0.28004, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29635, val loss: 0.27889, in 0.000s 1 tree, 103 leaves, max depth = 15, train loss: 0.29495, val loss: 0.27771, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29400, val loss: 0.27664, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.29294, val loss: 0.27581, in 0.000s 1 tree, 140 leaves, max depth = 19, train loss: 0.29124, val loss: 0.27474, in 0.016s 1 tree, 85 leaves, max depth = 18, train loss: 0.29019, val loss: 0.27380, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28928, val loss: 0.27279, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.28759, val loss: 0.27131, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.28592, val loss: 0.27038, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28509, val loss: 0.26946, in 0.000s 1 tree, 86 leaves, max depth = 17, train loss: 0.28409, val loss: 0.26859, in 0.016s 1 tree, 129 leaves, max depth = 23, train loss: 0.28256, val loss: 0.26746, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.28172, val loss: 0.26646, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28093, val loss: 0.26559, in 0.000s 1 tree, 140 leaves, max depth = 20, train loss: 0.27946, val loss: 0.26469, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.27862, val loss: 0.26397, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27731, val loss: 0.26317, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.27655, val loss: 0.26251, in 0.016s 1 tree, 129 leaves, max depth = 22, train loss: 0.27523, val loss: 0.26151, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27410, val loss: 0.26084, in 0.016s 1 tree, 140 leaves, max depth = 20, train loss: 0.27280, val loss: 0.26014, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27198, val loss: 0.25924, in 0.000s Fit 85 trees in 1.190 s, (5883 total leaves) Time spent computing histograms: 0.410s Time spent finding best splits: 0.086s Time spent applying splits: 0.100s Time spent predicting: 0.016s Trial 34, Fold 4: Log loss = 0.2763670443208256, Average precision = 0.9559006771424874, ROC-AUC = 0.9546079165981148, Elapsed Time = 1.2050694999998086 seconds Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 34, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 14, train loss: 0.65604, val loss: 0.65344, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.62471, val loss: 0.61997, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.59802, val loss: 0.59130, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.57510, val loss: 0.56637, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.55541, val loss: 0.54499, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.53731, val loss: 0.52774, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.52132, val loss: 0.51023, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.50645, val loss: 0.49620, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.49308, val loss: 0.48144, in 0.000s 1 tree, 105 leaves, max depth = 22, train loss: 0.48073, val loss: 0.46989, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.46990, val loss: 0.45785, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.45949, val loss: 0.44824, in 0.000s 1 tree, 68 leaves, max depth = 13, train loss: 0.45047, val loss: 0.43812, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.44241, val loss: 0.42900, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.43540, val loss: 0.42099, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.42689, val loss: 0.41333, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.42117, val loss: 0.40674, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.41382, val loss: 0.40021, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40852, val loss: 0.39513, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.40214, val loss: 0.38953, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.39658, val loss: 0.38469, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.39173, val loss: 0.38051, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.38719, val loss: 0.37617, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.38276, val loss: 0.37099, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.37885, val loss: 0.36726, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.37460, val loss: 0.36370, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.37089, val loss: 0.36062, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36745, val loss: 0.35734, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.36360, val loss: 0.35295, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.36035, val loss: 0.34910, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.35744, val loss: 0.34567, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35447, val loss: 0.34284, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35188, val loss: 0.34037, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.34842, val loss: 0.33763, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.34543, val loss: 0.33528, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34307, val loss: 0.33303, in 0.016s 1 tree, 86 leaves, max depth = 13, train loss: 0.34050, val loss: 0.33033, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.33823, val loss: 0.32759, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.33619, val loss: 0.32532, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.33341, val loss: 0.32319, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33126, val loss: 0.32113, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32937, val loss: 0.31933, in 0.000s 1 tree, 84 leaves, max depth = 14, train loss: 0.32726, val loss: 0.31710, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.32470, val loss: 0.31517, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32298, val loss: 0.31353, in 0.000s 1 tree, 104 leaves, max depth = 18, train loss: 0.32071, val loss: 0.31185, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.31894, val loss: 0.30969, in 0.016s 1 tree, 86 leaves, max depth = 18, train loss: 0.31725, val loss: 0.30792, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31566, val loss: 0.30640, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.31357, val loss: 0.30488, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.31193, val loss: 0.30315, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31047, val loss: 0.30175, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.30857, val loss: 0.30041, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.30720, val loss: 0.29869, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.30553, val loss: 0.29754, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30415, val loss: 0.29622, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.30272, val loss: 0.29471, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30150, val loss: 0.29353, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.29992, val loss: 0.29247, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29881, val loss: 0.29140, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.29749, val loss: 0.29003, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.29607, val loss: 0.28907, in 0.016s 1 tree, 63 leaves, max depth = 13, train loss: 0.29491, val loss: 0.28767, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29386, val loss: 0.28666, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.29257, val loss: 0.28581, in 0.000s 1 tree, 84 leaves, max depth = 16, train loss: 0.29143, val loss: 0.28463, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29046, val loss: 0.28369, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.28862, val loss: 0.28234, in 0.000s 1 tree, 84 leaves, max depth = 16, train loss: 0.28758, val loss: 0.28125, in 0.000s 1 tree, 140 leaves, max depth = 21, train loss: 0.28593, val loss: 0.28015, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28501, val loss: 0.27926, in 0.016s 1 tree, 128 leaves, max depth = 24, train loss: 0.28349, val loss: 0.27819, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28266, val loss: 0.27738, in 0.000s 1 tree, 84 leaves, max depth = 19, train loss: 0.28166, val loss: 0.27645, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.28017, val loss: 0.27547, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.27930, val loss: 0.27437, in 0.016s [77/85] 1 tree, 5 leaves, max depth = 3, train loss: 0.27852, val loss: 0.27361, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.27692, val loss: 0.27319, in 0.016s 1 tree, 140 leaves, max depth = 21, train loss: 0.27561, val loss: 0.27234, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.27474, val loss: 0.27145, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.27336, val loss: 0.27049, in 0.000s 1 tree, 129 leaves, max depth = 25, train loss: 0.27214, val loss: 0.26968, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.27138, val loss: 0.26886, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27032, val loss: 0.26821, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26952, val loss: 0.26741, in 0.016s Fit 85 trees in 1.330 s, (5901 total leaves) Time spent computing histograms: 0.461s Time spent finding best splits: 0.103s Time spent applying splits: 0.131s Time spent predicting: 0.000s Trial 34, Fold 5: Log loss = 0.2834021484954921, Average precision = 0.9528156004014571, ROC-AUC = 0.9512490199357152, Elapsed Time = 1.3338655999996263 seconds
Optimization Progress: 35%|###5 | 35/100 [06:56<12:58, 11.97s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 35, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 15 leaves, max depth = 6, train loss: 0.68744, val loss: 0.68725, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.68204, val loss: 0.68167, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.67677, val loss: 0.67623, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.67163, val loss: 0.67090, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.66660, val loss: 0.66570, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.66168, val loss: 0.66061, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.65688, val loss: 0.65564, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.65218, val loss: 0.65077, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.64759, val loss: 0.64601, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.64310, val loss: 0.64136, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.63871, val loss: 0.63681, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.63442, val loss: 0.63235, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.63022, val loss: 0.62799, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.62611, val loss: 0.62373, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.62209, val loss: 0.61955, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.61816, val loss: 0.61547, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.61431, val loss: 0.61147, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.61055, val loss: 0.60755, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.60686, val loss: 0.60372, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.60326, val loss: 0.59996, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.59973, val loss: 0.59629, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.59632, val loss: 0.59275, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.59294, val loss: 0.58922, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.58963, val loss: 0.58577, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.58643, val loss: 0.58244, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.58326, val loss: 0.57913, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.58015, val loss: 0.57588, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.57710, val loss: 0.57270, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.57412, val loss: 0.56959, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.57124, val loss: 0.56659, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.56838, val loss: 0.56359, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.56557, val loss: 0.56066, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.56287, val loss: 0.55784, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.56017, val loss: 0.55502, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.55745, val loss: 0.55248, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.55483, val loss: 0.54974, in 0.000s 1 tree, 49 leaves, max depth = 11, train loss: 0.55218, val loss: 0.54728, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.54969, val loss: 0.54466, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.54711, val loss: 0.54227, in 0.016s [40/65] 1 tree, 31 leaves, max depth = 11, train loss: 0.54468, val loss: 0.53973, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.54218, val loss: 0.53740, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.53982, val loss: 0.53493, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.53738, val loss: 0.53267, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.53506, val loss: 0.53022, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.53268, val loss: 0.52803, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.53036, val loss: 0.52587, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.52815, val loss: 0.52356, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.52589, val loss: 0.52147, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.52375, val loss: 0.51921, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.52154, val loss: 0.51718, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.51946, val loss: 0.51498, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.51731, val loss: 0.51300, in 0.062s 1 tree, 22 leaves, max depth = 10, train loss: 0.51525, val loss: 0.51082, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.51316, val loss: 0.50890, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.51119, val loss: 0.50682, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.50915, val loss: 0.50494, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.50716, val loss: 0.50311, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.50525, val loss: 0.50110, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.50331, val loss: 0.49931, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.50146, val loss: 0.49736, in 0.000s 1 tree, 49 leaves, max depth = 11, train loss: 0.49956, val loss: 0.49561, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.49776, val loss: 0.49371, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.49591, val loss: 0.49201, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.49415, val loss: 0.49016, in 0.000s 1 tree, 49 leaves, max depth = 10, train loss: 0.49235, val loss: 0.48850, in 0.016s Fit 65 trees in 1.002 s, (1831 total leaves) Time spent computing histograms: 0.335s Time spent finding best splits: 0.067s Time spent applying splits: 0.065s Time spent predicting: 0.000s Trial 35, Fold 1: Log loss = 0.4928565760604082, Average precision = 0.9120125022297894, ROC-AUC = 0.9211213185213862, Elapsed Time = 1.0007270999994944 seconds Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 35, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 11, train loss: 0.68764, val loss: 0.68738, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.68233, val loss: 0.68181, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.67715, val loss: 0.67638, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.67208, val loss: 0.67106, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.66713, val loss: 0.66586, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.66229, val loss: 0.66078, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.65755, val loss: 0.65582, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.65293, val loss: 0.65096, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.64841, val loss: 0.64621, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.64399, val loss: 0.64156, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.63967, val loss: 0.63702, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.63544, val loss: 0.63257, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.63124, val loss: 0.62814, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.62713, val loss: 0.62379, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.62317, val loss: 0.61963, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.61923, val loss: 0.61547, in 0.000s 1 tree, 18 leaves, max depth = 8, train loss: 0.61538, val loss: 0.61139, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.61167, val loss: 0.60749, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.60798, val loss: 0.60358, in 0.000s 1 tree, 22 leaves, max depth = 10, train loss: 0.60444, val loss: 0.59984, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.60090, val loss: 0.59610, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.59749, val loss: 0.59251, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.59410, val loss: 0.58891, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.59078, val loss: 0.58539, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.58759, val loss: 0.58201, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.58446, val loss: 0.57871, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.58134, val loss: 0.57539, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.57834, val loss: 0.57222, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.57535, val loss: 0.56904, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.57242, val loss: 0.56592, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.56960, val loss: 0.56293, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.56679, val loss: 0.55994, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.56408, val loss: 0.55708, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.56143, val loss: 0.55427, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.55873, val loss: 0.55166, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.55616, val loss: 0.54893, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.55353, val loss: 0.54639, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.55103, val loss: 0.54375, in 0.000s 1 tree, 46 leaves, max depth = 12, train loss: 0.54848, val loss: 0.54128, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.54605, val loss: 0.53871, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.54357, val loss: 0.53631, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.54116, val loss: 0.53373, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.53874, val loss: 0.53140, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.53645, val loss: 0.52897, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.53410, val loss: 0.52670, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.53180, val loss: 0.52448, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.52959, val loss: 0.52214, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.52735, val loss: 0.51997, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.52521, val loss: 0.51769, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.52302, val loss: 0.51559, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.52094, val loss: 0.51337, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.51881, val loss: 0.51132, in 0.016s 1 tree, 29 leaves, max depth = 17, train loss: 0.51678, val loss: 0.50915, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.51471, val loss: 0.50715, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.51274, val loss: 0.50505, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.51072, val loss: 0.50311, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.50874, val loss: 0.50121, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.50684, val loss: 0.49918, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.50491, val loss: 0.49733, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.50306, val loss: 0.49535, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.50118, val loss: 0.49355, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.49937, val loss: 0.49162, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.49754, val loss: 0.48986, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.49578, val loss: 0.48799, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.49400, val loss: 0.48627, in 0.016s Fit 65 trees in 1.017 s, (2086 total leaves) Time spent computing histograms: 0.354s Time spent finding best splits: 0.055s Time spent applying splits: 0.055s Time spent predicting: 0.000s Trial 35, Fold 2: Log loss = 0.49437818976459447, Average precision = 0.9123666380898829, ROC-AUC = 0.927085492577864, Elapsed Time = 1.021703800000978 seconds Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 35, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 13 leaves, max depth = 8, train loss: 0.68755, val loss: 0.68738, in 0.000s 1 tree, 13 leaves, max depth = 8, train loss: 0.68223, val loss: 0.68189, in 0.016s 1 tree, 13 leaves, max depth = 8, train loss: 0.67703, val loss: 0.67652, in 0.016s 1 tree, 13 leaves, max depth = 8, train loss: 0.67195, val loss: 0.67127, in 0.000s 1 tree, 13 leaves, max depth = 8, train loss: 0.66699, val loss: 0.66615, in 0.016s 1 tree, 13 leaves, max depth = 8, train loss: 0.66214, val loss: 0.66113, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.65740, val loss: 0.65623, in 0.016s 1 tree, 13 leaves, max depth = 8, train loss: 0.65276, val loss: 0.65143, in 0.000s 1 tree, 13 leaves, max depth = 7, train loss: 0.64823, val loss: 0.64674, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.64383, val loss: 0.64220, in 0.000s 1 tree, 13 leaves, max depth = 8, train loss: 0.63950, val loss: 0.63771, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.63529, val loss: 0.63336, in 0.016s 1 tree, 14 leaves, max depth = 9, train loss: 0.63114, val loss: 0.62906, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.62711, val loss: 0.62490, in 0.016s 1 tree, 14 leaves, max depth = 9, train loss: 0.62314, val loss: 0.62078, in 0.000s 1 tree, 20 leaves, max depth = 11, train loss: 0.61928, val loss: 0.61679, in 0.016s 1 tree, 14 leaves, max depth = 9, train loss: 0.61548, val loss: 0.61285, in 0.000s 1 tree, 20 leaves, max depth = 11, train loss: 0.61179, val loss: 0.60903, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.60817, val loss: 0.60529, in 0.000s 1 tree, 14 leaves, max depth = 9, train loss: 0.60460, val loss: 0.60159, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.60114, val loss: 0.59800, in 0.000s 1 tree, 14 leaves, max depth = 9, train loss: 0.59772, val loss: 0.59445, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.59440, val loss: 0.59101, in 0.000s 1 tree, 14 leaves, max depth = 9, train loss: 0.59112, val loss: 0.58760, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.58794, val loss: 0.58430, in 0.000s [26/65] 1 tree, 15 leaves, max depth = 10, train loss: 0.58480, val loss: 0.58103, in 0.000s 1 tree, 20 leaves, max depth = 11, train loss: 0.58174, val loss: 0.57787, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.57875, val loss: 0.57477, in 0.000s 1 tree, 15 leaves, max depth = 10, train loss: 0.57580, val loss: 0.57169, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.57293, val loss: 0.56872, in 0.000s 1 tree, 15 leaves, max depth = 10, train loss: 0.57010, val loss: 0.56576, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.56734, val loss: 0.56291, in 0.016s 1 tree, 15 leaves, max depth = 10, train loss: 0.56462, val loss: 0.56007, in 0.000s 1 tree, 20 leaves, max depth = 11, train loss: 0.56198, val loss: 0.55733, in 0.016s 1 tree, 15 leaves, max depth = 10, train loss: 0.55937, val loss: 0.55460, in 0.000s 1 tree, 48 leaves, max depth = 13, train loss: 0.55670, val loss: 0.55213, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.55419, val loss: 0.54952, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.55159, val loss: 0.54712, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.54916, val loss: 0.54458, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.54663, val loss: 0.54225, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.54426, val loss: 0.53978, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.54181, val loss: 0.53752, in 0.000s 1 tree, 22 leaves, max depth = 11, train loss: 0.53951, val loss: 0.53512, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.53712, val loss: 0.53292, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.53489, val loss: 0.53058, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.53256, val loss: 0.52845, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.53039, val loss: 0.52617, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.52813, val loss: 0.52409, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.52591, val loss: 0.52206, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.52382, val loss: 0.51985, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.52167, val loss: 0.51786, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.51963, val loss: 0.51573, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.51753, val loss: 0.51380, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.51555, val loss: 0.51173, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.51350, val loss: 0.50985, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.51158, val loss: 0.50781, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.50958, val loss: 0.50599, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.50771, val loss: 0.50402, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.50576, val loss: 0.50224, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.50386, val loss: 0.50050, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.50205, val loss: 0.49860, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.50019, val loss: 0.49690, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.49843, val loss: 0.49505, in 0.000s 1 tree, 48 leaves, max depth = 13, train loss: 0.49662, val loss: 0.49340, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.49490, val loss: 0.49160, in 0.016s Fit 65 trees in 0.970 s, (1674 total leaves) Time spent computing histograms: 0.343s Time spent finding best splits: 0.047s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 35, Fold 3: Log loss = 0.4913007819756683, Average precision = 0.916790820488213, ROC-AUC = 0.9291073053808592, Elapsed Time = 0.9722562999995716 seconds Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 35, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 14 leaves, max depth = 6, train loss: 0.68757, val loss: 0.68725, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.68222, val loss: 0.68160, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.67700, val loss: 0.67607, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.67190, val loss: 0.67066, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.66691, val loss: 0.66537, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.66204, val loss: 0.66020, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.65728, val loss: 0.65515, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.65263, val loss: 0.65021, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.64807, val loss: 0.64537, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.64362, val loss: 0.64064, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.63927, val loss: 0.63600, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.63502, val loss: 0.63147, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.63086, val loss: 0.62704, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.62679, val loss: 0.62270, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.62280, val loss: 0.61845, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.61891, val loss: 0.61429, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.61510, val loss: 0.61021, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.61137, val loss: 0.60622, in 0.000s 1 tree, 22 leaves, max depth = 12, train loss: 0.60772, val loss: 0.60231, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.60414, val loss: 0.59847, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.60068, val loss: 0.59478, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.59725, val loss: 0.59110, in 0.000s 1 tree, 23 leaves, max depth = 12, train loss: 0.59390, val loss: 0.58749, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.59062, val loss: 0.58396, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.58743, val loss: 0.58056, in 0.016s 1 tree, 23 leaves, max depth = 12, train loss: 0.58429, val loss: 0.57716, in 0.000s 1 tree, 23 leaves, max depth = 12, train loss: 0.58120, val loss: 0.57384, in 0.016s [28/65] 1 tree, 24 leaves, max depth = 12, train loss: 0.57818, val loss: 0.57058, in 0.000s 1 tree, 24 leaves, max depth = 12, train loss: 0.57523, val loss: 0.56739, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.57236, val loss: 0.56431, in 0.000s 1 tree, 24 leaves, max depth = 12, train loss: 0.56952, val loss: 0.56124, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.56674, val loss: 0.55823, in 0.000s 1 tree, 24 leaves, max depth = 12, train loss: 0.56402, val loss: 0.55528, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.56138, val loss: 0.55244, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.55867, val loss: 0.54976, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.55610, val loss: 0.54700, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.55347, val loss: 0.54440, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.55095, val loss: 0.54167, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.54839, val loss: 0.53914, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.54597, val loss: 0.53654, in 0.000s 1 tree, 42 leaves, max depth = 12, train loss: 0.54348, val loss: 0.53408, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.54104, val loss: 0.53168, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.53872, val loss: 0.52916, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.53634, val loss: 0.52683, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.53408, val loss: 0.52438, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.53177, val loss: 0.52211, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.52958, val loss: 0.51973, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.52733, val loss: 0.51751, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.52520, val loss: 0.51520, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.52301, val loss: 0.51305, in 0.000s 1 tree, 24 leaves, max depth = 12, train loss: 0.52093, val loss: 0.51080, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.51880, val loss: 0.50870, in 0.000s 1 tree, 44 leaves, max depth = 12, train loss: 0.51672, val loss: 0.50665, in 0.016s 1 tree, 24 leaves, max depth = 12, train loss: 0.51472, val loss: 0.50447, in 0.000s 1 tree, 44 leaves, max depth = 12, train loss: 0.51268, val loss: 0.50248, in 0.016s [56/65] 1 tree, 24 leaves, max depth = 12, train loss: 0.51074, val loss: 0.50036, in 0.016s [57/65] 1 tree, 45 leaves, max depth = 12, train loss: 0.50876, val loss: 0.49841, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.50687, val loss: 0.49635, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.50494, val loss: 0.49445, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.50307, val loss: 0.49240, in 0.000s 1 tree, 44 leaves, max depth = 13, train loss: 0.50119, val loss: 0.49055, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.49940, val loss: 0.48859, in 0.000s 1 tree, 44 leaves, max depth = 13, train loss: 0.49756, val loss: 0.48679, in 0.000s 1 tree, 44 leaves, max depth = 13, train loss: 0.49577, val loss: 0.48503, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.49403, val loss: 0.48313, in 0.016s Fit 65 trees in 0.892 s, (1763 total leaves) Time spent computing histograms: 0.306s Time spent finding best splits: 0.040s Time spent applying splits: 0.040s Time spent predicting: 0.000s Trial 35, Fold 4: Log loss = 0.4927859316438387, Average precision = 0.916245593193003, ROC-AUC = 0.9274143192152431, Elapsed Time = 0.8943703999993886 seconds Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 35, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 10, train loss: 0.68753, val loss: 0.68718, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.68220, val loss: 0.68150, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.67698, val loss: 0.67595, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.67188, val loss: 0.67051, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.66689, val loss: 0.66519, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.66203, val loss: 0.66000, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.65726, val loss: 0.65491, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.65262, val loss: 0.64995, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.64806, val loss: 0.64508, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.64362, val loss: 0.64032, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.63926, val loss: 0.63566, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.63501, val loss: 0.63110, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.63085, val loss: 0.62663, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.62678, val loss: 0.62227, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.62274, val loss: 0.61791, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.61878, val loss: 0.61364, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.61496, val loss: 0.60954, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.61117, val loss: 0.60544, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.60746, val loss: 0.60144, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.60382, val loss: 0.59751, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.60027, val loss: 0.59366, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.59679, val loss: 0.58989, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.59344, val loss: 0.58628, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.59011, val loss: 0.58266, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.58689, val loss: 0.57918, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.58369, val loss: 0.57570, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58056, val loss: 0.57229, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57749, val loss: 0.56896, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.57448, val loss: 0.56568, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57154, val loss: 0.56248, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56866, val loss: 0.55933, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.56588, val loss: 0.55631, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.56311, val loss: 0.55328, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56040, val loss: 0.55032, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.55775, val loss: 0.54741, in 0.000s 1 tree, 40 leaves, max depth = 13, train loss: 0.55511, val loss: 0.54489, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.55257, val loss: 0.54212, in 0.000s 1 tree, 40 leaves, max depth = 13, train loss: 0.55001, val loss: 0.53966, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.54755, val loss: 0.53697, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.54506, val loss: 0.53459, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.54262, val loss: 0.53192, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.54020, val loss: 0.52961, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.53787, val loss: 0.52706, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.53551, val loss: 0.52481, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.53325, val loss: 0.52233, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.53096, val loss: 0.52015, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.52872, val loss: 0.51768, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.52649, val loss: 0.51556, in 0.000s 1 tree, 42 leaves, max depth = 13, train loss: 0.52430, val loss: 0.51348, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.52219, val loss: 0.51116, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.52006, val loss: 0.50914, in 0.000s 1 tree, 32 leaves, max depth = 10, train loss: 0.51800, val loss: 0.50687, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.51592, val loss: 0.50491, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.51391, val loss: 0.50270, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.51190, val loss: 0.50079, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.50995, val loss: 0.49864, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.50798, val loss: 0.49678, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.50608, val loss: 0.49469, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.50416, val loss: 0.49288, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.50228, val loss: 0.49079, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.50041, val loss: 0.48903, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.49858, val loss: 0.48731, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.49679, val loss: 0.48533, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.49501, val loss: 0.48365, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.49326, val loss: 0.48173, in 0.000s Fit 65 trees in 0.861 s, (1892 total leaves) Time spent computing histograms: 0.304s Time spent finding best splits: 0.041s Time spent applying splits: 0.041s Time spent predicting: 0.000s Trial 35, Fold 5: Log loss = 0.49773541235725716, Average precision = 0.9113811010579826, ROC-AUC = 0.9217625922003606, Elapsed Time = 0.8786968999993405 seconds
Optimization Progress: 36%|###6 | 36/100 [07:08<12:41, 11.90s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 36, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 15 leaves, max depth = 6, train loss: 0.67203, val loss: 0.67173, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.64883, val loss: 0.64854, in 0.000s 1 tree, 11 leaves, max depth = 6, train loss: 0.63083, val loss: 0.63017, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.61309, val loss: 0.61227, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.59605, val loss: 0.59496, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.58042, val loss: 0.57903, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.56613, val loss: 0.56452, in 0.016s 1 tree, 21 leaves, max depth = 6, train loss: 0.55287, val loss: 0.55115, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54003, val loss: 0.53814, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.52689, val loss: 0.52513, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.51701, val loss: 0.51499, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.50699, val loss: 0.50494, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.49662, val loss: 0.49455, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.48687, val loss: 0.48464, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.47487, val loss: 0.47280, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.46607, val loss: 0.46385, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.45818, val loss: 0.45578, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.45051, val loss: 0.44806, in 0.031s 1 tree, 6 leaves, max depth = 4, train loss: 0.44352, val loss: 0.44080, in 0.000s 1 tree, 15 leaves, max depth = 6, train loss: 0.43701, val loss: 0.43415, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.43012, val loss: 0.42724, in 0.016s Fit 21 trees in 0.595 s, (317 total leaves) Time spent computing histograms: 0.128s Time spent finding best splits: 0.012s Time spent applying splits: 0.008s Time spent predicting: 0.000s Trial 36, Fold 1: Log loss = 0.4301857673216879, Average precision = 0.9524029040859325, ROC-AUC = 0.9449978312613816, Elapsed Time = 0.6013696999998501 seconds Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 36, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67213, val loss: 0.67116, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.64904, val loss: 0.64762, in 0.025s 1 tree, 9 leaves, max depth = 5, train loss: 0.63110, val loss: 0.62876, in 0.007s 1 tree, 16 leaves, max depth = 6, train loss: 0.61269, val loss: 0.60995, in 0.031s 1 tree, 14 leaves, max depth = 5, train loss: 0.59576, val loss: 0.59279, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.58018, val loss: 0.57680, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.56589, val loss: 0.56193, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.55240, val loss: 0.54800, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.53940, val loss: 0.53478, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.52623, val loss: 0.52130, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.51637, val loss: 0.51100, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50619, val loss: 0.50043, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.49527, val loss: 0.48930, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.48534, val loss: 0.47909, in 0.031s 1 tree, 6 leaves, max depth = 3, train loss: 0.47334, val loss: 0.46706, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.46457, val loss: 0.45801, in 0.031s 1 tree, 13 leaves, max depth = 7, train loss: 0.45382, val loss: 0.44729, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.44675, val loss: 0.44008, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.43996, val loss: 0.43310, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.43343, val loss: 0.42674, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.42688, val loss: 0.41994, in 0.000s Fit 21 trees in 0.643 s, (287 total leaves) Time spent computing histograms: 0.149s Time spent finding best splits: 0.012s Time spent applying splits: 0.008s Time spent predicting: 0.000s Trial 36, Fold 2: Log loss = 0.42915409712140934, Average precision = 0.9483329192762742, ROC-AUC = 0.9447445341206413, Elapsed Time = 0.6612169999989419 seconds Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 36, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.143 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 11 leaves, max depth = 5, train loss: 0.67219, val loss: 0.67183, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.64908, val loss: 0.64859, in 0.000s 1 tree, 15 leaves, max depth = 8, train loss: 0.63112, val loss: 0.63017, in 0.031s 1 tree, 16 leaves, max depth = 6, train loss: 0.61350, val loss: 0.61224, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.59642, val loss: 0.59520, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.58089, val loss: 0.57936, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.56666, val loss: 0.56496, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.55325, val loss: 0.55150, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.54038, val loss: 0.53819, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.52731, val loss: 0.52502, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.51712, val loss: 0.51475, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.50690, val loss: 0.50426, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.49637, val loss: 0.49373, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.48654, val loss: 0.48385, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.47732, val loss: 0.47469, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.46857, val loss: 0.46593, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.45721, val loss: 0.45532, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45023, val loss: 0.44818, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.44326, val loss: 0.44133, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.43679, val loss: 0.43498, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.43075, val loss: 0.42901, in 0.016s Fit 21 trees in 0.643 s, (297 total leaves) Time spent computing histograms: 0.140s Time spent finding best splits: 0.012s Time spent applying splits: 0.008s Time spent predicting: 0.000s Trial 36, Fold 3: Log loss = 0.4271048823779159, Average precision = 0.9534852152384319, ROC-AUC = 0.9482975189249838, Elapsed Time = 0.6425328000004811 seconds Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 36, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.159 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 15 leaves, max depth = 6, train loss: 0.67212, val loss: 0.67149, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.64917, val loss: 0.64765, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.63125, val loss: 0.62907, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.61354, val loss: 0.61074, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.59675, val loss: 0.59298, in 0.019s 1 tree, 18 leaves, max depth = 6, train loss: 0.58124, val loss: 0.57680, in 0.013s 1 tree, 18 leaves, max depth = 7, train loss: 0.56701, val loss: 0.56201, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.55372, val loss: 0.54812, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.54078, val loss: 0.53446, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.52768, val loss: 0.52065, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.51741, val loss: 0.50975, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.50742, val loss: 0.49935, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.49837, val loss: 0.48986, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.48822, val loss: 0.47908, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.48048, val loss: 0.47076, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.47240, val loss: 0.46203, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.46540, val loss: 0.45467, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.45757, val loss: 0.44616, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.45065, val loss: 0.43878, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.44425, val loss: 0.43186, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.43762, val loss: 0.42464, in 0.016s Fit 21 trees in 0.643 s, (316 total leaves) Time spent computing histograms: 0.143s Time spent finding best splits: 0.012s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 36, Fold 4: Log loss = 0.4373566537181545, Average precision = 0.9497832966419613, ROC-AUC = 0.9433571535869868, Elapsed Time = 0.6570773000003101 seconds Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 36, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 16 leaves, max depth = 6, train loss: 0.67196, val loss: 0.67085, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.64876, val loss: 0.64661, in 0.016s 1 tree, 11 leaves, max depth = 8, train loss: 0.63074, val loss: 0.62734, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.61288, val loss: 0.60873, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.59587, val loss: 0.59070, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.58037, val loss: 0.57407, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.56713, val loss: 0.55997, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.55273, val loss: 0.54472, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.53949, val loss: 0.53058, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.52626, val loss: 0.51686, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.51593, val loss: 0.50572, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.50589, val loss: 0.49534, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.49665, val loss: 0.48572, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.48687, val loss: 0.47538, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47846, val loss: 0.46647, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.47090, val loss: 0.45854, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.46266, val loss: 0.44977, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.45560, val loss: 0.44230, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.44905, val loss: 0.43545, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.44193, val loss: 0.42788, in 0.000s 1 tree, 16 leaves, max depth = 9, train loss: 0.43561, val loss: 0.42133, in 0.031s Fit 21 trees in 0.642 s, (333 total leaves) Time spent computing histograms: 0.137s Time spent finding best splits: 0.013s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 36, Fold 5: Log loss = 0.44078513005362435, Average precision = 0.9495169038635765, ROC-AUC = 0.9432683425301451, Elapsed Time = 0.6378953999992518 seconds
Optimization Progress: 37%|###7 | 37/100 [07:18<11:52, 11.31s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 37, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 10, train loss: 0.67495, val loss: 0.67482, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.65724, val loss: 0.65695, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.64077, val loss: 0.64054, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.62631, val loss: 0.62619, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.61130, val loss: 0.61109, in 0.016s 1 tree, 66 leaves, max depth = 10, train loss: 0.59835, val loss: 0.59787, in 0.031s 1 tree, 66 leaves, max depth = 16, train loss: 0.58679, val loss: 0.58628, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.57493, val loss: 0.57441, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.56390, val loss: 0.56324, in 0.031s 1 tree, 66 leaves, max depth = 13, train loss: 0.55273, val loss: 0.55193, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.54219, val loss: 0.54137, in 0.016s Fit 11 trees in 0.532 s, (726 total leaves) Time spent computing histograms: 0.092s Time spent finding best splits: 0.020s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 37, Fold 1: Log loss = 0.5447501958689264, Average precision = 0.923759226037229, ROC-AUC = 0.932276730450285, Elapsed Time = 0.5382616000006237 seconds Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 37, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 12, train loss: 0.67491, val loss: 0.67488, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.65725, val loss: 0.65710, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.64050, val loss: 0.64020, in 0.031s 1 tree, 66 leaves, max depth = 17, train loss: 0.62601, val loss: 0.62566, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.61097, val loss: 0.61042, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.59763, val loss: 0.59692, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.58429, val loss: 0.58353, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.57186, val loss: 0.57115, in 0.031s 1 tree, 66 leaves, max depth = 14, train loss: 0.56021, val loss: 0.55946, in 0.031s 1 tree, 66 leaves, max depth = 11, train loss: 0.54943, val loss: 0.54862, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.53884, val loss: 0.53792, in 0.016s Fit 11 trees in 0.580 s, (726 total leaves) Time spent computing histograms: 0.093s Time spent finding best splits: 0.022s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 37, Fold 2: Log loss = 0.5415810970872361, Average precision = 0.9166716562949201, ROC-AUC = 0.9333092003771843, Elapsed Time = 0.6004226000004564 seconds Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 37, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 11, train loss: 0.67505, val loss: 0.67510, in 0.031s 1 tree, 66 leaves, max depth = 13, train loss: 0.65762, val loss: 0.65776, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.64113, val loss: 0.64134, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.62692, val loss: 0.62707, in 0.031s 1 tree, 66 leaves, max depth = 13, train loss: 0.61189, val loss: 0.61223, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.59878, val loss: 0.59919, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.58667, val loss: 0.58714, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.57446, val loss: 0.57494, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.56293, val loss: 0.56332, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.55215, val loss: 0.55263, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.54137, val loss: 0.54185, in 0.031s Fit 11 trees in 0.596 s, (726 total leaves) Time spent computing histograms: 0.092s Time spent finding best splits: 0.020s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 37, Fold 3: Log loss = 0.5414732280783043, Average precision = 0.9260159673414275, ROC-AUC = 0.9363839285714284, Elapsed Time = 0.597768299998279 seconds Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 37, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.190 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 12, train loss: 0.67506, val loss: 0.67440, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.65755, val loss: 0.65637, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.64099, val loss: 0.63931, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.62663, val loss: 0.62472, in 0.031s 1 tree, 66 leaves, max depth = 18, train loss: 0.61191, val loss: 0.60943, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.59869, val loss: 0.59588, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.58542, val loss: 0.58223, in 0.031s 1 tree, 66 leaves, max depth = 14, train loss: 0.57315, val loss: 0.56937, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.56174, val loss: 0.55759, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.55118, val loss: 0.54669, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.54061, val loss: 0.53566, in 0.031s Fit 11 trees in 0.612 s, (726 total leaves) Time spent computing histograms: 0.087s Time spent finding best splits: 0.020s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 37, Fold 4: Log loss = 0.5420898621380901, Average precision = 0.9209019511537924, ROC-AUC = 0.9337531980896108, Elapsed Time = 0.6224643000005017 seconds Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 37, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 14, train loss: 0.67501, val loss: 0.67422, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.65740, val loss: 0.65604, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.64071, val loss: 0.63888, in 0.031s 1 tree, 66 leaves, max depth = 16, train loss: 0.62738, val loss: 0.62485, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.61236, val loss: 0.60946, in 0.031s 1 tree, 66 leaves, max depth = 11, train loss: 0.59937, val loss: 0.59588, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.58713, val loss: 0.58337, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.57437, val loss: 0.57023, in 0.031s 1 tree, 66 leaves, max depth = 15, train loss: 0.56272, val loss: 0.55809, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.55205, val loss: 0.54700, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.54132, val loss: 0.53598, in 0.031s Fit 11 trees in 0.596 s, (726 total leaves) Time spent computing histograms: 0.092s Time spent finding best splits: 0.020s Time spent applying splits: 0.014s Time spent predicting: 0.016s Trial 37, Fold 5: Log loss = 0.5459636127053025, Average precision = 0.9183070200349506, ROC-AUC = 0.9307324378053993, Elapsed Time = 0.6025019000007887 seconds
Optimization Progress: 38%|###8 | 38/100 [07:27<11:05, 10.74s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 38, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.68529, val loss: 0.68505, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.67774, val loss: 0.67729, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.67044, val loss: 0.66980, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66340, val loss: 0.66250, in 0.000s 1 tree, 25 leaves, max depth = 15, train loss: 0.65656, val loss: 0.65546, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.64994, val loss: 0.64864, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.64359, val loss: 0.64211, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.63744, val loss: 0.63578, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.63142, val loss: 0.62957, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.62550, val loss: 0.62347, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.61988, val loss: 0.61765, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.61446, val loss: 0.61206, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.60914, val loss: 0.60657, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.60405, val loss: 0.60129, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.59905, val loss: 0.59611, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.59425, val loss: 0.59114, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.58953, val loss: 0.58628, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.58496, val loss: 0.58156, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58052, val loss: 0.57691, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.57612, val loss: 0.57233, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.57186, val loss: 0.56788, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.56772, val loss: 0.56356, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.56370, val loss: 0.55937, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.55980, val loss: 0.55530, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55607, val loss: 0.55145, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55217, val loss: 0.54778, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.54864, val loss: 0.54409, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.54521, val loss: 0.54055, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.54189, val loss: 0.53710, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53821, val loss: 0.53366, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.53502, val loss: 0.53037, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.53191, val loss: 0.52710, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.52880, val loss: 0.52383, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.52532, val loss: 0.52058, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.52233, val loss: 0.51743, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51949, val loss: 0.51448, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51672, val loss: 0.51160, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.51343, val loss: 0.50854, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.51077, val loss: 0.50578, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.50822, val loss: 0.50309, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.50571, val loss: 0.50048, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.50322, val loss: 0.49784, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.50085, val loss: 0.49536, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49777, val loss: 0.49250, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.49553, val loss: 0.49015, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.49332, val loss: 0.48785, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.49036, val loss: 0.48511, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.48819, val loss: 0.48280, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.48608, val loss: 0.48055, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.48324, val loss: 0.47793, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.48128, val loss: 0.47585, in 0.016s Fit 51 trees in 0.689 s, (1237 total leaves) Time spent computing histograms: 0.222s Time spent finding best splits: 0.027s Time spent applying splits: 0.027s Time spent predicting: 0.000s Trial 38, Fold 1: Log loss = 0.4815927550429081, Average precision = 0.9019773409073615, ROC-AUC = 0.9093855599049819, Elapsed Time = 0.7017431000003853 seconds Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 38, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 8, train loss: 0.68540, val loss: 0.68502, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.67789, val loss: 0.67716, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.67058, val loss: 0.66951, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66351, val loss: 0.66210, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.65671, val loss: 0.65496, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.65011, val loss: 0.64806, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.64373, val loss: 0.64136, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.63755, val loss: 0.63486, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.63154, val loss: 0.62851, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.62565, val loss: 0.62229, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.62002, val loss: 0.61639, in 0.016s 1 tree, 25 leaves, max depth = 15, train loss: 0.61457, val loss: 0.61064, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.60929, val loss: 0.60507, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.60421, val loss: 0.59971, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.59924, val loss: 0.59448, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.59442, val loss: 0.58938, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.58970, val loss: 0.58440, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.58512, val loss: 0.57956, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58068, val loss: 0.57487, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.57633, val loss: 0.57023, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.57214, val loss: 0.56580, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.56804, val loss: 0.56143, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.56406, val loss: 0.55718, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.56020, val loss: 0.55305, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55648, val loss: 0.54911, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55257, val loss: 0.54540, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.54903, val loss: 0.54166, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.54559, val loss: 0.53801, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.54226, val loss: 0.53447, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53858, val loss: 0.53098, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.53538, val loss: 0.52758, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.53226, val loss: 0.52429, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.52918, val loss: 0.52098, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.52571, val loss: 0.51769, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.52279, val loss: 0.51457, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51995, val loss: 0.51154, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.51719, val loss: 0.50860, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.51391, val loss: 0.50549, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51126, val loss: 0.50266, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.50870, val loss: 0.49996, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50621, val loss: 0.49729, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.50374, val loss: 0.49462, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50138, val loss: 0.49209, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.49831, val loss: 0.48918, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.49606, val loss: 0.48678, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49386, val loss: 0.48442, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.49091, val loss: 0.48164, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.48876, val loss: 0.47931, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.48667, val loss: 0.47704, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.48384, val loss: 0.47438, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.48187, val loss: 0.47229, in 0.000s Fit 51 trees in 0.737 s, (1237 total leaves) Time spent computing histograms: 0.245s Time spent finding best splits: 0.028s Time spent applying splits: 0.029s Time spent predicting: 0.000s Trial 38, Fold 2: Log loss = 0.4818755510950665, Average precision = 0.899719366512129, ROC-AUC = 0.9126473736555525, Elapsed Time = 0.7471654999990278 seconds Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 38, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.68537, val loss: 0.68512, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.67792, val loss: 0.67741, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.67066, val loss: 0.66988, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66363, val loss: 0.66261, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.65689, val loss: 0.65563, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.65032, val loss: 0.64884, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.64399, val loss: 0.64228, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.63787, val loss: 0.63593, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.63189, val loss: 0.62970, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.62602, val loss: 0.62363, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.62041, val loss: 0.61783, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.61501, val loss: 0.61223, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.60974, val loss: 0.60672, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.60468, val loss: 0.60147, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.59972, val loss: 0.59628, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.59495, val loss: 0.59130, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.59026, val loss: 0.58641, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.58572, val loss: 0.58167, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58130, val loss: 0.57707, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.57695, val loss: 0.57256, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.57273, val loss: 0.56819, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.56864, val loss: 0.56394, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.56466, val loss: 0.55982, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.56081, val loss: 0.55581, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.55712, val loss: 0.55194, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55318, val loss: 0.54824, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.54966, val loss: 0.54457, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.54625, val loss: 0.54101, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.54296, val loss: 0.53756, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.53925, val loss: 0.53408, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.53608, val loss: 0.53076, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53299, val loss: 0.52753, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.52991, val loss: 0.52432, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.52642, val loss: 0.52104, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.52347, val loss: 0.51796, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.52066, val loss: 0.51499, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51793, val loss: 0.51211, in 0.016s [38/51] 1 tree, 25 leaves, max depth = 7, train loss: 0.51462, val loss: 0.50901, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.51200, val loss: 0.50624, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.50946, val loss: 0.50358, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.50699, val loss: 0.50096, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.50453, val loss: 0.49839, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50220, val loss: 0.49591, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.49909, val loss: 0.49300, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.49688, val loss: 0.49066, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49470, val loss: 0.48834, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.49173, val loss: 0.48556, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48959, val loss: 0.48331, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48750, val loss: 0.48113, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.48465, val loss: 0.47846, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.48270, val loss: 0.47641, in 0.000s Fit 51 trees in 0.799 s, (1237 total leaves) Time spent computing histograms: 0.268s Time spent finding best splits: 0.030s Time spent applying splits: 0.032s Time spent predicting: 0.000s Trial 38, Fold 3: Log loss = 0.4776605345559353, Average precision = 0.908762631514317, ROC-AUC = 0.9183635095732487, Elapsed Time = 0.7995752000006178 seconds Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 38, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 11, train loss: 0.68539, val loss: 0.68493, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.67788, val loss: 0.67701, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.67061, val loss: 0.66935, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66358, val loss: 0.66194, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.65678, val loss: 0.65474, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.65019, val loss: 0.64777, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.64385, val loss: 0.64106, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.63770, val loss: 0.63455, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.63172, val loss: 0.62819, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.62585, val loss: 0.62191, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.62024, val loss: 0.61592, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.61483, val loss: 0.61016, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.60954, val loss: 0.60453, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.60446, val loss: 0.59907, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.59949, val loss: 0.59376, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.59470, val loss: 0.58864, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59000, val loss: 0.58363, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.58544, val loss: 0.57876, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58101, val loss: 0.57403, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.57667, val loss: 0.56933, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.57245, val loss: 0.56477, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.56836, val loss: 0.56034, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.56440, val loss: 0.55603, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.56054, val loss: 0.55184, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.55684, val loss: 0.54787, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55300, val loss: 0.54408, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.54947, val loss: 0.54024, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.54605, val loss: 0.53655, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.54274, val loss: 0.53296, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53911, val loss: 0.52938, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.53592, val loss: 0.52594, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.53282, val loss: 0.52254, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.52975, val loss: 0.51917, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.52632, val loss: 0.51579, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.52338, val loss: 0.51256, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.52055, val loss: 0.50949, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.51781, val loss: 0.50650, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51456, val loss: 0.50330, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.51193, val loss: 0.50043, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.50938, val loss: 0.49762, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.50690, val loss: 0.49490, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.50444, val loss: 0.49217, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.50210, val loss: 0.48960, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.49906, val loss: 0.48662, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.49682, val loss: 0.48415, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.49463, val loss: 0.48174, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.49172, val loss: 0.47890, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48958, val loss: 0.47651, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.48750, val loss: 0.47418, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.48470, val loss: 0.47145, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.48275, val loss: 0.46925, in 0.016s Fit 51 trees in 0.799 s, (1237 total leaves) Time spent computing histograms: 0.257s Time spent finding best splits: 0.031s Time spent applying splits: 0.032s Time spent predicting: 0.047s Trial 38, Fold 4: Log loss = 0.4817802276329943, Average precision = 0.9027976986375478, ROC-AUC = 0.9135190222250835, Elapsed Time = 0.7933245000003808 seconds Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 38, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.172 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.68526, val loss: 0.68471, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.67770, val loss: 0.67668, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.67035, val loss: 0.66885, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66324, val loss: 0.66126, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.65640, val loss: 0.65396, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.64976, val loss: 0.64687, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.64334, val loss: 0.64001, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.63714, val loss: 0.63336, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.63108, val loss: 0.62688, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.62516, val loss: 0.62052, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.61950, val loss: 0.61439, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.61403, val loss: 0.60851, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.60868, val loss: 0.60277, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.60356, val loss: 0.59720, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.59852, val loss: 0.59178, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.59367, val loss: 0.58654, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.58893, val loss: 0.58143, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.58436, val loss: 0.57643, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.57989, val loss: 0.57159, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.57551, val loss: 0.56683, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.57126, val loss: 0.56221, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.56713, val loss: 0.55772, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.56313, val loss: 0.55335, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55924, val loss: 0.54911, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.55552, val loss: 0.54505, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55167, val loss: 0.54135, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.54810, val loss: 0.53745, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.54465, val loss: 0.53367, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.54129, val loss: 0.52999, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.53765, val loss: 0.52651, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.53444, val loss: 0.52298, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53130, val loss: 0.51951, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.52820, val loss: 0.51610, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.52476, val loss: 0.51283, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.52178, val loss: 0.50956, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.51895, val loss: 0.50644, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.51620, val loss: 0.50340, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.51295, val loss: 0.50031, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.51029, val loss: 0.49738, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.50772, val loss: 0.49449, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.50520, val loss: 0.49171, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.50271, val loss: 0.48895, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.50035, val loss: 0.48632, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49730, val loss: 0.48344, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.49504, val loss: 0.48093, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.49282, val loss: 0.47846, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48990, val loss: 0.47571, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.48773, val loss: 0.47329, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.48561, val loss: 0.47094, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48281, val loss: 0.46831, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.48083, val loss: 0.46608, in 0.000s Fit 51 trees in 0.813 s, (1237 total leaves) Time spent computing histograms: 0.269s Time spent finding best splits: 0.033s Time spent applying splits: 0.033s Time spent predicting: 0.000s Trial 38, Fold 5: Log loss = 0.4873302464752492, Average precision = 0.8985605879198201, ROC-AUC = 0.9079279861769133, Elapsed Time = 0.8260637000003044 seconds
Optimization Progress: 39%|###9 | 39/100 [07:38<11:00, 10.82s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 39, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 16, train loss: 0.68187, val loss: 0.68155, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.67129, val loss: 0.67067, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.66120, val loss: 0.66028, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.65157, val loss: 0.65035, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.64237, val loss: 0.64087, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.63357, val loss: 0.63179, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.62517, val loss: 0.62311, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.61712, val loss: 0.61480, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.60943, val loss: 0.60685, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.60206, val loss: 0.59922, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.59501, val loss: 0.59192, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.58825, val loss: 0.58492, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.58188, val loss: 0.57829, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.57567, val loss: 0.57184, in 0.000s 1 tree, 66 leaves, max depth = 16, train loss: 0.56971, val loss: 0.56566, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.56405, val loss: 0.55985, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.55857, val loss: 0.55415, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.55301, val loss: 0.54893, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.54793, val loss: 0.54362, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.54268, val loss: 0.53870, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.53782, val loss: 0.53363, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.53285, val loss: 0.52898, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.52827, val loss: 0.52419, in 0.000s 1 tree, 66 leaves, max depth = 17, train loss: 0.52387, val loss: 0.51958, in 0.031s 1 tree, 106 leaves, max depth = 16, train loss: 0.51922, val loss: 0.51525, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.51477, val loss: 0.51110, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.51049, val loss: 0.50711, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.50653, val loss: 0.50295, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.50247, val loss: 0.49918, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.49867, val loss: 0.49518, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.49502, val loss: 0.49133, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.49120, val loss: 0.48780, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.48754, val loss: 0.48440, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.48418, val loss: 0.48086, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.48069, val loss: 0.47763, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.47733, val loss: 0.47453, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.47415, val loss: 0.47117, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.47095, val loss: 0.46822, in 0.016s 1 tree, 67 leaves, max depth = 17, train loss: 0.46798, val loss: 0.46506, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.46493, val loss: 0.46226, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.46206, val loss: 0.45921, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.45915, val loss: 0.45655, in 0.016s 1 tree, 67 leaves, max depth = 17, train loss: 0.45646, val loss: 0.45368, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.45369, val loss: 0.45114, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.45101, val loss: 0.44869, in 0.016s 1 tree, 67 leaves, max depth = 17, train loss: 0.44848, val loss: 0.44599, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.44593, val loss: 0.44366, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.44350, val loss: 0.44108, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.44106, val loss: 0.43886, in 0.016s 1 tree, 65 leaves, max depth = 14, train loss: 0.43879, val loss: 0.43647, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.43645, val loss: 0.43435, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.43425, val loss: 0.43201, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.43202, val loss: 0.42999, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.42986, val loss: 0.42805, in 0.016s [55/82] 1 tree, 67 leaves, max depth = 13, train loss: 0.42780, val loss: 0.42582, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.42574, val loss: 0.42397, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.42377, val loss: 0.42183, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.42179, val loss: 0.42006, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.41985, val loss: 0.41800, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.41796, val loss: 0.41631, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.41617, val loss: 0.41436, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.41435, val loss: 0.41274, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.41259, val loss: 0.41118, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.41095, val loss: 0.40936, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.40937, val loss: 0.40761, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.40765, val loss: 0.40578, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.40597, val loss: 0.40431, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40447, val loss: 0.40264, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.40286, val loss: 0.40124, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40142, val loss: 0.39963, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.39984, val loss: 0.39795, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.39829, val loss: 0.39661, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39692, val loss: 0.39507, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.39541, val loss: 0.39348, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.39393, val loss: 0.39220, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.39249, val loss: 0.39064, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39118, val loss: 0.38917, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.38992, val loss: 0.38776, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.38871, val loss: 0.38640, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.38734, val loss: 0.38496, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.38599, val loss: 0.38356, in 0.000s 1 tree, 105 leaves, max depth = 13, train loss: 0.38456, val loss: 0.38235, in 0.016s Fit 82 trees in 1.173 s, (6143 total leaves) Time spent computing histograms: 0.399s Time spent finding best splits: 0.091s Time spent applying splits: 0.104s Time spent predicting: 0.000s Trial 39, Fold 1: Log loss = 0.3873480272135086, Average precision = 0.9476433109414608, ROC-AUC = 0.9442173392743628, Elapsed Time = 1.1750902000003407 seconds Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 39, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 16, train loss: 0.68205, val loss: 0.68153, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.67152, val loss: 0.67051, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.66149, val loss: 0.65999, in 0.000s 1 tree, 66 leaves, max depth = 16, train loss: 0.65190, val loss: 0.64994, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.64275, val loss: 0.64033, in 0.000s 1 tree, 66 leaves, max depth = 16, train loss: 0.63400, val loss: 0.63114, in 0.000s 1 tree, 66 leaves, max depth = 16, train loss: 0.62564, val loss: 0.62235, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.61771, val loss: 0.61406, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.61005, val loss: 0.60599, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.60279, val loss: 0.59838, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.59577, val loss: 0.59097, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.58911, val loss: 0.58398, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.58273, val loss: 0.57729, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.57654, val loss: 0.57074, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.57060, val loss: 0.56446, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.56497, val loss: 0.55852, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.55951, val loss: 0.55273, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.55394, val loss: 0.54741, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.54887, val loss: 0.54206, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.54361, val loss: 0.53704, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.53882, val loss: 0.53196, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.53385, val loss: 0.52722, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.52934, val loss: 0.52246, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.52494, val loss: 0.51778, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.52029, val loss: 0.51335, in 0.000s 1 tree, 106 leaves, max depth = 15, train loss: 0.51583, val loss: 0.50911, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.51155, val loss: 0.50504, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.50760, val loss: 0.50085, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.50353, val loss: 0.49699, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.49979, val loss: 0.49302, in 0.000s 1 tree, 64 leaves, max depth = 16, train loss: 0.49618, val loss: 0.48921, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.49236, val loss: 0.48559, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.48869, val loss: 0.48211, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.48534, val loss: 0.47856, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.48184, val loss: 0.47525, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.47848, val loss: 0.47208, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.47535, val loss: 0.46873, in 0.016s 1 tree, 106 leaves, max depth = 19, train loss: 0.47215, val loss: 0.46571, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.46917, val loss: 0.46255, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.46612, val loss: 0.45968, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.46330, val loss: 0.45667, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.46038, val loss: 0.45393, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.45770, val loss: 0.45108, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.45492, val loss: 0.44846, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.45224, val loss: 0.44596, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.44972, val loss: 0.44327, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.44716, val loss: 0.44087, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.44477, val loss: 0.43831, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.44232, val loss: 0.43602, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.44005, val loss: 0.43357, in 0.016s 1 tree, 106 leaves, max depth = 20, train loss: 0.43770, val loss: 0.43139, in 0.016s 1 tree, 106 leaves, max depth = 20, train loss: 0.43544, val loss: 0.42929, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.43330, val loss: 0.42698, in 0.000s 1 tree, 106 leaves, max depth = 20, train loss: 0.43114, val loss: 0.42498, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.42909, val loss: 0.42278, in 0.016s 1 tree, 106 leaves, max depth = 20, train loss: 0.42701, val loss: 0.42086, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.42506, val loss: 0.41876, in 0.016s 1 tree, 106 leaves, max depth = 20, train loss: 0.42307, val loss: 0.41693, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.42121, val loss: 0.41492, in 0.000s 1 tree, 105 leaves, max depth = 20, train loss: 0.41931, val loss: 0.41317, in 0.031s 1 tree, 68 leaves, max depth = 12, train loss: 0.41747, val loss: 0.41117, in 0.000s 1 tree, 105 leaves, max depth = 20, train loss: 0.41564, val loss: 0.40949, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.41388, val loss: 0.40787, in 0.031s 1 tree, 3 leaves, max depth = 2, train loss: 0.41230, val loss: 0.40624, in 0.000s 1 tree, 68 leaves, max depth = 12, train loss: 0.41062, val loss: 0.40444, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40912, val loss: 0.40288, in 0.000s 1 tree, 106 leaves, max depth = 19, train loss: 0.40743, val loss: 0.40134, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.40581, val loss: 0.39986, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40436, val loss: 0.39837, in 0.000s 1 tree, 66 leaves, max depth = 15, train loss: 0.40281, val loss: 0.39668, in 0.016s 1 tree, 106 leaves, max depth = 19, train loss: 0.40125, val loss: 0.39526, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39987, val loss: 0.39384, in 0.000s 1 tree, 68 leaves, max depth = 12, train loss: 0.39838, val loss: 0.39224, in 0.016s 1 tree, 106 leaves, max depth = 19, train loss: 0.39688, val loss: 0.39088, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39557, val loss: 0.38952, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.39415, val loss: 0.38799, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39289, val loss: 0.38669, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39169, val loss: 0.38543, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39053, val loss: 0.38422, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.38915, val loss: 0.38272, in 0.000s 1 tree, 67 leaves, max depth = 11, train loss: 0.38780, val loss: 0.38129, in 0.016s 1 tree, 106 leaves, max depth = 19, train loss: 0.38636, val loss: 0.38000, in 0.016s Fit 82 trees in 1.284 s, (6181 total leaves) Time spent computing histograms: 0.431s Time spent finding best splits: 0.100s Time spent applying splits: 0.115s Time spent predicting: 0.000s Trial 39, Fold 2: Log loss = 0.38897657752043585, Average precision = 0.9443297070994459, ROC-AUC = 0.9444997649426803, Elapsed Time = 1.2912431999993714 seconds Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 39, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 12, train loss: 0.68203, val loss: 0.68171, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.67165, val loss: 0.67095, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.66167, val loss: 0.66065, in 0.000s 1 tree, 66 leaves, max depth = 12, train loss: 0.65213, val loss: 0.65082, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.64310, val loss: 0.64144, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.63449, val loss: 0.63256, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.62623, val loss: 0.62397, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.61826, val loss: 0.61573, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.61063, val loss: 0.60785, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.60342, val loss: 0.60038, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.59649, val loss: 0.59316, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.58978, val loss: 0.58622, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.58342, val loss: 0.57958, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.57734, val loss: 0.57328, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.57143, val loss: 0.56715, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.56583, val loss: 0.56128, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.56039, val loss: 0.55564, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.55483, val loss: 0.55049, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.54978, val loss: 0.54524, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.54453, val loss: 0.54038, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.53971, val loss: 0.53536, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.53475, val loss: 0.53077, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.53026, val loss: 0.52609, in 0.000s 1 tree, 68 leaves, max depth = 13, train loss: 0.52595, val loss: 0.52160, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.52130, val loss: 0.51731, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.51685, val loss: 0.51321, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.51257, val loss: 0.50928, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.50864, val loss: 0.50516, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.50458, val loss: 0.50143, in 0.000s 1 tree, 68 leaves, max depth = 13, train loss: 0.50086, val loss: 0.49752, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.49730, val loss: 0.49375, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.49348, val loss: 0.49025, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.48981, val loss: 0.48690, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.48629, val loss: 0.48368, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.48300, val loss: 0.48021, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.47965, val loss: 0.47717, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.47656, val loss: 0.47386, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.47336, val loss: 0.47096, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.47040, val loss: 0.46783, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.46736, val loss: 0.46507, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.46455, val loss: 0.46209, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.46164, val loss: 0.45946, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.45897, val loss: 0.45663, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.45620, val loss: 0.45413, in 0.031s 1 tree, 104 leaves, max depth = 15, train loss: 0.45352, val loss: 0.45172, in 0.000s 1 tree, 68 leaves, max depth = 14, train loss: 0.45102, val loss: 0.44905, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.44846, val loss: 0.44676, in 0.016s 1 tree, 67 leaves, max depth = 17, train loss: 0.44611, val loss: 0.44421, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.44367, val loss: 0.44203, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.44132, val loss: 0.43993, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.43907, val loss: 0.43752, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.43682, val loss: 0.43552, in 0.000s 1 tree, 67 leaves, max depth = 18, train loss: 0.43470, val loss: 0.43322, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.43255, val loss: 0.43130, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.43050, val loss: 0.42910, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.42844, val loss: 0.42728, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.42649, val loss: 0.42517, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.42452, val loss: 0.42343, in 0.031s 1 tree, 67 leaves, max depth = 16, train loss: 0.42269, val loss: 0.42142, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.42080, val loss: 0.41975, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.41898, val loss: 0.41777, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.41716, val loss: 0.41619, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.41542, val loss: 0.41467, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.41377, val loss: 0.41313, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.41218, val loss: 0.41166, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.41048, val loss: 0.40979, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.40882, val loss: 0.40833, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40731, val loss: 0.40693, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.40585, val loss: 0.40558, in 0.000s 1 tree, 104 leaves, max depth = 13, train loss: 0.40425, val loss: 0.40419, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.40270, val loss: 0.40249, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40131, val loss: 0.40120, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.39978, val loss: 0.39988, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.39831, val loss: 0.39826, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39698, val loss: 0.39703, in 0.000s 1 tree, 66 leaves, max depth = 12, train loss: 0.39557, val loss: 0.39549, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39430, val loss: 0.39431, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.39308, val loss: 0.39318, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39191, val loss: 0.39209, in 0.000s 1 tree, 66 leaves, max depth = 11, train loss: 0.39057, val loss: 0.39062, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.38924, val loss: 0.38916, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.38777, val loss: 0.38791, in 0.016s Fit 82 trees in 1.298 s, (6025 total leaves) Time spent computing histograms: 0.445s Time spent finding best splits: 0.100s Time spent applying splits: 0.117s Time spent predicting: 0.000s Trial 39, Fold 3: Log loss = 0.38644798890290544, Average precision = 0.9486453359713937, ROC-AUC = 0.9473633085253923, Elapsed Time = 1.3085611999995308 seconds Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 39, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 16, train loss: 0.68206, val loss: 0.68143, in 0.000s 1 tree, 65 leaves, max depth = 16, train loss: 0.67159, val loss: 0.67034, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.66160, val loss: 0.65975, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.65206, val loss: 0.64962, in 0.000s 1 tree, 65 leaves, max depth = 16, train loss: 0.64295, val loss: 0.63994, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.63425, val loss: 0.63067, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.62593, val loss: 0.62179, in 0.000s 1 tree, 65 leaves, max depth = 16, train loss: 0.61797, val loss: 0.61329, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.61035, val loss: 0.60514, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.60306, val loss: 0.59733, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.59608, val loss: 0.58984, in 0.000s 1 tree, 65 leaves, max depth = 16, train loss: 0.58940, val loss: 0.58266, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.58303, val loss: 0.57579, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.57688, val loss: 0.56917, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.57100, val loss: 0.56281, in 0.000s 1 tree, 68 leaves, max depth = 18, train loss: 0.56536, val loss: 0.55678, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.55994, val loss: 0.55091, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.55436, val loss: 0.54549, in 0.031s 1 tree, 68 leaves, max depth = 15, train loss: 0.54929, val loss: 0.53998, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.54402, val loss: 0.53486, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.53922, val loss: 0.52964, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.53424, val loss: 0.52481, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.52973, val loss: 0.51989, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.52539, val loss: 0.51513, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.52073, val loss: 0.51063, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.51626, val loss: 0.50631, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.51197, val loss: 0.50216, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.50803, val loss: 0.49782, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.50395, val loss: 0.49389, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.50022, val loss: 0.48978, in 0.016s 1 tree, 64 leaves, max depth = 13, train loss: 0.49662, val loss: 0.48588, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.49279, val loss: 0.48219, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.48912, val loss: 0.47865, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.48558, val loss: 0.47525, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.48229, val loss: 0.47160, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.47893, val loss: 0.46836, in 0.016s 1 tree, 63 leaves, max depth = 13, train loss: 0.47579, val loss: 0.46494, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.47259, val loss: 0.46186, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.46963, val loss: 0.45857, in 0.000s 1 tree, 106 leaves, max depth = 16, train loss: 0.46657, val loss: 0.45564, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.46377, val loss: 0.45250, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.46085, val loss: 0.44971, in 0.016s 1 tree, 68 leaves, max depth = 19, train loss: 0.45817, val loss: 0.44675, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.45539, val loss: 0.44409, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.45271, val loss: 0.44153, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.45019, val loss: 0.43872, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.44764, val loss: 0.43627, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.44525, val loss: 0.43360, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.44281, val loss: 0.43126, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.44054, val loss: 0.42874, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.43820, val loss: 0.42652, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.43596, val loss: 0.42437, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.43382, val loss: 0.42199, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.43167, val loss: 0.41994, in 0.016s 1 tree, 68 leaves, max depth = 17, train loss: 0.42962, val loss: 0.41762, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.42756, val loss: 0.41566, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.42561, val loss: 0.41345, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.42363, val loss: 0.41157, in 0.016s 1 tree, 68 leaves, max depth = 17, train loss: 0.42176, val loss: 0.40949, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.41987, val loss: 0.40769, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.41805, val loss: 0.40563, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.41624, val loss: 0.40391, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.41449, val loss: 0.40226, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41292, val loss: 0.40059, in 0.000s 1 tree, 68 leaves, max depth = 18, train loss: 0.41124, val loss: 0.39867, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40973, val loss: 0.39707, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.40807, val loss: 0.39550, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40663, val loss: 0.39396, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.40502, val loss: 0.39246, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.40346, val loss: 0.39067, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40208, val loss: 0.38921, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.40055, val loss: 0.38777, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39923, val loss: 0.38636, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.39774, val loss: 0.38498, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.39627, val loss: 0.38330, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.39486, val loss: 0.38169, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39360, val loss: 0.38035, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39240, val loss: 0.37906, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39128, val loss: 0.37787, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.38994, val loss: 0.37634, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.38860, val loss: 0.37484, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.38719, val loss: 0.37352, in 0.016s Fit 82 trees in 1.299 s, (6190 total leaves) Time spent computing histograms: 0.448s Time spent finding best splits: 0.103s Time spent applying splits: 0.117s Time spent predicting: 0.031s Trial 39, Fold 4: Log loss = 0.3883433454778819, Average precision = 0.9477614116286428, ROC-AUC = 0.9447609003292871, Elapsed Time = 1.3085276000001613 seconds Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 39, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 13, train loss: 0.68203, val loss: 0.68127, in 0.000s 1 tree, 64 leaves, max depth = 13, train loss: 0.67156, val loss: 0.67007, in 0.016s 1 tree, 64 leaves, max depth = 13, train loss: 0.66158, val loss: 0.65936, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.65192, val loss: 0.64903, in 0.000s 1 tree, 64 leaves, max depth = 13, train loss: 0.64281, val loss: 0.63924, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.63398, val loss: 0.62977, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.62554, val loss: 0.62071, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.61746, val loss: 0.61203, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.60974, val loss: 0.60371, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.60234, val loss: 0.59574, in 0.000s 1 tree, 65 leaves, max depth = 13, train loss: 0.59535, val loss: 0.58814, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.58856, val loss: 0.58080, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.58214, val loss: 0.57381, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.57590, val loss: 0.56704, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.56991, val loss: 0.56054, in 0.016s 1 tree, 65 leaves, max depth = 19, train loss: 0.56425, val loss: 0.55443, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.55875, val loss: 0.54842, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.55346, val loss: 0.54265, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.54844, val loss: 0.53719, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.54317, val loss: 0.53217, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.53812, val loss: 0.52737, in 0.000s 1 tree, 65 leaves, max depth = 13, train loss: 0.53349, val loss: 0.52226, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.52871, val loss: 0.51773, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.52428, val loss: 0.51286, in 0.000s 1 tree, 105 leaves, max depth = 13, train loss: 0.51975, val loss: 0.50858, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.51541, val loss: 0.50448, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.51136, val loss: 0.49998, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.50724, val loss: 0.49610, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.50341, val loss: 0.49185, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.49950, val loss: 0.48818, in 0.016s 1 tree, 65 leaves, max depth = 14, train loss: 0.49587, val loss: 0.48419, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.49215, val loss: 0.48071, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.48869, val loss: 0.47690, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.48516, val loss: 0.47360, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.48177, val loss: 0.47043, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.47853, val loss: 0.46686, in 0.016s [37/82] 1 tree, 105 leaves, max depth = 14, train loss: 0.47530, val loss: 0.46385, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.47223, val loss: 0.46045, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.46915, val loss: 0.45760, in 0.032s 1 tree, 67 leaves, max depth = 13, train loss: 0.46623, val loss: 0.45436, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.46343, val loss: 0.45125, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.46052, val loss: 0.44856, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.45786, val loss: 0.44559, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.45508, val loss: 0.44303, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.45240, val loss: 0.44057, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.44982, val loss: 0.43821, in 0.000s 1 tree, 65 leaves, max depth = 17, train loss: 0.44737, val loss: 0.43548, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.44491, val loss: 0.43323, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.44255, val loss: 0.43059, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.44020, val loss: 0.42845, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.43795, val loss: 0.42593, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.43570, val loss: 0.42388, in 0.016s 1 tree, 66 leaves, max depth = 19, train loss: 0.43358, val loss: 0.42151, in 0.000s 1 tree, 105 leaves, max depth = 16, train loss: 0.43142, val loss: 0.41956, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42935, val loss: 0.41769, in 0.016s 1 tree, 64 leaves, max depth = 17, train loss: 0.42735, val loss: 0.41545, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42536, val loss: 0.41366, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.42343, val loss: 0.41147, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.42152, val loss: 0.40976, in 0.016s 1 tree, 64 leaves, max depth = 19, train loss: 0.41970, val loss: 0.40771, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.41793, val loss: 0.40569, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.41611, val loss: 0.40407, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.41435, val loss: 0.40251, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.41275, val loss: 0.40096, in 0.000s 1 tree, 105 leaves, max depth = 15, train loss: 0.41106, val loss: 0.39947, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.40940, val loss: 0.39758, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40787, val loss: 0.39611, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.40626, val loss: 0.39469, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.40480, val loss: 0.39327, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.40325, val loss: 0.39191, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.40169, val loss: 0.39013, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40029, val loss: 0.38879, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.39880, val loss: 0.38749, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.39732, val loss: 0.38579, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39599, val loss: 0.38451, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.39457, val loss: 0.38286, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39330, val loss: 0.38163, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.39207, val loss: 0.38046, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39089, val loss: 0.37932, in 0.000s 1 tree, 66 leaves, max depth = 12, train loss: 0.38955, val loss: 0.37776, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.38823, val loss: 0.37619, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.38680, val loss: 0.37496, in 0.016s Fit 82 trees in 1.283 s, (6101 total leaves) Time spent computing histograms: 0.427s Time spent finding best splits: 0.098s Time spent applying splits: 0.112s Time spent predicting: 0.016s Trial 39, Fold 5: Log loss = 0.3938249430487527, Average precision = 0.9446614020697768, ROC-AUC = 0.941758430410791, Elapsed Time = 1.2814244000001054 seconds
Optimization Progress: 40%|#### | 40/100 [07:52<11:48, 11.80s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 40, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.126 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 11, train loss: 0.67951, val loss: 0.67910, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.66665, val loss: 0.66584, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.65451, val loss: 0.65332, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.64315, val loss: 0.64166, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.63231, val loss: 0.63046, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.62205, val loss: 0.61985, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.61254, val loss: 0.61001, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.60333, val loss: 0.60047, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.59460, val loss: 0.59142, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.58632, val loss: 0.58282, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.57846, val loss: 0.57467, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.57110, val loss: 0.56707, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.56415, val loss: 0.55980, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.55742, val loss: 0.55279, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.55103, val loss: 0.54612, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.54434, val loss: 0.53986, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.53841, val loss: 0.53366, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.53217, val loss: 0.52784, in 0.000s 1 tree, 33 leaves, max depth = 12, train loss: 0.52676, val loss: 0.52213, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.52093, val loss: 0.51671, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.51539, val loss: 0.51156, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.51012, val loss: 0.50668, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.50519, val loss: 0.50149, in 0.000s 1 tree, 32 leaves, max depth = 13, train loss: 0.50051, val loss: 0.49656, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.49564, val loss: 0.49206, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.49101, val loss: 0.48779, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.48679, val loss: 0.48330, in 0.000s 1 tree, 33 leaves, max depth = 12, train loss: 0.48277, val loss: 0.47903, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.47847, val loss: 0.47508, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.47437, val loss: 0.47133, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.47069, val loss: 0.46748, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.46682, val loss: 0.46396, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.46329, val loss: 0.46020, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.45965, val loss: 0.45689, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.45617, val loss: 0.45374, in 0.000s Fit 35 trees in 0.533 s, (1386 total leaves) Time spent computing histograms: 0.144s Time spent finding best splits: 0.024s Time spent applying splits: 0.025s Time spent predicting: 0.000s Trial 40, Fold 1: Log loss = 0.4573582800681732, Average precision = 0.9182890017667679, ROC-AUC = 0.9282691942329476, Elapsed Time = 0.5440377000013541 seconds Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 40, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 11, train loss: 0.67970, val loss: 0.67910, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.66701, val loss: 0.66584, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.65504, val loss: 0.65332, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.64374, val loss: 0.64147, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.63306, val loss: 0.63028, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.62297, val loss: 0.61968, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.61342, val loss: 0.60964, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.60425, val loss: 0.59993, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.59557, val loss: 0.59072, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.58744, val loss: 0.58215, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57972, val loss: 0.57402, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.57237, val loss: 0.56624, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.56541, val loss: 0.55890, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.55878, val loss: 0.55187, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55238, val loss: 0.54506, in 0.000s 1 tree, 52 leaves, max depth = 10, train loss: 0.54578, val loss: 0.53877, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.53993, val loss: 0.53256, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.53378, val loss: 0.52670, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.52836, val loss: 0.52095, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.52261, val loss: 0.51549, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.51714, val loss: 0.51031, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.51195, val loss: 0.50539, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.50710, val loss: 0.50023, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.50249, val loss: 0.49530, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.49769, val loss: 0.49076, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.49312, val loss: 0.48645, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.48889, val loss: 0.48193, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.48486, val loss: 0.47763, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.48062, val loss: 0.47365, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.47657, val loss: 0.46986, in 0.016s 1 tree, 32 leaves, max depth = 16, train loss: 0.47286, val loss: 0.46588, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.46906, val loss: 0.46232, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.46558, val loss: 0.45859, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.46199, val loss: 0.45524, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.45856, val loss: 0.45205, in 0.016s Fit 35 trees in 0.610 s, (1341 total leaves) Time spent computing histograms: 0.147s Time spent finding best splits: 0.025s Time spent applying splits: 0.025s Time spent predicting: 0.000s Trial 40, Fold 2: Log loss = 0.4593806726958846, Average precision = 0.9127169139881373, ROC-AUC = 0.9285808911297653, Elapsed Time = 0.6046727999982977 seconds Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 40, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 11, train loss: 0.67980, val loss: 0.67938, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.66721, val loss: 0.66640, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.65522, val loss: 0.65400, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.64399, val loss: 0.64240, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.63328, val loss: 0.63130, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.62323, val loss: 0.62091, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.61375, val loss: 0.61104, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.60464, val loss: 0.60159, in 0.000s 1 tree, 30 leaves, max depth = 8, train loss: 0.59600, val loss: 0.59262, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.58790, val loss: 0.58421, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.58022, val loss: 0.57624, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.57297, val loss: 0.56867, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.56605, val loss: 0.56147, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.55933, val loss: 0.55450, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.55296, val loss: 0.54787, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.54630, val loss: 0.54169, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.54042, val loss: 0.53555, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.53421, val loss: 0.52981, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.52883, val loss: 0.52417, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.52302, val loss: 0.51880, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.51751, val loss: 0.51372, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.51227, val loss: 0.50888, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.50745, val loss: 0.50382, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.50277, val loss: 0.49889, in 0.000s 1 tree, 55 leaves, max depth = 13, train loss: 0.49793, val loss: 0.49445, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.49332, val loss: 0.49022, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.48912, val loss: 0.48579, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.48512, val loss: 0.48156, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.48083, val loss: 0.47764, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.47676, val loss: 0.47391, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.47309, val loss: 0.47000, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.46925, val loss: 0.46650, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.46580, val loss: 0.46284, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.46217, val loss: 0.45954, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.45872, val loss: 0.45641, in 0.016s Fit 35 trees in 0.627 s, (1343 total leaves) Time spent computing histograms: 0.159s Time spent finding best splits: 0.026s Time spent applying splits: 0.026s Time spent predicting: 0.000s Trial 40, Fold 3: Log loss = 0.4559647888321185, Average precision = 0.9192760922522056, ROC-AUC = 0.9317236362454374, Elapsed Time = 0.6254176999991614 seconds Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 40, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 14, train loss: 0.67964, val loss: 0.67882, in 0.016s 1 tree, 29 leaves, max depth = 14, train loss: 0.66689, val loss: 0.66529, in 0.000s 1 tree, 29 leaves, max depth = 14, train loss: 0.65487, val loss: 0.65250, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.64359, val loss: 0.64053, in 0.000s 1 tree, 29 leaves, max depth = 14, train loss: 0.63285, val loss: 0.62906, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.62268, val loss: 0.61820, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.61319, val loss: 0.60811, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.60407, val loss: 0.59832, in 0.000s 1 tree, 30 leaves, max depth = 14, train loss: 0.59542, val loss: 0.58902, in 0.000s 1 tree, 30 leaves, max depth = 14, train loss: 0.58723, val loss: 0.58020, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.57946, val loss: 0.57181, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.57214, val loss: 0.56393, in 0.016s 1 tree, 33 leaves, max depth = 13, train loss: 0.56523, val loss: 0.55645, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.55858, val loss: 0.54922, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.55225, val loss: 0.54233, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.54555, val loss: 0.53577, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.53969, val loss: 0.52937, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.53343, val loss: 0.52325, in 0.016s 1 tree, 33 leaves, max depth = 13, train loss: 0.52806, val loss: 0.51738, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.52220, val loss: 0.51168, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.51664, val loss: 0.50626, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.51135, val loss: 0.50111, in 0.016s 1 tree, 32 leaves, max depth = 14, train loss: 0.50650, val loss: 0.49577, in 0.000s 1 tree, 32 leaves, max depth = 14, train loss: 0.50188, val loss: 0.49067, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.49699, val loss: 0.48592, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.49234, val loss: 0.48140, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.48791, val loss: 0.47710, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.48379, val loss: 0.47255, in 0.000s 1 tree, 58 leaves, max depth = 13, train loss: 0.47962, val loss: 0.46850, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.47573, val loss: 0.46418, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.47206, val loss: 0.46013, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.46818, val loss: 0.45637, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.46471, val loss: 0.45249, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.46104, val loss: 0.44895, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.45755, val loss: 0.44557, in 0.000s Fit 35 trees in 0.596 s, (1392 total leaves) Time spent computing histograms: 0.151s Time spent finding best splits: 0.026s Time spent applying splits: 0.026s Time spent predicting: 0.000s Trial 40, Fold 4: Log loss = 0.45723244672666946, Average precision = 0.917938386877304, ROC-AUC = 0.9294905440269343, Elapsed Time = 0.6093828999983089 seconds Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 40, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 32 leaves, max depth = 12, train loss: 0.67949, val loss: 0.67861, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.66662, val loss: 0.66489, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.65446, val loss: 0.65191, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.64311, val loss: 0.63976, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.63225, val loss: 0.62813, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.62197, val loss: 0.61710, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.61235, val loss: 0.60677, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.60313, val loss: 0.59684, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.59439, val loss: 0.58740, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.58610, val loss: 0.57845, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.57824, val loss: 0.56993, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.57086, val loss: 0.56192, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.56386, val loss: 0.55431, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.55713, val loss: 0.54696, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.55073, val loss: 0.53997, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.54421, val loss: 0.53375, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.53827, val loss: 0.52724, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.53219, val loss: 0.52146, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.52674, val loss: 0.51547, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.52105, val loss: 0.51005, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.51566, val loss: 0.50492, in 0.000s 1 tree, 33 leaves, max depth = 12, train loss: 0.51061, val loss: 0.49936, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.50556, val loss: 0.49459, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.50090, val loss: 0.48945, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.49617, val loss: 0.48497, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.49182, val loss: 0.48016, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.48737, val loss: 0.47596, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.48331, val loss: 0.47144, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.47912, val loss: 0.46751, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.47513, val loss: 0.46377, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47139, val loss: 0.45960, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.46764, val loss: 0.45609, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.46408, val loss: 0.45211, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.46053, val loss: 0.44880, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.45725, val loss: 0.44509, in 0.016s Fit 35 trees in 0.626 s, (1355 total leaves) Time spent computing histograms: 0.164s Time spent finding best splits: 0.026s Time spent applying splits: 0.026s Time spent predicting: 0.000s Trial 40, Fold 5: Log loss = 0.4623714122151564, Average precision = 0.9148543808130059, ROC-AUC = 0.925672444865578, Elapsed Time = 0.6308587999992596 seconds
Optimization Progress: 41%|####1 | 41/100 [08:02<10:59, 11.18s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 41, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 26 leaves, max depth = 11, train loss: 0.65757, val loss: 0.65670, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.62692, val loss: 0.62497, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.60070, val loss: 0.59791, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.57855, val loss: 0.57500, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.55919, val loss: 0.55488, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.54133, val loss: 0.53809, in 0.000s 1 tree, 31 leaves, max depth = 14, train loss: 0.52568, val loss: 0.52173, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.51091, val loss: 0.50792, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.49790, val loss: 0.49422, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.48555, val loss: 0.48276, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.47475, val loss: 0.47127, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.46427, val loss: 0.46161, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.45521, val loss: 0.45183, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.44704, val loss: 0.44305, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.44003, val loss: 0.43551, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.43145, val loss: 0.42780, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.42540, val loss: 0.42119, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.42023, val loss: 0.41556, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.41506, val loss: 0.40991, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41052, val loss: 0.40494, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40610, val loss: 0.40008, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.40086, val loss: 0.39564, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.39414, val loss: 0.38976, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.39059, val loss: 0.38594, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38680, val loss: 0.38176, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.38090, val loss: 0.37675, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.37572, val loss: 0.37238, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.37236, val loss: 0.36866, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.36778, val loss: 0.36480, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.36374, val loss: 0.36150, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.36089, val loss: 0.35842, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35802, val loss: 0.35506, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.35543, val loss: 0.35228, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.35178, val loss: 0.34930, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.34858, val loss: 0.34669, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34598, val loss: 0.34360, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.34373, val loss: 0.34107, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.34083, val loss: 0.33887, in 0.016s Fit 38 trees in 0.642 s, (1301 total leaves) Time spent computing histograms: 0.187s Time spent finding best splits: 0.030s Time spent applying splits: 0.031s Time spent predicting: 0.000s Trial 41, Fold 1: Log loss = 0.34574342825086174, Average precision = 0.9481375195266801, ROC-AUC = 0.945188492063492, Elapsed Time = 0.6465646999986348 seconds Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 41, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 10, train loss: 0.65762, val loss: 0.65594, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.62741, val loss: 0.62422, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.60154, val loss: 0.59697, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.57922, val loss: 0.57339, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.55994, val loss: 0.55297, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.54195, val loss: 0.53566, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.52626, val loss: 0.51900, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.51138, val loss: 0.50481, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49827, val loss: 0.49077, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.48580, val loss: 0.47889, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.47504, val loss: 0.46737, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.46447, val loss: 0.45732, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.45547, val loss: 0.44765, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.44738, val loss: 0.43881, in 0.000s 1 tree, 28 leaves, max depth = 16, train loss: 0.44054, val loss: 0.43136, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.43185, val loss: 0.42328, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.42587, val loss: 0.41668, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.42080, val loss: 0.41118, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.41579, val loss: 0.40600, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.41134, val loss: 0.40103, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40705, val loss: 0.39658, in 0.000s 1 tree, 65 leaves, max depth = 10, train loss: 0.40185, val loss: 0.39205, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.39502, val loss: 0.38582, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.39142, val loss: 0.38187, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38775, val loss: 0.37804, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.38173, val loss: 0.37262, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.37643, val loss: 0.36776, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37330, val loss: 0.36452, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.36860, val loss: 0.36029, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.36446, val loss: 0.35655, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.36158, val loss: 0.35345, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35878, val loss: 0.35055, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.35625, val loss: 0.34772, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.35250, val loss: 0.34441, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.34921, val loss: 0.34147, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34668, val loss: 0.33883, in 0.000s 1 tree, 37 leaves, max depth = 11, train loss: 0.34449, val loss: 0.33647, in 0.000s 1 tree, 51 leaves, max depth = 13, train loss: 0.34151, val loss: 0.33388, in 0.016s Fit 38 trees in 0.751 s, (1278 total leaves) Time spent computing histograms: 0.214s Time spent finding best splits: 0.034s Time spent applying splits: 0.035s Time spent predicting: 0.000s Trial 41, Fold 2: Log loss = 0.34458307528002785, Average precision = 0.9450730202217352, ROC-AUC = 0.9462417536459682, Elapsed Time = 0.7594802000003256 seconds Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 41, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.205 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 9, train loss: 0.65779, val loss: 0.65641, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.62758, val loss: 0.62519, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.60187, val loss: 0.59830, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.57979, val loss: 0.57524, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.56060, val loss: 0.55532, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.54267, val loss: 0.53863, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.52711, val loss: 0.52225, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.51228, val loss: 0.50855, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49921, val loss: 0.49478, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.48680, val loss: 0.48334, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.47606, val loss: 0.47207, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.46554, val loss: 0.46249, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.45657, val loss: 0.45301, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.44853, val loss: 0.44443, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.44164, val loss: 0.43702, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.43303, val loss: 0.42922, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.42708, val loss: 0.42277, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.42181, val loss: 0.41699, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.41660, val loss: 0.41220, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.41215, val loss: 0.40735, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40768, val loss: 0.40326, in 0.000s 1 tree, 63 leaves, max depth = 10, train loss: 0.40253, val loss: 0.39897, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.39584, val loss: 0.39298, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.39221, val loss: 0.38896, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38839, val loss: 0.38545, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.38250, val loss: 0.38021, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.37733, val loss: 0.37565, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.37394, val loss: 0.37253, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.36936, val loss: 0.36860, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.36535, val loss: 0.36521, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.36250, val loss: 0.36198, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35960, val loss: 0.35930, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.35706, val loss: 0.35647, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.35343, val loss: 0.35348, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.35025, val loss: 0.35094, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34763, val loss: 0.34851, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.34547, val loss: 0.34605, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.34260, val loss: 0.34369, in 0.016s Fit 38 trees in 0.846 s, (1241 total leaves) Time spent computing histograms: 0.240s Time spent finding best splits: 0.039s Time spent applying splits: 0.039s Time spent predicting: 0.000s Trial 41, Fold 3: Log loss = 0.3399037601455915, Average precision = 0.9499262049311503, ROC-AUC = 0.9491582926823396, Elapsed Time = 0.8536236999989342 seconds Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 41, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.204 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 10, train loss: 0.65777, val loss: 0.65584, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.62740, val loss: 0.62347, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.60138, val loss: 0.59562, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.57921, val loss: 0.57190, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.56002, val loss: 0.55118, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.54240, val loss: 0.53375, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.52671, val loss: 0.51681, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.51216, val loss: 0.50245, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.49907, val loss: 0.48825, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.48690, val loss: 0.47624, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.47617, val loss: 0.46448, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.46589, val loss: 0.45437, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.45687, val loss: 0.44432, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.44882, val loss: 0.43525, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.44186, val loss: 0.42734, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.43345, val loss: 0.41917, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42749, val loss: 0.41234, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.42236, val loss: 0.40639, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41737, val loss: 0.40110, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41292, val loss: 0.39592, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40864, val loss: 0.39139, in 0.000s 1 tree, 65 leaves, max depth = 10, train loss: 0.40358, val loss: 0.38684, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.39700, val loss: 0.38054, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.39348, val loss: 0.37638, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38982, val loss: 0.37250, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.38403, val loss: 0.36701, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.37896, val loss: 0.36219, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37584, val loss: 0.35886, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.37134, val loss: 0.35460, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.36739, val loss: 0.35086, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.36456, val loss: 0.34755, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36176, val loss: 0.34456, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.35920, val loss: 0.34160, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.35564, val loss: 0.33831, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.35254, val loss: 0.33536, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35000, val loss: 0.33264, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.34785, val loss: 0.33013, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.34503, val loss: 0.32754, in 0.016s Fit 38 trees in 0.860 s, (1267 total leaves) Time spent computing histograms: 0.238s Time spent finding best splits: 0.039s Time spent applying splits: 0.039s Time spent predicting: 0.031s Trial 41, Fold 4: Log loss = 0.3451491793687913, Average precision = 0.948128226802262, ROC-AUC = 0.9461292534889876, Elapsed Time = 0.857237800000803 seconds Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 41, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.189 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 10, train loss: 0.65751, val loss: 0.65527, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.62691, val loss: 0.62237, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.60094, val loss: 0.59422, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.57858, val loss: 0.57007, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55918, val loss: 0.54879, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.54135, val loss: 0.53196, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.52559, val loss: 0.51452, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.51084, val loss: 0.50075, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.49775, val loss: 0.48623, in 0.000s 1 tree, 52 leaves, max depth = 13, train loss: 0.48541, val loss: 0.47479, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.47451, val loss: 0.46267, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.46406, val loss: 0.45304, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.45494, val loss: 0.44281, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.44676, val loss: 0.43357, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.43984, val loss: 0.42565, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.43126, val loss: 0.41793, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.42521, val loss: 0.41096, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42006, val loss: 0.40497, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41504, val loss: 0.40012, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.41052, val loss: 0.39483, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40622, val loss: 0.39071, in 0.000s 1 tree, 63 leaves, max depth = 10, train loss: 0.40111, val loss: 0.38667, in 0.031s 1 tree, 52 leaves, max depth = 12, train loss: 0.39436, val loss: 0.38083, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.39077, val loss: 0.37653, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38709, val loss: 0.37302, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.38113, val loss: 0.36795, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.37590, val loss: 0.36358, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37279, val loss: 0.36050, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.36814, val loss: 0.35663, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.36406, val loss: 0.35330, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.36119, val loss: 0.34982, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35840, val loss: 0.34706, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.35583, val loss: 0.34398, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.35213, val loss: 0.34100, in 0.016s 1 tree, 52 leaves, max depth = 15, train loss: 0.34889, val loss: 0.33843, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34637, val loss: 0.33594, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.34408, val loss: 0.33343, in 0.016s 1 tree, 54 leaves, max depth = 15, train loss: 0.34114, val loss: 0.33111, in 0.016s Fit 38 trees in 0.862 s, (1298 total leaves) Time spent computing histograms: 0.233s Time spent finding best splits: 0.036s Time spent applying splits: 0.037s Time spent predicting: 0.000s Trial 41, Fold 5: Log loss = 0.34752194627777483, Average precision = 0.947238785766397, ROC-AUC = 0.9448383033275738, Elapsed Time = 0.8585003999996843 seconds
Optimization Progress: 42%|####2 | 42/100 [08:13<10:41, 11.06s/it]
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 42, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 13 leaves, max depth = 5, train loss: 0.68314, val loss: 0.68317, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.67366, val loss: 0.67373, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.66449, val loss: 0.66459, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.65563, val loss: 0.65577, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.64705, val loss: 0.64724, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.63875, val loss: 0.63897, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.63053, val loss: 0.63071, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.62275, val loss: 0.62293, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.61521, val loss: 0.61540, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.60771, val loss: 0.60786, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.60045, val loss: 0.60056, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.59340, val loss: 0.59347, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.58656, val loss: 0.58660, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.57992, val loss: 0.57992, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.57348, val loss: 0.57344, in 0.000s 1 tree, 17 leaves, max depth = 5, train loss: 0.56723, val loss: 0.56715, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.56115, val loss: 0.56104, in 0.016s Fit 17 trees in 0.532 s, (262 total leaves) Time spent computing histograms: 0.101s Time spent finding best splits: 0.008s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 42, Fold 1: Log loss = 0.5622161983440589, Average precision = 0.9122596955287137, ROC-AUC = 0.9279834644135683, Elapsed Time = 0.5323338999987755 seconds Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 42, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 14 leaves, max depth = 5, train loss: 0.68307, val loss: 0.68285, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.67339, val loss: 0.67297, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.66404, val loss: 0.66341, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.65500, val loss: 0.65418, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.64624, val loss: 0.64523, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.63778, val loss: 0.63658, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.62957, val loss: 0.62819, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.62163, val loss: 0.62007, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.61393, val loss: 0.61221, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.60647, val loss: 0.60458, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.59924, val loss: 0.59718, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.59222, val loss: 0.59000, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.58541, val loss: 0.58304, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.57880, val loss: 0.57626, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.57239, val loss: 0.56967, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.56615, val loss: 0.56330, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.56010, val loss: 0.55709, in 0.016s Fit 17 trees in 0.580 s, (244 total leaves) Time spent computing histograms: 0.120s Time spent finding best splits: 0.009s Time spent applying splits: 0.006s Time spent predicting: 0.016s Trial 42, Fold 2: Log loss = 0.5607065966168168, Average precision = 0.9051654203427068, ROC-AUC = 0.9282734501164058, Elapsed Time = 0.5888044000002992 seconds Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 42, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 13 leaves, max depth = 6, train loss: 0.68317, val loss: 0.68310, in 0.000s 1 tree, 13 leaves, max depth = 6, train loss: 0.67368, val loss: 0.67354, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.66450, val loss: 0.66429, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.65562, val loss: 0.65534, in 0.000s 1 tree, 16 leaves, max depth = 5, train loss: 0.64689, val loss: 0.64656, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.63844, val loss: 0.63806, in 0.000s 1 tree, 16 leaves, max depth = 5, train loss: 0.63025, val loss: 0.62983, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.62232, val loss: 0.62186, in 0.000s 1 tree, 16 leaves, max depth = 5, train loss: 0.61464, val loss: 0.61413, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.60720, val loss: 0.60664, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.59998, val loss: 0.59937, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.59297, val loss: 0.59233, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.58618, val loss: 0.58549, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.57959, val loss: 0.57888, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.57319, val loss: 0.57246, in 0.016s 1 tree, 21 leaves, max depth = 6, train loss: 0.56698, val loss: 0.56623, in 0.000s 1 tree, 20 leaves, max depth = 6, train loss: 0.56094, val loss: 0.56016, in 0.031s Fit 17 trees in 0.563 s, (279 total leaves) Time spent computing histograms: 0.109s Time spent finding best splits: 0.009s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 42, Fold 3: Log loss = 0.5586542076405022, Average precision = 0.9144956673682674, ROC-AUC = 0.9326203758992979, Elapsed Time = 0.5713990999993257 seconds Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 42, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 13 leaves, max depth = 5, train loss: 0.68327, val loss: 0.68287, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.67384, val loss: 0.67303, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.66471, val loss: 0.66352, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.65589, val loss: 0.65432, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.64718, val loss: 0.64521, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.63873, val loss: 0.63639, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.63056, val loss: 0.62785, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.62265, val loss: 0.61957, in 0.000s 1 tree, 16 leaves, max depth = 5, train loss: 0.61497, val loss: 0.61153, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.60754, val loss: 0.60375, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.60034, val loss: 0.59623, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.59333, val loss: 0.58889, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.58655, val loss: 0.58181, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.57996, val loss: 0.57490, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.57357, val loss: 0.56822, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.56737, val loss: 0.56168, in 0.000s 1 tree, 16 leaves, max depth = 5, train loss: 0.56133, val loss: 0.55534, in 0.031s Fit 17 trees in 0.578 s, (259 total leaves) Time spent computing histograms: 0.110s Time spent finding best splits: 0.009s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 42, Fold 4: Log loss = 0.5609712921412567, Average precision = 0.9131328401100529, ROC-AUC = 0.9301575826615258, Elapsed Time = 0.5790532999999414 seconds Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 42, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 15 leaves, max depth = 5, train loss: 0.68300, val loss: 0.68258, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.67333, val loss: 0.67250, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.66399, val loss: 0.66276, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.65495, val loss: 0.65333, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.64621, val loss: 0.64421, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.63775, val loss: 0.63539, in 0.000s 1 tree, 15 leaves, max depth = 5, train loss: 0.62956, val loss: 0.62684, in 0.000s 1 tree, 16 leaves, max depth = 5, train loss: 0.62162, val loss: 0.61855, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.61394, val loss: 0.61053, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.60648, val loss: 0.60275, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.59926, val loss: 0.59521, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.59225, val loss: 0.58789, in 0.016s 1 tree, 18 leaves, max depth = 5, train loss: 0.58545, val loss: 0.58078, in 0.016s 1 tree, 18 leaves, max depth = 5, train loss: 0.57885, val loss: 0.57389, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.57245, val loss: 0.56719, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.56622, val loss: 0.56070, in 0.016s 1 tree, 18 leaves, max depth = 5, train loss: 0.56018, val loss: 0.55437, in 0.016s Fit 17 trees in 0.595 s, (272 total leaves) Time spent computing histograms: 0.122s Time spent finding best splits: 0.010s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 42, Fold 5: Log loss = 0.5621848627885069, Average precision = 0.9096137397135172, ROC-AUC = 0.9274200248964197, Elapsed Time = 0.605163199999879 seconds
Optimization Progress: 43%|####3 | 43/100 [08:22<10:07, 10.65s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 43, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.127 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 10, train loss: 0.68834, val loss: 0.68819, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.68379, val loss: 0.68350, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.67926, val loss: 0.67884, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.67485, val loss: 0.67427, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.67055, val loss: 0.66984, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.66632, val loss: 0.66546, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.66220, val loss: 0.66121, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.65815, val loss: 0.65703, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.65413, val loss: 0.65288, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.65013, val loss: 0.64874, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.64630, val loss: 0.64476, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.64255, val loss: 0.64089, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.63888, val loss: 0.63709, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.63529, val loss: 0.63337, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.63175, val loss: 0.62972, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.62828, val loss: 0.62613, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.62482, val loss: 0.62256, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.62146, val loss: 0.61905, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.61814, val loss: 0.61559, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.61482, val loss: 0.61215, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.61156, val loss: 0.60876, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.60837, val loss: 0.60543, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.60530, val loss: 0.60224, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.60222, val loss: 0.59903, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.59927, val loss: 0.59594, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.59633, val loss: 0.59291, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.59349, val loss: 0.58993, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.59071, val loss: 0.58705, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.58798, val loss: 0.58422, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.58530, val loss: 0.58144, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.58267, val loss: 0.57870, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.58005, val loss: 0.57596, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.57742, val loss: 0.57321, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.57492, val loss: 0.57062, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.57239, val loss: 0.56797, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.56993, val loss: 0.56541, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.56751, val loss: 0.56291, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.56519, val loss: 0.56049, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.56286, val loss: 0.55807, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.56060, val loss: 0.55570, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55835, val loss: 0.55336, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.55610, val loss: 0.55100, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55393, val loss: 0.54874, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.55170, val loss: 0.54668, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.54959, val loss: 0.54445, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.54751, val loss: 0.54229, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.54534, val loss: 0.54029, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.54330, val loss: 0.53815, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.54130, val loss: 0.53607, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.53920, val loss: 0.53413, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.53728, val loss: 0.53210, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.53523, val loss: 0.53021, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.53337, val loss: 0.52827, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.53154, val loss: 0.52636, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.52970, val loss: 0.52444, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.52772, val loss: 0.52261, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.52593, val loss: 0.52072, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.52417, val loss: 0.51887, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.52224, val loss: 0.51710, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.52055, val loss: 0.51534, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.51886, val loss: 0.51357, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.51698, val loss: 0.51185, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.51533, val loss: 0.51012, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.51371, val loss: 0.50842, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.51215, val loss: 0.50678, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.51058, val loss: 0.50514, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.50904, val loss: 0.50352, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.50752, val loss: 0.50193, in 0.000s 1 tree, 50 leaves, max depth = 10, train loss: 0.50573, val loss: 0.50029, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.50396, val loss: 0.49868, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.50246, val loss: 0.49709, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.50101, val loss: 0.49557, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.49962, val loss: 0.49408, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.49822, val loss: 0.49261, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.49685, val loss: 0.49117, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.49549, val loss: 0.48972, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.49418, val loss: 0.48832, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.49291, val loss: 0.48697, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.49162, val loss: 0.48562, in 0.000s Fit 79 trees in 0.877 s, (2337 total leaves) Time spent computing histograms: 0.344s Time spent finding best splits: 0.053s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 43, Fold 1: Log loss = 0.49087564799547156, Average precision = 0.9016494024047659, ROC-AUC = 0.9077731953465921, Elapsed Time = 0.8890620000001945 seconds Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 43, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 8, train loss: 0.68846, val loss: 0.68822, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.68389, val loss: 0.68343, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.67937, val loss: 0.67870, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.67494, val loss: 0.67406, in 0.000s 1 tree, 28 leaves, max depth = 13, train loss: 0.67064, val loss: 0.66954, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.66640, val loss: 0.66509, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.66225, val loss: 0.66074, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.65819, val loss: 0.65648, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.65418, val loss: 0.65226, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.65026, val loss: 0.64814, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.64642, val loss: 0.64410, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.64266, val loss: 0.64015, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.63897, val loss: 0.63626, in 0.000s 1 tree, 26 leaves, max depth = 8, train loss: 0.63538, val loss: 0.63247, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.63183, val loss: 0.62873, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.62834, val loss: 0.62506, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.62489, val loss: 0.62142, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.62152, val loss: 0.61788, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.61819, val loss: 0.61438, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.61489, val loss: 0.61089, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.61166, val loss: 0.60746, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.60848, val loss: 0.60410, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.60541, val loss: 0.60086, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.60241, val loss: 0.59769, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.59945, val loss: 0.59457, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.59653, val loss: 0.59148, in 0.016s [27/79] 1 tree, 30 leaves, max depth = 10, train loss: 0.59368, val loss: 0.58847, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.59088, val loss: 0.58551, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.58814, val loss: 0.58261, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.58545, val loss: 0.57977, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.58281, val loss: 0.57696, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.58017, val loss: 0.57418, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.57756, val loss: 0.57140, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.57506, val loss: 0.56874, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.57253, val loss: 0.56606, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.57012, val loss: 0.56350, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.56772, val loss: 0.56095, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.56539, val loss: 0.55847, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.56307, val loss: 0.55601, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.56081, val loss: 0.55362, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.55857, val loss: 0.55124, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.55634, val loss: 0.54886, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.55420, val loss: 0.54658, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.55192, val loss: 0.54437, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.54980, val loss: 0.54212, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.54773, val loss: 0.53992, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.54552, val loss: 0.53778, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.54348, val loss: 0.53562, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.54151, val loss: 0.53352, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.53936, val loss: 0.53145, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.53744, val loss: 0.52940, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.53534, val loss: 0.52737, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.53347, val loss: 0.52538, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.53164, val loss: 0.52342, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.52983, val loss: 0.52149, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.52780, val loss: 0.51953, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.52601, val loss: 0.51762, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.52428, val loss: 0.51577, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.52230, val loss: 0.51386, in 0.000s 1 tree, 30 leaves, max depth = 14, train loss: 0.52062, val loss: 0.51206, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.51896, val loss: 0.51028, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.51704, val loss: 0.50843, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.51540, val loss: 0.50667, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.51379, val loss: 0.50494, in 0.000s 1 tree, 30 leaves, max depth = 14, train loss: 0.51223, val loss: 0.50326, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.51068, val loss: 0.50159, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.50915, val loss: 0.49995, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.50766, val loss: 0.49835, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.50583, val loss: 0.49659, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.50402, val loss: 0.49485, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.50253, val loss: 0.49324, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.50110, val loss: 0.49170, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.49970, val loss: 0.49020, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.49834, val loss: 0.48872, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.49699, val loss: 0.48727, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.49564, val loss: 0.48582, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.49433, val loss: 0.48442, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.49306, val loss: 0.48304, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.49180, val loss: 0.48168, in 0.016s Fit 79 trees in 0.970 s, (2345 total leaves) Time spent computing histograms: 0.363s Time spent finding best splits: 0.054s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 43, Fold 2: Log loss = 0.49183921891558047, Average precision = 0.8987425486197136, ROC-AUC = 0.9109339894098256, Elapsed Time = 0.9627939000001788 seconds Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 43, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 9, train loss: 0.68841, val loss: 0.68825, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.68388, val loss: 0.68356, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.67939, val loss: 0.67891, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.67499, val loss: 0.67436, in 0.000s 1 tree, 26 leaves, max depth = 10, train loss: 0.67073, val loss: 0.66993, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.66652, val loss: 0.66560, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.66242, val loss: 0.66134, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.65840, val loss: 0.65716, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.65441, val loss: 0.65302, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.65045, val loss: 0.64893, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.64664, val loss: 0.64500, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.64292, val loss: 0.64114, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.63927, val loss: 0.63734, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.63570, val loss: 0.63364, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.63219, val loss: 0.62998, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.62874, val loss: 0.62640, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.62531, val loss: 0.62283, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.62197, val loss: 0.61938, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.61866, val loss: 0.61595, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.61538, val loss: 0.61255, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.61215, val loss: 0.60921, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.60898, val loss: 0.60594, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.60594, val loss: 0.60279, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.60289, val loss: 0.59963, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.59995, val loss: 0.59660, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.59705, val loss: 0.59357, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.59422, val loss: 0.59065, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.59146, val loss: 0.58775, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.58875, val loss: 0.58492, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.58609, val loss: 0.58214, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.58347, val loss: 0.57940, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.58086, val loss: 0.57668, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.57826, val loss: 0.57398, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.57578, val loss: 0.57138, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.57327, val loss: 0.56878, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.57088, val loss: 0.56628, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.56849, val loss: 0.56378, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.56619, val loss: 0.56136, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.56388, val loss: 0.55895, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.56164, val loss: 0.55662, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.55941, val loss: 0.55429, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.55719, val loss: 0.55198, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.55504, val loss: 0.54973, in 0.000s 1 tree, 50 leaves, max depth = 12, train loss: 0.55278, val loss: 0.54764, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.55068, val loss: 0.54544, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.54862, val loss: 0.54328, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.54642, val loss: 0.54125, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.54441, val loss: 0.53912, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.54245, val loss: 0.53710, in 0.000s 1 tree, 50 leaves, max depth = 12, train loss: 0.54032, val loss: 0.53513, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.53842, val loss: 0.53315, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.53634, val loss: 0.53123, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.53449, val loss: 0.52928, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.53268, val loss: 0.52736, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.53087, val loss: 0.52545, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.52885, val loss: 0.52359, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.52707, val loss: 0.52172, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.52534, val loss: 0.51989, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.52338, val loss: 0.51808, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.52172, val loss: 0.51633, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.52005, val loss: 0.51456, in 0.000s 1 tree, 49 leaves, max depth = 13, train loss: 0.51815, val loss: 0.51281, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.51652, val loss: 0.51109, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.51492, val loss: 0.50939, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.51338, val loss: 0.50776, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.51184, val loss: 0.50612, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.51032, val loss: 0.50451, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.50882, val loss: 0.50293, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.50700, val loss: 0.50126, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.50522, val loss: 0.49961, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.50373, val loss: 0.49806, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.50231, val loss: 0.49655, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.50093, val loss: 0.49511, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.49955, val loss: 0.49364, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.49820, val loss: 0.49221, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.49685, val loss: 0.49079, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.49556, val loss: 0.48944, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.49430, val loss: 0.48809, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.49303, val loss: 0.48674, in 0.016s Fit 79 trees in 0.955 s, (2315 total leaves) Time spent computing histograms: 0.352s Time spent finding best splits: 0.055s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 43, Fold 3: Log loss = 0.48784348922773413, Average precision = 0.9067597255312809, ROC-AUC = 0.9150396738449983, Elapsed Time = 0.9549069000004238 seconds Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 43, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 11, train loss: 0.68844, val loss: 0.68818, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.68389, val loss: 0.68339, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.67939, val loss: 0.67864, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.67499, val loss: 0.67401, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.67070, val loss: 0.66948, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.66649, val loss: 0.66500, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.66237, val loss: 0.66064, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.65833, val loss: 0.65637, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.65434, val loss: 0.65213, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.65044, val loss: 0.64798, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.64663, val loss: 0.64394, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.64289, val loss: 0.63997, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.63922, val loss: 0.63607, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.63565, val loss: 0.63226, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.63212, val loss: 0.62852, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.62865, val loss: 0.62484, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.62521, val loss: 0.62118, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.62187, val loss: 0.61759, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61856, val loss: 0.61408, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.61528, val loss: 0.61056, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.61206, val loss: 0.60710, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.60890, val loss: 0.60371, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.60585, val loss: 0.60043, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.60280, val loss: 0.59715, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.59987, val loss: 0.59401, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.59696, val loss: 0.59089, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.59413, val loss: 0.58787, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.59136, val loss: 0.58489, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.58863, val loss: 0.58198, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.58595, val loss: 0.57912, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.58333, val loss: 0.57629, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58071, val loss: 0.57349, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.57812, val loss: 0.57068, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.57563, val loss: 0.56800, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.57312, val loss: 0.56528, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.57072, val loss: 0.56270, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.56832, val loss: 0.56011, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.56600, val loss: 0.55762, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.56369, val loss: 0.55512, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.56145, val loss: 0.55270, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55921, val loss: 0.55028, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.55699, val loss: 0.54786, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.55484, val loss: 0.54553, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.55254, val loss: 0.54325, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.55044, val loss: 0.54098, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.54837, val loss: 0.53874, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.54614, val loss: 0.53652, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.54411, val loss: 0.53432, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.54213, val loss: 0.53217, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.53997, val loss: 0.53002, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.53807, val loss: 0.52795, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.53596, val loss: 0.52586, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.53410, val loss: 0.52384, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.53228, val loss: 0.52185, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.53046, val loss: 0.51986, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.52842, val loss: 0.51784, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.52664, val loss: 0.51591, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.52490, val loss: 0.51400, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.52291, val loss: 0.51203, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.52124, val loss: 0.51020, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.51957, val loss: 0.50837, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.51763, val loss: 0.50646, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.51600, val loss: 0.50467, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.51440, val loss: 0.50291, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.51285, val loss: 0.50120, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.51130, val loss: 0.49950, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.50977, val loss: 0.49782, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.50828, val loss: 0.49617, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.50643, val loss: 0.49435, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.50462, val loss: 0.49256, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.50314, val loss: 0.49091, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.50172, val loss: 0.48934, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.50034, val loss: 0.48783, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.49896, val loss: 0.48630, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.49760, val loss: 0.48480, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.49626, val loss: 0.48332, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.49497, val loss: 0.48189, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.49370, val loss: 0.48048, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.49243, val loss: 0.47907, in 0.016s Fit 79 trees in 1.033 s, (2401 total leaves) Time spent computing histograms: 0.394s Time spent finding best splits: 0.063s Time spent applying splits: 0.054s Time spent predicting: 0.016s Trial 43, Fold 4: Log loss = 0.4912654405101837, Average precision = 0.8998129762974338, ROC-AUC = 0.9107013204861334, Elapsed Time = 1.0364403000003222 seconds Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 43, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 12, train loss: 0.68834, val loss: 0.68802, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.68374, val loss: 0.68312, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.67920, val loss: 0.67829, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.67474, val loss: 0.67354, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.67041, val loss: 0.66892, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.66615, val loss: 0.66438, in 0.016s 1 tree, 28 leaves, max depth = 13, train loss: 0.66198, val loss: 0.65993, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.65790, val loss: 0.65556, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.65386, val loss: 0.65126, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.64992, val loss: 0.64705, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.64606, val loss: 0.64291, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.64228, val loss: 0.63886, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.63857, val loss: 0.63488, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.63495, val loss: 0.63097, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.63138, val loss: 0.62715, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.62787, val loss: 0.62338, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.62440, val loss: 0.61966, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.62101, val loss: 0.61602, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61766, val loss: 0.61242, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.61441, val loss: 0.60892, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.61116, val loss: 0.60542, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.60803, val loss: 0.60204, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.60494, val loss: 0.59872, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.60193, val loss: 0.59546, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.59896, val loss: 0.59225, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.59601, val loss: 0.58907, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.59315, val loss: 0.58597, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.59033, val loss: 0.58293, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.58758, val loss: 0.57995, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.58488, val loss: 0.57702, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.58221, val loss: 0.57414, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.57957, val loss: 0.57127, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.57694, val loss: 0.56842, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.57442, val loss: 0.56568, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.57189, val loss: 0.56293, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.56946, val loss: 0.56029, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.56707, val loss: 0.55768, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.56473, val loss: 0.55513, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.56242, val loss: 0.55262, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.56015, val loss: 0.55013, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.55792, val loss: 0.54770, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.55568, val loss: 0.54525, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.55352, val loss: 0.54289, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.55125, val loss: 0.54069, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.54912, val loss: 0.53837, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.54703, val loss: 0.53608, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.54482, val loss: 0.53396, in 0.031s 1 tree, 29 leaves, max depth = 8, train loss: 0.54278, val loss: 0.53171, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.54080, val loss: 0.52954, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.53866, val loss: 0.52748, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.53673, val loss: 0.52535, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.53464, val loss: 0.52334, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.53276, val loss: 0.52128, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.53092, val loss: 0.51925, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.52907, val loss: 0.51722, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.52705, val loss: 0.51528, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.52525, val loss: 0.51330, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.52351, val loss: 0.51138, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.52154, val loss: 0.50950, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.51985, val loss: 0.50762, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.51819, val loss: 0.50578, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.51628, val loss: 0.50395, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.51462, val loss: 0.50213, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.51300, val loss: 0.50034, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.51144, val loss: 0.49860, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.50987, val loss: 0.49686, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.50832, val loss: 0.49515, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.50683, val loss: 0.49349, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.50500, val loss: 0.49175, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.50320, val loss: 0.49004, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.50171, val loss: 0.48837, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.50026, val loss: 0.48678, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.49886, val loss: 0.48521, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.49746, val loss: 0.48365, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.49608, val loss: 0.48212, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.49472, val loss: 0.48060, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.49341, val loss: 0.47913, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.49213, val loss: 0.47769, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.49084, val loss: 0.47625, in 0.000s Fit 79 trees in 1.112 s, (2344 total leaves) Time spent computing histograms: 0.421s Time spent finding best splits: 0.074s Time spent applying splits: 0.072s Time spent predicting: 0.031s Trial 43, Fold 5: Log loss = 0.49696672240271467, Average precision = 0.8970724235639036, ROC-AUC = 0.9054165691247237, Elapsed Time = 1.1303874000004726 seconds
Optimization Progress: 44%|####4 | 44/100 [08:34<10:11, 10.92s/it]
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 44, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 26 leaves, max depth = 7, train loss: 0.67205, val loss: 0.67196, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.65221, val loss: 0.65221, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.63397, val loss: 0.63387, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.61672, val loss: 0.61668, in 0.016s 1 tree, 38 leaves, max depth = 7, train loss: 0.60176, val loss: 0.60165, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.58762, val loss: 0.58727, in 0.016s 1 tree, 33 leaves, max depth = 7, train loss: 0.57428, val loss: 0.57371, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.56062, val loss: 0.56003, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.54851, val loss: 0.54784, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.53724, val loss: 0.53658, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.52592, val loss: 0.52539, in 0.016s 1 tree, 39 leaves, max depth = 8, train loss: 0.51609, val loss: 0.51539, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.50597, val loss: 0.50541, in 0.016s 1 tree, 39 leaves, max depth = 8, train loss: 0.49656, val loss: 0.49587, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.48761, val loss: 0.48667, in 0.000s 1 tree, 43 leaves, max depth = 11, train loss: 0.47885, val loss: 0.47785, in 0.031s 1 tree, 40 leaves, max depth = 13, train loss: 0.47078, val loss: 0.46990, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.46288, val loss: 0.46196, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.45558, val loss: 0.45451, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.44853, val loss: 0.44744, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.44180, val loss: 0.44070, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.43546, val loss: 0.43437, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.42619, val loss: 0.42549, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.41750, val loss: 0.41718, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.41195, val loss: 0.41167, in 0.016s 1 tree, 42 leaves, max depth = 7, train loss: 0.40683, val loss: 0.40642, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.39910, val loss: 0.39913, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.39426, val loss: 0.39432, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.38712, val loss: 0.38755, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.38039, val loss: 0.38116, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.37601, val loss: 0.37684, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.37193, val loss: 0.37282, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.36588, val loss: 0.36714, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.36238, val loss: 0.36371, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.35869, val loss: 0.36011, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.35355, val loss: 0.35501, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34871, val loss: 0.35022, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.34395, val loss: 0.34594, in 0.016s 1 tree, 46 leaves, max depth = 16, train loss: 0.34025, val loss: 0.34238, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.33565, val loss: 0.33817, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.33281, val loss: 0.33546, in 0.016s 1 tree, 63 leaves, max depth = 14, train loss: 0.32869, val loss: 0.33177, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.32490, val loss: 0.32804, in 0.016s 1 tree, 47 leaves, max depth = 16, train loss: 0.32175, val loss: 0.32498, in 0.016s 1 tree, 64 leaves, max depth = 14, train loss: 0.31806, val loss: 0.32171, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.31471, val loss: 0.31840, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.31230, val loss: 0.31602, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30921, val loss: 0.31302, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.30629, val loss: 0.31013, in 0.016s 1 tree, 50 leaves, max depth = 16, train loss: 0.30335, val loss: 0.30718, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.30033, val loss: 0.30454, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.29747, val loss: 0.30205, in 0.032s 1 tree, 65 leaves, max depth = 12, train loss: 0.29477, val loss: 0.29969, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.29237, val loss: 0.29735, in 0.016s 1 tree, 54 leaves, max depth = 16, train loss: 0.28976, val loss: 0.29474, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.28784, val loss: 0.29298, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.28549, val loss: 0.29097, in 0.031s 1 tree, 51 leaves, max depth = 14, train loss: 0.28373, val loss: 0.28931, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.28152, val loss: 0.28741, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.27952, val loss: 0.28544, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.27749, val loss: 0.28371, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.27567, val loss: 0.28195, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.27395, val loss: 0.28025, in 0.016s 1 tree, 46 leaves, max depth = 18, train loss: 0.27197, val loss: 0.27835, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.27020, val loss: 0.27688, in 0.016s 1 tree, 55 leaves, max depth = 15, train loss: 0.26791, val loss: 0.27455, in 0.031s 1 tree, 66 leaves, max depth = 13, train loss: 0.26626, val loss: 0.27317, in 0.016s 1 tree, 42 leaves, max depth = 15, train loss: 0.26473, val loss: 0.27161, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.26320, val loss: 0.27035, in 0.016s 1 tree, 39 leaves, max depth = 14, train loss: 0.26183, val loss: 0.26896, in 0.016s 1 tree, 30 leaves, max depth = 7, train loss: 0.25995, val loss: 0.26706, in 0.016s 1 tree, 69 leaves, max depth = 13, train loss: 0.25856, val loss: 0.26586, in 0.016s 1 tree, 42 leaves, max depth = 16, train loss: 0.25728, val loss: 0.26455, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.25611, val loss: 0.26339, in 0.016s 1 tree, 47 leaves, max depth = 16, train loss: 0.25455, val loss: 0.26195, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.25289, val loss: 0.26027, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.25170, val loss: 0.25930, in 0.016s 1 tree, 43 leaves, max depth = 16, train loss: 0.25062, val loss: 0.25819, in 0.016s 1 tree, 48 leaves, max depth = 18, train loss: 0.24923, val loss: 0.25693, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.24815, val loss: 0.25607, in 0.016s 1 tree, 43 leaves, max depth = 15, train loss: 0.24716, val loss: 0.25503, in 0.016s 1 tree, 55 leaves, max depth = 18, train loss: 0.24568, val loss: 0.25350, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.24470, val loss: 0.25273, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.24380, val loss: 0.25179, in 0.031s 1 tree, 56 leaves, max depth = 16, train loss: 0.24242, val loss: 0.25037, in 0.016s Fit 85 trees in 1.721 s, (3900 total leaves) Time spent computing histograms: 0.533s Time spent finding best splits: 0.108s Time spent applying splits: 0.079s Time spent predicting: 0.000s Trial 44, Fold 1: Log loss = 0.2527531781390743, Average precision = 0.9657645417180376, ROC-AUC = 0.9605238041467716, Elapsed Time = 1.7215439999999944 seconds Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 44, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 24 leaves, max depth = 6, train loss: 0.67210, val loss: 0.67157, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.65188, val loss: 0.65105, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.63335, val loss: 0.63239, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.61578, val loss: 0.61457, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.59954, val loss: 0.59802, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.58422, val loss: 0.58243, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.56999, val loss: 0.56795, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.55749, val loss: 0.55515, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.54478, val loss: 0.54216, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.53282, val loss: 0.52998, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.52216, val loss: 0.51922, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.51146, val loss: 0.50840, in 0.031s 1 tree, 39 leaves, max depth = 10, train loss: 0.50147, val loss: 0.49823, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.49198, val loss: 0.48855, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.48301, val loss: 0.47943, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.47469, val loss: 0.47101, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.46697, val loss: 0.46310, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.45933, val loss: 0.45534, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.45210, val loss: 0.44797, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.44526, val loss: 0.44099, in 0.031s 1 tree, 36 leaves, max depth = 7, train loss: 0.43889, val loss: 0.43456, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.42954, val loss: 0.42537, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.42371, val loss: 0.41956, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.41516, val loss: 0.41116, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.40996, val loss: 0.40596, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.40213, val loss: 0.39828, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.39738, val loss: 0.39355, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.39021, val loss: 0.38655, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.38345, val loss: 0.37994, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.37932, val loss: 0.37587, in 0.016s 1 tree, 46 leaves, max depth = 8, train loss: 0.37524, val loss: 0.37184, in 0.031s 1 tree, 47 leaves, max depth = 9, train loss: 0.37138, val loss: 0.36805, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.36559, val loss: 0.36245, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.36189, val loss: 0.35874, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.35670, val loss: 0.35366, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.35184, val loss: 0.34888, in 0.016s 1 tree, 38 leaves, max depth = 14, train loss: 0.34797, val loss: 0.34518, in 0.016s 1 tree, 64 leaves, max depth = 15, train loss: 0.34332, val loss: 0.34093, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.33904, val loss: 0.33676, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.33553, val loss: 0.33339, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.33162, val loss: 0.32957, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.32791, val loss: 0.32598, in 0.016s 1 tree, 39 leaves, max depth = 17, train loss: 0.32469, val loss: 0.32291, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.32127, val loss: 0.31956, in 0.016s 1 tree, 40 leaves, max depth = 17, train loss: 0.31825, val loss: 0.31666, in 0.016s 1 tree, 47 leaves, max depth = 17, train loss: 0.31524, val loss: 0.31374, in 0.016s 1 tree, 48 leaves, max depth = 18, train loss: 0.31238, val loss: 0.31098, in 0.016s 1 tree, 26 leaves, max depth = 14, train loss: 0.30932, val loss: 0.30804, in 0.016s 1 tree, 26 leaves, max depth = 14, train loss: 0.30645, val loss: 0.30528, in 0.016s 1 tree, 53 leaves, max depth = 16, train loss: 0.30380, val loss: 0.30273, in 0.016s 1 tree, 61 leaves, max depth = 16, train loss: 0.30064, val loss: 0.29986, in 0.016s 1 tree, 62 leaves, max depth = 16, train loss: 0.29765, val loss: 0.29717, in 0.031s 1 tree, 51 leaves, max depth = 18, train loss: 0.29524, val loss: 0.29485, in 0.016s 1 tree, 65 leaves, max depth = 15, train loss: 0.29246, val loss: 0.29236, in 0.016s 1 tree, 65 leaves, max depth = 16, train loss: 0.28984, val loss: 0.29002, in 0.031s 1 tree, 63 leaves, max depth = 15, train loss: 0.28737, val loss: 0.28781, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.28521, val loss: 0.28573, in 0.016s 1 tree, 43 leaves, max depth = 16, train loss: 0.28313, val loss: 0.28374, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.28090, val loss: 0.28173, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.27879, val loss: 0.27985, in 0.031s 1 tree, 65 leaves, max depth = 15, train loss: 0.27680, val loss: 0.27809, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.27492, val loss: 0.27632, in 0.031s 1 tree, 26 leaves, max depth = 11, train loss: 0.27316, val loss: 0.27462, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.27135, val loss: 0.27300, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.26974, val loss: 0.27144, in 0.016s 1 tree, 66 leaves, max depth = 16, train loss: 0.26808, val loss: 0.26998, in 0.031s 1 tree, 33 leaves, max depth = 16, train loss: 0.26660, val loss: 0.26856, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.26522, val loss: 0.26731, in 0.016s 1 tree, 63 leaves, max depth = 16, train loss: 0.26371, val loss: 0.26599, in 0.031s 1 tree, 63 leaves, max depth = 16, train loss: 0.26228, val loss: 0.26475, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.26099, val loss: 0.26351, in 0.016s 1 tree, 53 leaves, max depth = 17, train loss: 0.25934, val loss: 0.26197, in 0.031s 1 tree, 65 leaves, max depth = 16, train loss: 0.25804, val loss: 0.26082, in 0.016s 1 tree, 59 leaves, max depth = 17, train loss: 0.25650, val loss: 0.25942, in 0.031s 1 tree, 34 leaves, max depth = 12, train loss: 0.25535, val loss: 0.25834, in 0.016s 1 tree, 44 leaves, max depth = 17, train loss: 0.25398, val loss: 0.25702, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.25288, val loss: 0.25598, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.25172, val loss: 0.25498, in 0.031s 1 tree, 32 leaves, max depth = 11, train loss: 0.25073, val loss: 0.25408, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.24972, val loss: 0.25337, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.24873, val loss: 0.25240, in 0.016s 1 tree, 52 leaves, max depth = 17, train loss: 0.24740, val loss: 0.25118, in 0.031s 1 tree, 72 leaves, max depth = 17, train loss: 0.24637, val loss: 0.25044, in 0.016s 1 tree, 52 leaves, max depth = 17, train loss: 0.24512, val loss: 0.24931, in 0.031s 1 tree, 65 leaves, max depth = 17, train loss: 0.24415, val loss: 0.24851, in 0.016s Fit 85 trees in 1.908 s, (3644 total leaves) Time spent computing histograms: 0.606s Time spent finding best splits: 0.121s Time spent applying splits: 0.087s Time spent predicting: 0.000s Trial 44, Fold 2: Log loss = 0.24867228386667295, Average precision = 0.9662767909458208, ROC-AUC = 0.9632831058279951, Elapsed Time = 1.924355299999661 seconds Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 44, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 7, train loss: 0.67210, val loss: 0.67202, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65208, val loss: 0.65216, in 0.031s 1 tree, 27 leaves, max depth = 7, train loss: 0.63387, val loss: 0.63392, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.61667, val loss: 0.61675, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.60062, val loss: 0.60066, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.58539, val loss: 0.58551, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.57116, val loss: 0.57136, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.55877, val loss: 0.55893, in 0.031s 1 tree, 37 leaves, max depth = 9, train loss: 0.54613, val loss: 0.54641, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.53427, val loss: 0.53463, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.52346, val loss: 0.52395, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.51289, val loss: 0.51341, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.50292, val loss: 0.50347, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.49343, val loss: 0.49413, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.48474, val loss: 0.48547, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.47628, val loss: 0.47707, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.46856, val loss: 0.46940, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.46098, val loss: 0.46191, in 0.031s 1 tree, 37 leaves, max depth = 12, train loss: 0.45380, val loss: 0.45479, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.44700, val loss: 0.44803, in 0.016s 1 tree, 39 leaves, max depth = 8, train loss: 0.44060, val loss: 0.44180, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.43110, val loss: 0.43302, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.42523, val loss: 0.42720, in 0.031s 1 tree, 44 leaves, max depth = 15, train loss: 0.41654, val loss: 0.41921, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.40849, val loss: 0.41188, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.40330, val loss: 0.40682, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.39588, val loss: 0.40002, in 0.016s 1 tree, 34 leaves, max depth = 7, train loss: 0.39137, val loss: 0.39556, in 0.031s 1 tree, 45 leaves, max depth = 11, train loss: 0.38449, val loss: 0.38932, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.37800, val loss: 0.38344, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.37190, val loss: 0.37793, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.36788, val loss: 0.37396, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.36437, val loss: 0.37051, in 0.031s 1 tree, 40 leaves, max depth = 8, train loss: 0.36076, val loss: 0.36706, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.35566, val loss: 0.36246, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35087, val loss: 0.35814, in 0.016s 1 tree, 40 leaves, max depth = 14, train loss: 0.34698, val loss: 0.35415, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.34262, val loss: 0.35022, in 0.031s 1 tree, 39 leaves, max depth = 14, train loss: 0.33900, val loss: 0.34650, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.33473, val loss: 0.34288, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.33071, val loss: 0.33949, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.32695, val loss: 0.33613, in 0.016s 1 tree, 40 leaves, max depth = 16, train loss: 0.32372, val loss: 0.33276, in 0.031s 1 tree, 62 leaves, max depth = 12, train loss: 0.32012, val loss: 0.32978, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.31707, val loss: 0.32647, in 0.031s 1 tree, 32 leaves, max depth = 11, train loss: 0.31381, val loss: 0.32353, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.31138, val loss: 0.32139, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.30837, val loss: 0.31870, in 0.016s 1 tree, 42 leaves, max depth = 16, train loss: 0.30570, val loss: 0.31590, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.30270, val loss: 0.31345, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.30003, val loss: 0.31108, in 0.031s 1 tree, 63 leaves, max depth = 15, train loss: 0.29729, val loss: 0.30887, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.29531, val loss: 0.30703, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.29277, val loss: 0.30496, in 0.016s 1 tree, 51 leaves, max depth = 8, train loss: 0.29086, val loss: 0.30331, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.28849, val loss: 0.30137, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.28633, val loss: 0.29947, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.28416, val loss: 0.29771, in 0.031s 1 tree, 33 leaves, max depth = 12, train loss: 0.28218, val loss: 0.29592, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.28019, val loss: 0.29427, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.27838, val loss: 0.29265, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.27640, val loss: 0.29055, in 0.031s 1 tree, 61 leaves, max depth = 16, train loss: 0.27460, val loss: 0.28918, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.27295, val loss: 0.28778, in 0.031s 1 tree, 62 leaves, max depth = 13, train loss: 0.27131, val loss: 0.28639, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.26983, val loss: 0.28505, in 0.016s 1 tree, 44 leaves, max depth = 16, train loss: 0.26803, val loss: 0.28310, in 0.031s 1 tree, 65 leaves, max depth = 15, train loss: 0.26653, val loss: 0.28193, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.26519, val loss: 0.28072, in 0.016s 1 tree, 42 leaves, max depth = 15, train loss: 0.26352, val loss: 0.27892, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.26227, val loss: 0.27785, in 0.016s 1 tree, 45 leaves, max depth = 14, train loss: 0.26071, val loss: 0.27619, in 0.031s 1 tree, 69 leaves, max depth = 11, train loss: 0.25918, val loss: 0.27474, in 0.016s 1 tree, 63 leaves, max depth = 16, train loss: 0.25790, val loss: 0.27370, in 0.031s 1 tree, 44 leaves, max depth = 13, train loss: 0.25673, val loss: 0.27256, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.25519, val loss: 0.27082, in 0.016s 1 tree, 62 leaves, max depth = 19, train loss: 0.25403, val loss: 0.26996, in 0.031s 1 tree, 43 leaves, max depth = 13, train loss: 0.25297, val loss: 0.26893, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.25189, val loss: 0.26814, in 0.031s 1 tree, 64 leaves, max depth = 15, train loss: 0.25087, val loss: 0.26728, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.24996, val loss: 0.26652, in 0.016s 1 tree, 49 leaves, max depth = 16, train loss: 0.24869, val loss: 0.26515, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.24778, val loss: 0.26429, in 0.031s 1 tree, 46 leaves, max depth = 16, train loss: 0.24657, val loss: 0.26297, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.24572, val loss: 0.26217, in 0.016s Fit 85 trees in 2.029 s, (3817 total leaves) Time spent computing histograms: 0.649s Time spent finding best splits: 0.132s Time spent applying splits: 0.095s Time spent predicting: 0.016s Trial 44, Fold 3: Log loss = 0.24909530956709083, Average precision = 0.9658513285518209, ROC-AUC = 0.9626928313798384, Elapsed Time = 2.0412577999995847 seconds Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 44, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 23 leaves, max depth = 7, train loss: 0.67219, val loss: 0.67130, in 0.016s 1 tree, 23 leaves, max depth = 7, train loss: 0.65285, val loss: 0.65112, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.63549, val loss: 0.63305, in 0.016s 1 tree, 26 leaves, max depth = 7, train loss: 0.61862, val loss: 0.61541, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.60253, val loss: 0.59870, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.58742, val loss: 0.58303, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.57448, val loss: 0.56937, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.56112, val loss: 0.55550, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.54938, val loss: 0.54323, in 0.016s 1 tree, 26 leaves, max depth = 8, train loss: 0.53785, val loss: 0.53110, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.52673, val loss: 0.51947, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.51690, val loss: 0.50902, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.50670, val loss: 0.49838, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.49701, val loss: 0.48830, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.48860, val loss: 0.47946, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.47991, val loss: 0.47039, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.46908, val loss: 0.45941, in 0.031s 1 tree, 45 leaves, max depth = 10, train loss: 0.46192, val loss: 0.45187, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.45441, val loss: 0.44407, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.44731, val loss: 0.43668, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.44058, val loss: 0.42967, in 0.031s 1 tree, 38 leaves, max depth = 11, train loss: 0.43424, val loss: 0.42301, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.42820, val loss: 0.41657, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.42244, val loss: 0.41054, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.41437, val loss: 0.40236, in 0.031s 1 tree, 58 leaves, max depth = 13, train loss: 0.40657, val loss: 0.39448, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.40164, val loss: 0.38922, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.39684, val loss: 0.38411, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.38980, val loss: 0.37699, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.38323, val loss: 0.37037, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.37896, val loss: 0.36585, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.37494, val loss: 0.36160, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.37138, val loss: 0.35795, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.36565, val loss: 0.35214, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.36044, val loss: 0.34672, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.35553, val loss: 0.34160, in 0.016s 1 tree, 40 leaves, max depth = 14, train loss: 0.35159, val loss: 0.33759, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.34693, val loss: 0.33320, in 0.031s 1 tree, 31 leaves, max depth = 14, train loss: 0.34261, val loss: 0.32870, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.33898, val loss: 0.32503, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.33505, val loss: 0.32086, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.33100, val loss: 0.31713, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.32754, val loss: 0.31356, in 0.016s 1 tree, 40 leaves, max depth = 14, train loss: 0.32438, val loss: 0.31036, in 0.031s 1 tree, 49 leaves, max depth = 13, train loss: 0.32194, val loss: 0.30777, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.31835, val loss: 0.30447, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.31509, val loss: 0.30107, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.31182, val loss: 0.29809, in 0.031s 1 tree, 20 leaves, max depth = 8, train loss: 0.30887, val loss: 0.29501, in 0.016s 1 tree, 42 leaves, max depth = 15, train loss: 0.30618, val loss: 0.29227, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.30325, val loss: 0.28962, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.30063, val loss: 0.28687, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.29815, val loss: 0.28428, in 0.016s 1 tree, 51 leaves, max depth = 15, train loss: 0.29555, val loss: 0.28158, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.29301, val loss: 0.27931, in 0.031s 1 tree, 59 leaves, max depth = 12, train loss: 0.29061, val loss: 0.27716, in 0.016s 1 tree, 69 leaves, max depth = 9, train loss: 0.28877, val loss: 0.27526, in 0.016s 1 tree, 61 leaves, max depth = 13, train loss: 0.28654, val loss: 0.27323, in 0.031s 1 tree, 33 leaves, max depth = 11, train loss: 0.28451, val loss: 0.27106, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.28245, val loss: 0.26922, in 0.031s 1 tree, 35 leaves, max depth = 15, train loss: 0.28060, val loss: 0.26726, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.27870, val loss: 0.26559, in 0.016s 1 tree, 39 leaves, max depth = 15, train loss: 0.27669, val loss: 0.26355, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.27503, val loss: 0.26175, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.27298, val loss: 0.25961, in 0.031s 1 tree, 41 leaves, max depth = 13, train loss: 0.27118, val loss: 0.25775, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.26931, val loss: 0.25578, in 0.016s 1 tree, 70 leaves, max depth = 15, train loss: 0.26769, val loss: 0.25434, in 0.031s 1 tree, 71 leaves, max depth = 15, train loss: 0.26617, val loss: 0.25301, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.26478, val loss: 0.25151, in 0.016s 1 tree, 69 leaves, max depth = 15, train loss: 0.26337, val loss: 0.25026, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.26209, val loss: 0.24891, in 0.016s 1 tree, 52 leaves, max depth = 15, train loss: 0.26042, val loss: 0.24717, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.25915, val loss: 0.24592, in 0.031s 1 tree, 64 leaves, max depth = 15, train loss: 0.25791, val loss: 0.24481, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.25673, val loss: 0.24366, in 0.031s 1 tree, 56 leaves, max depth = 14, train loss: 0.25523, val loss: 0.24212, in 0.016s 1 tree, 71 leaves, max depth = 15, train loss: 0.25408, val loss: 0.24112, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.25305, val loss: 0.24014, in 0.031s 1 tree, 38 leaves, max depth = 11, train loss: 0.25207, val loss: 0.23907, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.25105, val loss: 0.23819, in 0.016s 1 tree, 91 leaves, max depth = 13, train loss: 0.25006, val loss: 0.23747, in 0.031s 1 tree, 52 leaves, max depth = 13, train loss: 0.24869, val loss: 0.23606, in 0.016s 1 tree, 69 leaves, max depth = 13, train loss: 0.24773, val loss: 0.23520, in 0.016s 1 tree, 92 leaves, max depth = 13, train loss: 0.24680, val loss: 0.23454, in 0.031s Fit 85 trees in 2.049 s, (3880 total leaves) Time spent computing histograms: 0.643s Time spent finding best splits: 0.138s Time spent applying splits: 0.099s Time spent predicting: 0.000s Trial 44, Fold 4: Log loss = 0.24959408851622875, Average precision = 0.9669935233028454, ROC-AUC = 0.9628425502693191, Elapsed Time = 2.0559398000004876 seconds Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 44, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 24 leaves, max depth = 7, train loss: 0.67203, val loss: 0.67121, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.65215, val loss: 0.65058, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.63366, val loss: 0.63152, in 0.000s 1 tree, 30 leaves, max depth = 7, train loss: 0.61643, val loss: 0.61361, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.59999, val loss: 0.59658, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.58547, val loss: 0.58147, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.57106, val loss: 0.56656, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55842, val loss: 0.55332, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.54642, val loss: 0.54085, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.53459, val loss: 0.52842, in 0.031s 1 tree, 39 leaves, max depth = 12, train loss: 0.52314, val loss: 0.51659, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.51321, val loss: 0.50617, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.50364, val loss: 0.49622, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.49477, val loss: 0.48718, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.48636, val loss: 0.47846, in 0.031s 1 tree, 34 leaves, max depth = 7, train loss: 0.47785, val loss: 0.46962, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.46982, val loss: 0.46128, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.46216, val loss: 0.45333, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45491, val loss: 0.44580, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.44781, val loss: 0.43852, in 0.031s 1 tree, 39 leaves, max depth = 12, train loss: 0.44113, val loss: 0.43165, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.43474, val loss: 0.42512, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.42896, val loss: 0.41897, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.42021, val loss: 0.41023, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.41177, val loss: 0.40187, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.40709, val loss: 0.39711, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.40202, val loss: 0.39197, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.39447, val loss: 0.38453, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.38977, val loss: 0.37975, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.38572, val loss: 0.37561, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.37917, val loss: 0.36918, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.37501, val loss: 0.36499, in 0.031s 1 tree, 26 leaves, max depth = 11, train loss: 0.36909, val loss: 0.35903, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.36353, val loss: 0.35338, in 0.016s 1 tree, 42 leaves, max depth = 15, train loss: 0.35927, val loss: 0.34917, in 0.016s 1 tree, 43 leaves, max depth = 15, train loss: 0.35521, val loss: 0.34516, in 0.016s 1 tree, 63 leaves, max depth = 15, train loss: 0.34999, val loss: 0.34050, in 0.031s 1 tree, 22 leaves, max depth = 7, train loss: 0.34520, val loss: 0.33565, in 0.016s 1 tree, 62 leaves, max depth = 15, train loss: 0.34052, val loss: 0.33149, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.33621, val loss: 0.32712, in 0.016s 1 tree, 43 leaves, max depth = 15, train loss: 0.33270, val loss: 0.32365, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.32873, val loss: 0.31963, in 0.016s 1 tree, 43 leaves, max depth = 15, train loss: 0.32540, val loss: 0.31639, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.32226, val loss: 0.31335, in 0.016s 1 tree, 62 leaves, max depth = 15, train loss: 0.31836, val loss: 0.30997, in 0.016s 1 tree, 63 leaves, max depth = 15, train loss: 0.31469, val loss: 0.30672, in 0.031s 1 tree, 27 leaves, max depth = 12, train loss: 0.31137, val loss: 0.30337, in 0.016s 1 tree, 64 leaves, max depth = 15, train loss: 0.30801, val loss: 0.30049, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.30568, val loss: 0.29821, in 0.031s 1 tree, 63 leaves, max depth = 15, train loss: 0.30256, val loss: 0.29548, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.29974, val loss: 0.29256, in 0.016s 1 tree, 65 leaves, max depth = 14, train loss: 0.29689, val loss: 0.29009, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.29420, val loss: 0.28738, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.29169, val loss: 0.28476, in 0.016s 1 tree, 34 leaves, max depth = 15, train loss: 0.28932, val loss: 0.28230, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.28749, val loss: 0.28057, in 0.031s 1 tree, 63 leaves, max depth = 12, train loss: 0.28505, val loss: 0.27849, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.28274, val loss: 0.27658, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.28068, val loss: 0.27443, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.27903, val loss: 0.27288, in 0.031s 1 tree, 52 leaves, max depth = 14, train loss: 0.27681, val loss: 0.27064, in 0.016s 1 tree, 65 leaves, max depth = 15, train loss: 0.27476, val loss: 0.26892, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.27295, val loss: 0.26704, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.27107, val loss: 0.26554, in 0.016s 1 tree, 53 leaves, max depth = 16, train loss: 0.26907, val loss: 0.26357, in 0.031s 1 tree, 37 leaves, max depth = 12, train loss: 0.26748, val loss: 0.26190, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.26578, val loss: 0.26050, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.26429, val loss: 0.25895, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.26272, val loss: 0.25767, in 0.031s 1 tree, 44 leaves, max depth = 13, train loss: 0.26130, val loss: 0.25624, in 0.016s 1 tree, 45 leaves, max depth = 14, train loss: 0.25961, val loss: 0.25467, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.25816, val loss: 0.25351, in 0.031s 1 tree, 46 leaves, max depth = 13, train loss: 0.25684, val loss: 0.25220, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.25529, val loss: 0.25075, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.25344, val loss: 0.24885, in 0.016s 1 tree, 63 leaves, max depth = 18, train loss: 0.25214, val loss: 0.24778, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.25100, val loss: 0.24657, in 0.031s 1 tree, 55 leaves, max depth = 14, train loss: 0.24945, val loss: 0.24504, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.24828, val loss: 0.24413, in 0.031s 1 tree, 33 leaves, max depth = 17, train loss: 0.24725, val loss: 0.24305, in 0.016s 1 tree, 42 leaves, max depth = 13, train loss: 0.24624, val loss: 0.24204, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.24519, val loss: 0.24119, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.24418, val loss: 0.24057, in 0.031s 1 tree, 57 leaves, max depth = 14, train loss: 0.24280, val loss: 0.23921, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.24161, val loss: 0.23814, in 0.031s Fit 85 trees in 2.018 s, (3727 total leaves) Time spent computing histograms: 0.640s Time spent finding best splits: 0.129s Time spent applying splits: 0.093s Time spent predicting: 0.031s Trial 44, Fold 5: Log loss = 0.25348226199119334, Average precision = 0.964379013424272, ROC-AUC = 0.9602610779778162, Elapsed Time = 2.019451999998637 seconds
Optimization Progress: 45%|####5 | 45/100 [08:50<11:33, 12.62s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 45, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 47 leaves, max depth = 11, train loss: 0.65211, val loss: 0.65237, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.61671, val loss: 0.61729, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.58596, val loss: 0.58668, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.55998, val loss: 0.56050, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.53547, val loss: 0.53585, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.51513, val loss: 0.51539, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.49554, val loss: 0.49563, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.47806, val loss: 0.47795, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.46248, val loss: 0.46226, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.44852, val loss: 0.44814, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.43593, val loss: 0.43551, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.41817, val loss: 0.41837, in 0.016s 1 tree, 57 leaves, max depth = 9, train loss: 0.40785, val loss: 0.40788, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.39293, val loss: 0.39359, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.38415, val loss: 0.38484, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.37156, val loss: 0.37281, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.36457, val loss: 0.36579, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.35444, val loss: 0.35578, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.34694, val loss: 0.34834, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.33832, val loss: 0.33980, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.33177, val loss: 0.33345, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.32408, val loss: 0.32670, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.31820, val loss: 0.32078, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.31169, val loss: 0.31436, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.30558, val loss: 0.30909, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.30023, val loss: 0.30389, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.29511, val loss: 0.29956, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.29067, val loss: 0.29520, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.28628, val loss: 0.29099, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.28233, val loss: 0.28732, in 0.016s Fit 30 trees in 0.721 s, (1383 total leaves) Time spent computing histograms: 0.161s Time spent finding best splits: 0.028s Time spent applying splits: 0.023s Time spent predicting: 0.000s Trial 45, Fold 1: Log loss = 0.287727311305024, Average precision = 0.9614434610463919, ROC-AUC = 0.9557613168724279, Elapsed Time = 0.7307570999983 seconds Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 45, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 10, train loss: 0.65176, val loss: 0.65098, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.61607, val loss: 0.61459, in 0.016s 1 tree, 51 leaves, max depth = 9, train loss: 0.58506, val loss: 0.58286, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.55912, val loss: 0.55660, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.53470, val loss: 0.53154, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.51431, val loss: 0.51097, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.49489, val loss: 0.49101, in 0.016s 1 tree, 49 leaves, max depth = 9, train loss: 0.47748, val loss: 0.47333, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.46223, val loss: 0.45771, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.44269, val loss: 0.43837, in 0.031s 1 tree, 58 leaves, max depth = 9, train loss: 0.43023, val loss: 0.42573, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.41857, val loss: 0.41395, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.40804, val loss: 0.40331, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.39323, val loss: 0.38871, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.38023, val loss: 0.37595, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.37220, val loss: 0.36777, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.36535, val loss: 0.36099, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.35530, val loss: 0.35114, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.34647, val loss: 0.34254, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.33927, val loss: 0.33561, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.33150, val loss: 0.32845, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.32454, val loss: 0.32161, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.31854, val loss: 0.31586, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.31238, val loss: 0.31023, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.30687, val loss: 0.30480, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.30172, val loss: 0.29996, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.29679, val loss: 0.29547, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.29240, val loss: 0.29119, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.28808, val loss: 0.28710, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.28412, val loss: 0.28351, in 0.000s Fit 30 trees in 0.799 s, (1390 total leaves) Time spent computing histograms: 0.195s Time spent finding best splits: 0.032s Time spent applying splits: 0.027s Time spent predicting: 0.000s Trial 45, Fold 2: Log loss = 0.28666063128091607, Average precision = 0.9612289400368368, ROC-AUC = 0.9578255725975062, Elapsed Time = 0.8044456999996328 seconds Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 45, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.159 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 10, train loss: 0.65237, val loss: 0.65226, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.61727, val loss: 0.61706, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.58671, val loss: 0.58640, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.56054, val loss: 0.56038, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.53621, val loss: 0.53613, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.51577, val loss: 0.51584, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.49645, val loss: 0.49658, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.47921, val loss: 0.47937, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.46380, val loss: 0.46392, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.44995, val loss: 0.45018, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.43753, val loss: 0.43779, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.41978, val loss: 0.42138, in 0.000s 1 tree, 60 leaves, max depth = 11, train loss: 0.40953, val loss: 0.41135, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.39462, val loss: 0.39768, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.38592, val loss: 0.38897, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.37333, val loss: 0.37751, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.36638, val loss: 0.37069, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.35618, val loss: 0.36137, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.34717, val loss: 0.35318, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.34005, val loss: 0.34571, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.33202, val loss: 0.33894, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.32498, val loss: 0.33248, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.31903, val loss: 0.32624, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.31263, val loss: 0.32101, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.30694, val loss: 0.31633, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.30180, val loss: 0.31157, in 0.000s 1 tree, 49 leaves, max depth = 12, train loss: 0.29699, val loss: 0.30647, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.29239, val loss: 0.30282, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.28825, val loss: 0.29915, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.28417, val loss: 0.29480, in 0.016s Fit 30 trees in 0.815 s, (1345 total leaves) Time spent computing histograms: 0.189s Time spent finding best splits: 0.032s Time spent applying splits: 0.027s Time spent predicting: 0.000s Trial 45, Fold 3: Log loss = 0.2838568494431637, Average precision = 0.9616779725832294, ROC-AUC = 0.957759032706995, Elapsed Time = 0.8189179000000877 seconds Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 45, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 10, train loss: 0.65248, val loss: 0.65076, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.61743, val loss: 0.61438, in 0.031s 1 tree, 47 leaves, max depth = 10, train loss: 0.58690, val loss: 0.58236, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.56104, val loss: 0.55526, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.53656, val loss: 0.52966, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.51633, val loss: 0.50842, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.49689, val loss: 0.48814, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.47959, val loss: 0.46988, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.46411, val loss: 0.45344, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.45025, val loss: 0.43888, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.43772, val loss: 0.42558, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.42022, val loss: 0.40783, in 0.031s 1 tree, 60 leaves, max depth = 11, train loss: 0.40997, val loss: 0.39678, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.39528, val loss: 0.38190, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.38658, val loss: 0.37264, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.37417, val loss: 0.36009, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.36726, val loss: 0.35272, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.35719, val loss: 0.34231, in 0.000s 1 tree, 26 leaves, max depth = 12, train loss: 0.34831, val loss: 0.33310, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.34113, val loss: 0.32580, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.33463, val loss: 0.31921, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.32713, val loss: 0.31218, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.32046, val loss: 0.30596, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.31425, val loss: 0.29951, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.30873, val loss: 0.29376, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.30360, val loss: 0.28860, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.29877, val loss: 0.28358, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.29401, val loss: 0.27922, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.28977, val loss: 0.27537, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.28579, val loss: 0.27122, in 0.016s Fit 30 trees in 0.830 s, (1371 total leaves) Time spent computing histograms: 0.194s Time spent finding best splits: 0.033s Time spent applying splits: 0.027s Time spent predicting: 0.000s Trial 45, Fold 4: Log loss = 0.2852568290057377, Average precision = 0.9622366021799464, ROC-AUC = 0.9579074926500568, Elapsed Time = 0.8325492999993003 seconds Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 45, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 11, train loss: 0.65224, val loss: 0.65067, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.61645, val loss: 0.61336, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.58518, val loss: 0.58074, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.55912, val loss: 0.55340, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.53470, val loss: 0.52790, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.51432, val loss: 0.50646, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.49490, val loss: 0.48611, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.47758, val loss: 0.46801, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.46203, val loss: 0.45182, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.44234, val loss: 0.43204, in 0.016s 1 tree, 57 leaves, max depth = 9, train loss: 0.42975, val loss: 0.41885, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.41818, val loss: 0.40677, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.40776, val loss: 0.39574, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.39296, val loss: 0.38104, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.37986, val loss: 0.36802, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.37172, val loss: 0.35953, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.36482, val loss: 0.35243, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.35463, val loss: 0.34207, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.34563, val loss: 0.33289, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.33841, val loss: 0.32569, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.33066, val loss: 0.31870, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.32361, val loss: 0.31147, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.31758, val loss: 0.30555, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.31142, val loss: 0.30024, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.30584, val loss: 0.29453, in 0.000s 1 tree, 50 leaves, max depth = 11, train loss: 0.30076, val loss: 0.28957, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.29582, val loss: 0.28537, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.29139, val loss: 0.28069, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.28699, val loss: 0.27636, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.28302, val loss: 0.27291, in 0.016s Fit 30 trees in 0.830 s, (1348 total leaves) Time spent computing histograms: 0.189s Time spent finding best splits: 0.032s Time spent applying splits: 0.027s Time spent predicting: 0.000s Trial 45, Fold 5: Log loss = 0.2918554722452596, Average precision = 0.9588748949249177, ROC-AUC = 0.9535981271946937, Elapsed Time = 0.8407050000005256 seconds
Optimization Progress: 46%|####6 | 46/100 [09:01<10:47, 11.99s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 46, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.190 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 11, train loss: 0.68146, val loss: 0.68153, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.67023, val loss: 0.67037, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.65945, val loss: 0.65964, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.64907, val loss: 0.64933, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.63909, val loss: 0.63941, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.62948, val loss: 0.62986, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.62022, val loss: 0.62065, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.61129, val loss: 0.61177, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.60268, val loss: 0.60319, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.59438, val loss: 0.59493, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.58636, val loss: 0.58695, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.57862, val loss: 0.57925, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.57114, val loss: 0.57180, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.56392, val loss: 0.56458, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.55694, val loss: 0.55763, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.55020, val loss: 0.55090, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.54353, val loss: 0.54416, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.53704, val loss: 0.53761, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.53075, val loss: 0.53127, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52472, val loss: 0.52515, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51883, val loss: 0.51921, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51316, val loss: 0.51347, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.50764, val loss: 0.50789, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50232, val loss: 0.50250, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.49713, val loss: 0.49726, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49211, val loss: 0.49218, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.48724, val loss: 0.48726, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.48252, val loss: 0.48249, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47794, val loss: 0.47787, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47350, val loss: 0.47339, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46919, val loss: 0.46902, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.46501, val loss: 0.46480, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46095, val loss: 0.46069, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.45700, val loss: 0.45670, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.45317, val loss: 0.45282, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44945, val loss: 0.44905, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.44584, val loss: 0.44541, in 0.016s Fit 37 trees in 1.035 s, (1517 total leaves) Time spent computing histograms: 0.268s Time spent finding best splits: 0.052s Time spent applying splits: 0.035s Time spent predicting: 0.000s Trial 46, Fold 1: Log loss = 0.44786320029330523, Average precision = 0.9199227180329279, ROC-AUC = 0.9306717593190043, Elapsed Time = 1.0404746000003797 seconds Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 46, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 11, train loss: 0.68142, val loss: 0.68126, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.67015, val loss: 0.66985, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.65932, val loss: 0.65887, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.64890, val loss: 0.64833, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.63889, val loss: 0.63818, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.62924, val loss: 0.62841, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.61995, val loss: 0.61899, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.61099, val loss: 0.60991, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.60235, val loss: 0.60117, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.59390, val loss: 0.59255, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.58572, val loss: 0.58422, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.57781, val loss: 0.57618, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.57016, val loss: 0.56842, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.56278, val loss: 0.56092, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.55568, val loss: 0.55367, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54878, val loss: 0.54665, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54210, val loss: 0.53988, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53568, val loss: 0.53332, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52943, val loss: 0.52698, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52338, val loss: 0.52083, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51755, val loss: 0.51492, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.51190, val loss: 0.50920, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50640, val loss: 0.50361, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50109, val loss: 0.49820, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49592, val loss: 0.49296, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49094, val loss: 0.48791, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.48608, val loss: 0.48297, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.48137, val loss: 0.47819, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47683, val loss: 0.47358, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47239, val loss: 0.46906, in 0.031s 1 tree, 41 leaves, max depth = 9, train loss: 0.46808, val loss: 0.46468, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46393, val loss: 0.46047, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45987, val loss: 0.45635, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45595, val loss: 0.45238, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45213, val loss: 0.44849, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.44841, val loss: 0.44471, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44483, val loss: 0.44107, in 0.016s Fit 37 trees in 1.002 s, (1517 total leaves) Time spent computing histograms: 0.273s Time spent finding best splits: 0.045s Time spent applying splits: 0.030s Time spent predicting: 0.000s Trial 46, Fold 2: Log loss = 0.4479834310936089, Average precision = 0.9135194459769991, ROC-AUC = 0.9308501275136641, Elapsed Time = 1.01119479999943 seconds Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 46, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 9, train loss: 0.68137, val loss: 0.68143, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.67007, val loss: 0.67017, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.65919, val loss: 0.65935, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.64875, val loss: 0.64892, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.63868, val loss: 0.63890, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.62898, val loss: 0.62924, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.61966, val loss: 0.61993, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.61065, val loss: 0.61097, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.60196, val loss: 0.60227, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.59357, val loss: 0.59393, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.58546, val loss: 0.58589, in 0.031s 1 tree, 41 leaves, max depth = 8, train loss: 0.57765, val loss: 0.57806, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.57008, val loss: 0.57057, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.56277, val loss: 0.56332, in 0.063s 1 tree, 41 leaves, max depth = 8, train loss: 0.55571, val loss: 0.55631, in 0.047s 1 tree, 41 leaves, max depth = 8, train loss: 0.54888, val loss: 0.54954, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.54228, val loss: 0.54292, in 0.031s 1 tree, 41 leaves, max depth = 9, train loss: 0.53588, val loss: 0.53658, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52970, val loss: 0.53044, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.52376, val loss: 0.52449, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.51796, val loss: 0.51867, in 0.031s 1 tree, 41 leaves, max depth = 9, train loss: 0.51239, val loss: 0.51309, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.50700, val loss: 0.50770, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.50172, val loss: 0.50240, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.49666, val loss: 0.49736, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.49169, val loss: 0.49237, in 0.031s 1 tree, 41 leaves, max depth = 9, train loss: 0.48693, val loss: 0.48761, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.48226, val loss: 0.48293, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47778, val loss: 0.47844, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47343, val loss: 0.47412, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.46916, val loss: 0.46985, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.46506, val loss: 0.46574, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.46104, val loss: 0.46171, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45717, val loss: 0.45788, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45335, val loss: 0.45412, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44785, val loss: 0.44901, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44429, val loss: 0.44547, in 0.016s Fit 37 trees in 1.079 s, (1517 total leaves) Time spent computing histograms: 0.312s Time spent finding best splits: 0.063s Time spent applying splits: 0.050s Time spent predicting: 0.016s Trial 46, Fold 3: Log loss = 0.441979098882662, Average precision = 0.9501071518342736, ROC-AUC = 0.9485726806059848, Elapsed Time = 1.092893199998798 seconds Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 46, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 11, train loss: 0.68149, val loss: 0.68109, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.67029, val loss: 0.66950, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.65952, val loss: 0.65836, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.64917, val loss: 0.64763, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.63921, val loss: 0.63731, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.62962, val loss: 0.62735, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.62039, val loss: 0.61776, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.61149, val loss: 0.60852, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.60291, val loss: 0.59959, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.59463, val loss: 0.59099, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.58648, val loss: 0.58249, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.57861, val loss: 0.57428, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.57102, val loss: 0.56636, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.56368, val loss: 0.55869, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.55658, val loss: 0.55127, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54973, val loss: 0.54410, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54309, val loss: 0.53718, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53668, val loss: 0.53046, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53047, val loss: 0.52397, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52446, val loss: 0.51768, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51864, val loss: 0.51159, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51300, val loss: 0.50569, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50753, val loss: 0.49999, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50223, val loss: 0.49446, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49710, val loss: 0.48908, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49212, val loss: 0.48386, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.48729, val loss: 0.47881, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48261, val loss: 0.47390, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.47807, val loss: 0.46914, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.47367, val loss: 0.46451, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46940, val loss: 0.46003, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46525, val loss: 0.45566, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46123, val loss: 0.45140, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45732, val loss: 0.44727, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.45353, val loss: 0.44325, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.44984, val loss: 0.43933, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.44627, val loss: 0.43554, in 0.016s Fit 37 trees in 1.064 s, (1517 total leaves) Time spent computing histograms: 0.276s Time spent finding best splits: 0.044s Time spent applying splits: 0.032s Time spent predicting: 0.000s Trial 46, Fold 4: Log loss = 0.44626257917322193, Average precision = 0.9205991301635942, ROC-AUC = 0.9328650054483764, Elapsed Time = 1.0834144000000379 seconds Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 46, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 12, train loss: 0.68147, val loss: 0.68108, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.67026, val loss: 0.66948, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.65948, val loss: 0.65835, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.64896, val loss: 0.64744, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.63883, val loss: 0.63695, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.62908, val loss: 0.62684, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.61969, val loss: 0.61710, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.61064, val loss: 0.60771, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.60191, val loss: 0.59865, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.59349, val loss: 0.58991, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.58536, val loss: 0.58147, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.57752, val loss: 0.57333, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.56994, val loss: 0.56546, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.56259, val loss: 0.55781, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.55552, val loss: 0.55045, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.54865, val loss: 0.54330, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.54201, val loss: 0.53638, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.53561, val loss: 0.52973, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.52939, val loss: 0.52325, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52340, val loss: 0.51701, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.51757, val loss: 0.51092, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.51191, val loss: 0.50504, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.50645, val loss: 0.49938, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.50115, val loss: 0.49384, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49602, val loss: 0.48851, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49102, val loss: 0.48329, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48621, val loss: 0.47827, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.48153, val loss: 0.47341, in 0.031s [29/37] 1 tree, 41 leaves, max depth = 11, train loss: 0.47697, val loss: 0.46865, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.47254, val loss: 0.46402, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46828, val loss: 0.45957, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.46411, val loss: 0.45521, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.46008, val loss: 0.45101, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.45615, val loss: 0.44690, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.45235, val loss: 0.44297, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.44864, val loss: 0.43908, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.44506, val loss: 0.43536, in 0.031s Fit 37 trees in 0.985 s, (1517 total leaves) Time spent computing histograms: 0.252s Time spent finding best splits: 0.045s Time spent applying splits: 0.035s Time spent predicting: 0.000s Trial 46, Fold 5: Log loss = 0.45017790444983935, Average precision = 0.9146362790173244, ROC-AUC = 0.9284197462051541, Elapsed Time = 0.9993957999995473 seconds
Optimization Progress: 47%|####6 | 47/100 [09:14<10:56, 12.39s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 47, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.127 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 17, train loss: 0.67073, val loss: 0.67007, in 0.000s 1 tree, 65 leaves, max depth = 17, train loss: 0.65039, val loss: 0.64911, in 0.016s 1 tree, 65 leaves, max depth = 17, train loss: 0.63191, val loss: 0.63002, in 0.016s 1 tree, 65 leaves, max depth = 17, train loss: 0.61507, val loss: 0.61262, in 0.000s 1 tree, 65 leaves, max depth = 17, train loss: 0.59970, val loss: 0.59670, in 0.016s 1 tree, 65 leaves, max depth = 17, train loss: 0.58565, val loss: 0.58213, in 0.000s 1 tree, 65 leaves, max depth = 17, train loss: 0.57279, val loss: 0.56878, in 0.016s 1 tree, 65 leaves, max depth = 17, train loss: 0.56100, val loss: 0.55651, in 0.000s 1 tree, 65 leaves, max depth = 17, train loss: 0.55018, val loss: 0.54524, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.53934, val loss: 0.53514, in 0.016s 1 tree, 67 leaves, max depth = 18, train loss: 0.52979, val loss: 0.52515, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.52013, val loss: 0.51619, in 0.016s 1 tree, 67 leaves, max depth = 17, train loss: 0.51166, val loss: 0.50729, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.50302, val loss: 0.49931, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.49507, val loss: 0.49199, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.48782, val loss: 0.48436, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.48066, val loss: 0.47780, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.47418, val loss: 0.47094, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.46771, val loss: 0.46504, in 0.000s 1 tree, 68 leaves, max depth = 14, train loss: 0.46189, val loss: 0.45887, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.45602, val loss: 0.45354, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.45075, val loss: 0.44796, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.44542, val loss: 0.44315, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.44050, val loss: 0.43872, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.43586, val loss: 0.43375, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.43137, val loss: 0.42973, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.42717, val loss: 0.42521, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.42306, val loss: 0.42157, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.41924, val loss: 0.41747, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.41548, val loss: 0.41416, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41218, val loss: 0.41052, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.40876, val loss: 0.40686, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.40533, val loss: 0.40388, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40234, val loss: 0.40057, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.39918, val loss: 0.39786, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39643, val loss: 0.39481, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.39342, val loss: 0.39161, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.39052, val loss: 0.38916, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38801, val loss: 0.38635, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.38532, val loss: 0.38412, in 0.000s 1 tree, 68 leaves, max depth = 13, train loss: 0.38258, val loss: 0.38119, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38028, val loss: 0.37861, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.37779, val loss: 0.37657, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37566, val loss: 0.37417, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.37320, val loss: 0.37158, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37124, val loss: 0.36936, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.36898, val loss: 0.36700, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.36665, val loss: 0.36514, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.36483, val loss: 0.36307, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.36276, val loss: 0.36092, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.36059, val loss: 0.35921, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35890, val loss: 0.35728, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.35700, val loss: 0.35531, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.35497, val loss: 0.35374, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35339, val loss: 0.35192, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.35165, val loss: 0.35012, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.34974, val loss: 0.34868, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34827, val loss: 0.34697, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.34666, val loss: 0.34531, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34528, val loss: 0.34371, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.34370, val loss: 0.34190, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.34189, val loss: 0.34056, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34059, val loss: 0.33905, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.33918, val loss: 0.33760, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.33748, val loss: 0.33635, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.33605, val loss: 0.33471, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33483, val loss: 0.33327, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.33351, val loss: 0.33176, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33190, val loss: 0.33060, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33075, val loss: 0.32923, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.32955, val loss: 0.32802, in 0.016s Fit 71 trees in 0.861 s, (4031 total leaves) Time spent computing histograms: 0.294s Time spent finding best splits: 0.060s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 47, Fold 1: Log loss = 0.3353796802663027, Average precision = 0.9461633419759852, ROC-AUC = 0.9449084380063377, Elapsed Time = 0.8692676999999094 seconds Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 47, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 68 leaves, max depth = 15, train loss: 0.67100, val loss: 0.66996, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.65092, val loss: 0.64891, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.63267, val loss: 0.62973, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.61604, val loss: 0.61223, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.60087, val loss: 0.59624, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.58701, val loss: 0.58159, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.57432, val loss: 0.56816, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.56270, val loss: 0.55583, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.55133, val loss: 0.54490, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.54103, val loss: 0.53402, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.53092, val loss: 0.52431, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.52179, val loss: 0.51466, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.51276, val loss: 0.50600, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.50464, val loss: 0.49741, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.49653, val loss: 0.48968, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.48929, val loss: 0.48201, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.48198, val loss: 0.47507, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.47524, val loss: 0.46869, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.46894, val loss: 0.46199, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.46284, val loss: 0.45624, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.45718, val loss: 0.45020, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.45164, val loss: 0.44500, in 0.016s 1 tree, 68 leaves, max depth = 17, train loss: 0.44654, val loss: 0.43955, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.44149, val loss: 0.43483, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.43688, val loss: 0.42991, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.43226, val loss: 0.42561, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.42800, val loss: 0.42166, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.42390, val loss: 0.41727, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.41999, val loss: 0.41367, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.41627, val loss: 0.40967, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.41267, val loss: 0.40637, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40946, val loss: 0.40305, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.40614, val loss: 0.39948, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.40284, val loss: 0.39648, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39994, val loss: 0.39346, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.39689, val loss: 0.39071, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39422, val loss: 0.38793, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.39129, val loss: 0.38479, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.38848, val loss: 0.38226, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38604, val loss: 0.37971, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.38343, val loss: 0.37739, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.38073, val loss: 0.37448, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37849, val loss: 0.37214, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.37607, val loss: 0.37000, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37400, val loss: 0.36783, in 0.000s 1 tree, 67 leaves, max depth = 11, train loss: 0.37155, val loss: 0.36519, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36964, val loss: 0.36319, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.36740, val loss: 0.36077, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.36513, val loss: 0.35879, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36336, val loss: 0.35692, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.36130, val loss: 0.35471, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.35918, val loss: 0.35288, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35753, val loss: 0.35114, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.35558, val loss: 0.34897, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.35359, val loss: 0.34726, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35205, val loss: 0.34564, in 0.000s 1 tree, 67 leaves, max depth = 11, train loss: 0.35031, val loss: 0.34376, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.34844, val loss: 0.34217, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34700, val loss: 0.34065, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.34528, val loss: 0.33878, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.34375, val loss: 0.33714, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.34199, val loss: 0.33564, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34063, val loss: 0.33420, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.33911, val loss: 0.33254, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33784, val loss: 0.33120, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.33616, val loss: 0.32979, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.33474, val loss: 0.32824, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33354, val loss: 0.32697, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.33225, val loss: 0.32560, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.33066, val loss: 0.32427, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32953, val loss: 0.32307, in 0.000s Fit 71 trees in 0.955 s, (4072 total leaves) Time spent computing histograms: 0.323s Time spent finding best splits: 0.067s Time spent applying splits: 0.067s Time spent predicting: 0.000s Trial 47, Fold 2: Log loss = 0.33349052582425665, Average precision = 0.9443356104667668, ROC-AUC = 0.9468951037363036, Elapsed Time = 0.9595272000005934 seconds Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 47, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 14, train loss: 0.67121, val loss: 0.67047, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.65104, val loss: 0.64967, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.63295, val loss: 0.63092, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.61621, val loss: 0.61363, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.60094, val loss: 0.59784, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.58698, val loss: 0.58338, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.57421, val loss: 0.57013, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.56267, val loss: 0.55807, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.55122, val loss: 0.54750, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.54088, val loss: 0.53672, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.53071, val loss: 0.52735, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.52170, val loss: 0.51786, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.51261, val loss: 0.50951, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.50461, val loss: 0.50105, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.49645, val loss: 0.49357, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.48932, val loss: 0.48600, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.48196, val loss: 0.47930, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47518, val loss: 0.47313, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.46898, val loss: 0.46651, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.46284, val loss: 0.46095, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.45728, val loss: 0.45497, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.45169, val loss: 0.44994, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.44669, val loss: 0.44454, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.44159, val loss: 0.43997, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.43708, val loss: 0.43510, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.43242, val loss: 0.43094, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.42812, val loss: 0.42712, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.42411, val loss: 0.42276, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.42016, val loss: 0.41927, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.41652, val loss: 0.41530, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.41289, val loss: 0.41211, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40959, val loss: 0.40904, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.40625, val loss: 0.40612, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40323, val loss: 0.40330, in 0.000s 1 tree, 66 leaves, max depth = 12, train loss: 0.40002, val loss: 0.39980, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39726, val loss: 0.39723, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.39420, val loss: 0.39459, in 0.000s 1 tree, 66 leaves, max depth = 12, train loss: 0.39131, val loss: 0.39142, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38879, val loss: 0.38907, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.38597, val loss: 0.38665, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38364, val loss: 0.38448, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.38102, val loss: 0.38226, in 0.000s 1 tree, 66 leaves, max depth = 12, train loss: 0.37843, val loss: 0.37942, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37629, val loss: 0.37742, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.37386, val loss: 0.37538, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.37141, val loss: 0.37270, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36943, val loss: 0.37085, in 0.000s 1 tree, 66 leaves, max depth = 11, train loss: 0.36720, val loss: 0.36840, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.36493, val loss: 0.36650, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36309, val loss: 0.36478, in 0.000s 1 tree, 67 leaves, max depth = 11, train loss: 0.36105, val loss: 0.36253, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.35892, val loss: 0.36078, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35722, val loss: 0.35918, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.35538, val loss: 0.35717, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35380, val loss: 0.35569, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.35180, val loss: 0.35405, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.35011, val loss: 0.35219, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34863, val loss: 0.35081, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.34675, val loss: 0.34928, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.34514, val loss: 0.34751, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34375, val loss: 0.34621, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.34219, val loss: 0.34436, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.34041, val loss: 0.34293, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33911, val loss: 0.34170, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33766, val loss: 0.33997, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.33598, val loss: 0.33863, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33475, val loss: 0.33747, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.33343, val loss: 0.33603, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33228, val loss: 0.33495, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.33098, val loss: 0.33340, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.32937, val loss: 0.33213, in 0.016s Fit 71 trees in 1.002 s, (4043 total leaves) Time spent computing histograms: 0.324s Time spent finding best splits: 0.070s Time spent applying splits: 0.069s Time spent predicting: 0.016s Trial 47, Fold 3: Log loss = 0.3298393523478352, Average precision = 0.9500604398322146, ROC-AUC = 0.9502438156716203, Elapsed Time = 1.0118590999991284 seconds Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 47, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 13, train loss: 0.67094, val loss: 0.66962, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.65081, val loss: 0.64823, in 0.000s 1 tree, 65 leaves, max depth = 13, train loss: 0.63251, val loss: 0.62873, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.61584, val loss: 0.61092, in 0.000s 1 tree, 65 leaves, max depth = 13, train loss: 0.60063, val loss: 0.59461, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.58672, val loss: 0.57966, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.57400, val loss: 0.56592, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.56234, val loss: 0.55330, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.55088, val loss: 0.54203, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.54060, val loss: 0.53085, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.53042, val loss: 0.52085, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.52137, val loss: 0.51098, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.51226, val loss: 0.50206, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.50418, val loss: 0.49319, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.49601, val loss: 0.48520, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.48885, val loss: 0.47732, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.48148, val loss: 0.47012, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47468, val loss: 0.46348, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.46846, val loss: 0.45660, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.46230, val loss: 0.45059, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.45672, val loss: 0.44438, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.45111, val loss: 0.43893, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.44605, val loss: 0.43331, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.44095, val loss: 0.42835, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.43642, val loss: 0.42325, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.43175, val loss: 0.41874, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.42743, val loss: 0.41456, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.42340, val loss: 0.41000, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.41943, val loss: 0.40617, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.41577, val loss: 0.40203, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.41213, val loss: 0.39852, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40893, val loss: 0.39506, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.40557, val loss: 0.39184, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.40234, val loss: 0.38814, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39945, val loss: 0.38500, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.39635, val loss: 0.38205, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39369, val loss: 0.37916, in 0.000s 1 tree, 66 leaves, max depth = 14, train loss: 0.39081, val loss: 0.37589, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.38795, val loss: 0.37318, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38551, val loss: 0.37053, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.38285, val loss: 0.36803, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38060, val loss: 0.36558, in 0.000s 1 tree, 66 leaves, max depth = 14, train loss: 0.37803, val loss: 0.36267, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.37556, val loss: 0.36036, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37348, val loss: 0.35810, in 0.000s 1 tree, 66 leaves, max depth = 13, train loss: 0.37114, val loss: 0.35544, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36923, val loss: 0.35336, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.36691, val loss: 0.35122, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.36477, val loss: 0.34879, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36299, val loss: 0.34685, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.36082, val loss: 0.34486, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.35886, val loss: 0.34264, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35721, val loss: 0.34083, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.35537, val loss: 0.33870, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.35333, val loss: 0.33684, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35178, val loss: 0.33515, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.35012, val loss: 0.33326, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.34820, val loss: 0.33152, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34675, val loss: 0.32994, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.34507, val loss: 0.32821, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.34325, val loss: 0.32658, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34189, val loss: 0.32509, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.34032, val loss: 0.32349, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33906, val loss: 0.32210, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.33767, val loss: 0.32056, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.33593, val loss: 0.31900, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33453, val loss: 0.31759, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33333, val loss: 0.31627, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.33168, val loss: 0.31480, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.33037, val loss: 0.31349, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32924, val loss: 0.31224, in 0.000s Fit 71 trees in 1.002 s, (4034 total leaves) Time spent computing histograms: 0.330s Time spent finding best splits: 0.071s Time spent applying splits: 0.070s Time spent predicting: 0.000s Trial 47, Fold 4: Log loss = 0.3291262557638443, Average precision = 0.9527007277982728, ROC-AUC = 0.9509306351352318, Elapsed Time = 1.0164748999995936 seconds Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 47, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 67 leaves, max depth = 14, train loss: 0.67071, val loss: 0.66926, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.65037, val loss: 0.64754, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.63187, val loss: 0.62774, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.61518, val loss: 0.60975, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.59978, val loss: 0.59316, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.58570, val loss: 0.57796, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.57282, val loss: 0.56400, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.56110, val loss: 0.55116, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.55024, val loss: 0.53931, in 0.000s 1 tree, 75 leaves, max depth = 17, train loss: 0.53943, val loss: 0.52898, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.52993, val loss: 0.51860, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.52029, val loss: 0.50942, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.51144, val loss: 0.50102, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.50328, val loss: 0.49205, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.49533, val loss: 0.48455, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.48805, val loss: 0.47650, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.48089, val loss: 0.46978, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.47438, val loss: 0.46249, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.46791, val loss: 0.45645, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.46206, val loss: 0.44992, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.45619, val loss: 0.44446, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.45077, val loss: 0.43944, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.44562, val loss: 0.43367, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.44068, val loss: 0.42912, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.43603, val loss: 0.42387, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.43151, val loss: 0.41974, in 0.016s [27/71] 1 tree, 67 leaves, max depth = 13, train loss: 0.42731, val loss: 0.41496, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.42317, val loss: 0.41121, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.41936, val loss: 0.40685, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.41557, val loss: 0.40343, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.41210, val loss: 0.39944, in 0.016s [32/71] 1 tree, 75 leaves, max depth = 16, train loss: 0.40861, val loss: 0.39631, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40543, val loss: 0.39325, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.40221, val loss: 0.39039, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39930, val loss: 0.38759, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.39627, val loss: 0.38409, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.39330, val loss: 0.38149, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39065, val loss: 0.37894, in 0.000s 1 tree, 75 leaves, max depth = 17, train loss: 0.38791, val loss: 0.37655, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38546, val loss: 0.37421, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.38274, val loss: 0.37104, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.38020, val loss: 0.36885, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37796, val loss: 0.36670, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37588, val loss: 0.36471, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.37338, val loss: 0.36185, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.37101, val loss: 0.35983, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36909, val loss: 0.35800, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.36684, val loss: 0.35535, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.36462, val loss: 0.35349, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36284, val loss: 0.35179, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.36078, val loss: 0.34935, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.35871, val loss: 0.34764, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.35676, val loss: 0.34539, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35511, val loss: 0.34381, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.35317, val loss: 0.34222, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.35142, val loss: 0.34012, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34987, val loss: 0.33864, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.34815, val loss: 0.33677, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.34631, val loss: 0.33529, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34487, val loss: 0.33390, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.34333, val loss: 0.33206, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34199, val loss: 0.33077, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.34045, val loss: 0.32910, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.33869, val loss: 0.32770, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33742, val loss: 0.32649, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.33599, val loss: 0.32493, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.33433, val loss: 0.32361, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33313, val loss: 0.32247, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.33184, val loss: 0.32090, in 0.000s 1 tree, 75 leaves, max depth = 17, train loss: 0.33028, val loss: 0.31967, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.32897, val loss: 0.31826, in 0.016s Fit 71 trees in 1.001 s, (4121 total leaves) Time spent computing histograms: 0.319s Time spent finding best splits: 0.069s Time spent applying splits: 0.070s Time spent predicting: 0.000s Trial 47, Fold 5: Log loss = 0.33656045996488004, Average precision = 0.9484047206945048, ROC-AUC = 0.9457466882187912, Elapsed Time = 1.0071942000013223 seconds
Optimization Progress: 48%|####8 | 48/100 [09:26<10:32, 12.16s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 48, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 8, train loss: 0.68062, val loss: 0.68020, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.66875, val loss: 0.66793, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.65749, val loss: 0.65628, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.64681, val loss: 0.64522, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.63667, val loss: 0.63471, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.62704, val loss: 0.62471, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.61789, val loss: 0.61520, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.60918, val loss: 0.60615, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.60090, val loss: 0.59753, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.59301, val loss: 0.58931, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.58550, val loss: 0.58148, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.57835, val loss: 0.57401, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.57153, val loss: 0.56689, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.56503, val loss: 0.56009, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.55888, val loss: 0.55368, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.55301, val loss: 0.54757, in 0.000s 1 tree, 38 leaves, max depth = 9, train loss: 0.54710, val loss: 0.54203, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.54157, val loss: 0.53622, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.53634, val loss: 0.53076, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.53088, val loss: 0.52566, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.52568, val loss: 0.52081, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.52072, val loss: 0.51620, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.51596, val loss: 0.51117, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.51130, val loss: 0.50685, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.50691, val loss: 0.50221, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.50252, val loss: 0.49814, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.49840, val loss: 0.49378, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.49426, val loss: 0.48996, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.49038, val loss: 0.48584, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.48647, val loss: 0.48225, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.48282, val loss: 0.47841, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.47913, val loss: 0.47503, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.47560, val loss: 0.47180, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.47221, val loss: 0.46819, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.46887, val loss: 0.46514, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46567, val loss: 0.46173, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.46250, val loss: 0.45885, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.45946, val loss: 0.45562, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.45646, val loss: 0.45291, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.45361, val loss: 0.44985, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.45089, val loss: 0.44692, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44806, val loss: 0.44438, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44536, val loss: 0.44196, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.44279, val loss: 0.43922, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44023, val loss: 0.43693, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.43778, val loss: 0.43476, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.43538, val loss: 0.43217, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.43305, val loss: 0.43010, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.43077, val loss: 0.42766, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.42856, val loss: 0.42571, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.42641, val loss: 0.42337, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.42430, val loss: 0.42152, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.42225, val loss: 0.41931, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.42024, val loss: 0.41757, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.41830, val loss: 0.41544, in 0.016s [56/80] 1 tree, 39 leaves, max depth = 9, train loss: 0.41638, val loss: 0.41379, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.41454, val loss: 0.41176, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41273, val loss: 0.40976, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.41091, val loss: 0.40820, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40918, val loss: 0.40629, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40753, val loss: 0.40445, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40579, val loss: 0.40299, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40422, val loss: 0.40124, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40256, val loss: 0.39985, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.40088, val loss: 0.39805, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39938, val loss: 0.39638, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.39780, val loss: 0.39507, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39637, val loss: 0.39348, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.39479, val loss: 0.39179, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.39328, val loss: 0.39055, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39192, val loss: 0.38903, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.39039, val loss: 0.38742, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.38895, val loss: 0.38626, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38765, val loss: 0.38480, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38640, val loss: 0.38341, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.38502, val loss: 0.38231, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.38358, val loss: 0.38078, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38239, val loss: 0.37944, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.38107, val loss: 0.37840, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.37972, val loss: 0.37697, in 0.016s Fit 80 trees in 0.846 s, (2050 total leaves) Time spent computing histograms: 0.312s Time spent finding best splits: 0.046s Time spent applying splits: 0.039s Time spent predicting: 0.016s Trial 48, Fold 1: Log loss = 0.380684813906018, Average precision = 0.9458659296414029, ROC-AUC = 0.9424333575897487, Elapsed Time = 0.8453929999996035 seconds Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 48, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 8, train loss: 0.68065, val loss: 0.68003, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.66881, val loss: 0.66760, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.65775, val loss: 0.65597, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.64709, val loss: 0.64476, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.63697, val loss: 0.63411, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.62736, val loss: 0.62397, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.61823, val loss: 0.61434, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.60967, val loss: 0.60530, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.60140, val loss: 0.59656, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.59353, val loss: 0.58822, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.58603, val loss: 0.58028, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.57900, val loss: 0.57283, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.57219, val loss: 0.56560, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.56570, val loss: 0.55870, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.55951, val loss: 0.55212, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.55371, val loss: 0.54594, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.54783, val loss: 0.54020, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.54241, val loss: 0.53442, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.53724, val loss: 0.52889, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.53179, val loss: 0.52360, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.52660, val loss: 0.51856, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.52166, val loss: 0.51376, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.51699, val loss: 0.50876, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.51234, val loss: 0.50425, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.50796, val loss: 0.49956, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.50358, val loss: 0.49531, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.49947, val loss: 0.49089, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.49533, val loss: 0.48689, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.49146, val loss: 0.48273, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.48756, val loss: 0.47896, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.48391, val loss: 0.47504, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48022, val loss: 0.47148, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.47670, val loss: 0.46808, in 0.016s [34/80] 1 tree, 21 leaves, max depth = 8, train loss: 0.47332, val loss: 0.46443, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.46998, val loss: 0.46123, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46680, val loss: 0.45778, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.46364, val loss: 0.45475, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46063, val loss: 0.45149, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.45764, val loss: 0.44862, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.45480, val loss: 0.44554, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.45208, val loss: 0.44259, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44926, val loss: 0.43989, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44656, val loss: 0.43731, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44398, val loss: 0.43484, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.44147, val loss: 0.43211, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.43902, val loss: 0.42977, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.43664, val loss: 0.42718, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.43431, val loss: 0.42496, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.43205, val loss: 0.42250, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.42983, val loss: 0.42039, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.42770, val loss: 0.41805, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.42558, val loss: 0.41605, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.42356, val loss: 0.41383, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.42154, val loss: 0.41192, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.41962, val loss: 0.40982, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.41770, val loss: 0.40800, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.41586, val loss: 0.40627, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41408, val loss: 0.40443, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.41229, val loss: 0.40246, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41061, val loss: 0.40072, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40887, val loss: 0.39908, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40726, val loss: 0.39741, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40560, val loss: 0.39586, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.40392, val loss: 0.39402, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40241, val loss: 0.39243, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.40082, val loss: 0.39095, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39937, val loss: 0.38944, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.39776, val loss: 0.38767, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.39625, val loss: 0.38626, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39487, val loss: 0.38482, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.39342, val loss: 0.38348, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.39189, val loss: 0.38182, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39058, val loss: 0.38044, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.37913, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.38793, val loss: 0.37785, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.38651, val loss: 0.37627, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38530, val loss: 0.37501, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.38398, val loss: 0.37379, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38283, val loss: 0.37259, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.38147, val loss: 0.37110, in 0.000s Fit 80 trees in 0.876 s, (2059 total leaves) Time spent computing histograms: 0.341s Time spent finding best splits: 0.050s Time spent applying splits: 0.043s Time spent predicting: 0.000s Trial 48, Fold 2: Log loss = 0.3832620626880714, Average precision = 0.9423748606646112, ROC-AUC = 0.9432571023274772, Elapsed Time = 0.8830567999993946 seconds Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 48, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 7, train loss: 0.68072, val loss: 0.68033, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.66895, val loss: 0.66819, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.65779, val loss: 0.65666, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.64720, val loss: 0.64572, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.63715, val loss: 0.63533, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.62760, val loss: 0.62544, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.61852, val loss: 0.61605, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.60989, val loss: 0.60710, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.60168, val loss: 0.59858, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.59386, val loss: 0.59047, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.58642, val loss: 0.58274, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.57932, val loss: 0.57537, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.57257, val loss: 0.56834, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.56613, val loss: 0.56163, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.55999, val loss: 0.55523, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.55385, val loss: 0.54946, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.54818, val loss: 0.54354, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54243, val loss: 0.53814, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.53713, val loss: 0.53259, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53173, val loss: 0.52753, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.52677, val loss: 0.52232, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52170, val loss: 0.51757, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.51685, val loss: 0.51304, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.51229, val loss: 0.50824, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50773, val loss: 0.50398, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.50345, val loss: 0.49947, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49915, val loss: 0.49546, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.49514, val loss: 0.49121, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49108, val loss: 0.48744, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.48730, val loss: 0.48343, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.48346, val loss: 0.47987, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.47991, val loss: 0.47610, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47628, val loss: 0.47274, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47281, val loss: 0.46953, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.46951, val loss: 0.46601, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46623, val loss: 0.46298, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.46312, val loss: 0.45966, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46000, val loss: 0.45679, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.45707, val loss: 0.45365, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.45411, val loss: 0.45093, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.45134, val loss: 0.44796, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44854, val loss: 0.44539, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44585, val loss: 0.44293, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.44326, val loss: 0.44015, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.44071, val loss: 0.43782, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.43826, val loss: 0.43517, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.43584, val loss: 0.43296, in 0.000s 1 tree, 19 leaves, max depth = 9, train loss: 0.43351, val loss: 0.43041, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43120, val loss: 0.42831, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.42903, val loss: 0.42591, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42683, val loss: 0.42392, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.42474, val loss: 0.42166, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42265, val loss: 0.41977, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42065, val loss: 0.41797, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.41868, val loss: 0.41579, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41683, val loss: 0.41407, in 0.016s [57/80] 1 tree, 41 leaves, max depth = 10, train loss: 0.41493, val loss: 0.41236, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41316, val loss: 0.41072, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.41133, val loss: 0.40868, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.40952, val loss: 0.40707, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40785, val loss: 0.40552, in 0.000s 1 tree, 40 leaves, max depth = 10, train loss: 0.40613, val loss: 0.40398, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40453, val loss: 0.40250, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.40283, val loss: 0.40060, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.40118, val loss: 0.39915, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39967, val loss: 0.39774, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.39806, val loss: 0.39594, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.39649, val loss: 0.39456, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39505, val loss: 0.39322, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39367, val loss: 0.39194, in 0.000s 1 tree, 40 leaves, max depth = 9, train loss: 0.39216, val loss: 0.39062, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.39063, val loss: 0.38890, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.38768, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.38788, val loss: 0.38643, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38662, val loss: 0.38526, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.38525, val loss: 0.38408, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.38380, val loss: 0.38245, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38259, val loss: 0.38133, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.38128, val loss: 0.38020, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38012, val loss: 0.37913, in 0.000s Fit 80 trees in 0.938 s, (2110 total leaves) Time spent computing histograms: 0.345s Time spent finding best splits: 0.051s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 48, Fold 3: Log loss = 0.3766158102262939, Average precision = 0.9477626147986153, ROC-AUC = 0.9475018298966589, Elapsed Time = 0.9445581999989372 seconds Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 48, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 9, train loss: 0.68079, val loss: 0.68008, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.66909, val loss: 0.66768, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.65800, val loss: 0.65590, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.64747, val loss: 0.64471, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.63748, val loss: 0.63407, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.62799, val loss: 0.62394, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.61897, val loss: 0.61429, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.61039, val loss: 0.60510, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.60223, val loss: 0.59634, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.59446, val loss: 0.58799, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.58707, val loss: 0.58002, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.58003, val loss: 0.57241, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.57332, val loss: 0.56515, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.56693, val loss: 0.55822, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.56083, val loss: 0.55159, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.55502, val loss: 0.54525, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.54914, val loss: 0.53933, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.54371, val loss: 0.53340, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.53854, val loss: 0.52773, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.53311, val loss: 0.52227, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.52794, val loss: 0.51706, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.52302, val loss: 0.51210, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.51835, val loss: 0.50697, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.51371, val loss: 0.50230, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.50933, val loss: 0.49747, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.50496, val loss: 0.49307, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.50084, val loss: 0.48852, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.49673, val loss: 0.48438, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.49286, val loss: 0.48009, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.48897, val loss: 0.47618, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.48534, val loss: 0.47218, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.48167, val loss: 0.46849, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.47817, val loss: 0.46496, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.47478, val loss: 0.46119, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.47146, val loss: 0.45785, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.46827, val loss: 0.45429, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.46513, val loss: 0.45113, in 0.016s 1 tree, 20 leaves, max depth = 12, train loss: 0.46212, val loss: 0.44776, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.45915, val loss: 0.44477, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.45630, val loss: 0.44157, in 0.000s 1 tree, 20 leaves, max depth = 12, train loss: 0.45358, val loss: 0.43851, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.45078, val loss: 0.43570, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.44811, val loss: 0.43301, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.44555, val loss: 0.43044, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.44303, val loss: 0.42759, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.44060, val loss: 0.42514, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.43821, val loss: 0.42244, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.43590, val loss: 0.42011, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.43364, val loss: 0.41756, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.43145, val loss: 0.41535, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.42930, val loss: 0.41290, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.42721, val loss: 0.41080, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.42519, val loss: 0.40851, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.42320, val loss: 0.40651, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.42128, val loss: 0.40431, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.41939, val loss: 0.40240, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.41758, val loss: 0.40058, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41580, val loss: 0.39866, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41411, val loss: 0.39683, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.41232, val loss: 0.39479, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.41061, val loss: 0.39307, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40900, val loss: 0.39134, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.40732, val loss: 0.38941, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.40569, val loss: 0.38777, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40416, val loss: 0.38613, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40270, val loss: 0.38455, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.40115, val loss: 0.38300, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.39957, val loss: 0.38119, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39819, val loss: 0.37969, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.39670, val loss: 0.37821, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39537, val loss: 0.37678, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.39395, val loss: 0.37536, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.39247, val loss: 0.37367, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39121, val loss: 0.37230, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.38985, val loss: 0.37095, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.38845, val loss: 0.36934, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38724, val loss: 0.36803, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.38593, val loss: 0.36674, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38478, val loss: 0.36548, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.38339, val loss: 0.36388, in 0.016s Fit 80 trees in 0.925 s, (2004 total leaves) Time spent computing histograms: 0.343s Time spent finding best splits: 0.050s Time spent applying splits: 0.043s Time spent predicting: 0.000s Trial 48, Fold 4: Log loss = 0.38315428635904075, Average precision = 0.945962665443955, ROC-AUC = 0.9426270026061598, Elapsed Time = 0.938886599999023 seconds Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 48, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 8, train loss: 0.68073, val loss: 0.67990, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.66897, val loss: 0.66733, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.65776, val loss: 0.65533, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.64712, val loss: 0.64392, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.63707, val loss: 0.63313, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.62748, val loss: 0.62280, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.61836, val loss: 0.61297, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.60973, val loss: 0.60365, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.60147, val loss: 0.59471, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.59361, val loss: 0.58620, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.58617, val loss: 0.57811, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.57904, val loss: 0.57035, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.57225, val loss: 0.56294, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.56581, val loss: 0.55590, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.55964, val loss: 0.54914, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.55375, val loss: 0.54268, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.54790, val loss: 0.53706, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.54242, val loss: 0.53103, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.53720, val loss: 0.52527, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.53179, val loss: 0.52010, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.52664, val loss: 0.51518, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.52184, val loss: 0.50986, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.51699, val loss: 0.50525, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.51249, val loss: 0.50025, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.50792, val loss: 0.49592, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.50357, val loss: 0.49179, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.49941, val loss: 0.48716, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.49530, val loss: 0.48328, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.49139, val loss: 0.47892, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48751, val loss: 0.47527, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.48383, val loss: 0.47115, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48017, val loss: 0.46771, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.47670, val loss: 0.46382, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.47324, val loss: 0.46058, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46996, val loss: 0.45690, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.46668, val loss: 0.45384, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.46355, val loss: 0.45093, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.46050, val loss: 0.44749, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.45752, val loss: 0.44473, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.45464, val loss: 0.44147, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.45189, val loss: 0.43835, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.44909, val loss: 0.43577, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44641, val loss: 0.43330, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.44383, val loss: 0.43037, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.44129, val loss: 0.42804, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.43885, val loss: 0.42526, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.43643, val loss: 0.42305, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.43411, val loss: 0.42094, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.43182, val loss: 0.41832, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.42962, val loss: 0.41633, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.42745, val loss: 0.41384, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.42535, val loss: 0.41194, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.42330, val loss: 0.40958, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.42130, val loss: 0.40779, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.41935, val loss: 0.40553, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.41744, val loss: 0.40382, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.41558, val loss: 0.40167, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41381, val loss: 0.39997, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.41199, val loss: 0.39836, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41030, val loss: 0.39674, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40857, val loss: 0.39521, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.40686, val loss: 0.39322, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40526, val loss: 0.39169, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.40361, val loss: 0.39024, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40208, val loss: 0.38878, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.40050, val loss: 0.38741, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.39889, val loss: 0.38550, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39744, val loss: 0.38411, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.39593, val loss: 0.38281, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39455, val loss: 0.38148, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.39303, val loss: 0.37968, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.39159, val loss: 0.37844, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39027, val loss: 0.37718, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38900, val loss: 0.37597, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.38763, val loss: 0.37480, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.38620, val loss: 0.37310, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38499, val loss: 0.37195, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.38367, val loss: 0.37083, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38251, val loss: 0.36973, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.38116, val loss: 0.36811, in 0.000s Fit 80 trees in 0.925 s, (2055 total leaves) Time spent computing histograms: 0.338s Time spent finding best splits: 0.051s Time spent applying splits: 0.044s Time spent predicting: 0.016s Trial 48, Fold 5: Log loss = 0.3854740829711175, Average precision = 0.9450464468313988, ROC-AUC = 0.9415032978466454, Elapsed Time = 0.9400613999987399 seconds
Optimization Progress: 49%|####9 | 49/100 [09:37<10:02, 11.81s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 49, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.173 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.64967, val loss: 0.64819, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.61370, val loss: 0.61102, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.58401, val loss: 0.58024, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.55969, val loss: 0.55494, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.53764, val loss: 0.53467, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.51850, val loss: 0.51461, in 0.000s 1 tree, 40 leaves, max depth = 13, train loss: 0.50111, val loss: 0.49877, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.48634, val loss: 0.48316, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.47365, val loss: 0.46970, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.46025, val loss: 0.45770, in 0.000s 1 tree, 40 leaves, max depth = 11, train loss: 0.44879, val loss: 0.44734, in 0.016s Fit 11 trees in 0.376 s, (194 total leaves) Time spent computing histograms: 0.050s Time spent finding best splits: 0.007s Time spent applying splits: 0.005s Time spent predicting: 0.000s Trial 49, Fold 1: Log loss = 0.44881063567524554, Average precision = 0.9169794849465421, ROC-AUC = 0.9279338014940995, Elapsed Time = 0.3875459000009869 seconds Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 49, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0.189 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.64957, val loss: 0.64758, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.61375, val loss: 0.60978, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.58416, val loss: 0.57844, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.55976, val loss: 0.55260, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.53801, val loss: 0.53182, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.51902, val loss: 0.51149, in 0.000s 1 tree, 45 leaves, max depth = 14, train loss: 0.50178, val loss: 0.49514, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.48697, val loss: 0.47920, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.47434, val loss: 0.46545, in 0.000s 1 tree, 46 leaves, max depth = 14, train loss: 0.46095, val loss: 0.45293, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.44960, val loss: 0.44238, in 0.016s Fit 11 trees in 0.423 s, (233 total leaves) Time spent computing histograms: 0.055s Time spent finding best splits: 0.008s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 49, Fold 2: Log loss = 0.4507257352537522, Average precision = 0.9085994456637089, ROC-AUC = 0.9257551956165527, Elapsed Time = 0.43212510000012117 seconds Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 49, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0.173 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.64996, val loss: 0.64830, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.61434, val loss: 0.61120, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.58491, val loss: 0.58043, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.56077, val loss: 0.55505, in 0.000s 1 tree, 44 leaves, max depth = 14, train loss: 0.53865, val loss: 0.53467, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.51975, val loss: 0.51466, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.50227, val loss: 0.49871, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.48767, val loss: 0.48304, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.47517, val loss: 0.46960, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.46140, val loss: 0.45717, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.44957, val loss: 0.44655, in 0.016s Fit 11 trees in 0.423 s, (244 total leaves) Time spent computing histograms: 0.055s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 49, Fold 3: Log loss = 0.44611069590815944, Average precision = 0.9203295690550128, ROC-AUC = 0.9321823895320279, Elapsed Time = 0.42323920000126236 seconds Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 49, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.173 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 5, train loss: 0.64985, val loss: 0.64751, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.61415, val loss: 0.60949, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.58463, val loss: 0.57790, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.56034, val loss: 0.55181, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.53858, val loss: 0.53068, in 0.000s 1 tree, 14 leaves, max depth = 6, train loss: 0.51959, val loss: 0.51003, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.50202, val loss: 0.49318, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.48731, val loss: 0.47699, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.47475, val loss: 0.46303, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.46148, val loss: 0.45029, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.45011, val loss: 0.43939, in 0.000s Fit 11 trees in 0.407 s, (228 total leaves) Time spent computing histograms: 0.054s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 49, Fold 4: Log loss = 0.45057004868577993, Average precision = 0.9144949064218189, ROC-AUC = 0.9272061304367524, Elapsed Time = 0.42175010000028124 seconds Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 49, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.174 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 5 leaves, max depth = 4, train loss: 0.64929, val loss: 0.64638, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61341, val loss: 0.60766, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.58357, val loss: 0.57537, in 0.016s 1 tree, 5 leaves, max depth = 4, train loss: 0.55901, val loss: 0.54834, in 0.000s 1 tree, 46 leaves, max depth = 16, train loss: 0.53697, val loss: 0.52722, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.51789, val loss: 0.50611, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.50044, val loss: 0.48961, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.48557, val loss: 0.47292, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.47283, val loss: 0.45868, in 0.000s 1 tree, 40 leaves, max depth = 10, train loss: 0.45932, val loss: 0.44610, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.44784, val loss: 0.43552, in 0.016s Fit 11 trees in 0.424 s, (228 total leaves) Time spent computing histograms: 0.054s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 49, Fold 5: Log loss = 0.45311330864184113, Average precision = 0.9136391070882067, ROC-AUC = 0.9254636122103934, Elapsed Time = 0.42471040000054927 seconds
Optimization Progress: 50%|##### | 50/100 [09:46<09:11, 11.02s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 50, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 93 leaves, max depth = 19, train loss: 0.67691, val loss: 0.67669, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.66030, val loss: 0.66018, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.64396, val loss: 0.64362, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.63039, val loss: 0.62996, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.61710, val loss: 0.61688, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.60445, val loss: 0.60429, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.59204, val loss: 0.59153, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.57944, val loss: 0.57868, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.56946, val loss: 0.56846, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.55847, val loss: 0.55736, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.54744, val loss: 0.54619, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.53563, val loss: 0.53445, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.52659, val loss: 0.52521, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.51887, val loss: 0.51738, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.51029, val loss: 0.50873, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.50283, val loss: 0.50141, in 0.016s 1 tree, 93 leaves, max depth = 20, train loss: 0.49582, val loss: 0.49441, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.48930, val loss: 0.48782, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.48209, val loss: 0.48047, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.47611, val loss: 0.47438, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.47000, val loss: 0.46813, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.46400, val loss: 0.46182, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.45805, val loss: 0.45567, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.45242, val loss: 0.44982, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.44640, val loss: 0.44398, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.44121, val loss: 0.43887, in 0.016s 1 tree, 15 leaves, max depth = 10, train loss: 0.43418, val loss: 0.43195, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.42909, val loss: 0.42692, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.42403, val loss: 0.42180, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.41933, val loss: 0.41696, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.41568, val loss: 0.41328, in 0.031s 1 tree, 93 leaves, max depth = 18, train loss: 0.41229, val loss: 0.40987, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.40863, val loss: 0.40613, in 0.016s 1 tree, 93 leaves, max depth = 22, train loss: 0.40466, val loss: 0.40203, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.40183, val loss: 0.39911, in 0.031s 1 tree, 93 leaves, max depth = 13, train loss: 0.39699, val loss: 0.39437, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.39320, val loss: 0.39046, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.38875, val loss: 0.38606, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.38455, val loss: 0.38189, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.37911, val loss: 0.37662, in 0.000s 1 tree, 34 leaves, max depth = 13, train loss: 0.37666, val loss: 0.37410, in 0.016s 1 tree, 14 leaves, max depth = 8, train loss: 0.37159, val loss: 0.36924, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.36869, val loss: 0.36636, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.36650, val loss: 0.36412, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.36391, val loss: 0.36149, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.35946, val loss: 0.35716, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.35696, val loss: 0.35467, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.35512, val loss: 0.35292, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.35318, val loss: 0.35100, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.35115, val loss: 0.34906, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.34746, val loss: 0.34561, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.34361, val loss: 0.34186, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.34156, val loss: 0.33997, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.33846, val loss: 0.33677, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.33484, val loss: 0.33356, in 0.000s 1 tree, 23 leaves, max depth = 8, train loss: 0.33145, val loss: 0.33027, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.32824, val loss: 0.32715, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.32645, val loss: 0.32547, in 0.031s Fit 58 trees in 1.283 s, (4131 total leaves) Time spent computing histograms: 0.386s Time spent finding best splits: 0.075s Time spent applying splits: 0.066s Time spent predicting: 0.000s Trial 50, Fold 1: Log loss = 0.332543159481717, Average precision = 0.960439148332299, ROC-AUC = 0.9515212984471115, Elapsed Time = 1.2974546999994345 seconds Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 50, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 93 leaves, max depth = 14, train loss: 0.67712, val loss: 0.67655, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.66063, val loss: 0.65961, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.64447, val loss: 0.64340, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.62999, val loss: 0.62871, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.61492, val loss: 0.61359, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.60232, val loss: 0.60093, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.59010, val loss: 0.58830, in 0.031s 1 tree, 44 leaves, max depth = 13, train loss: 0.57760, val loss: 0.57544, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.56719, val loss: 0.56463, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.55622, val loss: 0.55374, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.54582, val loss: 0.54325, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.53720, val loss: 0.53424, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.52793, val loss: 0.52493, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.51891, val loss: 0.51599, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.51000, val loss: 0.50705, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.50179, val loss: 0.49877, in 0.031s 1 tree, 93 leaves, max depth = 11, train loss: 0.49437, val loss: 0.49129, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.48643, val loss: 0.48321, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.47894, val loss: 0.47571, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.47201, val loss: 0.46873, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.46525, val loss: 0.46193, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.45915, val loss: 0.45589, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.45419, val loss: 0.45077, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.44872, val loss: 0.44524, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.44314, val loss: 0.43967, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.43878, val loss: 0.43512, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.43392, val loss: 0.43036, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.42930, val loss: 0.42583, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.42494, val loss: 0.42148, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.42125, val loss: 0.41768, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.41641, val loss: 0.41302, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.41129, val loss: 0.40802, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.40671, val loss: 0.40348, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.40006, val loss: 0.39694, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.39391, val loss: 0.39091, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.38824, val loss: 0.38532, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.38280, val loss: 0.38002, in 0.000s 1 tree, 9 leaves, max depth = 4, train loss: 0.37745, val loss: 0.37473, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.37253, val loss: 0.36987, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.36838, val loss: 0.36592, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.36414, val loss: 0.36161, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.36101, val loss: 0.35856, in 0.031s 1 tree, 15 leaves, max depth = 6, train loss: 0.35658, val loss: 0.35428, in 0.000s 1 tree, 32 leaves, max depth = 9, train loss: 0.35267, val loss: 0.35069, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.34950, val loss: 0.34769, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.34791, val loss: 0.34631, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.34401, val loss: 0.34257, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.34155, val loss: 0.34014, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.33926, val loss: 0.33803, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.33713, val loss: 0.33602, in 0.016s 1 tree, 93 leaves, max depth = 20, train loss: 0.33436, val loss: 0.33322, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.33118, val loss: 0.33035, in 0.031s 1 tree, 93 leaves, max depth = 13, train loss: 0.32926, val loss: 0.32849, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.32614, val loss: 0.32572, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.32423, val loss: 0.32381, in 0.016s 1 tree, 93 leaves, max depth = 20, train loss: 0.32235, val loss: 0.32212, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.31964, val loss: 0.31937, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.31793, val loss: 0.31775, in 0.016s Fit 58 trees in 1.361 s, (4202 total leaves) Time spent computing histograms: 0.402s Time spent finding best splits: 0.078s Time spent applying splits: 0.070s Time spent predicting: 0.016s Trial 50, Fold 2: Log loss = 0.322366115525635, Average precision = 0.9586962201836953, ROC-AUC = 0.9532182063351058, Elapsed Time = 1.3756073000004108 seconds Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 50, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 93 leaves, max depth = 16, train loss: 0.67699, val loss: 0.67687, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.66059, val loss: 0.66047, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.64392, val loss: 0.64371, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.63036, val loss: 0.63012, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.61582, val loss: 0.61557, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.60270, val loss: 0.60208, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.58979, val loss: 0.58909, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.57838, val loss: 0.57759, in 0.031s 1 tree, 93 leaves, max depth = 13, train loss: 0.56866, val loss: 0.56741, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.55778, val loss: 0.55665, in 0.031s 1 tree, 93 leaves, max depth = 18, train loss: 0.54750, val loss: 0.54645, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.53805, val loss: 0.53703, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.52802, val loss: 0.52715, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.51983, val loss: 0.51872, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.51229, val loss: 0.51097, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.50429, val loss: 0.50288, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.49686, val loss: 0.49552, in 0.031s 1 tree, 93 leaves, max depth = 18, train loss: 0.48960, val loss: 0.48841, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.48313, val loss: 0.48199, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.47729, val loss: 0.47620, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.47177, val loss: 0.47068, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.46518, val loss: 0.46392, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.45945, val loss: 0.45830, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.45456, val loss: 0.45366, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.44924, val loss: 0.44841, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.44406, val loss: 0.44327, in 0.016s 1 tree, 14 leaves, max depth = 8, train loss: 0.43627, val loss: 0.43604, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.43106, val loss: 0.43091, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.42579, val loss: 0.42578, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.42053, val loss: 0.42059, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.41531, val loss: 0.41527, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.41015, val loss: 0.41007, in 0.031s 1 tree, 93 leaves, max depth = 20, train loss: 0.40523, val loss: 0.40504, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.40136, val loss: 0.40132, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.39694, val loss: 0.39681, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.39388, val loss: 0.39364, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.39107, val loss: 0.39071, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.38772, val loss: 0.38755, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.38493, val loss: 0.38493, in 0.016s 1 tree, 11 leaves, max depth = 4, train loss: 0.37915, val loss: 0.37970, in 0.016s 1 tree, 28 leaves, max depth = 13, train loss: 0.37680, val loss: 0.37723, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.37407, val loss: 0.37460, in 0.016s 1 tree, 11 leaves, max depth = 4, train loss: 0.36892, val loss: 0.36985, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.36651, val loss: 0.36755, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.36409, val loss: 0.36527, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.36140, val loss: 0.36232, in 0.031s 1 tree, 93 leaves, max depth = 19, train loss: 0.35951, val loss: 0.36054, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.35618, val loss: 0.35716, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.35396, val loss: 0.35499, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.35196, val loss: 0.35315, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.34994, val loss: 0.35125, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.34801, val loss: 0.34936, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.34655, val loss: 0.34796, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.34205, val loss: 0.34429, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.34021, val loss: 0.34257, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.33858, val loss: 0.34114, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.33663, val loss: 0.33936, in 0.031s 1 tree, 93 leaves, max depth = 19, train loss: 0.33458, val loss: 0.33749, in 0.016s Fit 58 trees in 1.456 s, (4741 total leaves) Time spent computing histograms: 0.423s Time spent finding best splits: 0.089s Time spent applying splits: 0.080s Time spent predicting: 0.000s Trial 50, Fold 3: Log loss = 0.33476056901817486, Average precision = 0.9590247945597814, ROC-AUC = 0.9532110773757355, Elapsed Time = 1.46114309999939 seconds Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 50, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 93 leaves, max depth = 16, train loss: 0.67706, val loss: 0.67662, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.66065, val loss: 0.65963, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.64404, val loss: 0.64234, in 0.031s 1 tree, 93 leaves, max depth = 23, train loss: 0.63095, val loss: 0.62891, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.61819, val loss: 0.61587, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.60411, val loss: 0.60139, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.59182, val loss: 0.58841, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.57956, val loss: 0.57559, in 0.031s 1 tree, 86 leaves, max depth = 14, train loss: 0.56887, val loss: 0.56458, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.55849, val loss: 0.55400, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.54768, val loss: 0.54265, in 0.031s 1 tree, 93 leaves, max depth = 18, train loss: 0.53934, val loss: 0.53406, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.52996, val loss: 0.52418, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.52248, val loss: 0.51605, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.51347, val loss: 0.50652, in 0.031s 1 tree, 93 leaves, max depth = 12, train loss: 0.50528, val loss: 0.49792, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.49812, val loss: 0.49043, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.49035, val loss: 0.48221, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.48311, val loss: 0.47458, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.47595, val loss: 0.46706, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.46919, val loss: 0.45981, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.46239, val loss: 0.45270, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.45693, val loss: 0.44701, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.45220, val loss: 0.44209, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.44424, val loss: 0.43376, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.43880, val loss: 0.42793, in 0.016s 1 tree, 93 leaves, max depth = 21, train loss: 0.43397, val loss: 0.42281, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.42901, val loss: 0.41748, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.42493, val loss: 0.41326, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.42092, val loss: 0.40902, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.41696, val loss: 0.40488, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.41041, val loss: 0.39815, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.40405, val loss: 0.39168, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.40064, val loss: 0.38802, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.39686, val loss: 0.38430, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.39228, val loss: 0.37961, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.38937, val loss: 0.37661, in 0.031s 1 tree, 8 leaves, max depth = 4, train loss: 0.38389, val loss: 0.37111, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.37882, val loss: 0.36581, in 0.016s 1 tree, 10 leaves, max depth = 4, train loss: 0.37444, val loss: 0.36104, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.36958, val loss: 0.35611, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.36581, val loss: 0.35236, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.36170, val loss: 0.34789, in 0.000s 1 tree, 93 leaves, max depth = 15, train loss: 0.35880, val loss: 0.34478, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.35558, val loss: 0.34145, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.35367, val loss: 0.33964, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.35128, val loss: 0.33703, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.34947, val loss: 0.33508, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.34721, val loss: 0.33300, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.34421, val loss: 0.33030, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.34245, val loss: 0.32862, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.34063, val loss: 0.32694, in 0.031s 1 tree, 84 leaves, max depth = 13, train loss: 0.33614, val loss: 0.32258, in 0.016s 1 tree, 11 leaves, max depth = 4, train loss: 0.33288, val loss: 0.31933, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.32976, val loss: 0.31607, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.32660, val loss: 0.31279, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.32374, val loss: 0.30998, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.32194, val loss: 0.30798, in 0.016s Fit 58 trees in 1.393 s, (4225 total leaves) Time spent computing histograms: 0.404s Time spent finding best splits: 0.080s Time spent applying splits: 0.071s Time spent predicting: 0.000s Trial 50, Fold 4: Log loss = 0.3254950150277615, Average precision = 0.9597598144707746, ROC-AUC = 0.9523395232611097, Elapsed Time = 1.4036365000010846 seconds Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 50, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 93 leaves, max depth = 18, train loss: 0.67693, val loss: 0.67613, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.65889, val loss: 0.65740, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.64240, val loss: 0.64030, in 0.016s 1 tree, 93 leaves, max depth = 22, train loss: 0.62800, val loss: 0.62499, in 0.032s 1 tree, 93 leaves, max depth = 16, train loss: 0.61531, val loss: 0.61168, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.60245, val loss: 0.59855, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.59016, val loss: 0.58574, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.57771, val loss: 0.57269, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.56684, val loss: 0.56124, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.55727, val loss: 0.55110, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.54609, val loss: 0.53956, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.53762, val loss: 0.53042, in 0.019s 1 tree, 93 leaves, max depth = 13, train loss: 0.52816, val loss: 0.52042, in 0.029s 1 tree, 93 leaves, max depth = 13, train loss: 0.52061, val loss: 0.51222, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.51164, val loss: 0.50288, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.50482, val loss: 0.49561, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.49646, val loss: 0.48679, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.48951, val loss: 0.47963, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.48216, val loss: 0.47189, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.47498, val loss: 0.46430, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.46818, val loss: 0.45719, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.46127, val loss: 0.45012, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.45575, val loss: 0.44449, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.45080, val loss: 0.43934, in 0.031s 1 tree, 25 leaves, max depth = 8, train loss: 0.44602, val loss: 0.43428, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.44189, val loss: 0.42987, in 0.000s 1 tree, 43 leaves, max depth = 11, train loss: 0.43739, val loss: 0.42532, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.43228, val loss: 0.42012, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.42779, val loss: 0.41536, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.42416, val loss: 0.41150, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.41970, val loss: 0.40683, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.41632, val loss: 0.40326, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.41188, val loss: 0.39883, in 0.031s 1 tree, 11 leaves, max depth = 4, train loss: 0.40569, val loss: 0.39258, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.40210, val loss: 0.38896, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.39591, val loss: 0.38266, in 0.016s 1 tree, 87 leaves, max depth = 12, train loss: 0.39288, val loss: 0.37956, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.39003, val loss: 0.37664, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.38470, val loss: 0.37127, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.37984, val loss: 0.36636, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.37727, val loss: 0.36380, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.37367, val loss: 0.36030, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.36912, val loss: 0.35571, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.36596, val loss: 0.35251, in 0.016s 1 tree, 93 leaves, max depth = 18, train loss: 0.36289, val loss: 0.34947, in 0.016s 1 tree, 93 leaves, max depth = 21, train loss: 0.36098, val loss: 0.34743, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.35840, val loss: 0.34480, in 0.031s 1 tree, 27 leaves, max depth = 10, train loss: 0.35658, val loss: 0.34275, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.35402, val loss: 0.34021, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.35000, val loss: 0.33615, in 0.016s 1 tree, 93 leaves, max depth = 17, train loss: 0.34782, val loss: 0.33397, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.34502, val loss: 0.33118, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.34276, val loss: 0.32882, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.34009, val loss: 0.32605, in 0.031s 1 tree, 93 leaves, max depth = 20, train loss: 0.33788, val loss: 0.32392, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.33598, val loss: 0.32193, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.33236, val loss: 0.31829, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.32964, val loss: 0.31577, in 0.016s Fit 58 trees in 1.377 s, (4221 total leaves) Time spent computing histograms: 0.400s Time spent finding best splits: 0.082s Time spent applying splits: 0.072s Time spent predicting: 0.016s Trial 50, Fold 5: Log loss = 0.34125740585444037, Average precision = 0.9561758490398942, ROC-AUC = 0.9489523066347101, Elapsed Time = 1.3802941999983886 seconds
Optimization Progress: 51%|#####1 | 51/100 [10:01<09:53, 12.12s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 51, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.125 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 69 leaves, max depth = 14, train loss: 0.64940, val loss: 0.64810, in 0.016s 1 tree, 73 leaves, max depth = 19, train loss: 0.61326, val loss: 0.61086, in 0.000s 1 tree, 73 leaves, max depth = 17, train loss: 0.58337, val loss: 0.58023, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.55886, val loss: 0.55494, in 0.000s 1 tree, 115 leaves, max depth = 16, train loss: 0.53645, val loss: 0.53415, in 0.016s 1 tree, 74 leaves, max depth = 19, train loss: 0.51724, val loss: 0.51411, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.49958, val loss: 0.49786, in 0.000s 1 tree, 69 leaves, max depth = 16, train loss: 0.48468, val loss: 0.48225, in 0.016s 1 tree, 74 leaves, max depth = 19, train loss: 0.47194, val loss: 0.46881, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.45826, val loss: 0.45640, in 0.000s 1 tree, 114 leaves, max depth = 17, train loss: 0.44667, val loss: 0.44597, in 0.016s Fit 11 trees in 0.360 s, (959 total leaves) Time spent computing histograms: 0.047s Time spent finding best splits: 0.011s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 51, Fold 1: Log loss = 0.44821471458833306, Average precision = 0.9172839742555474, ROC-AUC = 0.927829023936183, Elapsed Time = 0.36683610000000044 seconds Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 51, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 72 leaves, max depth = 18, train loss: 0.64926, val loss: 0.64729, in 0.000s 1 tree, 71 leaves, max depth = 15, train loss: 0.61334, val loss: 0.60958, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.58351, val loss: 0.57807, in 0.016s 1 tree, 71 leaves, max depth = 20, train loss: 0.55890, val loss: 0.55214, in 0.000s 1 tree, 112 leaves, max depth = 15, train loss: 0.53652, val loss: 0.53076, in 0.016s 1 tree, 73 leaves, max depth = 15, train loss: 0.51744, val loss: 0.51051, in 0.016s 1 tree, 112 leaves, max depth = 15, train loss: 0.49976, val loss: 0.49373, in 0.000s 1 tree, 72 leaves, max depth = 18, train loss: 0.48486, val loss: 0.47787, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47209, val loss: 0.46413, in 0.016s 1 tree, 111 leaves, max depth = 15, train loss: 0.45839, val loss: 0.45127, in 0.000s 1 tree, 112 leaves, max depth = 15, train loss: 0.44679, val loss: 0.44042, in 0.016s Fit 11 trees in 0.377 s, (954 total leaves) Time spent computing histograms: 0.051s Time spent finding best splits: 0.012s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 51, Fold 2: Log loss = 0.4485400674319748, Average precision = 0.9115079649576727, ROC-AUC = 0.9271467228093008, Elapsed Time = 0.38675879999937024 seconds Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 51, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.126 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 69 leaves, max depth = 16, train loss: 0.64962, val loss: 0.64818, in 0.000s 1 tree, 73 leaves, max depth = 11, train loss: 0.61380, val loss: 0.61092, in 0.016s 1 tree, 72 leaves, max depth = 12, train loss: 0.58423, val loss: 0.58013, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.55987, val loss: 0.55478, in 0.016s 1 tree, 110 leaves, max depth = 13, train loss: 0.53762, val loss: 0.53429, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.51860, val loss: 0.51459, in 0.000s 1 tree, 110 leaves, max depth = 14, train loss: 0.50103, val loss: 0.49851, in 0.016s 1 tree, 70 leaves, max depth = 15, train loss: 0.48624, val loss: 0.48290, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.47360, val loss: 0.46939, in 0.000s 1 tree, 110 leaves, max depth = 14, train loss: 0.46000, val loss: 0.45707, in 0.016s 1 tree, 110 leaves, max depth = 13, train loss: 0.44849, val loss: 0.44672, in 0.016s Fit 11 trees in 0.360 s, (937 total leaves) Time spent computing histograms: 0.045s Time spent finding best splits: 0.011s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 51, Fold 3: Log loss = 0.4468223273437511, Average precision = 0.9191827663788117, ROC-AUC = 0.9306622660862364, Elapsed Time = 0.36531429999922693 seconds Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 51, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 12, train loss: 0.64952, val loss: 0.64734, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.61360, val loss: 0.60925, in 0.016s 1 tree, 72 leaves, max depth = 13, train loss: 0.58385, val loss: 0.57766, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.55935, val loss: 0.55142, in 0.000s 1 tree, 111 leaves, max depth = 14, train loss: 0.53742, val loss: 0.52987, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.51833, val loss: 0.50917, in 0.016s 1 tree, 111 leaves, max depth = 13, train loss: 0.50107, val loss: 0.49227, in 0.000s 1 tree, 69 leaves, max depth = 18, train loss: 0.48619, val loss: 0.47602, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.47353, val loss: 0.46203, in 0.016s 1 tree, 111 leaves, max depth = 13, train loss: 0.46021, val loss: 0.44909, in 0.000s 1 tree, 110 leaves, max depth = 14, train loss: 0.44894, val loss: 0.43815, in 0.016s Fit 11 trees in 0.378 s, (939 total leaves) Time spent computing histograms: 0.049s Time spent finding best splits: 0.011s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 51, Fold 4: Log loss = 0.4507458387276249, Average precision = 0.9130938405507721, ROC-AUC = 0.9259754024287599, Elapsed Time = 0.37635580000096525 seconds Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 51, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.127 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 68 leaves, max depth = 13, train loss: 0.64895, val loss: 0.64615, in 0.000s 1 tree, 69 leaves, max depth = 12, train loss: 0.61287, val loss: 0.60750, in 0.000s 1 tree, 74 leaves, max depth = 15, train loss: 0.58282, val loss: 0.57515, in 0.016s 1 tree, 71 leaves, max depth = 14, train loss: 0.55807, val loss: 0.54831, in 0.000s 1 tree, 114 leaves, max depth = 15, train loss: 0.53602, val loss: 0.52714, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.51672, val loss: 0.50600, in 0.016s 1 tree, 114 leaves, max depth = 16, train loss: 0.49935, val loss: 0.48944, in 0.016s 1 tree, 69 leaves, max depth = 15, train loss: 0.48426, val loss: 0.47274, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.47146, val loss: 0.45848, in 0.016s 1 tree, 114 leaves, max depth = 15, train loss: 0.45802, val loss: 0.44588, in 0.016s 1 tree, 114 leaves, max depth = 15, train loss: 0.44668, val loss: 0.43528, in 0.000s Fit 11 trees in 0.361 s, (957 total leaves) Time spent computing histograms: 0.052s Time spent finding best splits: 0.011s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 51, Fold 5: Log loss = 0.4527107971310902, Average precision = 0.9107154606342183, ROC-AUC = 0.924461958642216, Elapsed Time = 0.3719161000008171 seconds
Optimization Progress: 52%|#####2 | 52/100 [10:09<08:46, 10.97s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 52, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 66 leaves, max depth = 10, train loss: 0.65370, val loss: 0.65348, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.61890, val loss: 0.61892, in 0.016s 1 tree, 100 leaves, max depth = 12, train loss: 0.58839, val loss: 0.58842, in 0.031s 1 tree, 106 leaves, max depth = 13, train loss: 0.56160, val loss: 0.56168, in 0.016s 1 tree, 105 leaves, max depth = 12, train loss: 0.53781, val loss: 0.53800, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.51668, val loss: 0.51697, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.49788, val loss: 0.49834, in 0.016s 1 tree, 108 leaves, max depth = 12, train loss: 0.48101, val loss: 0.48159, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.46603, val loss: 0.46647, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.45190, val loss: 0.45229, in 0.031s 1 tree, 103 leaves, max depth = 12, train loss: 0.43940, val loss: 0.43978, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.42183, val loss: 0.42258, in 0.031s 1 tree, 120 leaves, max depth = 15, train loss: 0.41137, val loss: 0.41191, in 0.016s 1 tree, 94 leaves, max depth = 14, train loss: 0.39639, val loss: 0.39753, in 0.016s 1 tree, 111 leaves, max depth = 15, train loss: 0.38739, val loss: 0.38863, in 0.031s 1 tree, 94 leaves, max depth = 12, train loss: 0.37471, val loss: 0.37665, in 0.016s 1 tree, 108 leaves, max depth = 13, train loss: 0.36718, val loss: 0.36926, in 0.016s 1 tree, 96 leaves, max depth = 13, train loss: 0.35633, val loss: 0.35898, in 0.031s 1 tree, 111 leaves, max depth = 12, train loss: 0.35006, val loss: 0.35301, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.34132, val loss: 0.34437, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.33353, val loss: 0.33673, in 0.016s 1 tree, 120 leaves, max depth = 16, train loss: 0.32708, val loss: 0.33031, in 0.031s 1 tree, 71 leaves, max depth = 13, train loss: 0.32026, val loss: 0.32425, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.31410, val loss: 0.31816, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.30864, val loss: 0.31282, in 0.016s 1 tree, 121 leaves, max depth = 16, train loss: 0.30337, val loss: 0.30774, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.29820, val loss: 0.30317, in 0.016s 1 tree, 131 leaves, max depth = 18, train loss: 0.29462, val loss: 0.29965, in 0.031s 1 tree, 72 leaves, max depth = 14, train loss: 0.29010, val loss: 0.29567, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.28605, val loss: 0.29213, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.28272, val loss: 0.28908, in 0.031s 1 tree, 47 leaves, max depth = 13, train loss: 0.27917, val loss: 0.28566, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.27600, val loss: 0.28260, in 0.016s 1 tree, 120 leaves, max depth = 17, train loss: 0.27228, val loss: 0.27895, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.26925, val loss: 0.27635, in 0.016s 1 tree, 53 leaves, max depth = 16, train loss: 0.26652, val loss: 0.27351, in 0.016s 1 tree, 131 leaves, max depth = 14, train loss: 0.26299, val loss: 0.27006, in 0.031s 1 tree, 73 leaves, max depth = 13, train loss: 0.26053, val loss: 0.26794, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.25836, val loss: 0.26573, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.25625, val loss: 0.26394, in 0.016s 1 tree, 51 leaves, max depth = 17, train loss: 0.25427, val loss: 0.26181, in 0.016s 1 tree, 118 leaves, max depth = 15, train loss: 0.25150, val loss: 0.25915, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.24858, val loss: 0.25623, in 0.031s 1 tree, 121 leaves, max depth = 17, train loss: 0.24623, val loss: 0.25401, in 0.031s 1 tree, 83 leaves, max depth = 19, train loss: 0.24446, val loss: 0.25248, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.24263, val loss: 0.25104, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.24099, val loss: 0.24920, in 0.031s 1 tree, 131 leaves, max depth = 18, train loss: 0.23840, val loss: 0.24660, in 0.016s Fit 48 trees in 1.237 s, (4395 total leaves) Time spent computing histograms: 0.326s Time spent finding best splits: 0.090s Time spent applying splits: 0.079s Time spent predicting: 0.000s Trial 52, Fold 1: Log loss = 0.24882506581240213, Average precision = 0.9662038143774188, ROC-AUC = 0.9609069180969588, Elapsed Time = 1.2518823999998858 seconds Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 52, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 68 leaves, max depth = 12, train loss: 0.65352, val loss: 0.65276, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.61851, val loss: 0.61723, in 0.031s 1 tree, 96 leaves, max depth = 14, train loss: 0.58731, val loss: 0.58581, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.55988, val loss: 0.55818, in 0.031s 1 tree, 98 leaves, max depth = 14, train loss: 0.53563, val loss: 0.53374, in 0.031s 1 tree, 103 leaves, max depth = 14, train loss: 0.51409, val loss: 0.51187, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.49495, val loss: 0.49261, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.47775, val loss: 0.47514, in 0.031s 1 tree, 110 leaves, max depth = 12, train loss: 0.46249, val loss: 0.45972, in 0.016s 1 tree, 112 leaves, max depth = 15, train loss: 0.44880, val loss: 0.44604, in 0.031s 1 tree, 103 leaves, max depth = 16, train loss: 0.43625, val loss: 0.43341, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.41929, val loss: 0.41669, in 0.016s 1 tree, 122 leaves, max depth = 17, train loss: 0.40911, val loss: 0.40661, in 0.031s 1 tree, 103 leaves, max depth = 14, train loss: 0.39460, val loss: 0.39253, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.38574, val loss: 0.38398, in 0.016s 1 tree, 125 leaves, max depth = 19, train loss: 0.37802, val loss: 0.37638, in 0.031s 1 tree, 92 leaves, max depth = 16, train loss: 0.36608, val loss: 0.36476, in 0.016s 1 tree, 126 leaves, max depth = 18, train loss: 0.35961, val loss: 0.35842, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.34997, val loss: 0.34900, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.34247, val loss: 0.34198, in 0.031s 1 tree, 121 leaves, max depth = 17, train loss: 0.33575, val loss: 0.33555, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.32783, val loss: 0.32788, in 0.031s 1 tree, 65 leaves, max depth = 12, train loss: 0.32042, val loss: 0.32104, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.31382, val loss: 0.31497, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.30781, val loss: 0.30909, in 0.031s 1 tree, 120 leaves, max depth = 17, train loss: 0.30257, val loss: 0.30414, in 0.016s 1 tree, 69 leaves, max depth = 12, train loss: 0.29727, val loss: 0.29926, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.29246, val loss: 0.29453, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.28800, val loss: 0.29044, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.28403, val loss: 0.28651, in 0.031s 1 tree, 131 leaves, max depth = 18, train loss: 0.27969, val loss: 0.28259, in 0.032s 1 tree, 131 leaves, max depth = 17, train loss: 0.27567, val loss: 0.27903, in 0.016s 1 tree, 71 leaves, max depth = 14, train loss: 0.27216, val loss: 0.27581, in 0.031s 1 tree, 45 leaves, max depth = 12, train loss: 0.26899, val loss: 0.27274, in 0.016s 1 tree, 120 leaves, max depth = 19, train loss: 0.26563, val loss: 0.26966, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.26277, val loss: 0.26706, in 0.031s 1 tree, 45 leaves, max depth = 13, train loss: 0.26019, val loss: 0.26457, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.25789, val loss: 0.26237, in 0.031s 1 tree, 131 leaves, max depth = 19, train loss: 0.25487, val loss: 0.25983, in 0.031s 1 tree, 45 leaves, max depth = 13, train loss: 0.25286, val loss: 0.25795, in 0.016s 1 tree, 119 leaves, max depth = 19, train loss: 0.25028, val loss: 0.25565, in 0.016s 1 tree, 71 leaves, max depth = 13, train loss: 0.24821, val loss: 0.25380, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.24604, val loss: 0.25215, in 0.031s 1 tree, 131 leaves, max depth = 17, train loss: 0.24365, val loss: 0.25017, in 0.031s 1 tree, 71 leaves, max depth = 14, train loss: 0.24202, val loss: 0.24873, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.24035, val loss: 0.24758, in 0.031s 1 tree, 68 leaves, max depth = 13, train loss: 0.23888, val loss: 0.24631, in 0.016s 1 tree, 52 leaves, max depth = 14, train loss: 0.23742, val loss: 0.24485, in 0.031s Fit 48 trees in 1.454 s, (4229 total leaves) Time spent computing histograms: 0.381s Time spent finding best splits: 0.102s Time spent applying splits: 0.088s Time spent predicting: 0.031s Trial 52, Fold 2: Log loss = 0.2419004480603451, Average precision = 0.9674886909543102, ROC-AUC = 0.9645675749580949, Elapsed Time = 1.456512400000065 seconds Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 52, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 69 leaves, max depth = 12, train loss: 0.65379, val loss: 0.65364, in 0.016s 1 tree, 86 leaves, max depth = 12, train loss: 0.61857, val loss: 0.61869, in 0.031s 1 tree, 100 leaves, max depth = 14, train loss: 0.58817, val loss: 0.58829, in 0.031s 1 tree, 97 leaves, max depth = 12, train loss: 0.56106, val loss: 0.56143, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.53743, val loss: 0.53784, in 0.031s 1 tree, 103 leaves, max depth = 14, train loss: 0.51604, val loss: 0.51667, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.49697, val loss: 0.49789, in 0.031s 1 tree, 102 leaves, max depth = 12, train loss: 0.47989, val loss: 0.48103, in 0.016s 1 tree, 120 leaves, max depth = 13, train loss: 0.46470, val loss: 0.46613, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.45084, val loss: 0.45248, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.43230, val loss: 0.43529, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.41575, val loss: 0.42007, in 0.031s 1 tree, 83 leaves, max depth = 14, train loss: 0.40546, val loss: 0.40995, in 0.031s 1 tree, 121 leaves, max depth = 14, train loss: 0.39589, val loss: 0.40075, in 0.016s 1 tree, 120 leaves, max depth = 16, train loss: 0.38687, val loss: 0.39214, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.37904, val loss: 0.38461, in 0.031s 1 tree, 42 leaves, max depth = 12, train loss: 0.36747, val loss: 0.37405, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.35730, val loss: 0.36475, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.34794, val loss: 0.35642, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.34064, val loss: 0.34895, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.33398, val loss: 0.34215, in 0.031s 1 tree, 70 leaves, max depth = 12, train loss: 0.32622, val loss: 0.33586, in 0.031s 1 tree, 45 leaves, max depth = 12, train loss: 0.31933, val loss: 0.32969, in 0.016s 1 tree, 117 leaves, max depth = 16, train loss: 0.31375, val loss: 0.32390, in 0.031s 1 tree, 70 leaves, max depth = 13, train loss: 0.30754, val loss: 0.31897, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.30205, val loss: 0.31401, in 0.031s 1 tree, 121 leaves, max depth = 13, train loss: 0.29798, val loss: 0.31049, in 0.016s 1 tree, 119 leaves, max depth = 18, train loss: 0.29352, val loss: 0.30590, in 0.031s 1 tree, 71 leaves, max depth = 14, train loss: 0.28871, val loss: 0.30207, in 0.016s 1 tree, 120 leaves, max depth = 18, train loss: 0.28478, val loss: 0.29809, in 0.031s 1 tree, 73 leaves, max depth = 13, train loss: 0.28057, val loss: 0.29494, in 0.031s 1 tree, 43 leaves, max depth = 12, train loss: 0.27678, val loss: 0.29154, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.27339, val loss: 0.28809, in 0.031s 1 tree, 131 leaves, max depth = 16, train loss: 0.27004, val loss: 0.28451, in 0.031s 1 tree, 71 leaves, max depth = 13, train loss: 0.26672, val loss: 0.28211, in 0.031s 1 tree, 94 leaves, max depth = 17, train loss: 0.26325, val loss: 0.27918, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.26046, val loss: 0.27703, in 0.031s 1 tree, 44 leaves, max depth = 14, train loss: 0.25796, val loss: 0.27482, in 0.016s 1 tree, 72 leaves, max depth = 13, train loss: 0.25561, val loss: 0.27314, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.25346, val loss: 0.27114, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.25081, val loss: 0.26820, in 0.016s 1 tree, 40 leaves, max depth = 13, train loss: 0.24891, val loss: 0.26662, in 0.031s 1 tree, 73 leaves, max depth = 13, train loss: 0.24705, val loss: 0.26531, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.24545, val loss: 0.26382, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.24373, val loss: 0.26283, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.24210, val loss: 0.26127, in 0.031s 1 tree, 58 leaves, max depth = 14, train loss: 0.24061, val loss: 0.25983, in 0.016s 1 tree, 131 leaves, max depth = 18, train loss: 0.23803, val loss: 0.25698, in 0.031s Fit 48 trees in 1.549 s, (4221 total leaves) Time spent computing histograms: 0.394s Time spent finding best splits: 0.114s Time spent applying splits: 0.098s Time spent predicting: 0.000s Trial 52, Fold 3: Log loss = 0.24269607928834366, Average precision = 0.9655662806077301, ROC-AUC = 0.9624913320684575, Elapsed Time = 1.555675900001006 seconds Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 52, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 71 leaves, max depth = 12, train loss: 0.65354, val loss: 0.65202, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.61862, val loss: 0.61578, in 0.031s 1 tree, 101 leaves, max depth = 16, train loss: 0.58794, val loss: 0.58395, in 0.016s 1 tree, 99 leaves, max depth = 13, train loss: 0.56067, val loss: 0.55556, in 0.031s 1 tree, 103 leaves, max depth = 14, train loss: 0.53660, val loss: 0.53051, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.51512, val loss: 0.50806, in 0.031s 1 tree, 102 leaves, max depth = 14, train loss: 0.49603, val loss: 0.48810, in 0.016s 1 tree, 108 leaves, max depth = 14, train loss: 0.47889, val loss: 0.47012, in 0.031s 1 tree, 114 leaves, max depth = 13, train loss: 0.46372, val loss: 0.45401, in 0.031s 1 tree, 95 leaves, max depth = 14, train loss: 0.44419, val loss: 0.43419, in 0.016s 1 tree, 110 leaves, max depth = 14, train loss: 0.43143, val loss: 0.42076, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.41517, val loss: 0.40437, in 0.031s 1 tree, 87 leaves, max depth = 13, train loss: 0.40487, val loss: 0.39337, in 0.016s 1 tree, 122 leaves, max depth = 13, train loss: 0.39539, val loss: 0.38323, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.38657, val loss: 0.37421, in 0.031s 1 tree, 131 leaves, max depth = 15, train loss: 0.37909, val loss: 0.36632, in 0.031s 1 tree, 91 leaves, max depth = 17, train loss: 0.36712, val loss: 0.35433, in 0.016s 1 tree, 126 leaves, max depth = 18, train loss: 0.36058, val loss: 0.34755, in 0.031s 1 tree, 96 leaves, max depth = 18, train loss: 0.35030, val loss: 0.33728, in 0.031s 1 tree, 46 leaves, max depth = 14, train loss: 0.34168, val loss: 0.32832, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.33481, val loss: 0.32139, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.32704, val loss: 0.31409, in 0.031s 1 tree, 131 leaves, max depth = 15, train loss: 0.32071, val loss: 0.30766, in 0.016s 1 tree, 96 leaves, max depth = 18, train loss: 0.31363, val loss: 0.30059, in 0.031s 1 tree, 113 leaves, max depth = 18, train loss: 0.30927, val loss: 0.29607, in 0.016s 1 tree, 71 leaves, max depth = 13, train loss: 0.30334, val loss: 0.29055, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.29863, val loss: 0.28578, in 0.016s 1 tree, 131 leaves, max depth = 15, train loss: 0.29402, val loss: 0.28107, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.28916, val loss: 0.27598, in 0.016s 1 tree, 71 leaves, max depth = 14, train loss: 0.28458, val loss: 0.27171, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.28050, val loss: 0.26750, in 0.016s 1 tree, 69 leaves, max depth = 14, train loss: 0.27661, val loss: 0.26397, in 0.016s 1 tree, 131 leaves, max depth = 14, train loss: 0.27286, val loss: 0.26014, in 0.031s 1 tree, 72 leaves, max depth = 14, train loss: 0.26949, val loss: 0.25709, in 0.016s 1 tree, 122 leaves, max depth = 16, train loss: 0.26685, val loss: 0.25468, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.26390, val loss: 0.25162, in 0.016s 1 tree, 131 leaves, max depth = 14, train loss: 0.26072, val loss: 0.24837, in 0.031s 1 tree, 72 leaves, max depth = 16, train loss: 0.25797, val loss: 0.24582, in 0.016s 1 tree, 131 leaves, max depth = 20, train loss: 0.25428, val loss: 0.24258, in 0.031s 1 tree, 55 leaves, max depth = 12, train loss: 0.25180, val loss: 0.24015, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.24951, val loss: 0.23802, in 0.016s 1 tree, 131 leaves, max depth = 21, train loss: 0.24631, val loss: 0.23524, in 0.016s 1 tree, 56 leaves, max depth = 16, train loss: 0.24432, val loss: 0.23338, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.24241, val loss: 0.23162, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.24071, val loss: 0.22979, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.23912, val loss: 0.22855, in 0.031s 1 tree, 75 leaves, max depth = 13, train loss: 0.23749, val loss: 0.22706, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.23581, val loss: 0.22576, in 0.016s Fit 48 trees in 1.425 s, (4499 total leaves) Time spent computing histograms: 0.369s Time spent finding best splits: 0.101s Time spent applying splits: 0.088s Time spent predicting: 0.000s Trial 52, Fold 4: Log loss = 0.24011756494161798, Average precision = 0.9684737999022944, ROC-AUC = 0.9645331713126891, Elapsed Time = 1.4310083000000304 seconds Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 52, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 12, train loss: 0.65348, val loss: 0.65181, in 0.016s 1 tree, 90 leaves, max depth = 14, train loss: 0.61798, val loss: 0.61507, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.58694, val loss: 0.58286, in 0.016s 1 tree, 110 leaves, max depth = 13, train loss: 0.56174, val loss: 0.55693, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.53773, val loss: 0.53183, in 0.031s 1 tree, 103 leaves, max depth = 14, train loss: 0.51594, val loss: 0.50921, in 0.016s 1 tree, 118 leaves, max depth = 15, train loss: 0.49745, val loss: 0.48993, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.47993, val loss: 0.47181, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.46458, val loss: 0.45581, in 0.031s 1 tree, 99 leaves, max depth = 12, train loss: 0.44531, val loss: 0.43637, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.43220, val loss: 0.42288, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.41571, val loss: 0.40652, in 0.016s 1 tree, 114 leaves, max depth = 14, train loss: 0.40467, val loss: 0.39516, in 0.031s 1 tree, 111 leaves, max depth = 13, train loss: 0.39470, val loss: 0.38490, in 0.016s 1 tree, 116 leaves, max depth = 13, train loss: 0.38600, val loss: 0.37581, in 0.016s 1 tree, 98 leaves, max depth = 15, train loss: 0.37336, val loss: 0.36330, in 0.016s 1 tree, 108 leaves, max depth = 13, train loss: 0.36559, val loss: 0.35539, in 0.031s 1 tree, 98 leaves, max depth = 15, train loss: 0.35482, val loss: 0.34476, in 0.016s 1 tree, 110 leaves, max depth = 13, train loss: 0.34822, val loss: 0.33808, in 0.016s 1 tree, 117 leaves, max depth = 16, train loss: 0.34101, val loss: 0.33094, in 0.031s 1 tree, 98 leaves, max depth = 14, train loss: 0.33208, val loss: 0.32221, in 0.016s 1 tree, 119 leaves, max depth = 16, train loss: 0.32582, val loss: 0.31605, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.31869, val loss: 0.30875, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.31291, val loss: 0.30299, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.30637, val loss: 0.29700, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.30066, val loss: 0.29120, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.29554, val loss: 0.28598, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.29099, val loss: 0.28134, in 0.016s 1 tree, 131 leaves, max depth = 16, train loss: 0.28627, val loss: 0.27669, in 0.031s 1 tree, 73 leaves, max depth = 12, train loss: 0.28163, val loss: 0.27267, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.27746, val loss: 0.26897, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.27405, val loss: 0.26547, in 0.016s 1 tree, 120 leaves, max depth = 17, train loss: 0.27040, val loss: 0.26210, in 0.031s 1 tree, 75 leaves, max depth = 14, train loss: 0.26697, val loss: 0.25905, in 0.016s 1 tree, 131 leaves, max depth = 16, train loss: 0.26348, val loss: 0.25561, in 0.016s 1 tree, 131 leaves, max depth = 23, train loss: 0.25932, val loss: 0.25186, in 0.031s 1 tree, 123 leaves, max depth = 18, train loss: 0.25652, val loss: 0.24925, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.25376, val loss: 0.24683, in 0.031s 1 tree, 46 leaves, max depth = 12, train loss: 0.25129, val loss: 0.24437, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.24884, val loss: 0.24223, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.24663, val loss: 0.24028, in 0.016s 1 tree, 56 leaves, max depth = 15, train loss: 0.24458, val loss: 0.23826, in 0.016s 1 tree, 57 leaves, max depth = 15, train loss: 0.24274, val loss: 0.23640, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.24097, val loss: 0.23499, in 0.016s 1 tree, 131 leaves, max depth = 18, train loss: 0.23903, val loss: 0.23380, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.23751, val loss: 0.23233, in 0.016s 1 tree, 121 leaves, max depth = 18, train loss: 0.23545, val loss: 0.23050, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.23394, val loss: 0.22978, in 0.016s Fit 48 trees in 1.267 s, (4439 total leaves) Time spent computing histograms: 0.329s Time spent finding best splits: 0.089s Time spent applying splits: 0.079s Time spent predicting: 0.000s Trial 52, Fold 5: Log loss = 0.24758770280829395, Average precision = 0.9640340686720158, ROC-AUC = 0.9600119651450124, Elapsed Time = 1.2826208999995288 seconds
Optimization Progress: 53%|#####3 | 53/100 [10:22<09:09, 11.70s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 53, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 37 leaves, max depth = 8, train loss: 0.68173, val loss: 0.68168, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.67031, val loss: 0.67032, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.65933, val loss: 0.65942, in 0.016s 1 tree, 51 leaves, max depth = 8, train loss: 0.64935, val loss: 0.64935, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.63979, val loss: 0.63967, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.62996, val loss: 0.62992, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.62072, val loss: 0.62061, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.61171, val loss: 0.61150, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.60292, val loss: 0.60277, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.59489, val loss: 0.59460, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.58658, val loss: 0.58624, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.57902, val loss: 0.57864, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.57123, val loss: 0.57083, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.56383, val loss: 0.56348, in 0.016s 1 tree, 50 leaves, max depth = 8, train loss: 0.55665, val loss: 0.55618, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.54991, val loss: 0.54937, in 0.016s 1 tree, 52 leaves, max depth = 8, train loss: 0.54320, val loss: 0.54254, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.53673, val loss: 0.53614, in 0.016s 1 tree, 34 leaves, max depth = 8, train loss: 0.53054, val loss: 0.52986, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.52474, val loss: 0.52403, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.51874, val loss: 0.51801, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.51346, val loss: 0.51262, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.50792, val loss: 0.50713, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.50282, val loss: 0.50196, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.49771, val loss: 0.49684, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.49266, val loss: 0.49186, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.48790, val loss: 0.48711, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.48333, val loss: 0.48252, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.47859, val loss: 0.47778, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.47445, val loss: 0.47355, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.47028, val loss: 0.46937, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.46434, val loss: 0.46362, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.46017, val loss: 0.45939, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.45454, val loss: 0.45394, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.45077, val loss: 0.45018, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.44691, val loss: 0.44620, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.44184, val loss: 0.44125, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.43826, val loss: 0.43756, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.43488, val loss: 0.43420, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.43133, val loss: 0.43061, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.42788, val loss: 0.42718, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.42462, val loss: 0.42386, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.42146, val loss: 0.42064, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.41858, val loss: 0.41773, in 0.031s 1 tree, 52 leaves, max depth = 10, train loss: 0.41548, val loss: 0.41465, in 0.016s 1 tree, 69 leaves, max depth = 12, train loss: 0.41282, val loss: 0.41198, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.41014, val loss: 0.40938, in 0.016s 1 tree, 70 leaves, max depth = 11, train loss: 0.40758, val loss: 0.40679, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.40477, val loss: 0.40401, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.40222, val loss: 0.40140, in 0.016s 1 tree, 51 leaves, max depth = 10, train loss: 0.39956, val loss: 0.39877, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.39539, val loss: 0.39485, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.39136, val loss: 0.39106, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.38890, val loss: 0.38863, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.38650, val loss: 0.38627, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.38271, val loss: 0.38271, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.37924, val loss: 0.37930, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.37587, val loss: 0.37599, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.37263, val loss: 0.37279, in 0.016s Fit 59 trees in 1.236 s, (3029 total leaves) Time spent computing histograms: 0.378s Time spent finding best splits: 0.060s Time spent applying splits: 0.048s Time spent predicting: 0.016s Trial 53, Fold 1: Log loss = 0.37719282831701495, Average precision = 0.9540985323839766, ROC-AUC = 0.9484556400347954, Elapsed Time = 1.235247900000104 seconds Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 53, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.126 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 39 leaves, max depth = 7, train loss: 0.68182, val loss: 0.68164, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.67031, val loss: 0.66989, in 0.016s 1 tree, 49 leaves, max depth = 8, train loss: 0.65940, val loss: 0.65880, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.64920, val loss: 0.64844, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.63965, val loss: 0.63878, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.62965, val loss: 0.62862, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.62006, val loss: 0.61886, in 0.016s 1 tree, 38 leaves, max depth = 8, train loss: 0.61100, val loss: 0.60957, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.60209, val loss: 0.60051, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.59415, val loss: 0.59250, in 0.000s 1 tree, 47 leaves, max depth = 11, train loss: 0.58584, val loss: 0.58404, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.57828, val loss: 0.57635, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.57048, val loss: 0.56839, in 0.031s 1 tree, 47 leaves, max depth = 11, train loss: 0.56298, val loss: 0.56076, in 0.000s 1 tree, 46 leaves, max depth = 8, train loss: 0.55579, val loss: 0.55349, in 0.031s 1 tree, 38 leaves, max depth = 10, train loss: 0.54890, val loss: 0.54649, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.54263, val loss: 0.54019, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.53670, val loss: 0.53406, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.53038, val loss: 0.52768, in 0.016s 1 tree, 51 leaves, max depth = 9, train loss: 0.52426, val loss: 0.52151, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.51876, val loss: 0.51585, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.51290, val loss: 0.50991, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.50734, val loss: 0.50430, in 0.016s 1 tree, 34 leaves, max depth = 8, train loss: 0.50202, val loss: 0.49886, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.49669, val loss: 0.49346, in 0.016s 1 tree, 53 leaves, max depth = 9, train loss: 0.49167, val loss: 0.48839, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.48685, val loss: 0.48345, in 0.016s 1 tree, 42 leaves, max depth = 8, train loss: 0.48216, val loss: 0.47860, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.47743, val loss: 0.47381, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.47285, val loss: 0.46918, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.46869, val loss: 0.46498, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.46441, val loss: 0.46058, in 0.016s 1 tree, 57 leaves, max depth = 9, train loss: 0.46030, val loss: 0.45649, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.45463, val loss: 0.45089, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.44925, val loss: 0.44563, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.44536, val loss: 0.44170, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.44015, val loss: 0.43656, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.43661, val loss: 0.43294, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.43327, val loss: 0.42958, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.42977, val loss: 0.42604, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.42671, val loss: 0.42301, in 0.016s 1 tree, 62 leaves, max depth = 10, train loss: 0.42349, val loss: 0.41979, in 0.031s 1 tree, 61 leaves, max depth = 10, train loss: 0.42036, val loss: 0.41668, in 0.016s 1 tree, 74 leaves, max depth = 15, train loss: 0.41748, val loss: 0.41387, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.41455, val loss: 0.41092, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.41008, val loss: 0.40654, in 0.016s 1 tree, 64 leaves, max depth = 9, train loss: 0.40757, val loss: 0.40402, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.40472, val loss: 0.40116, in 0.016s 1 tree, 53 leaves, max depth = 15, train loss: 0.40066, val loss: 0.39721, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.39655, val loss: 0.39318, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.39403, val loss: 0.39065, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.39160, val loss: 0.38818, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.38928, val loss: 0.38586, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.38560, val loss: 0.38227, in 0.016s 1 tree, 54 leaves, max depth = 14, train loss: 0.38194, val loss: 0.37870, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.37974, val loss: 0.37645, in 0.016s 1 tree, 65 leaves, max depth = 11, train loss: 0.37756, val loss: 0.37425, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.37531, val loss: 0.37195, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.37209, val loss: 0.36879, in 0.016s Fit 59 trees in 1.251 s, (3004 total leaves) Time spent computing histograms: 0.376s Time spent finding best splits: 0.066s Time spent applying splits: 0.054s Time spent predicting: 0.000s Trial 53, Fold 2: Log loss = 0.37552071858201236, Average precision = 0.9503976093764905, ROC-AUC = 0.9489623638177436, Elapsed Time = 1.2549022000002878 seconds Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 53, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 39 leaves, max depth = 8, train loss: 0.68181, val loss: 0.68159, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.67045, val loss: 0.67018, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.65954, val loss: 0.65922, in 0.016s 1 tree, 50 leaves, max depth = 8, train loss: 0.64958, val loss: 0.64910, in 0.016s 1 tree, 49 leaves, max depth = 8, train loss: 0.64008, val loss: 0.63964, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.63069, val loss: 0.63028, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.62150, val loss: 0.62117, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.61235, val loss: 0.61200, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.60388, val loss: 0.60357, in 0.016s 1 tree, 50 leaves, max depth = 9, train loss: 0.59583, val loss: 0.59557, in 0.016s 1 tree, 42 leaves, max depth = 8, train loss: 0.58759, val loss: 0.58736, in 0.016s 1 tree, 51 leaves, max depth = 9, train loss: 0.58010, val loss: 0.57993, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.57251, val loss: 0.57232, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.56552, val loss: 0.56540, in 0.031s 1 tree, 41 leaves, max depth = 12, train loss: 0.55819, val loss: 0.55811, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.55146, val loss: 0.55147, in 0.016s 1 tree, 65 leaves, max depth = 11, train loss: 0.54520, val loss: 0.54526, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.53903, val loss: 0.53917, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.53314, val loss: 0.53335, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52685, val loss: 0.52711, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.52125, val loss: 0.52155, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.51531, val loss: 0.51568, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.51006, val loss: 0.51048, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.50499, val loss: 0.50538, in 0.016s 1 tree, 40 leaves, max depth = 8, train loss: 0.50023, val loss: 0.50058, in 0.016s 1 tree, 47 leaves, max depth = 8, train loss: 0.49552, val loss: 0.49593, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.48900, val loss: 0.48983, in 0.031s 1 tree, 35 leaves, max depth = 7, train loss: 0.48415, val loss: 0.48500, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.47947, val loss: 0.48029, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.47340, val loss: 0.47464, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.46880, val loss: 0.47008, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.46463, val loss: 0.46601, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.46028, val loss: 0.46172, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.45482, val loss: 0.45669, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.44950, val loss: 0.45171, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.44546, val loss: 0.44772, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.44154, val loss: 0.44385, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.43787, val loss: 0.44023, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.43450, val loss: 0.43693, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.42983, val loss: 0.43258, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.42512, val loss: 0.42825, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.42055, val loss: 0.42403, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.41716, val loss: 0.42069, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.41388, val loss: 0.41744, in 0.016s 1 tree, 52 leaves, max depth = 15, train loss: 0.40976, val loss: 0.41366, in 0.031s 1 tree, 45 leaves, max depth = 13, train loss: 0.40662, val loss: 0.41057, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.40358, val loss: 0.40756, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.40062, val loss: 0.40464, in 0.000s 1 tree, 48 leaves, max depth = 13, train loss: 0.39776, val loss: 0.40180, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.39496, val loss: 0.39906, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.39236, val loss: 0.39652, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.38971, val loss: 0.39392, in 0.016s 1 tree, 55 leaves, max depth = 15, train loss: 0.38600, val loss: 0.39054, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.38347, val loss: 0.38806, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.38130, val loss: 0.38591, in 0.016s 1 tree, 63 leaves, max depth = 14, train loss: 0.37906, val loss: 0.38376, in 0.031s 1 tree, 49 leaves, max depth = 12, train loss: 0.37671, val loss: 0.38146, in 0.000s 1 tree, 55 leaves, max depth = 15, train loss: 0.37329, val loss: 0.37838, in 0.031s 1 tree, 61 leaves, max depth = 9, train loss: 0.37113, val loss: 0.37626, in 0.016s Fit 59 trees in 1.345 s, (2943 total leaves) Time spent computing histograms: 0.411s Time spent finding best splits: 0.067s Time spent applying splits: 0.055s Time spent predicting: 0.016s Trial 53, Fold 3: Log loss = 0.37057968492038945, Average precision = 0.9563776470250097, ROC-AUC = 0.9524284052932168, Elapsed Time = 1.3416933999997127 seconds Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 53, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 8, train loss: 0.68182, val loss: 0.68135, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.67044, val loss: 0.66956, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.65950, val loss: 0.65821, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.64982, val loss: 0.64805, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.63982, val loss: 0.63759, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.63054, val loss: 0.62793, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.62125, val loss: 0.61820, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.61205, val loss: 0.60863, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.60374, val loss: 0.59997, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.59579, val loss: 0.59166, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.58759, val loss: 0.58314, in 0.016s 1 tree, 57 leaves, max depth = 14, train loss: 0.58012, val loss: 0.57535, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.57241, val loss: 0.56725, in 0.016s 1 tree, 58 leaves, max depth = 9, train loss: 0.56549, val loss: 0.55999, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.55816, val loss: 0.55233, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.55159, val loss: 0.54548, in 0.016s 1 tree, 60 leaves, max depth = 16, train loss: 0.54547, val loss: 0.53905, in 0.031s 1 tree, 50 leaves, max depth = 10, train loss: 0.53956, val loss: 0.53297, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.53319, val loss: 0.52626, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.52704, val loss: 0.51977, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.52126, val loss: 0.51375, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.51537, val loss: 0.50758, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.50977, val loss: 0.50167, in 0.016s 1 tree, 43 leaves, max depth = 9, train loss: 0.50441, val loss: 0.49602, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.49906, val loss: 0.49041, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.49403, val loss: 0.48511, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.48911, val loss: 0.47989, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.48281, val loss: 0.47346, in 0.031s 1 tree, 66 leaves, max depth = 15, train loss: 0.47846, val loss: 0.46889, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.47426, val loss: 0.46455, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.47009, val loss: 0.46027, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.46614, val loss: 0.45611, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.46049, val loss: 0.45035, in 0.031s 1 tree, 54 leaves, max depth = 13, train loss: 0.45503, val loss: 0.44483, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.45129, val loss: 0.44098, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.44731, val loss: 0.43681, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.44387, val loss: 0.43309, in 0.016s 1 tree, 65 leaves, max depth = 17, train loss: 0.44049, val loss: 0.42953, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.43682, val loss: 0.42568, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.43326, val loss: 0.42193, in 0.000s 1 tree, 49 leaves, max depth = 11, train loss: 0.42981, val loss: 0.41829, in 0.031s 1 tree, 60 leaves, max depth = 12, train loss: 0.42510, val loss: 0.41352, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.42190, val loss: 0.41012, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.41740, val loss: 0.40557, in 0.016s 1 tree, 66 leaves, max depth = 10, train loss: 0.41436, val loss: 0.40233, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.41018, val loss: 0.39809, in 0.031s 1 tree, 72 leaves, max depth = 12, train loss: 0.40613, val loss: 0.39398, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.40320, val loss: 0.39089, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.40037, val loss: 0.38790, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.39760, val loss: 0.38498, in 0.016s 1 tree, 45 leaves, max depth = 9, train loss: 0.39504, val loss: 0.38224, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.39242, val loss: 0.37948, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.38988, val loss: 0.37677, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.38631, val loss: 0.37315, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.38409, val loss: 0.37091, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.38172, val loss: 0.36839, in 0.031s 1 tree, 69 leaves, max depth = 15, train loss: 0.37965, val loss: 0.36620, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.37738, val loss: 0.36381, in 0.016s 1 tree, 65 leaves, max depth = 14, train loss: 0.37398, val loss: 0.36039, in 0.016s Fit 59 trees in 1.376 s, (3193 total leaves) Time spent computing histograms: 0.434s Time spent finding best splits: 0.074s Time spent applying splits: 0.061s Time spent predicting: 0.000s Trial 53, Fold 4: Log loss = 0.3745417032972952, Average precision = 0.9548845195369, ROC-AUC = 0.9501723183139361, Elapsed Time = 1.3895826000007219 seconds Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 53, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 9, train loss: 0.68162, val loss: 0.68100, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.67017, val loss: 0.66914, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.65905, val loss: 0.65762, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.64873, val loss: 0.64679, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.63915, val loss: 0.63675, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.62918, val loss: 0.62645, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.61979, val loss: 0.61659, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.61058, val loss: 0.60702, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.60218, val loss: 0.59827, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.59403, val loss: 0.58976, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.58569, val loss: 0.58114, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.57815, val loss: 0.57328, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.57047, val loss: 0.56529, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.56349, val loss: 0.55799, in 0.016s 1 tree, 42 leaves, max depth = 13, train loss: 0.55618, val loss: 0.55042, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.54953, val loss: 0.54350, in 0.016s 1 tree, 62 leaves, max depth = 10, train loss: 0.54329, val loss: 0.53697, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.53732, val loss: 0.53088, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.53136, val loss: 0.52467, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.52510, val loss: 0.51820, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.51951, val loss: 0.51238, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.51363, val loss: 0.50630, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.50839, val loss: 0.50085, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.50324, val loss: 0.49562, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.49850, val loss: 0.49073, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.49391, val loss: 0.48599, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.48748, val loss: 0.47952, in 0.016s 1 tree, 36 leaves, max depth = 7, train loss: 0.48268, val loss: 0.47448, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.47802, val loss: 0.46958, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.47371, val loss: 0.46519, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.46915, val loss: 0.46049, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.46505, val loss: 0.45626, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.46074, val loss: 0.45182, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.45517, val loss: 0.44622, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.45108, val loss: 0.44202, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.44579, val loss: 0.43674, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.44069, val loss: 0.43165, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.43712, val loss: 0.42787, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.43344, val loss: 0.42407, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.42865, val loss: 0.41929, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.42401, val loss: 0.41465, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.41948, val loss: 0.41013, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.41611, val loss: 0.40668, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.41283, val loss: 0.40334, in 0.016s 1 tree, 53 leaves, max depth = 15, train loss: 0.40878, val loss: 0.39917, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.40566, val loss: 0.39599, in 0.031s 1 tree, 49 leaves, max depth = 13, train loss: 0.40263, val loss: 0.39289, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.39968, val loss: 0.38989, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.39684, val loss: 0.38696, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.39406, val loss: 0.38411, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.39149, val loss: 0.38139, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.38886, val loss: 0.37871, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.38630, val loss: 0.37609, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.38282, val loss: 0.37257, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.38060, val loss: 0.37036, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.37836, val loss: 0.36797, in 0.016s 1 tree, 77 leaves, max depth = 11, train loss: 0.37626, val loss: 0.36584, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.37400, val loss: 0.36352, in 0.016s 1 tree, 72 leaves, max depth = 13, train loss: 0.37200, val loss: 0.36151, in 0.016s Fit 59 trees in 1.330 s, (2959 total leaves) Time spent computing histograms: 0.412s Time spent finding best splits: 0.067s Time spent applying splits: 0.054s Time spent predicting: 0.000s Trial 53, Fold 5: Log loss = 0.3786991673644685, Average precision = 0.9511013074293633, ROC-AUC = 0.9479524366906341, Elapsed Time = 1.3397103999996034 seconds
Optimization Progress: 54%|#####4 | 54/100 [10:36<09:18, 12.14s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 54, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.126 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 74 leaves, max depth = 19, train loss: 0.64843, val loss: 0.64717, in 0.016s 1 tree, 76 leaves, max depth = 19, train loss: 0.61204, val loss: 0.60963, in 0.000s 1 tree, 79 leaves, max depth = 19, train loss: 0.58201, val loss: 0.57855, in 0.016s 1 tree, 76 leaves, max depth = 17, train loss: 0.55730, val loss: 0.55297, in 0.016s 1 tree, 132 leaves, max depth = 16, train loss: 0.53483, val loss: 0.53240, in 0.016s 1 tree, 79 leaves, max depth = 19, train loss: 0.51546, val loss: 0.51214, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.49773, val loss: 0.49607, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.48253, val loss: 0.48004, in 0.016s 1 tree, 132 leaves, max depth = 15, train loss: 0.46827, val loss: 0.46726, in 0.016s 1 tree, 81 leaves, max depth = 19, train loss: 0.45626, val loss: 0.45455, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.44458, val loss: 0.44423, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.43486, val loss: 0.43399, in 0.000s 1 tree, 123 leaves, max depth = 14, train loss: 0.42523, val loss: 0.42559, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.41702, val loss: 0.41851, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.40946, val loss: 0.41047, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40288, val loss: 0.40318, in 0.016s 1 tree, 82 leaves, max depth = 16, train loss: 0.39661, val loss: 0.39655, in 0.000s 1 tree, 130 leaves, max depth = 16, train loss: 0.38983, val loss: 0.39093, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38442, val loss: 0.38487, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.37863, val loss: 0.38022, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37403, val loss: 0.37505, in 0.000s 1 tree, 85 leaves, max depth = 15, train loss: 0.36899, val loss: 0.36967, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.36400, val loss: 0.36579, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36010, val loss: 0.36136, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.35581, val loss: 0.35697, in 0.000s 1 tree, 126 leaves, max depth = 14, train loss: 0.35149, val loss: 0.35374, in 0.031s 1 tree, 86 leaves, max depth = 16, train loss: 0.34799, val loss: 0.35007, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34463, val loss: 0.34620, in 0.000s 1 tree, 126 leaves, max depth = 15, train loss: 0.34084, val loss: 0.34347, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33791, val loss: 0.34007, in 0.000s 1 tree, 109 leaves, max depth = 13, train loss: 0.33453, val loss: 0.33617, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33201, val loss: 0.33320, in 0.016s 1 tree, 108 leaves, max depth = 13, train loss: 0.32916, val loss: 0.32993, in 0.016s 1 tree, 122 leaves, max depth = 15, train loss: 0.32567, val loss: 0.32751, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32341, val loss: 0.32483, in 0.000s 1 tree, 85 leaves, max depth = 15, train loss: 0.32095, val loss: 0.32236, in 0.016s 1 tree, 121 leaves, max depth = 15, train loss: 0.31786, val loss: 0.32030, in 0.031s 1 tree, 107 leaves, max depth = 13, train loss: 0.31554, val loss: 0.31757, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31349, val loss: 0.31510, in 0.000s 1 tree, 123 leaves, max depth = 16, train loss: 0.31075, val loss: 0.31334, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.30894, val loss: 0.31113, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.30686, val loss: 0.30872, in 0.000s 1 tree, 82 leaves, max depth = 14, train loss: 0.30509, val loss: 0.30695, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.30260, val loss: 0.30540, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30094, val loss: 0.30334, in 0.016s 1 tree, 120 leaves, max depth = 16, train loss: 0.29877, val loss: 0.30208, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.29704, val loss: 0.30002, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.29561, val loss: 0.29856, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29408, val loss: 0.29665, in 0.000s 1 tree, 123 leaves, max depth = 16, train loss: 0.29209, val loss: 0.29551, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.29070, val loss: 0.29383, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.28932, val loss: 0.29207, in 0.000s 1 tree, 125 leaves, max depth = 16, train loss: 0.28755, val loss: 0.29109, in 0.016s 1 tree, 111 leaves, max depth = 15, train loss: 0.28620, val loss: 0.28980, in 0.016s 1 tree, 176 leaves, max depth = 20, train loss: 0.28417, val loss: 0.28865, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28287, val loss: 0.28699, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.28173, val loss: 0.28561, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28061, val loss: 0.28414, in 0.000s 1 tree, 126 leaves, max depth = 17, train loss: 0.27908, val loss: 0.28334, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.27808, val loss: 0.28201, in 0.000s 1 tree, 110 leaves, max depth = 14, train loss: 0.27608, val loss: 0.28086, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.27498, val loss: 0.27952, in 0.016s 1 tree, 182 leaves, max depth = 20, train loss: 0.27304, val loss: 0.27829, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27211, val loss: 0.27704, in 0.016s 1 tree, 84 leaves, max depth = 20, train loss: 0.27104, val loss: 0.27603, in 0.016s 1 tree, 163 leaves, max depth = 27, train loss: 0.26927, val loss: 0.27515, in 0.016s 1 tree, 175 leaves, max depth = 21, train loss: 0.26764, val loss: 0.27434, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26676, val loss: 0.27315, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.26580, val loss: 0.27233, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.26503, val loss: 0.27127, in 0.016s 1 tree, 165 leaves, max depth = 27, train loss: 0.26351, val loss: 0.27061, in 0.016s 1 tree, 108 leaves, max depth = 15, train loss: 0.26252, val loss: 0.26971, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26183, val loss: 0.26873, in 0.016s 1 tree, 177 leaves, max depth = 21, train loss: 0.26035, val loss: 0.26808, in 0.016s Fit 74 trees in 1.236 s, (6131 total leaves) Time spent computing histograms: 0.410s Time spent finding best splits: 0.103s Time spent applying splits: 0.112s Time spent predicting: 0.016s Trial 54, Fold 1: Log loss = 0.2760671375760067, Average precision = 0.9524626419368991, ROC-AUC = 0.9524715405333066, Elapsed Time = 1.2402317999985826 seconds Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 54, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 78 leaves, max depth = 19, train loss: 0.64875, val loss: 0.64656, in 0.016s 1 tree, 77 leaves, max depth = 16, train loss: 0.61286, val loss: 0.60902, in 0.016s 1 tree, 80 leaves, max depth = 20, train loss: 0.58280, val loss: 0.57733, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.55806, val loss: 0.55117, in 0.016s 1 tree, 132 leaves, max depth = 17, train loss: 0.53579, val loss: 0.52983, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.51670, val loss: 0.50966, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.49910, val loss: 0.49290, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.48410, val loss: 0.47699, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.46992, val loss: 0.46358, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.45802, val loss: 0.45083, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.44642, val loss: 0.43995, in 0.016s 1 tree, 77 leaves, max depth = 17, train loss: 0.43683, val loss: 0.42969, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.42720, val loss: 0.42075, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.41900, val loss: 0.41320, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.41140, val loss: 0.40505, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40501, val loss: 0.39841, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.39883, val loss: 0.39176, in 0.000s 1 tree, 124 leaves, max depth = 15, train loss: 0.39204, val loss: 0.38562, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38680, val loss: 0.38015, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.38099, val loss: 0.37499, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37655, val loss: 0.37031, in 0.000s 1 tree, 84 leaves, max depth = 12, train loss: 0.37131, val loss: 0.36470, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.36634, val loss: 0.36036, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36255, val loss: 0.35636, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.35820, val loss: 0.35171, in 0.016s 1 tree, 127 leaves, max depth = 19, train loss: 0.35390, val loss: 0.34801, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.35031, val loss: 0.34426, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34704, val loss: 0.34080, in 0.000s 1 tree, 127 leaves, max depth = 18, train loss: 0.34326, val loss: 0.33762, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.34041, val loss: 0.33459, in 0.000s 1 tree, 99 leaves, max depth = 15, train loss: 0.33699, val loss: 0.33095, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33453, val loss: 0.32833, in 0.016s 1 tree, 87 leaves, max depth = 16, train loss: 0.33170, val loss: 0.32533, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.32823, val loss: 0.32244, in 0.016s 1 tree, 101 leaves, max depth = 16, train loss: 0.32554, val loss: 0.31960, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.32333, val loss: 0.31722, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.32026, val loss: 0.31471, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.31792, val loss: 0.31223, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31593, val loss: 0.31009, in 0.000s 1 tree, 130 leaves, max depth = 19, train loss: 0.31319, val loss: 0.30785, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.31113, val loss: 0.30587, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30934, val loss: 0.30394, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.30743, val loss: 0.30195, in 0.016s 1 tree, 127 leaves, max depth = 20, train loss: 0.30495, val loss: 0.29995, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30334, val loss: 0.29820, in 0.000s 1 tree, 131 leaves, max depth = 20, train loss: 0.30117, val loss: 0.29647, in 0.031s 1 tree, 102 leaves, max depth = 17, train loss: 0.29942, val loss: 0.29461, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.29788, val loss: 0.29322, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.29639, val loss: 0.29160, in 0.016s 1 tree, 130 leaves, max depth = 17, train loss: 0.29440, val loss: 0.29003, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.29299, val loss: 0.28856, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29164, val loss: 0.28708, in 0.000s 1 tree, 130 leaves, max depth = 19, train loss: 0.28985, val loss: 0.28569, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.28764, val loss: 0.28416, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.28632, val loss: 0.28274, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.28506, val loss: 0.28135, in 0.000s 1 tree, 158 leaves, max depth = 21, train loss: 0.28320, val loss: 0.28065, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28208, val loss: 0.27940, in 0.016s 1 tree, 85 leaves, max depth = 20, train loss: 0.28077, val loss: 0.27834, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.27979, val loss: 0.27724, in 0.016s 1 tree, 108 leaves, max depth = 15, train loss: 0.27786, val loss: 0.27594, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.27672, val loss: 0.27473, in 0.016s 1 tree, 182 leaves, max depth = 19, train loss: 0.27482, val loss: 0.27404, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27390, val loss: 0.27301, in 0.016s 1 tree, 130 leaves, max depth = 17, train loss: 0.27256, val loss: 0.27206, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.27151, val loss: 0.27110, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27066, val loss: 0.27015, in 0.000s 1 tree, 187 leaves, max depth = 18, train loss: 0.26879, val loss: 0.26947, in 0.016s [69/74] 1 tree, 113 leaves, max depth = 15, train loss: 0.26762, val loss: 0.26843, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26685, val loss: 0.26755, in 0.016s 1 tree, 157 leaves, max depth = 21, train loss: 0.26538, val loss: 0.26712, in 0.016s 1 tree, 108 leaves, max depth = 23, train loss: 0.26427, val loss: 0.26617, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26357, val loss: 0.26536, in 0.016s 1 tree, 186 leaves, max depth = 18, train loss: 0.26191, val loss: 0.26482, in 0.016s Fit 74 trees in 1.330 s, (6067 total leaves) Time spent computing histograms: 0.421s Time spent finding best splits: 0.109s Time spent applying splits: 0.120s Time spent predicting: 0.016s Trial 54, Fold 2: Log loss = 0.27454362553156353, Average precision = 0.9524217169185072, ROC-AUC = 0.9541073724840596, Elapsed Time = 1.341459000001123 seconds Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 54, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.205 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 80 leaves, max depth = 15, train loss: 0.64887, val loss: 0.64767, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.61312, val loss: 0.61053, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.58330, val loss: 0.57976, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.55880, val loss: 0.55430, in 0.016s 1 tree, 130 leaves, max depth = 16, train loss: 0.53612, val loss: 0.53336, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.51725, val loss: 0.51360, in 0.031s 1 tree, 132 leaves, max depth = 16, train loss: 0.49934, val loss: 0.49717, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.48417, val loss: 0.48127, in 0.016s 1 tree, 134 leaves, max depth = 16, train loss: 0.46978, val loss: 0.46814, in 0.016s 1 tree, 78 leaves, max depth = 17, train loss: 0.45806, val loss: 0.45565, in 0.016s 1 tree, 134 leaves, max depth = 17, train loss: 0.44629, val loss: 0.44500, in 0.031s 1 tree, 77 leaves, max depth = 18, train loss: 0.43691, val loss: 0.43492, in 0.000s 1 tree, 133 leaves, max depth = 16, train loss: 0.42716, val loss: 0.42616, in 0.031s 1 tree, 134 leaves, max depth = 16, train loss: 0.41885, val loss: 0.41876, in 0.016s 1 tree, 87 leaves, max depth = 12, train loss: 0.41138, val loss: 0.41063, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40475, val loss: 0.40446, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39917, val loss: 0.39927, in 0.016s 1 tree, 131 leaves, max depth = 15, train loss: 0.39226, val loss: 0.39323, in 0.016s 1 tree, 87 leaves, max depth = 13, train loss: 0.38643, val loss: 0.38685, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.38056, val loss: 0.38177, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37590, val loss: 0.37742, in 0.016s 1 tree, 129 leaves, max depth = 16, train loss: 0.37085, val loss: 0.37313, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36686, val loss: 0.36940, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.36193, val loss: 0.36406, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.35777, val loss: 0.35965, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.35334, val loss: 0.35594, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.34957, val loss: 0.35284, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34612, val loss: 0.34959, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.34255, val loss: 0.34559, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33960, val loss: 0.34281, in 0.000s 1 tree, 103 leaves, max depth = 16, train loss: 0.33630, val loss: 0.33892, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33376, val loss: 0.33652, in 0.016s 1 tree, 122 leaves, max depth = 16, train loss: 0.33022, val loss: 0.33368, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.32737, val loss: 0.33029, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32510, val loss: 0.32814, in 0.016s 1 tree, 86 leaves, max depth = 16, train loss: 0.32258, val loss: 0.32558, in 0.000s 1 tree, 124 leaves, max depth = 15, train loss: 0.31939, val loss: 0.32309, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.31714, val loss: 0.32038, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.31507, val loss: 0.31842, in 0.000s 1 tree, 127 leaves, max depth = 15, train loss: 0.31223, val loss: 0.31622, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31040, val loss: 0.31448, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.30839, val loss: 0.31243, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.30655, val loss: 0.31023, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.30400, val loss: 0.30831, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30231, val loss: 0.30669, in 0.016s 1 tree, 129 leaves, max depth = 19, train loss: 0.30001, val loss: 0.30497, in 0.016s 1 tree, 102 leaves, max depth = 17, train loss: 0.29833, val loss: 0.30290, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.29635, val loss: 0.30143, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29476, val loss: 0.29991, in 0.016s 1 tree, 87 leaves, max depth = 13, train loss: 0.29324, val loss: 0.29836, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.29188, val loss: 0.29667, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29047, val loss: 0.29532, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.28858, val loss: 0.29402, in 0.016s 1 tree, 161 leaves, max depth = 24, train loss: 0.28644, val loss: 0.29312, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.28526, val loss: 0.29197, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28394, val loss: 0.29070, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.28276, val loss: 0.28925, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28162, val loss: 0.28814, in 0.016s [59/74] 1 tree, 127 leaves, max depth = 17, train loss: 0.28001, val loss: 0.28700, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27898, val loss: 0.28601, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.27692, val loss: 0.28467, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.27579, val loss: 0.28326, in 0.016s 1 tree, 175 leaves, max depth = 19, train loss: 0.27393, val loss: 0.28213, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.27298, val loss: 0.28120, in 0.000s 1 tree, 81 leaves, max depth = 15, train loss: 0.27188, val loss: 0.28019, in 0.000s 1 tree, 180 leaves, max depth = 19, train loss: 0.27007, val loss: 0.27918, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.26920, val loss: 0.27833, in 0.016s 1 tree, 161 leaves, max depth = 24, train loss: 0.26747, val loss: 0.27780, in 0.016s 1 tree, 113 leaves, max depth = 17, train loss: 0.26639, val loss: 0.27660, in 0.031s 1 tree, 182 leaves, max depth = 19, train loss: 0.26486, val loss: 0.27576, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26403, val loss: 0.27495, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.26317, val loss: 0.27384, in 0.016s 1 tree, 162 leaves, max depth = 24, train loss: 0.26170, val loss: 0.27349, in 0.031s 1 tree, 183 leaves, max depth = 19, train loss: 0.26039, val loss: 0.27279, in 0.016s Fit 74 trees in 1.549 s, (6333 total leaves) Time spent computing histograms: 0.471s Time spent finding best splits: 0.125s Time spent applying splits: 0.139s Time spent predicting: 0.016s Trial 54, Fold 3: Log loss = 0.26791224175750544, Average precision = 0.9581761600803326, ROC-AUC = 0.9569774140189347, Elapsed Time = 1.5581058999996458 seconds Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 54, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 76 leaves, max depth = 17, train loss: 0.64889, val loss: 0.64626, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.61281, val loss: 0.60783, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.58304, val loss: 0.57592, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.55838, val loss: 0.54963, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.53601, val loss: 0.52804, in 0.016s 1 tree, 81 leaves, max depth = 18, train loss: 0.51684, val loss: 0.50717, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.49919, val loss: 0.49024, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.48424, val loss: 0.47395, in 0.016s 1 tree, 129 leaves, max depth = 18, train loss: 0.47006, val loss: 0.46042, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.45807, val loss: 0.44708, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.44649, val loss: 0.43609, in 0.016s 1 tree, 84 leaves, max depth = 18, train loss: 0.43687, val loss: 0.42531, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.42731, val loss: 0.41629, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.41917, val loss: 0.40864, in 0.031s 1 tree, 84 leaves, max depth = 15, train loss: 0.41166, val loss: 0.40008, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40529, val loss: 0.39321, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.39910, val loss: 0.38612, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.39237, val loss: 0.37990, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38713, val loss: 0.37423, in 0.000s 1 tree, 127 leaves, max depth = 16, train loss: 0.38138, val loss: 0.36899, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37693, val loss: 0.36417, in 0.016s 1 tree, 86 leaves, max depth = 15, train loss: 0.37201, val loss: 0.35853, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.36705, val loss: 0.35407, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36328, val loss: 0.34997, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.35903, val loss: 0.34528, in 0.000s 1 tree, 124 leaves, max depth = 15, train loss: 0.35471, val loss: 0.34149, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.35127, val loss: 0.33750, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34801, val loss: 0.33396, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.34423, val loss: 0.33067, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34139, val loss: 0.32757, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.33808, val loss: 0.32419, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33564, val loss: 0.32152, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.33300, val loss: 0.31855, in 0.016s 1 tree, 122 leaves, max depth = 16, train loss: 0.32952, val loss: 0.31558, in 0.031s 1 tree, 106 leaves, max depth = 17, train loss: 0.32690, val loss: 0.31293, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32469, val loss: 0.31051, in 0.000s 1 tree, 122 leaves, max depth = 18, train loss: 0.32158, val loss: 0.30790, in 0.031s 1 tree, 104 leaves, max depth = 16, train loss: 0.31927, val loss: 0.30560, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31729, val loss: 0.30343, in 0.000s 1 tree, 119 leaves, max depth = 18, train loss: 0.31451, val loss: 0.30115, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.31249, val loss: 0.29916, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31071, val loss: 0.29720, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.30897, val loss: 0.29531, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.30645, val loss: 0.29323, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30485, val loss: 0.29146, in 0.016s 1 tree, 123 leaves, max depth = 19, train loss: 0.30259, val loss: 0.28973, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.30086, val loss: 0.28808, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.29942, val loss: 0.28677, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29794, val loss: 0.28513, in 0.000s 1 tree, 126 leaves, max depth = 15, train loss: 0.29594, val loss: 0.28356, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.29452, val loss: 0.28223, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.29318, val loss: 0.28074, in 0.000s 1 tree, 108 leaves, max depth = 17, train loss: 0.29069, val loss: 0.27878, in 0.016s 1 tree, 176 leaves, max depth = 26, train loss: 0.28861, val loss: 0.27773, in 0.031s 1 tree, 106 leaves, max depth = 17, train loss: 0.28728, val loss: 0.27648, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28603, val loss: 0.27508, in 0.000s 1 tree, 174 leaves, max depth = 22, train loss: 0.28391, val loss: 0.27410, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.28281, val loss: 0.27288, in 0.000s 1 tree, 128 leaves, max depth = 14, train loss: 0.28121, val loss: 0.27174, in 0.031s 1 tree, 111 leaves, max depth = 16, train loss: 0.27988, val loss: 0.27062, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27888, val loss: 0.26950, in 0.000s 1 tree, 177 leaves, max depth = 26, train loss: 0.27705, val loss: 0.26864, in 0.031s 1 tree, 82 leaves, max depth = 15, train loss: 0.27593, val loss: 0.26727, in 0.016s 1 tree, 158 leaves, max depth = 28, train loss: 0.27411, val loss: 0.26609, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27316, val loss: 0.26503, in 0.016s 1 tree, 113 leaves, max depth = 16, train loss: 0.27198, val loss: 0.26419, in 0.016s 1 tree, 175 leaves, max depth = 23, train loss: 0.27041, val loss: 0.26348, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26954, val loss: 0.26252, in 0.016s 1 tree, 107 leaves, max depth = 19, train loss: 0.26861, val loss: 0.26171, in 0.016s 1 tree, 176 leaves, max depth = 23, train loss: 0.26720, val loss: 0.26108, in 0.016s 1 tree, 161 leaves, max depth = 26, train loss: 0.26567, val loss: 0.26010, in 0.031s 1 tree, 175 leaves, max depth = 21, train loss: 0.26405, val loss: 0.25945, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.26322, val loss: 0.25852, in 0.000s 1 tree, 52 leaves, max depth = 10, train loss: 0.26233, val loss: 0.25781, in 0.016s Fit 74 trees in 1.424 s, (6314 total leaves) Time spent computing histograms: 0.459s Time spent finding best splits: 0.118s Time spent applying splits: 0.130s Time spent predicting: 0.016s Trial 54, Fold 4: Log loss = 0.27029483261290455, Average precision = 0.9573415530068022, ROC-AUC = 0.9565255036469547, Elapsed Time = 1.4256296999992628 seconds Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 54, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.172 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 79 leaves, max depth = 16, train loss: 0.64882, val loss: 0.64587, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.61270, val loss: 0.60711, in 0.016s 1 tree, 82 leaves, max depth = 21, train loss: 0.58241, val loss: 0.57442, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.55762, val loss: 0.54747, in 0.000s 1 tree, 133 leaves, max depth = 16, train loss: 0.53517, val loss: 0.52612, in 0.016s 1 tree, 81 leaves, max depth = 21, train loss: 0.51564, val loss: 0.50472, in 0.031s 1 tree, 132 leaves, max depth = 16, train loss: 0.49794, val loss: 0.48806, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.48261, val loss: 0.47111, in 0.016s 1 tree, 132 leaves, max depth = 20, train loss: 0.46838, val loss: 0.45786, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.45638, val loss: 0.44452, in 0.016s 1 tree, 134 leaves, max depth = 17, train loss: 0.44474, val loss: 0.43381, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.43510, val loss: 0.42297, in 0.016s 1 tree, 131 leaves, max depth = 19, train loss: 0.42544, val loss: 0.41420, in 0.016s 1 tree, 132 leaves, max depth = 19, train loss: 0.41722, val loss: 0.40677, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.40957, val loss: 0.39811, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40314, val loss: 0.39191, in 0.000s 1 tree, 82 leaves, max depth = 18, train loss: 0.39675, val loss: 0.38456, in 0.016s 1 tree, 131 leaves, max depth = 19, train loss: 0.38994, val loss: 0.37860, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.38468, val loss: 0.37355, in 0.000s 1 tree, 129 leaves, max depth = 20, train loss: 0.37886, val loss: 0.36851, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37438, val loss: 0.36423, in 0.016s 1 tree, 130 leaves, max depth = 20, train loss: 0.36937, val loss: 0.35994, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.36440, val loss: 0.35410, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36059, val loss: 0.35046, in 0.000s 1 tree, 83 leaves, max depth = 14, train loss: 0.35629, val loss: 0.34534, in 0.016s 1 tree, 131 leaves, max depth = 18, train loss: 0.35193, val loss: 0.34173, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.34841, val loss: 0.33773, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34514, val loss: 0.33459, in 0.016s 1 tree, 130 leaves, max depth = 19, train loss: 0.34130, val loss: 0.33147, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33845, val loss: 0.32874, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.33493, val loss: 0.32510, in 0.016s 1 tree, 130 leaves, max depth = 22, train loss: 0.33154, val loss: 0.32237, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32901, val loss: 0.31996, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.32597, val loss: 0.31680, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.32351, val loss: 0.31381, in 0.016s 1 tree, 130 leaves, max depth = 23, train loss: 0.32044, val loss: 0.31141, in 0.016s 1 tree, 110 leaves, max depth = 15, train loss: 0.31806, val loss: 0.30900, in 0.016s 1 tree, 129 leaves, max depth = 24, train loss: 0.31541, val loss: 0.30694, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31306, val loss: 0.30467, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31105, val loss: 0.30274, in 0.000s 1 tree, 104 leaves, max depth = 16, train loss: 0.30887, val loss: 0.30053, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30713, val loss: 0.29887, in 0.016s 1 tree, 88 leaves, max depth = 13, train loss: 0.30530, val loss: 0.29665, in 0.016s 1 tree, 124 leaves, max depth = 22, train loss: 0.30271, val loss: 0.29467, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30113, val loss: 0.29316, in 0.000s 1 tree, 126 leaves, max depth = 22, train loss: 0.29887, val loss: 0.29145, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.29701, val loss: 0.28960, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.29547, val loss: 0.28806, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29400, val loss: 0.28665, in 0.000s 1 tree, 125 leaves, max depth = 22, train loss: 0.29193, val loss: 0.28509, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.29044, val loss: 0.28340, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28910, val loss: 0.28211, in 0.016s 1 tree, 126 leaves, max depth = 23, train loss: 0.28725, val loss: 0.28076, in 0.016s 1 tree, 110 leaves, max depth = 18, train loss: 0.28585, val loss: 0.27942, in 0.016s 1 tree, 179 leaves, max depth = 21, train loss: 0.28379, val loss: 0.27799, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28253, val loss: 0.27676, in 0.016s 1 tree, 184 leaves, max depth = 21, train loss: 0.28038, val loss: 0.27615, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27927, val loss: 0.27508, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.27803, val loss: 0.27389, in 0.016s 1 tree, 177 leaves, max depth = 22, train loss: 0.27617, val loss: 0.27259, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27516, val loss: 0.27162, in 0.016s 1 tree, 129 leaves, max depth = 22, train loss: 0.27371, val loss: 0.27055, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.27255, val loss: 0.26944, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27163, val loss: 0.26856, in 0.016s 1 tree, 158 leaves, max depth = 21, train loss: 0.26980, val loss: 0.26755, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.26868, val loss: 0.26629, in 0.016s 1 tree, 178 leaves, max depth = 22, train loss: 0.26716, val loss: 0.26528, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.26628, val loss: 0.26443, in 0.000s 1 tree, 101 leaves, max depth = 17, train loss: 0.26529, val loss: 0.26352, in 0.016s 1 tree, 186 leaves, max depth = 21, train loss: 0.26360, val loss: 0.26318, in 0.031s 1 tree, 101 leaves, max depth = 16, train loss: 0.26181, val loss: 0.26204, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.26095, val loss: 0.26113, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26012, val loss: 0.26033, in 0.000s 1 tree, 160 leaves, max depth = 26, train loss: 0.25868, val loss: 0.25963, in 0.016s Fit 74 trees in 1.391 s, (6375 total leaves) Time spent computing histograms: 0.444s Time spent finding best splits: 0.117s Time spent applying splits: 0.131s Time spent predicting: 0.016s Trial 54, Fold 5: Log loss = 0.27748913248953944, Average precision = 0.9528118582715788, ROC-AUC = 0.9521970904631849, Elapsed Time = 1.414216099999976 seconds
Optimization Progress: 55%|#####5 | 55/100 [10:49<09:26, 12.59s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 55, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 Binning 0.040 GB of training data: 0.205 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 11, train loss: 0.67308, val loss: 0.67327, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.65437, val loss: 0.65472, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.63687, val loss: 0.63735, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.62118, val loss: 0.62166, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.60572, val loss: 0.60620, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.59126, val loss: 0.59186, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.57761, val loss: 0.57821, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.56481, val loss: 0.56552, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.55271, val loss: 0.55342, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.54103, val loss: 0.54164, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.53002, val loss: 0.53053, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.51963, val loss: 0.52003, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.50978, val loss: 0.51010, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.50045, val loss: 0.50067, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.49163, val loss: 0.49181, in 0.016s Fit 15 trees in 0.705 s, (720 total leaves) Time spent computing histograms: 0.120s Time spent finding best splits: 0.025s Time spent applying splits: 0.016s Time spent predicting: 0.000s Trial 55, Fold 1: Log loss = 0.4936647614237157, Average precision = 0.9200493818973322, ROC-AUC = 0.9309645838411647, Elapsed Time = 0.7165882999997848 seconds Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 55, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.189 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 10, train loss: 0.67272, val loss: 0.67242, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.65384, val loss: 0.65320, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.63603, val loss: 0.63512, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.62028, val loss: 0.61923, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.60470, val loss: 0.60342, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.58994, val loss: 0.58846, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.57619, val loss: 0.57448, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.56313, val loss: 0.56116, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.55083, val loss: 0.54860, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.53933, val loss: 0.53690, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.52833, val loss: 0.52578, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.51795, val loss: 0.51523, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.50814, val loss: 0.50526, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.49888, val loss: 0.49584, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.49008, val loss: 0.48688, in 0.016s Fit 15 trees in 0.689 s, (720 total leaves) Time spent computing histograms: 0.119s Time spent finding best splits: 0.026s Time spent applying splits: 0.016s Time spent predicting: 0.016s Trial 55, Fold 2: Log loss = 0.4922320066760393, Average precision = 0.9170846482239512, ROC-AUC = 0.9323047059112742, Elapsed Time = 0.700506299999688 seconds Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 55, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 12, train loss: 0.67321, val loss: 0.67331, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.65463, val loss: 0.65480, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.63688, val loss: 0.63717, in 0.031s 1 tree, 48 leaves, max depth = 9, train loss: 0.62113, val loss: 0.62153, in 0.016s 1 tree, 48 leaves, max depth = 8, train loss: 0.60574, val loss: 0.60607, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.59103, val loss: 0.59146, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.57792, val loss: 0.57843, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.56484, val loss: 0.56542, in 0.016s 1 tree, 48 leaves, max depth = 9, train loss: 0.55250, val loss: 0.55314, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.54087, val loss: 0.54160, in 0.016s 1 tree, 48 leaves, max depth = 8, train loss: 0.52990, val loss: 0.53071, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.51951, val loss: 0.52041, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.50970, val loss: 0.51066, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.50040, val loss: 0.50143, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.49162, val loss: 0.49271, in 0.016s Fit 15 trees in 0.626 s, (720 total leaves) Time spent computing histograms: 0.114s Time spent finding best splits: 0.023s Time spent applying splits: 0.015s Time spent predicting: 0.016s Trial 55, Fold 3: Log loss = 0.49051110730915715, Average precision = 0.9220649795799083, ROC-AUC = 0.9346053472710462, Elapsed Time = 0.6382448000003933 seconds Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 55, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 11, train loss: 0.67318, val loss: 0.67245, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.65456, val loss: 0.65314, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.63718, val loss: 0.63508, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.62175, val loss: 0.61907, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.60642, val loss: 0.60308, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.59198, val loss: 0.58805, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.57833, val loss: 0.57381, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.56549, val loss: 0.56039, in 0.032s 1 tree, 48 leaves, max depth = 10, train loss: 0.55316, val loss: 0.54755, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.54228, val loss: 0.53615, in 0.016s 1 tree, 48 leaves, max depth = 8, train loss: 0.53138, val loss: 0.52466, in 0.020s 1 tree, 48 leaves, max depth = 10, train loss: 0.52096, val loss: 0.51377, in 0.011s 1 tree, 48 leaves, max depth = 10, train loss: 0.51110, val loss: 0.50345, in 0.031s 1 tree, 48 leaves, max depth = 10, train loss: 0.50178, val loss: 0.49369, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.49293, val loss: 0.48448, in 0.016s Fit 15 trees in 0.643 s, (720 total leaves) Time spent computing histograms: 0.117s Time spent finding best splits: 0.023s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 55, Fold 4: Log loss = 0.4929778956507077, Average precision = 0.9227369323898403, ROC-AUC = 0.9334807047599973, Elapsed Time = 0.6573267000003398 seconds Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 55, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 13, train loss: 0.67295, val loss: 0.67229, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.65417, val loss: 0.65295, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.63632, val loss: 0.63449, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.62056, val loss: 0.61813, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.60481, val loss: 0.60184, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.59003, val loss: 0.58654, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.57614, val loss: 0.57215, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.56307, val loss: 0.55862, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.55075, val loss: 0.54583, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.53977, val loss: 0.53446, in 0.016s 1 tree, 48 leaves, max depth = 8, train loss: 0.52889, val loss: 0.52312, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.51845, val loss: 0.51231, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.50858, val loss: 0.50208, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.49924, val loss: 0.49236, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.49040, val loss: 0.48320, in 0.031s Fit 15 trees in 0.658 s, (720 total leaves) Time spent computing histograms: 0.122s Time spent finding best splits: 0.023s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 55, Fold 5: Log loss = 0.49450128925862646, Average precision = 0.9170951877765856, ROC-AUC = 0.930760752838006, Elapsed Time = 0.6636056999996072 seconds
Optimization Progress: 56%|#####6 | 56/100 [11:00<08:50, 12.06s/it]
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 56, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 8, train loss: 0.68062, val loss: 0.68020, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.66875, val loss: 0.66793, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.65749, val loss: 0.65628, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.64681, val loss: 0.64522, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.63667, val loss: 0.63471, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.62704, val loss: 0.62471, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.61789, val loss: 0.61520, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.60918, val loss: 0.60615, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.60090, val loss: 0.59753, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.59301, val loss: 0.58931, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.58550, val loss: 0.58148, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.57835, val loss: 0.57401, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.57153, val loss: 0.56689, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.56503, val loss: 0.56009, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.55888, val loss: 0.55368, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.55301, val loss: 0.54757, in 0.000s 1 tree, 38 leaves, max depth = 9, train loss: 0.54710, val loss: 0.54203, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.54157, val loss: 0.53622, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.53634, val loss: 0.53076, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.53088, val loss: 0.52566, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.52568, val loss: 0.52081, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.52072, val loss: 0.51620, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.51596, val loss: 0.51117, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.51130, val loss: 0.50685, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.50691, val loss: 0.50221, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.50252, val loss: 0.49814, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.49840, val loss: 0.49378, in 0.019s 1 tree, 39 leaves, max depth = 9, train loss: 0.49426, val loss: 0.48996, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.49038, val loss: 0.48584, in 0.013s 1 tree, 39 leaves, max depth = 9, train loss: 0.48647, val loss: 0.48225, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.48282, val loss: 0.47841, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.47913, val loss: 0.47503, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.47560, val loss: 0.47180, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.47221, val loss: 0.46819, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.46887, val loss: 0.46514, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46567, val loss: 0.46173, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.46250, val loss: 0.45885, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.45946, val loss: 0.45562, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.45646, val loss: 0.45291, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.45361, val loss: 0.44985, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.45089, val loss: 0.44692, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44806, val loss: 0.44438, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44536, val loss: 0.44196, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.44279, val loss: 0.43922, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44023, val loss: 0.43693, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.43778, val loss: 0.43476, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.43538, val loss: 0.43217, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.43305, val loss: 0.43010, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.43077, val loss: 0.42766, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.42856, val loss: 0.42571, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.42641, val loss: 0.42337, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.42430, val loss: 0.42152, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.42225, val loss: 0.41931, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.42024, val loss: 0.41757, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.41830, val loss: 0.41544, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.41638, val loss: 0.41379, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.41454, val loss: 0.41176, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41273, val loss: 0.40976, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.41091, val loss: 0.40820, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40918, val loss: 0.40629, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40753, val loss: 0.40445, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40579, val loss: 0.40299, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40422, val loss: 0.40124, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40256, val loss: 0.39985, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.40088, val loss: 0.39805, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39938, val loss: 0.39638, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.39780, val loss: 0.39507, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39637, val loss: 0.39348, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.39479, val loss: 0.39179, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.39328, val loss: 0.39055, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39192, val loss: 0.38903, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.39039, val loss: 0.38742, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.38895, val loss: 0.38626, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38765, val loss: 0.38480, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38640, val loss: 0.38341, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.38502, val loss: 0.38231, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.38358, val loss: 0.38078, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38239, val loss: 0.37944, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.38107, val loss: 0.37840, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.37972, val loss: 0.37697, in 0.016s Fit 80 trees in 0.994 s, (2050 total leaves) Time spent computing histograms: 0.374s Time spent finding best splits: 0.059s Time spent applying splits: 0.049s Time spent predicting: 0.000s Trial 56, Fold 1: Log loss = 0.380684813906018, Average precision = 0.9458659296414029, ROC-AUC = 0.9424333575897487, Elapsed Time = 1.0017719000006764 seconds Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 56, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 8, train loss: 0.68065, val loss: 0.68003, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.66881, val loss: 0.66760, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.65775, val loss: 0.65597, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.64709, val loss: 0.64476, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.63697, val loss: 0.63411, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.62736, val loss: 0.62397, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.61823, val loss: 0.61434, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.60967, val loss: 0.60530, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.60140, val loss: 0.59656, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.59353, val loss: 0.58822, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.58603, val loss: 0.58028, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.57900, val loss: 0.57283, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.57219, val loss: 0.56560, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.56570, val loss: 0.55870, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.55951, val loss: 0.55212, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.55371, val loss: 0.54594, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.54783, val loss: 0.54020, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.54241, val loss: 0.53442, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.53724, val loss: 0.52889, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.53179, val loss: 0.52360, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.52660, val loss: 0.51856, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.52166, val loss: 0.51376, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.51699, val loss: 0.50876, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.51234, val loss: 0.50425, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.50796, val loss: 0.49956, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.50358, val loss: 0.49531, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.49947, val loss: 0.49089, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.49533, val loss: 0.48689, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.49146, val loss: 0.48273, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48756, val loss: 0.47896, in 0.000s 1 tree, 21 leaves, max depth = 10, train loss: 0.48391, val loss: 0.47504, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48022, val loss: 0.47148, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.47670, val loss: 0.46808, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.47332, val loss: 0.46443, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.46998, val loss: 0.46123, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46680, val loss: 0.45778, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.46364, val loss: 0.45475, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46063, val loss: 0.45149, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.45764, val loss: 0.44862, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.45480, val loss: 0.44554, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.45208, val loss: 0.44259, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44926, val loss: 0.43989, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44656, val loss: 0.43731, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.44398, val loss: 0.43484, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.44147, val loss: 0.43211, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.43902, val loss: 0.42977, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.43664, val loss: 0.42718, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.43431, val loss: 0.42496, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.43205, val loss: 0.42250, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.42983, val loss: 0.42039, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.42770, val loss: 0.41805, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.42558, val loss: 0.41605, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.42356, val loss: 0.41383, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.42154, val loss: 0.41192, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.41962, val loss: 0.40982, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.41770, val loss: 0.40800, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.41586, val loss: 0.40627, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41408, val loss: 0.40443, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.41229, val loss: 0.40246, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41061, val loss: 0.40072, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40887, val loss: 0.39908, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40726, val loss: 0.39741, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40560, val loss: 0.39586, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.40392, val loss: 0.39402, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40241, val loss: 0.39243, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40082, val loss: 0.39095, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39937, val loss: 0.38944, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.39776, val loss: 0.38767, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.39625, val loss: 0.38626, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39487, val loss: 0.38482, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.39342, val loss: 0.38348, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.39189, val loss: 0.38182, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39058, val loss: 0.38044, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.37913, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.38793, val loss: 0.37785, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.38651, val loss: 0.37627, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38530, val loss: 0.37501, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.38398, val loss: 0.37379, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38283, val loss: 0.37259, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.38147, val loss: 0.37110, in 0.016s Fit 80 trees in 1.095 s, (2059 total leaves) Time spent computing histograms: 0.404s Time spent finding best splits: 0.063s Time spent applying splits: 0.053s Time spent predicting: 0.000s Trial 56, Fold 2: Log loss = 0.3832620626880714, Average precision = 0.9423748606646112, ROC-AUC = 0.9432571023274772, Elapsed Time = 1.0971567000015057 seconds Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 56, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 7, train loss: 0.68072, val loss: 0.68033, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.66895, val loss: 0.66819, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.65779, val loss: 0.65666, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.64720, val loss: 0.64572, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.63715, val loss: 0.63533, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.62760, val loss: 0.62544, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.61852, val loss: 0.61605, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.60989, val loss: 0.60710, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.60168, val loss: 0.59858, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.59386, val loss: 0.59047, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.58642, val loss: 0.58274, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.57932, val loss: 0.57537, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.57257, val loss: 0.56834, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.56613, val loss: 0.56163, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.55999, val loss: 0.55523, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.55385, val loss: 0.54946, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.54818, val loss: 0.54354, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.54243, val loss: 0.53814, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.53713, val loss: 0.53259, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53173, val loss: 0.52753, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.52677, val loss: 0.52232, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52170, val loss: 0.51757, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.51685, val loss: 0.51304, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.51229, val loss: 0.50824, in 0.016s [25/80] 1 tree, 41 leaves, max depth = 10, train loss: 0.50773, val loss: 0.50398, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.50345, val loss: 0.49947, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49915, val loss: 0.49546, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.49514, val loss: 0.49121, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49108, val loss: 0.48744, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.48730, val loss: 0.48343, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.48346, val loss: 0.47987, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.47991, val loss: 0.47610, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.47628, val loss: 0.47274, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47281, val loss: 0.46953, in 0.031s 1 tree, 21 leaves, max depth = 8, train loss: 0.46951, val loss: 0.46601, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.46623, val loss: 0.46298, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46312, val loss: 0.45966, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46000, val loss: 0.45679, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.45707, val loss: 0.45365, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.45411, val loss: 0.45093, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.45134, val loss: 0.44796, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44854, val loss: 0.44539, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44585, val loss: 0.44293, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.44326, val loss: 0.44015, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.44071, val loss: 0.43782, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.43826, val loss: 0.43517, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.43584, val loss: 0.43296, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.43351, val loss: 0.43041, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43120, val loss: 0.42831, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.42903, val loss: 0.42591, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.42683, val loss: 0.42392, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.42474, val loss: 0.42166, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.42265, val loss: 0.41977, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42065, val loss: 0.41797, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.41868, val loss: 0.41579, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41683, val loss: 0.41407, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.41493, val loss: 0.41236, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41316, val loss: 0.41072, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.41133, val loss: 0.40868, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.40952, val loss: 0.40707, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40785, val loss: 0.40552, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.40613, val loss: 0.40398, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40453, val loss: 0.40250, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.40283, val loss: 0.40060, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.40118, val loss: 0.39915, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39967, val loss: 0.39774, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.39806, val loss: 0.39594, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.39649, val loss: 0.39456, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39505, val loss: 0.39322, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39367, val loss: 0.39194, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.39216, val loss: 0.39062, in 0.000s 1 tree, 20 leaves, max depth = 8, train loss: 0.39063, val loss: 0.38890, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.38768, in 0.000s 1 tree, 40 leaves, max depth = 9, train loss: 0.38788, val loss: 0.38643, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38662, val loss: 0.38526, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.38525, val loss: 0.38408, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.38380, val loss: 0.38245, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38259, val loss: 0.38133, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.38128, val loss: 0.38020, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38012, val loss: 0.37913, in 0.016s Fit 80 trees in 1.220 s, (2110 total leaves) Time spent computing histograms: 0.450s Time spent finding best splits: 0.082s Time spent applying splits: 0.069s Time spent predicting: 0.016s Trial 56, Fold 3: Log loss = 0.3766158102262939, Average precision = 0.9477626147986153, ROC-AUC = 0.9475018298966589, Elapsed Time = 1.228315799999109 seconds Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 56, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 9, train loss: 0.68079, val loss: 0.68008, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.66909, val loss: 0.66768, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.65800, val loss: 0.65590, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.64747, val loss: 0.64471, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.63748, val loss: 0.63407, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.62799, val loss: 0.62394, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.61897, val loss: 0.61429, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.61039, val loss: 0.60510, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.60223, val loss: 0.59634, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.59446, val loss: 0.58799, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.58707, val loss: 0.58002, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.58003, val loss: 0.57241, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.57332, val loss: 0.56515, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.56693, val loss: 0.55822, in 0.000s 1 tree, 20 leaves, max depth = 9, train loss: 0.56083, val loss: 0.55159, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.55502, val loss: 0.54525, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.54914, val loss: 0.53933, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.54371, val loss: 0.53340, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.53854, val loss: 0.52773, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.53311, val loss: 0.52227, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.52794, val loss: 0.51706, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.52302, val loss: 0.51210, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.51835, val loss: 0.50697, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.51371, val loss: 0.50230, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.50933, val loss: 0.49747, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.50496, val loss: 0.49307, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.50084, val loss: 0.48852, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.49673, val loss: 0.48438, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.49286, val loss: 0.48009, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.48897, val loss: 0.47618, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.48534, val loss: 0.47218, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.48167, val loss: 0.46849, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.47817, val loss: 0.46496, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.47478, val loss: 0.46119, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.47146, val loss: 0.45785, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.46827, val loss: 0.45429, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.46513, val loss: 0.45113, in 0.016s 1 tree, 20 leaves, max depth = 12, train loss: 0.46212, val loss: 0.44776, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.45915, val loss: 0.44477, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.45630, val loss: 0.44157, in 0.000s 1 tree, 20 leaves, max depth = 12, train loss: 0.45358, val loss: 0.43851, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.45078, val loss: 0.43570, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.44811, val loss: 0.43301, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.44555, val loss: 0.43044, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.44303, val loss: 0.42759, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.44060, val loss: 0.42514, in 0.000s 1 tree, 21 leaves, max depth = 11, train loss: 0.43821, val loss: 0.42244, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.43590, val loss: 0.42011, in 0.016s 1 tree, 20 leaves, max depth = 11, train loss: 0.43364, val loss: 0.41756, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.43145, val loss: 0.41535, in 0.016s 1 tree, 21 leaves, max depth = 11, train loss: 0.42930, val loss: 0.41290, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.42721, val loss: 0.41080, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.42519, val loss: 0.40851, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.42320, val loss: 0.40651, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.42128, val loss: 0.40431, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.41939, val loss: 0.40240, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.41758, val loss: 0.40058, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41580, val loss: 0.39866, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41411, val loss: 0.39683, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.41232, val loss: 0.39479, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.41061, val loss: 0.39307, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40900, val loss: 0.39134, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.40732, val loss: 0.38941, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.40569, val loss: 0.38777, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40416, val loss: 0.38613, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40270, val loss: 0.38455, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.40115, val loss: 0.38300, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.39957, val loss: 0.38119, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39819, val loss: 0.37969, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.39670, val loss: 0.37821, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39537, val loss: 0.37678, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.39395, val loss: 0.37536, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.39247, val loss: 0.37367, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39121, val loss: 0.37230, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.38985, val loss: 0.37095, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.38845, val loss: 0.36934, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38724, val loss: 0.36803, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.38593, val loss: 0.36674, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38478, val loss: 0.36548, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.38339, val loss: 0.36388, in 0.000s Fit 80 trees in 1.158 s, (2004 total leaves) Time spent computing histograms: 0.430s Time spent finding best splits: 0.067s Time spent applying splits: 0.056s Time spent predicting: 0.000s Trial 56, Fold 4: Log loss = 0.38315428635904075, Average precision = 0.945962665443955, ROC-AUC = 0.9426270026061598, Elapsed Time = 1.1737682000002678 seconds Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 56, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 8, train loss: 0.68073, val loss: 0.67990, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.66897, val loss: 0.66733, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.65776, val loss: 0.65533, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.64712, val loss: 0.64392, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.63707, val loss: 0.63313, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.62748, val loss: 0.62280, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.61836, val loss: 0.61297, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.60973, val loss: 0.60365, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.60147, val loss: 0.59471, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.59361, val loss: 0.58620, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.58617, val loss: 0.57811, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.57904, val loss: 0.57035, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.57225, val loss: 0.56294, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.56581, val loss: 0.55590, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.55964, val loss: 0.54914, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.55375, val loss: 0.54268, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.54790, val loss: 0.53706, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.54242, val loss: 0.53103, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.53720, val loss: 0.52527, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.53179, val loss: 0.52010, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.52664, val loss: 0.51518, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.52184, val loss: 0.50986, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.51699, val loss: 0.50525, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.51249, val loss: 0.50025, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.50792, val loss: 0.49592, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.50357, val loss: 0.49179, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.49941, val loss: 0.48716, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.49530, val loss: 0.48328, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.49139, val loss: 0.47892, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.48751, val loss: 0.47527, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.48383, val loss: 0.47115, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.48017, val loss: 0.46771, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.47670, val loss: 0.46382, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.47324, val loss: 0.46058, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.46996, val loss: 0.45690, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.46668, val loss: 0.45384, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.46355, val loss: 0.45093, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.46050, val loss: 0.44749, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.45752, val loss: 0.44473, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.45464, val loss: 0.44147, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.45189, val loss: 0.43835, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.44909, val loss: 0.43577, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.44641, val loss: 0.43330, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.44383, val loss: 0.43037, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.44129, val loss: 0.42804, in 0.000s 1 tree, 21 leaves, max depth = 7, train loss: 0.43885, val loss: 0.42526, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.43643, val loss: 0.42305, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.43411, val loss: 0.42094, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.43182, val loss: 0.41832, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.42962, val loss: 0.41633, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.42745, val loss: 0.41384, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.42535, val loss: 0.41194, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.42330, val loss: 0.40958, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.42130, val loss: 0.40779, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.41935, val loss: 0.40553, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.41744, val loss: 0.40382, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.41558, val loss: 0.40167, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41381, val loss: 0.39997, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.41199, val loss: 0.39836, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41030, val loss: 0.39674, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.40857, val loss: 0.39521, in 0.000s 1 tree, 21 leaves, max depth = 9, train loss: 0.40686, val loss: 0.39322, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40526, val loss: 0.39169, in 0.000s 1 tree, 39 leaves, max depth = 9, train loss: 0.40361, val loss: 0.39024, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40208, val loss: 0.38878, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.40050, val loss: 0.38741, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.39889, val loss: 0.38550, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39744, val loss: 0.38411, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.39593, val loss: 0.38281, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39455, val loss: 0.38148, in 0.000s 1 tree, 20 leaves, max depth = 10, train loss: 0.39303, val loss: 0.37968, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.39159, val loss: 0.37844, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39027, val loss: 0.37718, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38900, val loss: 0.37597, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.38763, val loss: 0.37480, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.38620, val loss: 0.37310, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38499, val loss: 0.37195, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.38367, val loss: 0.37083, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38251, val loss: 0.36973, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.38116, val loss: 0.36811, in 0.000s Fit 80 trees in 1.110 s, (2055 total leaves) Time spent computing histograms: 0.422s Time spent finding best splits: 0.065s Time spent applying splits: 0.055s Time spent predicting: 0.000s Trial 56, Fold 5: Log loss = 0.3854740829711175, Average precision = 0.9450464468313988, ROC-AUC = 0.9415032978466454, Elapsed Time = 1.122230999999374 seconds
Optimization Progress: 57%|#####6 | 57/100 [11:13<08:49, 12.32s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 57, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 32 leaves, max depth = 12, train loss: 0.67075, val loss: 0.67008, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.65045, val loss: 0.64913, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.63199, val loss: 0.63006, in 0.000s 1 tree, 30 leaves, max depth = 13, train loss: 0.61534, val loss: 0.61293, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.59998, val loss: 0.59701, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.58594, val loss: 0.58243, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.57308, val loss: 0.56906, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.56130, val loss: 0.55678, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.55050, val loss: 0.54550, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.53979, val loss: 0.53552, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.53024, val loss: 0.52551, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.52071, val loss: 0.51669, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.51237, val loss: 0.50788, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.50383, val loss: 0.50000, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.49628, val loss: 0.49203, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.48860, val loss: 0.48495, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.48186, val loss: 0.47782, in 0.000s 1 tree, 55 leaves, max depth = 10, train loss: 0.47494, val loss: 0.47147, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.46899, val loss: 0.46512, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.46272, val loss: 0.45937, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.45694, val loss: 0.45410, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.45169, val loss: 0.44850, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.44645, val loss: 0.44376, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.44177, val loss: 0.43878, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.43700, val loss: 0.43450, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.43259, val loss: 0.43056, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42840, val loss: 0.42602, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.42437, val loss: 0.42242, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.42047, val loss: 0.41825, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.41679, val loss: 0.41498, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.41336, val loss: 0.41133, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.41016, val loss: 0.40783, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.40680, val loss: 0.40490, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40388, val loss: 0.40169, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.40078, val loss: 0.39903, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.39774, val loss: 0.39575, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.39490, val loss: 0.39333, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.39211, val loss: 0.39033, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38947, val loss: 0.38743, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38703, val loss: 0.38474, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38479, val loss: 0.38226, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.38215, val loss: 0.38010, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.37967, val loss: 0.37745, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.37759, val loss: 0.37516, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.37514, val loss: 0.37319, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37330, val loss: 0.37104, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.37105, val loss: 0.36866, in 0.006s 1 tree, 55 leaves, max depth = 12, train loss: 0.36877, val loss: 0.36686, in 0.010s 1 tree, 3 leaves, max depth = 2, train loss: 0.36705, val loss: 0.36482, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.36499, val loss: 0.36265, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.36286, val loss: 0.36099, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36126, val loss: 0.35909, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.35937, val loss: 0.35710, in 0.000s 1 tree, 57 leaves, max depth = 10, train loss: 0.35738, val loss: 0.35557, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.35563, val loss: 0.35374, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35413, val loss: 0.35197, in 0.000s 1 tree, 30 leaves, max depth = 8, train loss: 0.35245, val loss: 0.35019, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35106, val loss: 0.34851, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.34916, val loss: 0.34710, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34785, val loss: 0.34553, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.34631, val loss: 0.34375, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.34452, val loss: 0.34243, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.34310, val loss: 0.34096, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34187, val loss: 0.33947, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.34049, val loss: 0.33789, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33933, val loss: 0.33649, in 0.000s 1 tree, 55 leaves, max depth = 10, train loss: 0.33764, val loss: 0.33526, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.33638, val loss: 0.33397, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.33479, val loss: 0.33282, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33368, val loss: 0.33146, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.33243, val loss: 0.33002, in 0.000s Fit 71 trees in 0.877 s, (2340 total leaves) Time spent computing histograms: 0.312s Time spent finding best splits: 0.051s Time spent applying splits: 0.047s Time spent predicting: 0.016s Trial 57, Fold 1: Log loss = 0.3367816199749105, Average precision = 0.9455012029625787, ROC-AUC = 0.9441351154031821, Elapsed Time = 0.8882864000006521 seconds Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 57, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 10, train loss: 0.67105, val loss: 0.67000, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.65101, val loss: 0.64897, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.63280, val loss: 0.62983, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.61622, val loss: 0.61235, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.60107, val loss: 0.59644, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.58723, val loss: 0.58189, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.57455, val loss: 0.56845, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.56293, val loss: 0.55620, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.55165, val loss: 0.54529, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.54140, val loss: 0.53445, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.53136, val loss: 0.52478, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.52227, val loss: 0.51507, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.51330, val loss: 0.50645, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.50521, val loss: 0.49786, in 0.016s 1 tree, 29 leaves, max depth = 15, train loss: 0.49779, val loss: 0.48992, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.48991, val loss: 0.48237, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.48328, val loss: 0.47523, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.47617, val loss: 0.46846, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.47022, val loss: 0.46210, in 0.000s 1 tree, 54 leaves, max depth = 14, train loss: 0.46377, val loss: 0.45598, in 0.016s 1 tree, 55 leaves, max depth = 15, train loss: 0.45782, val loss: 0.45036, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.45232, val loss: 0.44516, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.44724, val loss: 0.43969, in 0.000s 1 tree, 31 leaves, max depth = 15, train loss: 0.44259, val loss: 0.43465, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.43764, val loss: 0.43001, in 0.000s 1 tree, 55 leaves, max depth = 10, train loss: 0.43307, val loss: 0.42574, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.42891, val loss: 0.42125, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.42471, val loss: 0.41733, in 0.000s 1 tree, 55 leaves, max depth = 13, train loss: 0.42084, val loss: 0.41372, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.41712, val loss: 0.40970, in 0.000s 1 tree, 32 leaves, max depth = 13, train loss: 0.41373, val loss: 0.40598, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.41020, val loss: 0.40271, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40709, val loss: 0.39949, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.40382, val loss: 0.39649, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40097, val loss: 0.39354, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.39796, val loss: 0.39078, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.39505, val loss: 0.38758, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.39227, val loss: 0.38506, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38967, val loss: 0.38237, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38728, val loss: 0.37988, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.38459, val loss: 0.37694, in 0.000s 1 tree, 55 leaves, max depth = 13, train loss: 0.38202, val loss: 0.37464, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.37956, val loss: 0.37194, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37746, val loss: 0.36977, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.37507, val loss: 0.36763, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37312, val loss: 0.36561, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.37089, val loss: 0.36364, in 0.000s 1 tree, 32 leaves, max depth = 10, train loss: 0.36859, val loss: 0.36106, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36678, val loss: 0.35919, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.36511, val loss: 0.35745, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.36306, val loss: 0.35522, in 0.000s 1 tree, 56 leaves, max depth = 13, train loss: 0.36095, val loss: 0.35340, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35939, val loss: 0.35177, in 0.000s 1 tree, 33 leaves, max depth = 10, train loss: 0.35745, val loss: 0.34961, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.35547, val loss: 0.34789, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.35401, val loss: 0.34637, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.35227, val loss: 0.34447, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.35041, val loss: 0.34286, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34903, val loss: 0.34142, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.34738, val loss: 0.33965, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.34580, val loss: 0.33788, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34452, val loss: 0.33654, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.34274, val loss: 0.33503, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.34127, val loss: 0.33338, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.34006, val loss: 0.33211, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.33866, val loss: 0.33056, in 0.000s 1 tree, 57 leaves, max depth = 13, train loss: 0.33698, val loss: 0.32912, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33583, val loss: 0.32791, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.33451, val loss: 0.32643, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.33292, val loss: 0.32510, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33183, val loss: 0.32395, in 0.000s Fit 71 trees in 0.907 s, (2402 total leaves) Time spent computing histograms: 0.308s Time spent finding best splits: 0.048s Time spent applying splits: 0.046s Time spent predicting: 0.016s Trial 57, Fold 2: Log loss = 0.3348763084371926, Average precision = 0.9444874552828766, ROC-AUC = 0.9469416144325498, Elapsed Time = 0.906026099999508 seconds Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 57, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 10, train loss: 0.67125, val loss: 0.67053, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.65119, val loss: 0.64980, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.63312, val loss: 0.63109, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.61667, val loss: 0.61402, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.60145, val loss: 0.59824, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.58755, val loss: 0.58379, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.57497, val loss: 0.57068, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.56345, val loss: 0.55866, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.55209, val loss: 0.54807, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.54177, val loss: 0.53728, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.53168, val loss: 0.52789, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.52266, val loss: 0.51841, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.51365, val loss: 0.51005, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.50564, val loss: 0.50159, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.49810, val loss: 0.49364, in 0.000s 1 tree, 56 leaves, max depth = 13, train loss: 0.49018, val loss: 0.48632, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.48346, val loss: 0.47920, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.47632, val loss: 0.47261, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.47045, val loss: 0.46636, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.46399, val loss: 0.46041, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.45802, val loss: 0.45494, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.45251, val loss: 0.44990, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44749, val loss: 0.44452, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.44290, val loss: 0.43955, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.43794, val loss: 0.43503, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.43337, val loss: 0.43088, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.42913, val loss: 0.42705, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42511, val loss: 0.42268, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.42122, val loss: 0.41918, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.41757, val loss: 0.41520, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.41417, val loss: 0.41149, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.41063, val loss: 0.40831, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40743, val loss: 0.40536, in 0.000s 1 tree, 52 leaves, max depth = 13, train loss: 0.40418, val loss: 0.40245, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40124, val loss: 0.39974, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39854, val loss: 0.39724, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.39555, val loss: 0.39460, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.39265, val loss: 0.39142, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39017, val loss: 0.38913, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.38741, val loss: 0.38672, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38512, val loss: 0.38461, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.38256, val loss: 0.38239, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.37997, val loss: 0.37954, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.37787, val loss: 0.37759, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.37542, val loss: 0.37492, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.37304, val loss: 0.37286, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.37084, val loss: 0.37099, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.36860, val loss: 0.36852, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.36673, val loss: 0.36678, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36500, val loss: 0.36517, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.36301, val loss: 0.36298, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36141, val loss: 0.36149, in 0.000s 1 tree, 53 leaves, max depth = 15, train loss: 0.35931, val loss: 0.35972, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.35748, val loss: 0.35771, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.35554, val loss: 0.35609, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35403, val loss: 0.35469, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.35228, val loss: 0.35278, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35088, val loss: 0.35147, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.34926, val loss: 0.34966, in 0.016s [60/71] 1 tree, 3 leaves, max depth = 2, train loss: 0.34795, val loss: 0.34844, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.34644, val loss: 0.34664, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.34455, val loss: 0.34509, in 0.000s 1 tree, 54 leaves, max depth = 15, train loss: 0.34281, val loss: 0.34366, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34157, val loss: 0.34249, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.34014, val loss: 0.34078, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33897, val loss: 0.33969, in 0.000s 1 tree, 30 leaves, max depth = 8, train loss: 0.33760, val loss: 0.33817, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.33594, val loss: 0.33682, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33483, val loss: 0.33578, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.33355, val loss: 0.33425, in 0.000s 1 tree, 55 leaves, max depth = 13, train loss: 0.33198, val loss: 0.33299, in 0.016s Fit 71 trees in 0.908 s, (2362 total leaves) Time spent computing histograms: 0.316s Time spent finding best splits: 0.049s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 57, Fold 3: Log loss = 0.3302480489271217, Average precision = 0.9506856079438344, ROC-AUC = 0.950528232175362, Elapsed Time = 0.9109050000006391 seconds Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 57, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 11, train loss: 0.67097, val loss: 0.66966, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.65086, val loss: 0.64830, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.63258, val loss: 0.62882, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.61611, val loss: 0.61125, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.60089, val loss: 0.59495, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.58698, val loss: 0.57999, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.57425, val loss: 0.56625, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.56259, val loss: 0.55362, in 0.000s 1 tree, 55 leaves, max depth = 10, train loss: 0.55122, val loss: 0.54241, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.54094, val loss: 0.53123, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.53084, val loss: 0.52128, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.52175, val loss: 0.51134, in 0.000s 1 tree, 55 leaves, max depth = 11, train loss: 0.51272, val loss: 0.50248, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.50465, val loss: 0.49360, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.49723, val loss: 0.48540, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.48931, val loss: 0.47763, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.48274, val loss: 0.47035, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.47558, val loss: 0.46336, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.46971, val loss: 0.45683, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.46321, val loss: 0.45050, in 0.000s 1 tree, 55 leaves, max depth = 14, train loss: 0.45721, val loss: 0.44468, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.45166, val loss: 0.43927, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.44662, val loss: 0.43364, in 0.000s 1 tree, 30 leaves, max depth = 14, train loss: 0.44200, val loss: 0.42845, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.43701, val loss: 0.42362, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.43240, val loss: 0.41917, in 0.016s 1 tree, 56 leaves, max depth = 9, train loss: 0.42812, val loss: 0.41505, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.42410, val loss: 0.41050, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.42017, val loss: 0.40670, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.41652, val loss: 0.40254, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.41317, val loss: 0.39871, in 0.016s 1 tree, 55 leaves, max depth = 15, train loss: 0.40958, val loss: 0.39526, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40649, val loss: 0.39198, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.40318, val loss: 0.38882, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40035, val loss: 0.38581, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.39729, val loss: 0.38290, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.39439, val loss: 0.37957, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.39156, val loss: 0.37686, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38898, val loss: 0.37412, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38669, val loss: 0.37169, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38458, val loss: 0.36944, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.38194, val loss: 0.36693, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.37938, val loss: 0.36400, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37742, val loss: 0.36193, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.37496, val loss: 0.35961, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37262, val loss: 0.35694, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.37034, val loss: 0.35480, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.36820, val loss: 0.35234, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36638, val loss: 0.35040, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.36470, val loss: 0.34861, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36274, val loss: 0.34636, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.36058, val loss: 0.34435, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35900, val loss: 0.34268, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.35721, val loss: 0.34062, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.35517, val loss: 0.33874, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35370, val loss: 0.33717, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.35205, val loss: 0.33527, in 0.000s 1 tree, 56 leaves, max depth = 14, train loss: 0.35013, val loss: 0.33352, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34875, val loss: 0.33204, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.34717, val loss: 0.33037, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34588, val loss: 0.32900, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.34436, val loss: 0.32731, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.34252, val loss: 0.32564, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.34108, val loss: 0.32414, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.33986, val loss: 0.32284, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.33853, val loss: 0.32147, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.33677, val loss: 0.31988, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.33561, val loss: 0.31862, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.33397, val loss: 0.31716, in 0.000s 1 tree, 39 leaves, max depth = 14, train loss: 0.33270, val loss: 0.31588, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.33160, val loss: 0.31468, in 0.016s Fit 71 trees in 0.892 s, (2391 total leaves) Time spent computing histograms: 0.307s Time spent finding best splits: 0.048s Time spent applying splits: 0.045s Time spent predicting: 0.031s Trial 57, Fold 4: Log loss = 0.33093443406721534, Average precision = 0.9528355563995237, ROC-AUC = 0.9508373451027798, Elapsed Time = 0.8867230000014388 seconds Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 57, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.189 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 8, train loss: 0.67074, val loss: 0.66927, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.65042, val loss: 0.64756, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.63195, val loss: 0.62776, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.61530, val loss: 0.60976, in 0.000s 1 tree, 30 leaves, max depth = 8, train loss: 0.59992, val loss: 0.59317, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.58586, val loss: 0.57796, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.57299, val loss: 0.56398, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.56133, val loss: 0.55119, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.55050, val loss: 0.53934, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.53975, val loss: 0.52903, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.53028, val loss: 0.51863, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.52069, val loss: 0.50948, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.51189, val loss: 0.50110, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.50376, val loss: 0.49211, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.49627, val loss: 0.48372, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.48853, val loss: 0.47639, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.48185, val loss: 0.46893, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.47487, val loss: 0.46235, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.46889, val loss: 0.45564, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.46256, val loss: 0.44971, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.45672, val loss: 0.44424, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.45132, val loss: 0.43922, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.44619, val loss: 0.43344, in 0.000s 1 tree, 30 leaves, max depth = 13, train loss: 0.44148, val loss: 0.42809, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.43662, val loss: 0.42362, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.43214, val loss: 0.41948, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.42794, val loss: 0.41469, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.42383, val loss: 0.41096, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.42002, val loss: 0.40659, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.41625, val loss: 0.40317, in 0.000s 1 tree, 31 leaves, max depth = 13, train loss: 0.41281, val loss: 0.39919, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.40934, val loss: 0.39608, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40623, val loss: 0.39308, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.40304, val loss: 0.39024, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40019, val loss: 0.38748, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.39715, val loss: 0.38394, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.39421, val loss: 0.38134, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.39144, val loss: 0.37811, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38887, val loss: 0.37563, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.38615, val loss: 0.37327, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38388, val loss: 0.37101, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.38136, val loss: 0.36883, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.37889, val loss: 0.36590, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37680, val loss: 0.36382, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37487, val loss: 0.36191, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.37251, val loss: 0.35990, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.37027, val loss: 0.35724, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.36808, val loss: 0.35539, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36628, val loss: 0.35360, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.36422, val loss: 0.35116, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.36218, val loss: 0.34945, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.36050, val loss: 0.34778, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.35855, val loss: 0.34547, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.35663, val loss: 0.34388, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.35489, val loss: 0.34179, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35333, val loss: 0.34023, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.35164, val loss: 0.33839, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35019, val loss: 0.33694, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.34834, val loss: 0.33545, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.34698, val loss: 0.33410, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.34541, val loss: 0.33222, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.34415, val loss: 0.33097, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.34264, val loss: 0.32934, in 0.000s 1 tree, 52 leaves, max depth = 13, train loss: 0.34087, val loss: 0.32789, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.33967, val loss: 0.32671, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.33826, val loss: 0.32518, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.33659, val loss: 0.32385, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.33530, val loss: 0.32228, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.33416, val loss: 0.32115, in 0.000s 1 tree, 38 leaves, max depth = 11, train loss: 0.33291, val loss: 0.31979, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.33131, val loss: 0.31853, in 0.000s Fit 71 trees in 0.939 s, (2355 total leaves) Time spent computing histograms: 0.314s Time spent finding best splits: 0.050s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 57, Fold 5: Log loss = 0.3377299161193195, Average precision = 0.9483662589185281, ROC-AUC = 0.9455798450476561, Elapsed Time = 0.9485564999995404 seconds
Optimization Progress: 58%|#####8 | 58/100 [11:25<08:37, 12.31s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 58, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.127 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 23 leaves, max depth = 7, train loss: 0.67353, val loss: 0.67345, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.65484, val loss: 0.65486, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.63738, val loss: 0.63749, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.62104, val loss: 0.62122, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.60571, val loss: 0.60596, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.59129, val loss: 0.59158, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.57818, val loss: 0.57836, in 0.016s 1 tree, 30 leaves, max depth = 7, train loss: 0.56553, val loss: 0.56565, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.55345, val loss: 0.55357, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.54220, val loss: 0.54218, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.53143, val loss: 0.53152, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52150, val loss: 0.52151, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51159, val loss: 0.51154, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.50220, val loss: 0.50209, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49375, val loss: 0.49360, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.48540, val loss: 0.48507, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47740, val loss: 0.47702, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.46976, val loss: 0.46936, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46288, val loss: 0.46245, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.45611, val loss: 0.45554, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.44952, val loss: 0.44895, in 0.016s Fit 21 trees in 0.642 s, (690 total leaves) Time spent computing histograms: 0.136s Time spent finding best splits: 0.024s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 58, Fold 1: Log loss = 0.4518877100166728, Average precision = 0.9206472773505876, ROC-AUC = 0.9320100443067922, Elapsed Time = 0.6472635999998602 seconds Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 58, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 23 leaves, max depth = 6, train loss: 0.67342, val loss: 0.67295, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.65472, val loss: 0.65388, in 0.016s 1 tree, 23 leaves, max depth = 6, train loss: 0.63803, val loss: 0.63696, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.62176, val loss: 0.62029, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.60623, val loss: 0.60447, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.59173, val loss: 0.58972, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.57894, val loss: 0.57683, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.56588, val loss: 0.56355, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55357, val loss: 0.55098, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.54220, val loss: 0.53936, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.53111, val loss: 0.52815, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.52134, val loss: 0.51830, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.51139, val loss: 0.50822, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.50197, val loss: 0.49874, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.49368, val loss: 0.49039, in 0.016s 1 tree, 32 leaves, max depth = 7, train loss: 0.48530, val loss: 0.48200, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.47725, val loss: 0.47387, in 0.031s 1 tree, 30 leaves, max depth = 7, train loss: 0.46977, val loss: 0.46631, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.46252, val loss: 0.45897, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.45290, val loss: 0.44948, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.44631, val loss: 0.44279, in 0.016s Fit 21 trees in 0.704 s, (629 total leaves) Time spent computing histograms: 0.159s Time spent finding best splits: 0.027s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 58, Fold 2: Log loss = 0.448487289569763, Average precision = 0.9484778573320208, ROC-AUC = 0.9477508094985616, Elapsed Time = 0.7175649000000703 seconds Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 58, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 22 leaves, max depth = 6, train loss: 0.67356, val loss: 0.67346, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.65498, val loss: 0.65482, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.63752, val loss: 0.63738, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.62118, val loss: 0.62102, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.60585, val loss: 0.60572, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.59203, val loss: 0.59200, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.57827, val loss: 0.57828, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.56611, val loss: 0.56620, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.55449, val loss: 0.55463, in 0.016s 1 tree, 29 leaves, max depth = 7, train loss: 0.54314, val loss: 0.54327, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.53216, val loss: 0.53237, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.52236, val loss: 0.52267, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.51309, val loss: 0.51346, in 0.031s 1 tree, 33 leaves, max depth = 11, train loss: 0.50368, val loss: 0.50407, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.49536, val loss: 0.49585, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.48686, val loss: 0.48746, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.47623, val loss: 0.47751, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.46851, val loss: 0.46987, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.46127, val loss: 0.46278, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.45437, val loss: 0.45602, in 0.031s 1 tree, 32 leaves, max depth = 12, train loss: 0.44770, val loss: 0.44941, in 0.016s Fit 21 trees in 0.721 s, (671 total leaves) Time spent computing histograms: 0.151s Time spent finding best splits: 0.026s Time spent applying splits: 0.016s Time spent predicting: 0.000s Trial 58, Fold 3: Log loss = 0.4462454177332421, Average precision = 0.9515587752719641, ROC-AUC = 0.949697781129913, Elapsed Time = 0.7262883999992482 seconds Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 58, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 21 leaves, max depth = 6, train loss: 0.67371, val loss: 0.67290, in 0.016s 1 tree, 30 leaves, max depth = 6, train loss: 0.65546, val loss: 0.65372, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.63809, val loss: 0.63571, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.62178, val loss: 0.61879, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.60652, val loss: 0.60291, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.59219, val loss: 0.58797, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.57936, val loss: 0.57465, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.56655, val loss: 0.56107, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55430, val loss: 0.54834, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.54301, val loss: 0.53645, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.53205, val loss: 0.52505, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.52230, val loss: 0.51488, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.51311, val loss: 0.50525, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.50377, val loss: 0.49551, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.49551, val loss: 0.48688, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.48707, val loss: 0.47803, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47654, val loss: 0.46733, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46883, val loss: 0.45935, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.46168, val loss: 0.45166, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.45487, val loss: 0.44449, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.44828, val loss: 0.43758, in 0.016s Fit 21 trees in 0.705 s, (662 total leaves) Time spent computing histograms: 0.144s Time spent finding best splits: 0.024s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 58, Fold 4: Log loss = 0.44771803535883925, Average precision = 0.9527280687118671, ROC-AUC = 0.9486615711829772, Elapsed Time = 0.7152146000007633 seconds Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 58, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.190 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 22 leaves, max depth = 7, train loss: 0.67363, val loss: 0.67280, in 0.031s 1 tree, 24 leaves, max depth = 6, train loss: 0.65499, val loss: 0.65339, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.63814, val loss: 0.63576, in 0.031s 1 tree, 22 leaves, max depth = 6, train loss: 0.62188, val loss: 0.61872, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.60623, val loss: 0.60247, in 0.031s 1 tree, 32 leaves, max depth = 12, train loss: 0.59149, val loss: 0.58715, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.57873, val loss: 0.57364, in 0.031s 1 tree, 32 leaves, max depth = 12, train loss: 0.56566, val loss: 0.56005, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.55335, val loss: 0.54726, in 0.031s 1 tree, 26 leaves, max depth = 8, train loss: 0.54199, val loss: 0.53531, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.53095, val loss: 0.52384, in 0.031s 1 tree, 30 leaves, max depth = 11, train loss: 0.52113, val loss: 0.51360, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.51185, val loss: 0.50393, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.50247, val loss: 0.49420, in 0.031s 1 tree, 30 leaves, max depth = 11, train loss: 0.49415, val loss: 0.48554, in 0.021s 1 tree, 31 leaves, max depth = 11, train loss: 0.48563, val loss: 0.47669, in 0.027s 1 tree, 32 leaves, max depth = 11, train loss: 0.47755, val loss: 0.46825, in 0.031s 1 tree, 32 leaves, max depth = 11, train loss: 0.46982, val loss: 0.46021, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45987, val loss: 0.45024, in 0.016s 1 tree, 33 leaves, max depth = 7, train loss: 0.45299, val loss: 0.44307, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.44388, val loss: 0.43396, in 0.016s Fit 21 trees in 0.878 s, (624 total leaves) Time spent computing histograms: 0.198s Time spent finding best splits: 0.035s Time spent applying splits: 0.021s Time spent predicting: 0.000s Trial 58, Fold 5: Log loss = 0.44844346568526616, Average precision = 0.9489391251785544, ROC-AUC = 0.9459536629322036, Elapsed Time = 0.8952907000002597 seconds
Optimization Progress: 59%|#####8 | 59/100 [11:35<07:59, 11.70s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 59, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 11, train loss: 0.67087, val loss: 0.67019, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.65066, val loss: 0.64935, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.63229, val loss: 0.63037, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.61556, val loss: 0.61305, in 0.015s 1 tree, 30 leaves, max depth = 11, train loss: 0.60022, val loss: 0.59717, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.58627, val loss: 0.58267, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.57353, val loss: 0.56955, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.56189, val loss: 0.55740, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.55112, val loss: 0.54615, in 0.000s 1 tree, 48 leaves, max depth = 10, train loss: 0.54034, val loss: 0.53591, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.53088, val loss: 0.52601, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.52127, val loss: 0.51690, in 0.000s 1 tree, 49 leaves, max depth = 10, train loss: 0.51244, val loss: 0.50855, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.50432, val loss: 0.50000, in 0.000s 1 tree, 49 leaves, max depth = 11, train loss: 0.49640, val loss: 0.49253, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.48916, val loss: 0.48487, in 0.000s 1 tree, 49 leaves, max depth = 10, train loss: 0.48201, val loss: 0.47815, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.47553, val loss: 0.47126, in 0.000s 1 tree, 49 leaves, max depth = 10, train loss: 0.46907, val loss: 0.46521, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.46326, val loss: 0.45901, in 0.000s Fit 20 trees in 0.392 s, (692 total leaves) Time spent computing histograms: 0.070s Time spent finding best splits: 0.010s Time spent applying splits: 0.011s Time spent predicting: 0.000s Trial 59, Fold 1: Log loss = 0.46407318827883015, Average precision = 0.9176346016864347, ROC-AUC = 0.9272196263078151, Elapsed Time = 0.39520360000096844 seconds Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 59, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 11, train loss: 0.67095, val loss: 0.66977, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.65082, val loss: 0.64853, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.63265, val loss: 0.62950, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.61595, val loss: 0.61185, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.60083, val loss: 0.59597, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.58688, val loss: 0.58117, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.57423, val loss: 0.56784, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.56250, val loss: 0.55536, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.55187, val loss: 0.54413, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.54106, val loss: 0.53375, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.53166, val loss: 0.52380, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.52202, val loss: 0.51455, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.51318, val loss: 0.50608, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.50511, val loss: 0.49750, in 0.000s 1 tree, 52 leaves, max depth = 13, train loss: 0.49718, val loss: 0.48990, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.48998, val loss: 0.48223, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.48283, val loss: 0.47541, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47638, val loss: 0.46853, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.46992, val loss: 0.46237, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.46414, val loss: 0.45619, in 0.016s Fit 20 trees in 0.471 s, (731 total leaves) Time spent computing histograms: 0.095s Time spent finding best splits: 0.013s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 59, Fold 2: Log loss = 0.46482889485721857, Average precision = 0.9129098439490244, ROC-AUC = 0.9281474994544667, Elapsed Time = 0.46732349999911094 seconds Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 59, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 8, train loss: 0.67108, val loss: 0.67035, in 0.000s 1 tree, 29 leaves, max depth = 8, train loss: 0.65108, val loss: 0.64965, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.63301, val loss: 0.63105, in 0.000s 1 tree, 30 leaves, max depth = 8, train loss: 0.61633, val loss: 0.61377, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.60119, val loss: 0.59803, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.58738, val loss: 0.58360, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.57480, val loss: 0.57056, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.56326, val loss: 0.55859, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55256, val loss: 0.54743, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.54179, val loss: 0.53742, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.53193, val loss: 0.52828, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.52288, val loss: 0.51881, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.51407, val loss: 0.51067, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.50602, val loss: 0.50223, in 0.000s 1 tree, 48 leaves, max depth = 13, train loss: 0.49811, val loss: 0.49495, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.49092, val loss: 0.48739, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.48380, val loss: 0.48085, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.47737, val loss: 0.47407, in 0.000s 1 tree, 47 leaves, max depth = 11, train loss: 0.47093, val loss: 0.46817, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.46516, val loss: 0.46206, in 0.000s Fit 20 trees in 0.454 s, (703 total leaves) Time spent computing histograms: 0.090s Time spent finding best splits: 0.013s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 59, Fold 3: Log loss = 0.4618078758230681, Average precision = 0.917900894446191, ROC-AUC = 0.9314134868199285, Elapsed Time = 0.46097200000076555 seconds Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 59, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 10, train loss: 0.67106, val loss: 0.66979, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.65103, val loss: 0.64855, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.63283, val loss: 0.62918, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.61624, val loss: 0.61148, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.60105, val loss: 0.59516, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.58716, val loss: 0.58020, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.57453, val loss: 0.56658, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.56291, val loss: 0.55401, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.55229, val loss: 0.54247, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.54158, val loss: 0.53196, in 0.000s 1 tree, 29 leaves, max depth = 13, train loss: 0.53219, val loss: 0.52171, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.52264, val loss: 0.51235, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.51388, val loss: 0.50377, in 0.016s 1 tree, 29 leaves, max depth = 14, train loss: 0.50581, val loss: 0.49494, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.49796, val loss: 0.48726, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.49076, val loss: 0.47932, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.48369, val loss: 0.47241, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.47725, val loss: 0.46528, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.47086, val loss: 0.45904, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.46508, val loss: 0.45261, in 0.000s Fit 20 trees in 0.455 s, (701 total leaves) Time spent computing histograms: 0.083s Time spent finding best splits: 0.012s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 59, Fold 4: Log loss = 0.464658487404741, Average precision = 0.91684926447469, ROC-AUC = 0.9289775978740603, Elapsed Time = 0.46681159999934607 seconds Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 59, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 10, train loss: 0.67080, val loss: 0.66929, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.65069, val loss: 0.64783, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.63224, val loss: 0.62802, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.61557, val loss: 0.61014, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.60021, val loss: 0.59355, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.58630, val loss: 0.57853, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.57348, val loss: 0.56462, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.56180, val loss: 0.55193, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.55100, val loss: 0.54013, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.54049, val loss: 0.53009, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.53099, val loss: 0.51968, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.52161, val loss: 0.51079, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.51318, val loss: 0.50150, in 0.000s 1 tree, 48 leaves, max depth = 12, train loss: 0.50478, val loss: 0.49358, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.49726, val loss: 0.48527, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.48971, val loss: 0.47820, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.48277, val loss: 0.47171, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.47622, val loss: 0.46444, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.46996, val loss: 0.45858, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.46408, val loss: 0.45202, in 0.016s Fit 20 trees in 0.471 s, (693 total leaves) Time spent computing histograms: 0.082s Time spent finding best splits: 0.012s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 59, Fold 5: Log loss = 0.4690791333260642, Average precision = 0.9139430069964957, ROC-AUC = 0.9255519016034037, Elapsed Time = 0.47083159999965574 seconds
Optimization Progress: 60%|###### | 60/100 [11:45<07:18, 10.97s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 60, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 20 leaves, max depth = 7, train loss: 0.66210, val loss: 0.66184, in 0.016s 1 tree, 19 leaves, max depth = 5, train loss: 0.63373, val loss: 0.63343, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.60928, val loss: 0.60887, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.58612, val loss: 0.58565, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.56516, val loss: 0.56470, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.54680, val loss: 0.54608, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.52910, val loss: 0.52830, in 0.000s 1 tree, 22 leaves, max depth = 8, train loss: 0.51294, val loss: 0.51208, in 0.016s 1 tree, 20 leaves, max depth = 5, train loss: 0.49832, val loss: 0.49702, in 0.031s 1 tree, 23 leaves, max depth = 8, train loss: 0.48470, val loss: 0.48335, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.47226, val loss: 0.47086, in 0.000s 1 tree, 24 leaves, max depth = 8, train loss: 0.46079, val loss: 0.45927, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.45021, val loss: 0.44865, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44046, val loss: 0.43887, in 0.016s 1 tree, 21 leaves, max depth = 10, train loss: 0.42656, val loss: 0.42545, in 0.016s 1 tree, 20 leaves, max depth = 5, train loss: 0.41832, val loss: 0.41699, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.40625, val loss: 0.40542, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.39883, val loss: 0.39800, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.38826, val loss: 0.38790, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.38177, val loss: 0.38144, in 0.031s 1 tree, 24 leaves, max depth = 10, train loss: 0.37242, val loss: 0.37253, in 0.000s 1 tree, 23 leaves, max depth = 6, train loss: 0.36687, val loss: 0.36687, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.36185, val loss: 0.36191, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.35433, val loss: 0.35447, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.34746, val loss: 0.34768, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.34205, val loss: 0.34237, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.33599, val loss: 0.33638, in 0.000s Fit 27 trees in 0.673 s, (561 total leaves) Time spent computing histograms: 0.162s Time spent finding best splits: 0.016s Time spent applying splits: 0.012s Time spent predicting: 0.000s Trial 60, Fold 1: Log loss = 0.3384298929201707, Average precision = 0.9562246653547312, ROC-AUC = 0.9504353608350898, Elapsed Time = 0.6907088000007207 seconds Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 60, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.66211, val loss: 0.66122, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.63339, val loss: 0.63185, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.60759, val loss: 0.60548, in 0.016s 1 tree, 18 leaves, max depth = 5, train loss: 0.58433, val loss: 0.58174, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.56326, val loss: 0.56007, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54504, val loss: 0.54158, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.52744, val loss: 0.52354, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.51140, val loss: 0.50709, in 0.016s 1 tree, 19 leaves, max depth = 5, train loss: 0.49697, val loss: 0.49241, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.48353, val loss: 0.47873, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.47154, val loss: 0.46637, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.46018, val loss: 0.45481, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.44971, val loss: 0.44408, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.43534, val loss: 0.42988, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.42640, val loss: 0.42067, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.41802, val loss: 0.41204, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.40605, val loss: 0.40026, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.39886, val loss: 0.39288, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.38839, val loss: 0.38259, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.37879, val loss: 0.37315, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.37284, val loss: 0.36702, in 0.031s 1 tree, 24 leaves, max depth = 6, train loss: 0.36737, val loss: 0.36149, in 0.000s 1 tree, 23 leaves, max depth = 9, train loss: 0.35909, val loss: 0.35337, in 0.031s 1 tree, 25 leaves, max depth = 9, train loss: 0.35418, val loss: 0.34828, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.34730, val loss: 0.34152, in 0.000s 1 tree, 11 leaves, max depth = 5, train loss: 0.34110, val loss: 0.33548, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.33537, val loss: 0.33015, in 0.016s Fit 27 trees in 0.736 s, (551 total leaves) Time spent computing histograms: 0.178s Time spent finding best splits: 0.017s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 60, Fold 2: Log loss = 0.336854622544885, Average precision = 0.9510914200412619, ROC-AUC = 0.9497730004877174, Elapsed Time = 0.747653000000355 seconds Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 60, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.159 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.66187, val loss: 0.66194, in 0.000s 1 tree, 16 leaves, max depth = 6, train loss: 0.63335, val loss: 0.63344, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.60791, val loss: 0.60781, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.58479, val loss: 0.58460, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.56387, val loss: 0.56359, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.54557, val loss: 0.54526, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.52826, val loss: 0.52782, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.51233, val loss: 0.51179, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.49776, val loss: 0.49718, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.48442, val loss: 0.48381, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.47257, val loss: 0.47189, in 0.000s 1 tree, 26 leaves, max depth = 11, train loss: 0.46126, val loss: 0.46056, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.45082, val loss: 0.45009, in 0.016s 1 tree, 23 leaves, max depth = 11, train loss: 0.43627, val loss: 0.43653, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.42717, val loss: 0.42736, in 0.016s 1 tree, 21 leaves, max depth = 6, train loss: 0.41898, val loss: 0.41927, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.40678, val loss: 0.40804, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.39560, val loss: 0.39767, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.38865, val loss: 0.39069, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.38260, val loss: 0.38469, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.37317, val loss: 0.37612, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.36754, val loss: 0.37042, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.35967, val loss: 0.36317, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.35449, val loss: 0.35815, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.34753, val loss: 0.35183, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.34121, val loss: 0.34643, in 0.000s 1 tree, 16 leaves, max depth = 7, train loss: 0.33526, val loss: 0.34101, in 0.016s Fit 27 trees in 0.737 s, (570 total leaves) Time spent computing histograms: 0.167s Time spent finding best splits: 0.016s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 60, Fold 3: Log loss = 0.33306986354968326, Average precision = 0.9541527565952485, ROC-AUC = 0.9513584574934268, Elapsed Time = 0.7431777999991027 seconds Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 60, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 14 leaves, max depth = 5, train loss: 0.66229, val loss: 0.66093, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.63426, val loss: 0.63166, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.60898, val loss: 0.60531, in 0.000s 1 tree, 17 leaves, max depth = 5, train loss: 0.58617, val loss: 0.58139, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.56517, val loss: 0.55927, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.54704, val loss: 0.54015, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.52951, val loss: 0.52178, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.51356, val loss: 0.50489, in 0.031s 1 tree, 22 leaves, max depth = 7, train loss: 0.49915, val loss: 0.48959, in 0.016s 1 tree, 21 leaves, max depth = 6, train loss: 0.48578, val loss: 0.47544, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.47385, val loss: 0.46259, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.46248, val loss: 0.45058, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45207, val loss: 0.43952, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.44245, val loss: 0.42932, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.42867, val loss: 0.41532, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.42054, val loss: 0.40653, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.40856, val loss: 0.39438, in 0.000s 1 tree, 23 leaves, max depth = 8, train loss: 0.40127, val loss: 0.38659, in 0.031s 1 tree, 21 leaves, max depth = 7, train loss: 0.39078, val loss: 0.37596, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.38466, val loss: 0.36932, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.37535, val loss: 0.35991, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.36688, val loss: 0.35132, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.35915, val loss: 0.34351, in 0.000s 1 tree, 27 leaves, max depth = 7, train loss: 0.35426, val loss: 0.33812, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.34870, val loss: 0.33239, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.34244, val loss: 0.32578, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.33671, val loss: 0.32033, in 0.016s Fit 27 trees in 0.767 s, (580 total leaves) Time spent computing histograms: 0.177s Time spent finding best splits: 0.017s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 60, Fold 4: Log loss = 0.33538787395702674, Average precision = 0.9564025175831881, ROC-AUC = 0.9518338793631718, Elapsed Time = 0.7707864999993035 seconds Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 60, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 16 leaves, max depth = 5, train loss: 0.66177, val loss: 0.66029, in 0.031s 1 tree, 17 leaves, max depth = 5, train loss: 0.63313, val loss: 0.63036, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.60741, val loss: 0.60347, in 0.000s 1 tree, 19 leaves, max depth = 6, train loss: 0.58422, val loss: 0.57917, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.56321, val loss: 0.55722, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54497, val loss: 0.53804, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.52748, val loss: 0.51976, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.51150, val loss: 0.50304, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.49689, val loss: 0.48779, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.48349, val loss: 0.47377, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.47159, val loss: 0.46111, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.46024, val loss: 0.44924, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.44972, val loss: 0.43830, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.43530, val loss: 0.42382, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.42627, val loss: 0.41443, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.41792, val loss: 0.40570, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.40586, val loss: 0.39363, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.39860, val loss: 0.38611, in 0.000s 1 tree, 22 leaves, max depth = 9, train loss: 0.38803, val loss: 0.37560, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.37842, val loss: 0.36595, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.37239, val loss: 0.35972, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.36680, val loss: 0.35396, in 0.016s 1 tree, 19 leaves, max depth = 9, train loss: 0.35862, val loss: 0.34580, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.35371, val loss: 0.34078, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.34674, val loss: 0.33363, in 0.000s 1 tree, 11 leaves, max depth = 6, train loss: 0.34045, val loss: 0.32705, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.33469, val loss: 0.32115, in 0.016s Fit 27 trees in 0.736 s, (521 total leaves) Time spent computing histograms: 0.170s Time spent finding best splits: 0.016s Time spent applying splits: 0.013s Time spent predicting: 0.000s Trial 60, Fold 5: Log loss = 0.3408628390929188, Average precision = 0.9507184833691187, ROC-AUC = 0.9466764022815525, Elapsed Time = 0.7471552000006341 seconds
Optimization Progress: 61%|######1 | 61/100 [11:55<06:58, 10.73s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 61, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.141 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 16, train loss: 0.67118, val loss: 0.67058, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.65078, val loss: 0.64955, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.63224, val loss: 0.63040, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.61552, val loss: 0.61325, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.60027, val loss: 0.59751, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.58634, val loss: 0.58311, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.57368, val loss: 0.57001, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.56208, val loss: 0.55807, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.55132, val loss: 0.54691, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.54037, val loss: 0.53671, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.53034, val loss: 0.52738, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.52116, val loss: 0.51789, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.51282, val loss: 0.50919, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.50486, val loss: 0.50081, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.49779, val loss: 0.49341, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.48944, val loss: 0.48573, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.48306, val loss: 0.47911, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.47719, val loss: 0.47300, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47176, val loss: 0.46729, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.46658, val loss: 0.46176, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.46180, val loss: 0.45665, in 0.016s 1 tree, 75 leaves, max depth = 10, train loss: 0.45667, val loss: 0.45208, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.44994, val loss: 0.44598, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.44580, val loss: 0.44152, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.44213, val loss: 0.43768, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.43605, val loss: 0.43221, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.43043, val loss: 0.42717, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.42712, val loss: 0.42351, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.42195, val loss: 0.41892, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.41718, val loss: 0.41468, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.41414, val loss: 0.41147, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41116, val loss: 0.40817, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.40833, val loss: 0.40509, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.40397, val loss: 0.40129, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.40136, val loss: 0.39857, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39868, val loss: 0.39559, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.39640, val loss: 0.39320, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.39241, val loss: 0.38978, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.39023, val loss: 0.38755, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38779, val loss: 0.38483, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38554, val loss: 0.38232, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.38186, val loss: 0.37921, in 0.016s 1 tree, 75 leaves, max depth = 20, train loss: 0.37987, val loss: 0.37744, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.37659, val loss: 0.37469, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37450, val loss: 0.37235, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.37145, val loss: 0.36982, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.36863, val loss: 0.36750, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36680, val loss: 0.36553, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.36419, val loss: 0.36340, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.36177, val loss: 0.36145, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.36007, val loss: 0.35975, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.35784, val loss: 0.35796, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.35578, val loss: 0.35631, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.35423, val loss: 0.35461, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.35231, val loss: 0.35311, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35034, val loss: 0.35088, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.34857, val loss: 0.34952, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.34712, val loss: 0.34809, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34529, val loss: 0.34601, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34360, val loss: 0.34408, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.34223, val loss: 0.34255, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.34053, val loss: 0.34127, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.33897, val loss: 0.34012, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.33757, val loss: 0.33848, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.33611, val loss: 0.33742, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33450, val loss: 0.33558, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33301, val loss: 0.33386, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33164, val loss: 0.33227, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.33041, val loss: 0.33091, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.32927, val loss: 0.32980, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32784, val loss: 0.32880, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.32676, val loss: 0.32766, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32545, val loss: 0.32614, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.32444, val loss: 0.32516, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32323, val loss: 0.32374, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.32224, val loss: 0.32268, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32111, val loss: 0.32134, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.32018, val loss: 0.32053, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.31921, val loss: 0.31945, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.31815, val loss: 0.31818, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.31731, val loss: 0.31739, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.31585, val loss: 0.31637, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31474, val loss: 0.31580, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.31346, val loss: 0.31494, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.31228, val loss: 0.31416, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31123, val loss: 0.31291, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.31013, val loss: 0.31219, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30914, val loss: 0.31101, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.30821, val loss: 0.30995, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30728, val loss: 0.30884, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.30642, val loss: 0.30786, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.30535, val loss: 0.30719, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30447, val loss: 0.30613, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.30365, val loss: 0.30521, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30283, val loss: 0.30420, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.30207, val loss: 0.30327, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.30134, val loss: 0.30262, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.30062, val loss: 0.30174, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.29954, val loss: 0.30108, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.29878, val loss: 0.30025, in 0.000s Fit 100 trees in 1.251 s, (5865 total leaves) Time spent computing histograms: 0.465s Time spent finding best splits: 0.097s Time spent applying splits: 0.098s Time spent predicting: 0.000s Trial 61, Fold 1: Log loss = 0.3085742850973289, Average precision = 0.9500699490593552, ROC-AUC = 0.9481077755552687, Elapsed Time = 1.2683758000002854 seconds Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 61, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 20, train loss: 0.67109, val loss: 0.67018, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.65104, val loss: 0.64923, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.63257, val loss: 0.62980, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.61599, val loss: 0.61250, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.60083, val loss: 0.59659, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.58688, val loss: 0.58184, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.57420, val loss: 0.56855, in 0.016s 1 tree, 75 leaves, max depth = 23, train loss: 0.56258, val loss: 0.55628, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.55182, val loss: 0.54484, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.54094, val loss: 0.53435, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.53097, val loss: 0.52476, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.52180, val loss: 0.51505, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.51338, val loss: 0.50603, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.50546, val loss: 0.49756, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.49839, val loss: 0.49008, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.49009, val loss: 0.48213, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.48375, val loss: 0.47540, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47789, val loss: 0.46913, in 0.016s 1 tree, 75 leaves, max depth = 20, train loss: 0.47253, val loss: 0.46339, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.46739, val loss: 0.45783, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.46265, val loss: 0.45270, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.45829, val loss: 0.44795, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.45131, val loss: 0.44134, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.44743, val loss: 0.43711, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.44307, val loss: 0.43329, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.43702, val loss: 0.42759, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.43144, val loss: 0.42233, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42819, val loss: 0.41896, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.42307, val loss: 0.41414, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.41833, val loss: 0.40968, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.41527, val loss: 0.40642, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41236, val loss: 0.40339, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.40944, val loss: 0.40035, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.40515, val loss: 0.39634, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.40266, val loss: 0.39371, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40004, val loss: 0.39098, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.39773, val loss: 0.38854, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.39378, val loss: 0.38487, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.39159, val loss: 0.38258, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38921, val loss: 0.38010, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38701, val loss: 0.37780, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.38336, val loss: 0.37445, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.38086, val loss: 0.37235, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.37764, val loss: 0.36942, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37561, val loss: 0.36729, in 0.000s 1 tree, 75 leaves, max depth = 17, train loss: 0.37263, val loss: 0.36457, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.36988, val loss: 0.36206, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.36806, val loss: 0.36007, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.36551, val loss: 0.35776, in 0.000s 1 tree, 75 leaves, max depth = 17, train loss: 0.36316, val loss: 0.35565, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.36147, val loss: 0.35389, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.35929, val loss: 0.35196, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.35729, val loss: 0.35017, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.35574, val loss: 0.34846, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.35388, val loss: 0.34681, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35196, val loss: 0.34480, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.35023, val loss: 0.34328, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.34880, val loss: 0.34177, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34702, val loss: 0.33989, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34538, val loss: 0.33815, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.34403, val loss: 0.33668, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.34237, val loss: 0.33524, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.34084, val loss: 0.33392, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33945, val loss: 0.33248, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.33803, val loss: 0.33126, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33646, val loss: 0.32960, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33501, val loss: 0.32806, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33367, val loss: 0.32663, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.33239, val loss: 0.32535, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.33102, val loss: 0.32419, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.32975, val loss: 0.32288, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32864, val loss: 0.32174, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.32737, val loss: 0.32038, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32634, val loss: 0.31938, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32515, val loss: 0.31811, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.32413, val loss: 0.31703, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32302, val loss: 0.31584, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.32209, val loss: 0.31494, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.32112, val loss: 0.31397, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32007, val loss: 0.31286, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.31923, val loss: 0.31198, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.31780, val loss: 0.31076, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.31655, val loss: 0.31002, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.31529, val loss: 0.30896, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.31414, val loss: 0.30800, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31311, val loss: 0.30689, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.31203, val loss: 0.30600, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31106, val loss: 0.30495, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.31014, val loss: 0.30403, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30923, val loss: 0.30305, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.30838, val loss: 0.30221, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.30733, val loss: 0.30133, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30647, val loss: 0.30040, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.30568, val loss: 0.29969, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30487, val loss: 0.29882, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.30412, val loss: 0.29800, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.30334, val loss: 0.29723, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30263, val loss: 0.29646, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.30157, val loss: 0.29558, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.30085, val loss: 0.29495, in 0.016s Fit 100 trees in 1.392 s, (5890 total leaves) Time spent computing histograms: 0.499s Time spent finding best splits: 0.104s Time spent applying splits: 0.104s Time spent predicting: 0.000s Trial 61, Fold 2: Log loss = 0.3076580276331425, Average precision = 0.9456636992142133, ROC-AUC = 0.9476590020883531, Elapsed Time = 1.3889223000005586 seconds Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 61, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 14, train loss: 0.67132, val loss: 0.67060, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.65113, val loss: 0.64982, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.63278, val loss: 0.63089, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.61623, val loss: 0.61368, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.60113, val loss: 0.59809, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.58734, val loss: 0.58383, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.57475, val loss: 0.57075, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.56323, val loss: 0.55877, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.55255, val loss: 0.54756, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.54143, val loss: 0.53723, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.53124, val loss: 0.52780, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.52217, val loss: 0.51820, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.51389, val loss: 0.50953, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.50604, val loss: 0.50136, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.49897, val loss: 0.49384, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.49049, val loss: 0.48602, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.48419, val loss: 0.47944, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47840, val loss: 0.47338, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.47303, val loss: 0.46773, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.46794, val loss: 0.46241, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.46325, val loss: 0.45749, in 0.000s 1 tree, 75 leaves, max depth = 10, train loss: 0.45810, val loss: 0.45291, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.45124, val loss: 0.44664, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.44719, val loss: 0.44236, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.44354, val loss: 0.43852, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.43734, val loss: 0.43289, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.43162, val loss: 0.42770, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42827, val loss: 0.42461, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.42300, val loss: 0.41985, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.41813, val loss: 0.41547, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.41516, val loss: 0.41228, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41216, val loss: 0.40950, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.40935, val loss: 0.40643, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.40492, val loss: 0.40249, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.40240, val loss: 0.39969, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39970, val loss: 0.39718, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.39740, val loss: 0.39473, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.39334, val loss: 0.39117, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.39124, val loss: 0.38885, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38879, val loss: 0.38658, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38653, val loss: 0.38448, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.38277, val loss: 0.38119, in 0.016s 1 tree, 75 leaves, max depth = 22, train loss: 0.38070, val loss: 0.37961, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.37734, val loss: 0.37670, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37524, val loss: 0.37475, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.37213, val loss: 0.37209, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.36925, val loss: 0.36965, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36749, val loss: 0.36768, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.36482, val loss: 0.36544, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36236, val loss: 0.36337, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36075, val loss: 0.36157, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.35847, val loss: 0.35969, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.35637, val loss: 0.35796, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.35485, val loss: 0.35632, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.35290, val loss: 0.35473, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35092, val loss: 0.35288, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.34909, val loss: 0.35141, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.34773, val loss: 0.34988, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34589, val loss: 0.34816, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34419, val loss: 0.34657, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.34289, val loss: 0.34505, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.34115, val loss: 0.34367, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.33954, val loss: 0.34241, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.33822, val loss: 0.34077, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.33672, val loss: 0.33960, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33510, val loss: 0.33808, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33360, val loss: 0.33668, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33221, val loss: 0.33539, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33077, val loss: 0.33429, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.32951, val loss: 0.33273, in 0.010s 1 tree, 75 leaves, max depth = 16, train loss: 0.32841, val loss: 0.33151, in 0.006s 1 tree, 75 leaves, max depth = 20, train loss: 0.32737, val loss: 0.33024, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32605, val loss: 0.32900, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.32508, val loss: 0.32798, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32385, val loss: 0.32683, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.32290, val loss: 0.32575, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.32176, val loss: 0.32468, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.32089, val loss: 0.32373, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.31966, val loss: 0.32304, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.31857, val loss: 0.32203, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.31773, val loss: 0.32108, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31629, val loss: 0.32000, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.31522, val loss: 0.31940, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.31395, val loss: 0.31845, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.31279, val loss: 0.31760, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31172, val loss: 0.31660, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31062, val loss: 0.31582, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30962, val loss: 0.31487, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.30869, val loss: 0.31374, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30775, val loss: 0.31285, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.30690, val loss: 0.31181, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.30582, val loss: 0.31099, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30492, val loss: 0.31014, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.30414, val loss: 0.30935, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30331, val loss: 0.30857, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.30253, val loss: 0.30784, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.30174, val loss: 0.30687, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30101, val loss: 0.30619, in 0.000s 1 tree, 75 leaves, max depth = 17, train loss: 0.29992, val loss: 0.30539, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.29921, val loss: 0.30469, in 0.016s Fit 100 trees in 1.486 s, (5890 total leaves) Time spent computing histograms: 0.540s Time spent finding best splits: 0.113s Time spent applying splits: 0.115s Time spent predicting: 0.016s Trial 61, Fold 3: Log loss = 0.30098675456426244, Average precision = 0.9532472784465278, ROC-AUC = 0.9525602300854272, Elapsed Time = 1.483257500000036 seconds Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 61, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 13, train loss: 0.67122, val loss: 0.67007, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.65103, val loss: 0.64867, in 0.000s 1 tree, 75 leaves, max depth = 19, train loss: 0.63267, val loss: 0.62915, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.61605, val loss: 0.61152, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.60098, val loss: 0.59540, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.58710, val loss: 0.58060, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.57450, val loss: 0.56710, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.56297, val loss: 0.55477, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.55226, val loss: 0.54324, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.54103, val loss: 0.53232, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.53073, val loss: 0.52232, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.52163, val loss: 0.51247, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.51327, val loss: 0.50340, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.50546, val loss: 0.49482, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.49844, val loss: 0.48711, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.48987, val loss: 0.47884, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.48358, val loss: 0.47192, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.47778, val loss: 0.46552, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.47240, val loss: 0.45953, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.46734, val loss: 0.45385, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.46268, val loss: 0.44859, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.45839, val loss: 0.44372, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.45122, val loss: 0.43688, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.44741, val loss: 0.43253, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.44301, val loss: 0.42834, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.43676, val loss: 0.42240, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.43099, val loss: 0.41692, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42775, val loss: 0.41344, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.42243, val loss: 0.40840, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.41749, val loss: 0.40373, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.41452, val loss: 0.40032, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41162, val loss: 0.39719, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.40890, val loss: 0.39404, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.40440, val loss: 0.38980, in 0.016s 1 tree, 75 leaves, max depth = 10, train loss: 0.40141, val loss: 0.38711, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39879, val loss: 0.38428, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.39643, val loss: 0.38155, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.39244, val loss: 0.37783, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.39018, val loss: 0.37528, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38781, val loss: 0.37271, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38561, val loss: 0.37033, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.38192, val loss: 0.36692, in 0.016s 1 tree, 75 leaves, max depth = 10, train loss: 0.37958, val loss: 0.36487, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.37630, val loss: 0.36183, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37426, val loss: 0.35963, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.37120, val loss: 0.35680, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36837, val loss: 0.35419, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.36647, val loss: 0.35204, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.36385, val loss: 0.34963, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36141, val loss: 0.34741, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.35970, val loss: 0.34546, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.35744, val loss: 0.34340, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.35535, val loss: 0.34149, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.35383, val loss: 0.33972, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.35188, val loss: 0.33794, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34995, val loss: 0.33583, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.34813, val loss: 0.33418, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.34666, val loss: 0.33264, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34487, val loss: 0.33068, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34321, val loss: 0.32887, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.34180, val loss: 0.32746, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.34004, val loss: 0.32589, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.33842, val loss: 0.32442, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33700, val loss: 0.32296, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.33548, val loss: 0.32161, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33390, val loss: 0.31987, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33243, val loss: 0.31826, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33107, val loss: 0.31678, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32983, val loss: 0.31541, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.32855, val loss: 0.31412, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.32706, val loss: 0.31281, in 0.016s 1 tree, 75 leaves, max depth = 19, train loss: 0.32597, val loss: 0.31174, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32468, val loss: 0.31033, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.32365, val loss: 0.30918, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32245, val loss: 0.30786, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.32142, val loss: 0.30690, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32030, val loss: 0.30567, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.31936, val loss: 0.30467, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31840, val loss: 0.30374, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31735, val loss: 0.30259, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.31652, val loss: 0.30182, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.31500, val loss: 0.30049, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.31417, val loss: 0.29973, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.31275, val loss: 0.29850, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.31145, val loss: 0.29735, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.31041, val loss: 0.29621, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.30919, val loss: 0.29515, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30821, val loss: 0.29407, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.30733, val loss: 0.29325, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30642, val loss: 0.29224, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.30560, val loss: 0.29149, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.30441, val loss: 0.29047, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30353, val loss: 0.28950, in 0.000s 1 tree, 75 leaves, max depth = 12, train loss: 0.30281, val loss: 0.28875, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30200, val loss: 0.28785, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30125, val loss: 0.28701, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.30049, val loss: 0.28635, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29978, val loss: 0.28557, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.29857, val loss: 0.28455, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.29745, val loss: 0.28379, in 0.016s Fit 100 trees in 1.440 s, (5866 total leaves) Time spent computing histograms: 0.521s Time spent finding best splits: 0.112s Time spent applying splits: 0.113s Time spent predicting: 0.016s Trial 61, Fold 4: Log loss = 0.30024239566538646, Average precision = 0.9538280617924989, ROC-AUC = 0.9534089310452537, Elapsed Time = 1.4443295000000944 seconds Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 61, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 75 leaves, max depth = 14, train loss: 0.67094, val loss: 0.66952, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.65052, val loss: 0.64772, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.63196, val loss: 0.62785, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.61527, val loss: 0.60999, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.59998, val loss: 0.59356, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.58600, val loss: 0.57849, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.57323, val loss: 0.56476, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.56153, val loss: 0.55212, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.55072, val loss: 0.54028, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.53968, val loss: 0.52988, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.53016, val loss: 0.51947, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.52135, val loss: 0.50979, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.51332, val loss: 0.50101, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.50571, val loss: 0.49258, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.49892, val loss: 0.48498, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.48996, val loss: 0.47668, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.48386, val loss: 0.46985, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.47825, val loss: 0.46354, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.47311, val loss: 0.45781, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.46818, val loss: 0.45222, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.46364, val loss: 0.44705, in 0.000s 1 tree, 75 leaves, max depth = 11, train loss: 0.45834, val loss: 0.44256, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.45106, val loss: 0.43596, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.44715, val loss: 0.43147, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.44277, val loss: 0.42734, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.43644, val loss: 0.42165, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.43058, val loss: 0.41641, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42733, val loss: 0.41329, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.42192, val loss: 0.40848, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.41692, val loss: 0.40404, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.41393, val loss: 0.40052, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41101, val loss: 0.39772, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.40821, val loss: 0.39446, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.40363, val loss: 0.39045, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.40111, val loss: 0.38749, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39849, val loss: 0.38499, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.39619, val loss: 0.38229, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.39198, val loss: 0.37864, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.38984, val loss: 0.37606, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38746, val loss: 0.37379, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38527, val loss: 0.37171, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.38136, val loss: 0.36836, in 0.016s 1 tree, 75 leaves, max depth = 22, train loss: 0.37927, val loss: 0.36654, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.37577, val loss: 0.36357, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37373, val loss: 0.36164, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.37047, val loss: 0.35889, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.36746, val loss: 0.35637, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.36566, val loss: 0.35424, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.36286, val loss: 0.35191, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.36028, val loss: 0.34979, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.35865, val loss: 0.34779, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.35625, val loss: 0.34583, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.35403, val loss: 0.34403, in 0.016s 1 tree, 75 leaves, max depth = 17, train loss: 0.35254, val loss: 0.34241, in 0.000s 1 tree, 75 leaves, max depth = 14, train loss: 0.35047, val loss: 0.34074, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34853, val loss: 0.33889, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.34660, val loss: 0.33734, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.34519, val loss: 0.33559, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34340, val loss: 0.33387, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34174, val loss: 0.33228, in 0.000s 1 tree, 75 leaves, max depth = 13, train loss: 0.34039, val loss: 0.33083, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.33854, val loss: 0.32937, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.33683, val loss: 0.32803, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.33546, val loss: 0.32654, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33386, val loss: 0.32530, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33228, val loss: 0.32377, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33081, val loss: 0.32236, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.32946, val loss: 0.32106, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32824, val loss: 0.31976, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.32669, val loss: 0.31856, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.32548, val loss: 0.31724, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.32440, val loss: 0.31593, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32311, val loss: 0.31469, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.32212, val loss: 0.31346, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.32092, val loss: 0.31231, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31996, val loss: 0.31129, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.31884, val loss: 0.31022, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.31795, val loss: 0.30910, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.31703, val loss: 0.30811, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.31598, val loss: 0.30710, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31519, val loss: 0.30627, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.31358, val loss: 0.30505, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.31235, val loss: 0.30412, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.31093, val loss: 0.30307, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.30962, val loss: 0.30210, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30857, val loss: 0.30109, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.30734, val loss: 0.30020, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.30636, val loss: 0.29924, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.30547, val loss: 0.29829, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30455, val loss: 0.29740, in 0.000s 1 tree, 75 leaves, max depth = 19, train loss: 0.30373, val loss: 0.29652, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.30253, val loss: 0.29566, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30165, val loss: 0.29481, in 0.000s 1 tree, 75 leaves, max depth = 18, train loss: 0.30087, val loss: 0.29398, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30006, val loss: 0.29319, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.29930, val loss: 0.29247, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.29859, val loss: 0.29158, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29788, val loss: 0.29090, in 0.000s 1 tree, 75 leaves, max depth = 15, train loss: 0.29666, val loss: 0.29004, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.29594, val loss: 0.28928, in 0.016s Fit 100 trees in 1.423 s, (5890 total leaves) Time spent computing histograms: 0.524s Time spent finding best splits: 0.107s Time spent applying splits: 0.109s Time spent predicting: 0.000s Trial 61, Fold 5: Log loss = 0.3061034647699614, Average precision = 0.9509200647523018, ROC-AUC = 0.949326496107612, Elapsed Time = 1.433862500000032 seconds
Optimization Progress: 62%|######2 | 62/100 [12:09<07:22, 11.65s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 62, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 49 leaves, max depth = 10, train loss: 0.65202, val loss: 0.65248, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.61666, val loss: 0.61748, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.58585, val loss: 0.58689, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.55982, val loss: 0.56076, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.53538, val loss: 0.53637, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.51502, val loss: 0.51592, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.49548, val loss: 0.49624, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.47812, val loss: 0.47875, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.46256, val loss: 0.46300, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.44864, val loss: 0.44889, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.43600, val loss: 0.43617, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.41825, val loss: 0.41907, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.40780, val loss: 0.40866, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.39291, val loss: 0.39438, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.38413, val loss: 0.38563, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.37157, val loss: 0.37363, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.36408, val loss: 0.36625, in 0.018s 1 tree, 29 leaves, max depth = 10, train loss: 0.35393, val loss: 0.35621, in 0.014s 1 tree, 30 leaves, max depth = 10, train loss: 0.34493, val loss: 0.34728, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.33780, val loss: 0.34033, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.32994, val loss: 0.33341, in 0.016s 1 tree, 29 leaves, max depth = 14, train loss: 0.32291, val loss: 0.32650, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.31696, val loss: 0.32067, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.31072, val loss: 0.31525, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.30512, val loss: 0.30970, in 0.000s 1 tree, 62 leaves, max depth = 12, train loss: 0.29993, val loss: 0.30456, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.29493, val loss: 0.30030, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.29046, val loss: 0.29584, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.28614, val loss: 0.29161, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.28222, val loss: 0.28786, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.27833, val loss: 0.28460, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.27474, val loss: 0.28095, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.27141, val loss: 0.27764, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.26826, val loss: 0.27508, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.26547, val loss: 0.27226, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.26277, val loss: 0.27013, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.26041, val loss: 0.26773, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.25752, val loss: 0.26483, in 0.031s 1 tree, 45 leaves, max depth = 12, train loss: 0.25533, val loss: 0.26313, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.25334, val loss: 0.26095, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.25147, val loss: 0.25952, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.24968, val loss: 0.25771, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.24732, val loss: 0.25534, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.24490, val loss: 0.25281, in 0.016s 1 tree, 28 leaves, max depth = 14, train loss: 0.24338, val loss: 0.25121, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.24121, val loss: 0.24895, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.23958, val loss: 0.24769, in 0.016s 1 tree, 36 leaves, max depth = 17, train loss: 0.23820, val loss: 0.24609, in 0.016s 1 tree, 65 leaves, max depth = 14, train loss: 0.23601, val loss: 0.24377, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.23454, val loss: 0.24241, in 0.016s 1 tree, 52 leaves, max depth = 18, train loss: 0.23321, val loss: 0.24139, in 0.016s 1 tree, 61 leaves, max depth = 14, train loss: 0.23200, val loss: 0.23999, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.23010, val loss: 0.23790, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.22882, val loss: 0.23695, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.22775, val loss: 0.23587, in 0.016s 1 tree, 52 leaves, max depth = 19, train loss: 0.22665, val loss: 0.23494, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.22496, val loss: 0.23312, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.22389, val loss: 0.23216, in 0.016s 1 tree, 37 leaves, max depth = 15, train loss: 0.22299, val loss: 0.23110, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.22210, val loss: 0.23009, in 0.000s 1 tree, 61 leaves, max depth = 12, train loss: 0.22070, val loss: 0.22854, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.21942, val loss: 0.22725, in 0.031s 1 tree, 33 leaves, max depth = 11, train loss: 0.21844, val loss: 0.22649, in 0.000s 1 tree, 58 leaves, max depth = 15, train loss: 0.21752, val loss: 0.22595, in 0.016s Fit 64 trees in 1.315 s, (3062 total leaves) Time spent computing histograms: 0.388s Time spent finding best splits: 0.080s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 62, Fold 1: Log loss = 0.22921671941905178, Average precision = 0.9687370333075268, ROC-AUC = 0.9640845234510547, Elapsed Time = 1.327083500000299 seconds Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 62, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 49 leaves, max depth = 11, train loss: 0.65203, val loss: 0.65130, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.61660, val loss: 0.61514, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.58569, val loss: 0.58361, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.55961, val loss: 0.55727, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.53489, val loss: 0.53223, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.51451, val loss: 0.51166, in 0.031s 1 tree, 54 leaves, max depth = 10, train loss: 0.49491, val loss: 0.49176, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.47736, val loss: 0.47402, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.46184, val loss: 0.45820, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.44775, val loss: 0.44393, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.43516, val loss: 0.43111, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.41764, val loss: 0.41398, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.40729, val loss: 0.40362, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.39258, val loss: 0.38927, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.38378, val loss: 0.38042, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.37138, val loss: 0.36833, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.36453, val loss: 0.36152, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.35449, val loss: 0.35166, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.34695, val loss: 0.34416, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.33834, val loss: 0.33627, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.33052, val loss: 0.32866, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.32422, val loss: 0.32242, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.31737, val loss: 0.31613, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.31121, val loss: 0.31014, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.30570, val loss: 0.30483, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.30025, val loss: 0.29988, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.29531, val loss: 0.29504, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.29074, val loss: 0.29092, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.28663, val loss: 0.28685, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.28227, val loss: 0.28267, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.27875, val loss: 0.27926, in 0.000s 1 tree, 52 leaves, max depth = 14, train loss: 0.27500, val loss: 0.27565, in 0.031s 1 tree, 47 leaves, max depth = 13, train loss: 0.27165, val loss: 0.27267, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.26865, val loss: 0.27003, in 0.016s 1 tree, 30 leaves, max depth = 16, train loss: 0.26596, val loss: 0.26741, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.26270, val loss: 0.26441, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.26024, val loss: 0.26222, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.25798, val loss: 0.26003, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.25516, val loss: 0.25743, in 0.031s 1 tree, 52 leaves, max depth = 14, train loss: 0.25268, val loss: 0.25504, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.25034, val loss: 0.25322, in 0.016s 1 tree, 28 leaves, max depth = 16, train loss: 0.24859, val loss: 0.25154, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.24680, val loss: 0.25025, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.24460, val loss: 0.24825, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.24280, val loss: 0.24682, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.24132, val loss: 0.24533, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.23991, val loss: 0.24421, in 0.016s 1 tree, 63 leaves, max depth = 14, train loss: 0.23773, val loss: 0.24222, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.23619, val loss: 0.24103, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.23492, val loss: 0.24007, in 0.000s 1 tree, 55 leaves, max depth = 14, train loss: 0.23365, val loss: 0.23927, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.23174, val loss: 0.23751, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.23044, val loss: 0.23658, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.22938, val loss: 0.23578, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.22750, val loss: 0.23379, in 0.031s 1 tree, 36 leaves, max depth = 9, train loss: 0.22645, val loss: 0.23293, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.22472, val loss: 0.23099, in 0.000s 1 tree, 63 leaves, max depth = 13, train loss: 0.22379, val loss: 0.23040, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.22243, val loss: 0.22926, in 0.031s 1 tree, 48 leaves, max depth = 11, train loss: 0.22101, val loss: 0.22811, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.22009, val loss: 0.22747, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.21869, val loss: 0.22588, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.21793, val loss: 0.22523, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.21723, val loss: 0.22479, in 0.016s Fit 64 trees in 1.392 s, (3022 total leaves) Time spent computing histograms: 0.393s Time spent finding best splits: 0.081s Time spent applying splits: 0.060s Time spent predicting: 0.000s Trial 62, Fold 2: Log loss = 0.22243597504614823, Average precision = 0.96841896352419, ROC-AUC = 0.9656550754626563, Elapsed Time = 1.4002233000010165 seconds Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 62, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 52 leaves, max depth = 9, train loss: 0.65227, val loss: 0.65219, in 0.016s 1 tree, 52 leaves, max depth = 9, train loss: 0.61709, val loss: 0.61691, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.58642, val loss: 0.58609, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.56023, val loss: 0.56005, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.53580, val loss: 0.53570, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.51531, val loss: 0.51531, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.49588, val loss: 0.49595, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.47857, val loss: 0.47868, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.46308, val loss: 0.46324, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.44920, val loss: 0.44939, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.43044, val loss: 0.43204, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.41894, val loss: 0.42062, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.40844, val loss: 0.41007, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.39352, val loss: 0.39643, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.38473, val loss: 0.38766, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.37213, val loss: 0.37624, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.36467, val loss: 0.36878, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35443, val loss: 0.35947, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.34539, val loss: 0.35129, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.33837, val loss: 0.34408, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.33025, val loss: 0.33744, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.32321, val loss: 0.33096, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.31734, val loss: 0.32479, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.31090, val loss: 0.31958, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.30529, val loss: 0.31457, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.30039, val loss: 0.30950, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.29524, val loss: 0.30536, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.29077, val loss: 0.30134, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.28637, val loss: 0.29800, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.28266, val loss: 0.29464, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.27867, val loss: 0.29035, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.27510, val loss: 0.28765, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.27189, val loss: 0.28522, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.26904, val loss: 0.28270, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.26582, val loss: 0.27921, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.26319, val loss: 0.27732, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.26087, val loss: 0.27524, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.25786, val loss: 0.27188, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.25567, val loss: 0.27045, in 0.016s 1 tree, 33 leaves, max depth = 14, train loss: 0.25371, val loss: 0.26855, in 0.016s 1 tree, 62 leaves, max depth = 15, train loss: 0.25114, val loss: 0.26571, in 0.031s 1 tree, 28 leaves, max depth = 13, train loss: 0.24943, val loss: 0.26411, in 0.000s 1 tree, 61 leaves, max depth = 14, train loss: 0.24705, val loss: 0.26145, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.24512, val loss: 0.26008, in 0.016s 1 tree, 32 leaves, max depth = 15, train loss: 0.24367, val loss: 0.25868, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.24132, val loss: 0.25600, in 0.031s 1 tree, 59 leaves, max depth = 12, train loss: 0.23991, val loss: 0.25448, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.23801, val loss: 0.25288, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.23634, val loss: 0.25161, in 0.016s 1 tree, 54 leaves, max depth = 16, train loss: 0.23498, val loss: 0.25083, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.23301, val loss: 0.24862, in 0.031s 1 tree, 60 leaves, max depth = 11, train loss: 0.23188, val loss: 0.24743, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.23014, val loss: 0.24548, in 0.016s 1 tree, 54 leaves, max depth = 16, train loss: 0.22887, val loss: 0.24477, in 0.031s 1 tree, 60 leaves, max depth = 12, train loss: 0.22729, val loss: 0.24301, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.22605, val loss: 0.24216, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.22511, val loss: 0.24123, in 0.016s 1 tree, 62 leaves, max depth = 14, train loss: 0.22424, val loss: 0.24047, in 0.031s 1 tree, 58 leaves, max depth = 13, train loss: 0.22307, val loss: 0.23983, in 0.016s 1 tree, 61 leaves, max depth = 13, train loss: 0.22172, val loss: 0.23830, in 0.016s 1 tree, 54 leaves, max depth = 15, train loss: 0.22074, val loss: 0.23782, in 0.031s 1 tree, 61 leaves, max depth = 13, train loss: 0.21950, val loss: 0.23635, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.21873, val loss: 0.23583, in 0.031s 1 tree, 61 leaves, max depth = 12, train loss: 0.21763, val loss: 0.23451, in 0.016s Fit 64 trees in 1.470 s, (3165 total leaves) Time spent computing histograms: 0.402s Time spent finding best splits: 0.090s Time spent applying splits: 0.069s Time spent predicting: 0.000s Trial 62, Fold 3: Log loss = 0.22279210569114516, Average precision = 0.9677415351549978, ROC-AUC = 0.9644402623012396, Elapsed Time = 1.4759725999992952 seconds Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 62, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 49 leaves, max depth = 10, train loss: 0.65255, val loss: 0.65082, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.61746, val loss: 0.61419, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.58655, val loss: 0.58175, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.56076, val loss: 0.55467, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.53636, val loss: 0.52918, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.51632, val loss: 0.50806, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.49689, val loss: 0.48771, in 0.031s 1 tree, 53 leaves, max depth = 10, train loss: 0.47962, val loss: 0.46944, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.46417, val loss: 0.45303, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.45022, val loss: 0.43821, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.43776, val loss: 0.42501, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.42023, val loss: 0.40725, in 0.031s 1 tree, 58 leaves, max depth = 11, train loss: 0.41003, val loss: 0.39632, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.39531, val loss: 0.38140, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.38654, val loss: 0.37194, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.37410, val loss: 0.35932, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.36731, val loss: 0.35213, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.35720, val loss: 0.34169, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.34971, val loss: 0.33410, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.34115, val loss: 0.32526, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.33465, val loss: 0.31863, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.32705, val loss: 0.31153, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.32125, val loss: 0.30556, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.31476, val loss: 0.29885, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.30873, val loss: 0.29326, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.30339, val loss: 0.28771, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.29837, val loss: 0.28309, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.29394, val loss: 0.27846, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.28961, val loss: 0.27408, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.28557, val loss: 0.27040, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.28202, val loss: 0.26665, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.27811, val loss: 0.26245, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.27485, val loss: 0.25950, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.27193, val loss: 0.25642, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.26874, val loss: 0.25317, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.26610, val loss: 0.25077, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.26373, val loss: 0.24827, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.26079, val loss: 0.24509, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.25854, val loss: 0.24291, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.25651, val loss: 0.24086, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.25425, val loss: 0.23896, in 0.031s 1 tree, 63 leaves, max depth = 14, train loss: 0.25153, val loss: 0.23632, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.24948, val loss: 0.23449, in 0.016s 1 tree, 37 leaves, max depth = 14, train loss: 0.24787, val loss: 0.23286, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.24536, val loss: 0.23048, in 0.016s 1 tree, 61 leaves, max depth = 13, train loss: 0.24313, val loss: 0.22835, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.24163, val loss: 0.22728, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.23989, val loss: 0.22563, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.23821, val loss: 0.22422, in 0.031s 1 tree, 34 leaves, max depth = 10, train loss: 0.23670, val loss: 0.22297, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.23545, val loss: 0.22212, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.23362, val loss: 0.22039, in 0.016s 1 tree, 53 leaves, max depth = 16, train loss: 0.23235, val loss: 0.21937, in 0.016s 1 tree, 63 leaves, max depth = 14, train loss: 0.23068, val loss: 0.21778, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.22963, val loss: 0.21711, in 0.016s 1 tree, 57 leaves, max depth = 14, train loss: 0.22828, val loss: 0.21649, in 0.031s 1 tree, 57 leaves, max depth = 17, train loss: 0.22715, val loss: 0.21549, in 0.016s 1 tree, 36 leaves, max depth = 16, train loss: 0.22621, val loss: 0.21459, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.22481, val loss: 0.21328, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.22393, val loss: 0.21275, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.22267, val loss: 0.21159, in 0.016s 1 tree, 60 leaves, max depth = 14, train loss: 0.22174, val loss: 0.21092, in 0.031s 1 tree, 49 leaves, max depth = 10, train loss: 0.22033, val loss: 0.20976, in 0.016s 1 tree, 56 leaves, max depth = 15, train loss: 0.21934, val loss: 0.20943, in 0.016s Fit 64 trees in 1.486 s, (3143 total leaves) Time spent computing histograms: 0.422s Time spent finding best splits: 0.091s Time spent applying splits: 0.069s Time spent predicting: 0.000s Trial 62, Fold 4: Log loss = 0.2271394765584005, Average precision = 0.96831361157216, ROC-AUC = 0.963736480396575, Elapsed Time = 1.4979230000008101 seconds Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 62, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 12, train loss: 0.65172, val loss: 0.65012, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.61637, val loss: 0.61345, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.58523, val loss: 0.58104, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.55911, val loss: 0.55370, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.53479, val loss: 0.52842, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.51435, val loss: 0.50701, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.49472, val loss: 0.48653, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.47730, val loss: 0.46839, in 0.031s 1 tree, 52 leaves, max depth = 11, train loss: 0.46163, val loss: 0.45199, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.44762, val loss: 0.43744, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.43498, val loss: 0.42418, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.41734, val loss: 0.40664, in 0.016s 1 tree, 62 leaves, max depth = 10, train loss: 0.40698, val loss: 0.39594, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.39215, val loss: 0.38130, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.38334, val loss: 0.37232, in 0.031s 1 tree, 52 leaves, max depth = 13, train loss: 0.37081, val loss: 0.35994, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.36389, val loss: 0.35285, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.35368, val loss: 0.34251, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.34611, val loss: 0.33496, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.33747, val loss: 0.32620, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.33090, val loss: 0.31964, in 0.016s 1 tree, 45 leaves, max depth = 15, train loss: 0.32330, val loss: 0.31291, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.31745, val loss: 0.30698, in 0.031s 1 tree, 28 leaves, max depth = 10, train loss: 0.31090, val loss: 0.30036, in 0.000s 1 tree, 46 leaves, max depth = 14, train loss: 0.30486, val loss: 0.29509, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.29946, val loss: 0.28960, in 0.031s 1 tree, 46 leaves, max depth = 12, train loss: 0.29444, val loss: 0.28519, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.28997, val loss: 0.28046, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.28560, val loss: 0.27622, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.28177, val loss: 0.27233, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.27774, val loss: 0.26834, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.27407, val loss: 0.26521, in 0.016s 1 tree, 45 leaves, max depth = 14, train loss: 0.27078, val loss: 0.26241, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.26786, val loss: 0.25935, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.26456, val loss: 0.25627, in 0.031s 1 tree, 45 leaves, max depth = 13, train loss: 0.26187, val loss: 0.25411, in 0.016s 1 tree, 29 leaves, max depth = 14, train loss: 0.25950, val loss: 0.25162, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.25715, val loss: 0.24962, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.25496, val loss: 0.24732, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.25218, val loss: 0.24460, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.25026, val loss: 0.24304, in 0.016s 1 tree, 28 leaves, max depth = 14, train loss: 0.24852, val loss: 0.24124, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.24571, val loss: 0.23870, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.24351, val loss: 0.23664, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.24165, val loss: 0.23505, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.24013, val loss: 0.23399, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.23873, val loss: 0.23258, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.23653, val loss: 0.23026, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.23452, val loss: 0.22850, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.23326, val loss: 0.22752, in 0.016s 1 tree, 60 leaves, max depth = 13, train loss: 0.23203, val loss: 0.22672, in 0.031s 1 tree, 34 leaves, max depth = 14, train loss: 0.23089, val loss: 0.22553, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.22909, val loss: 0.22362, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.22747, val loss: 0.22223, in 0.016s 1 tree, 61 leaves, max depth = 14, train loss: 0.22646, val loss: 0.22165, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.22491, val loss: 0.22001, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.22368, val loss: 0.21900, in 0.032s 1 tree, 55 leaves, max depth = 24, train loss: 0.22255, val loss: 0.21805, in 0.016s 1 tree, 57 leaves, max depth = 15, train loss: 0.22118, val loss: 0.21663, in 0.016s 1 tree, 61 leaves, max depth = 14, train loss: 0.22033, val loss: 0.21618, in 0.031s 1 tree, 34 leaves, max depth = 12, train loss: 0.21934, val loss: 0.21546, in 0.016s 1 tree, 54 leaves, max depth = 22, train loss: 0.21841, val loss: 0.21470, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.21758, val loss: 0.21410, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.21634, val loss: 0.21271, in 0.016s Fit 64 trees in 1.470 s, (3061 total leaves) Time spent computing histograms: 0.421s Time spent finding best splits: 0.090s Time spent applying splits: 0.067s Time spent predicting: 0.000s Trial 62, Fold 5: Log loss = 0.23154348837633418, Average precision = 0.9660934449123427, ROC-AUC = 0.962099102614124, Elapsed Time = 1.485750599998937 seconds
Optimization Progress: 63%|######3 | 63/100 [12:23<07:36, 12.33s/it]
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 63, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.127 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67126, val loss: 0.67128, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65074, val loss: 0.65091, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63284, val loss: 0.63309, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.61529, val loss: 0.61548, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.59926, val loss: 0.59925, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.58432, val loss: 0.58410, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.57048, val loss: 0.57017, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.55672, val loss: 0.55634, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.54387, val loss: 0.54358, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.53159, val loss: 0.53115, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.52039, val loss: 0.51964, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.50980, val loss: 0.50867, in 0.000s 1 tree, 17 leaves, max depth = 5, train loss: 0.49953, val loss: 0.49822, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.49006, val loss: 0.48852, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.48094, val loss: 0.47922, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.47240, val loss: 0.47062, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.46457, val loss: 0.46251, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45380, val loss: 0.45208, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.44687, val loss: 0.44507, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.43993, val loss: 0.43803, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.43335, val loss: 0.43140, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42713, val loss: 0.42513, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.42124, val loss: 0.41921, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41253, val loss: 0.41087, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40731, val loss: 0.40553, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.40223, val loss: 0.40044, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39447, val loss: 0.39306, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.38716, val loss: 0.38607, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38268, val loss: 0.38155, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.37606, val loss: 0.37514, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36973, val loss: 0.36920, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36374, val loss: 0.36350, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35981, val loss: 0.35960, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35632, val loss: 0.35602, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35130, val loss: 0.35104, in 0.016s Fit 35 trees in 0.767 s, (595 total leaves) Time spent computing histograms: 0.219s Time spent finding best splits: 0.023s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 63, Fold 1: Log loss = 0.3543605450878688, Average precision = 0.9539285503411343, ROC-AUC = 0.9486214320366307, Elapsed Time = 0.7798387000002549 seconds Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 63, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67092, val loss: 0.67043, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65043, val loss: 0.64946, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63246, val loss: 0.63135, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.61431, val loss: 0.61282, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.59850, val loss: 0.59678, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.58377, val loss: 0.58184, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.57002, val loss: 0.56784, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.55598, val loss: 0.55350, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54284, val loss: 0.54008, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.53052, val loss: 0.52749, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.51927, val loss: 0.51591, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50869, val loss: 0.50506, in 0.000s 1 tree, 17 leaves, max depth = 5, train loss: 0.49846, val loss: 0.49459, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.48908, val loss: 0.48506, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.47997, val loss: 0.47575, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47135, val loss: 0.46703, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46361, val loss: 0.45907, in 0.000s 1 tree, 17 leaves, max depth = 5, train loss: 0.45295, val loss: 0.44850, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44294, val loss: 0.43857, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43599, val loss: 0.43144, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42943, val loss: 0.42474, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42309, val loss: 0.41831, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.41709, val loss: 0.41223, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.40879, val loss: 0.40405, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40349, val loss: 0.39874, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39840, val loss: 0.39352, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39102, val loss: 0.38625, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38406, val loss: 0.37939, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.37954, val loss: 0.37479, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37325, val loss: 0.36862, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36918, val loss: 0.36447, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36524, val loss: 0.36048, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.36193, val loss: 0.35725, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.35665, val loss: 0.35211, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.35169, val loss: 0.34724, in 0.016s Fit 35 trees in 0.814 s, (595 total leaves) Time spent computing histograms: 0.216s Time spent finding best splits: 0.024s Time spent applying splits: 0.014s Time spent predicting: 0.016s Trial 63, Fold 2: Log loss = 0.35372807073401374, Average precision = 0.9503790214942958, ROC-AUC = 0.9492831586362245, Elapsed Time = 0.8227070000011736 seconds Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 63, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67125, val loss: 0.67116, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65113, val loss: 0.65095, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63313, val loss: 0.63296, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.61532, val loss: 0.61508, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.59943, val loss: 0.59917, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.58467, val loss: 0.58442, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.57013, val loss: 0.56984, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.55622, val loss: 0.55590, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.54320, val loss: 0.54285, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.53100, val loss: 0.53063, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.51984, val loss: 0.51944, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50985, val loss: 0.50939, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49970, val loss: 0.49918, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49023, val loss: 0.48976, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.48118, val loss: 0.48070, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47267, val loss: 0.47215, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46487, val loss: 0.46434, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45726, val loss: 0.45673, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.44674, val loss: 0.44694, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.43983, val loss: 0.44008, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43332, val loss: 0.43358, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42715, val loss: 0.42743, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42132, val loss: 0.42165, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41260, val loss: 0.41354, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40448, val loss: 0.40615, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39935, val loss: 0.40106, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.39187, val loss: 0.39419, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.38479, val loss: 0.38774, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.38031, val loss: 0.38326, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.37394, val loss: 0.37755, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.36989, val loss: 0.37350, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36598, val loss: 0.36962, in 0.031s 1 tree, 17 leaves, max depth = 9, train loss: 0.36051, val loss: 0.36454, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.35535, val loss: 0.35981, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.35049, val loss: 0.35540, in 0.016s Fit 35 trees in 0.860 s, (595 total leaves) Time spent computing histograms: 0.221s Time spent finding best splits: 0.025s Time spent applying splits: 0.015s Time spent predicting: 0.000s Trial 63, Fold 3: Log loss = 0.3476500742964973, Average precision = 0.9537392723874637, ROC-AUC = 0.9517430217131685, Elapsed Time = 0.8657454000003781 seconds Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 63, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67139, val loss: 0.67049, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65096, val loss: 0.64924, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63320, val loss: 0.63096, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61575, val loss: 0.61260, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.59954, val loss: 0.59559, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.58466, val loss: 0.57993, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.57079, val loss: 0.56533, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55695, val loss: 0.55080, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54418, val loss: 0.53732, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.53194, val loss: 0.52444, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.52075, val loss: 0.51259, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50995, val loss: 0.50115, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49997, val loss: 0.49052, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.49068, val loss: 0.48061, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.48165, val loss: 0.47103, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47320, val loss: 0.46205, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46541, val loss: 0.45378, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45476, val loss: 0.44295, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.44472, val loss: 0.43274, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.43793, val loss: 0.42559, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43134, val loss: 0.41858, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42510, val loss: 0.41195, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41919, val loss: 0.40568, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41089, val loss: 0.39729, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40567, val loss: 0.39163, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40058, val loss: 0.38621, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.39319, val loss: 0.37873, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.38617, val loss: 0.37162, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.38185, val loss: 0.36695, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.37560, val loss: 0.36055, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37148, val loss: 0.35616, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.36751, val loss: 0.35194, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.36419, val loss: 0.34834, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.35887, val loss: 0.34283, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.35389, val loss: 0.33767, in 0.016s Fit 35 trees in 0.829 s, (595 total leaves) Time spent computing histograms: 0.215s Time spent finding best splits: 0.023s Time spent applying splits: 0.014s Time spent predicting: 0.016s Trial 63, Fold 4: Log loss = 0.35249242395515334, Average precision = 0.955026329538689, ROC-AUC = 0.9499984799387684, Elapsed Time = 0.8395970000001398 seconds Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 63, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67099, val loss: 0.67000, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65027, val loss: 0.64844, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63218, val loss: 0.62985, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.61443, val loss: 0.61133, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.59795, val loss: 0.59400, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.58305, val loss: 0.57837, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.56904, val loss: 0.56371, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.55512, val loss: 0.54918, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.54213, val loss: 0.53563, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.52990, val loss: 0.52288, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.51884, val loss: 0.51119, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.50835, val loss: 0.50010, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.49818, val loss: 0.48952, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.48876, val loss: 0.47961, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47971, val loss: 0.47020, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47116, val loss: 0.46129, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46337, val loss: 0.45305, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45575, val loss: 0.44509, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44532, val loss: 0.43466, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43846, val loss: 0.42751, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43190, val loss: 0.42072, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42570, val loss: 0.41431, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41987, val loss: 0.40827, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.41119, val loss: 0.39966, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40319, val loss: 0.39165, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39805, val loss: 0.38630, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.39062, val loss: 0.37893, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38361, val loss: 0.37199, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37912, val loss: 0.36733, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37285, val loss: 0.36107, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.36870, val loss: 0.35680, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.36282, val loss: 0.35099, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.35902, val loss: 0.34712, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35566, val loss: 0.34367, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.35064, val loss: 0.33854, in 0.016s Fit 35 trees in 0.844 s, (595 total leaves) Time spent computing histograms: 0.221s Time spent finding best splits: 0.024s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 63, Fold 5: Log loss = 0.3572451824505063, Average precision = 0.9509240981302716, ROC-AUC = 0.9475750515578842, Elapsed Time = 0.846569699999236 seconds
Optimization Progress: 64%|######4 | 64/100 [12:33<07:06, 11.84s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 64, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.015 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.67357, val loss: 0.67362, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.65487, val loss: 0.65513, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.63740, val loss: 0.63785, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.62105, val loss: 0.62163, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.60634, val loss: 0.60689, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.59292, val loss: 0.59325, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.58012, val loss: 0.58027, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.56701, val loss: 0.56709, in 0.031s 1 tree, 35 leaves, max depth = 7, train loss: 0.55542, val loss: 0.55543, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54369, val loss: 0.54376, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.53336, val loss: 0.53340, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.52284, val loss: 0.52287, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.51305, val loss: 0.51299, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.50361, val loss: 0.50362, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49492, val loss: 0.49469, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.48640, val loss: 0.48613, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.47877, val loss: 0.47848, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.47111, val loss: 0.47078, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.46380, val loss: 0.46344, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.45413, val loss: 0.45409, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.44762, val loss: 0.44747, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.44130, val loss: 0.44121, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43529, val loss: 0.43521, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.42956, val loss: 0.42950, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.42146, val loss: 0.42161, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.41667, val loss: 0.41668, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.41189, val loss: 0.41179, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40709, val loss: 0.40699, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39985, val loss: 0.40010, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39539, val loss: 0.39571, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.39139, val loss: 0.39174, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.38488, val loss: 0.38559, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.37874, val loss: 0.37981, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37491, val loss: 0.37609, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.36920, val loss: 0.37072, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.36566, val loss: 0.36729, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.36038, val loss: 0.36233, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.35564, val loss: 0.35762, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.35193, val loss: 0.35394, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.34758, val loss: 0.34966, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.34412, val loss: 0.34626, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.33998, val loss: 0.34259, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.33611, val loss: 0.33878, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.33294, val loss: 0.33564, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.32924, val loss: 0.33238, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.32577, val loss: 0.32895, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.32277, val loss: 0.32590, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.31951, val loss: 0.32303, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31638, val loss: 0.32030, in 0.016s 1 tree, 28 leaves, max depth = 13, train loss: 0.31340, val loss: 0.31738, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.31053, val loss: 0.31490, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.30778, val loss: 0.31217, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.30523, val loss: 0.30965, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.30265, val loss: 0.30742, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.30020, val loss: 0.30499, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29776, val loss: 0.30250, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29543, val loss: 0.30052, in 0.000s 1 tree, 29 leaves, max depth = 15, train loss: 0.29323, val loss: 0.29836, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.29104, val loss: 0.29622, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28893, val loss: 0.29445, in 0.031s 1 tree, 30 leaves, max depth = 10, train loss: 0.28694, val loss: 0.29248, in 0.000s 1 tree, 35 leaves, max depth = 8, train loss: 0.28484, val loss: 0.29035, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28294, val loss: 0.28877, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.28097, val loss: 0.28680, in 0.031s Fit 64 trees in 1.283 s, (2189 total leaves) Time spent computing histograms: 0.399s Time spent finding best splits: 0.068s Time spent applying splits: 0.045s Time spent predicting: 0.016s Trial 64, Fold 1: Log loss = 0.28649361238491966, Average precision = 0.961671769787424, ROC-AUC = 0.9561228479899437, Elapsed Time = 1.288647299999866 seconds Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 64, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 10, train loss: 0.67341, val loss: 0.67298, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.65468, val loss: 0.65387, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.63711, val loss: 0.63596, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.62073, val loss: 0.61924, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.60531, val loss: 0.60350, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.59066, val loss: 0.58871, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.57762, val loss: 0.57553, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.56480, val loss: 0.56253, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.55322, val loss: 0.55084, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.54179, val loss: 0.53918, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.53074, val loss: 0.52797, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52097, val loss: 0.51811, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51106, val loss: 0.50798, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.50169, val loss: 0.49854, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49340, val loss: 0.49018, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.48489, val loss: 0.48157, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47681, val loss: 0.47340, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46919, val loss: 0.46567, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.46217, val loss: 0.45851, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45575, val loss: 0.45204, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.44919, val loss: 0.44544, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.44298, val loss: 0.43912, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.43703, val loss: 0.43302, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.42842, val loss: 0.42456, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42031, val loss: 0.41659, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.41556, val loss: 0.41188, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.41053, val loss: 0.40672, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40603, val loss: 0.40223, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.39892, val loss: 0.39529, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39448, val loss: 0.39084, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39055, val loss: 0.38692, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.38415, val loss: 0.38066, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37810, val loss: 0.37476, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37430, val loss: 0.37097, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.36870, val loss: 0.36549, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.36518, val loss: 0.36189, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.35994, val loss: 0.35677, in 0.031s 1 tree, 29 leaves, max depth = 11, train loss: 0.35528, val loss: 0.35220, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.35211, val loss: 0.34907, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.34855, val loss: 0.34553, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.34429, val loss: 0.34163, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.34023, val loss: 0.33766, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.33641, val loss: 0.33394, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.33266, val loss: 0.33054, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.32911, val loss: 0.32727, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.32575, val loss: 0.32398, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.32272, val loss: 0.32099, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.31960, val loss: 0.31810, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.31660, val loss: 0.31517, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.31372, val loss: 0.31237, in 0.000s 1 tree, 35 leaves, max depth = 10, train loss: 0.31086, val loss: 0.30979, in 0.031s 1 tree, 29 leaves, max depth = 11, train loss: 0.30817, val loss: 0.30715, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.30559, val loss: 0.30461, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.30301, val loss: 0.30229, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.30060, val loss: 0.29995, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.29813, val loss: 0.29757, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29581, val loss: 0.29549, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.29365, val loss: 0.29336, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.29144, val loss: 0.29123, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.28925, val loss: 0.28913, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.28719, val loss: 0.28729, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.28528, val loss: 0.28543, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28338, val loss: 0.28375, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.28139, val loss: 0.28185, in 0.000s Fit 64 trees in 1.347 s, (2185 total leaves) Time spent computing histograms: 0.400s Time spent finding best splits: 0.067s Time spent applying splits: 0.046s Time spent predicting: 0.000s Trial 64, Fold 2: Log loss = 0.2846823637848209, Average precision = 0.9607167809910063, ROC-AUC = 0.9577754958283016, Elapsed Time = 1.3587115000009362 seconds Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 64, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.67361, val loss: 0.67350, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65501, val loss: 0.65486, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.63763, val loss: 0.63745, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.62137, val loss: 0.62113, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.60612, val loss: 0.60583, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59222, val loss: 0.59199, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.57843, val loss: 0.57823, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.56626, val loss: 0.56615, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.55459, val loss: 0.55459, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.54324, val loss: 0.54321, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.53223, val loss: 0.53227, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.52243, val loss: 0.52253, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.51255, val loss: 0.51271, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50320, val loss: 0.50338, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49488, val loss: 0.49513, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.48658, val loss: 0.48687, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.47859, val loss: 0.47889, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.47113, val loss: 0.47148, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.46394, val loss: 0.46430, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.45420, val loss: 0.45523, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.44758, val loss: 0.44872, in 0.031s 1 tree, 35 leaves, max depth = 7, train loss: 0.44141, val loss: 0.44267, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.43546, val loss: 0.43675, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42976, val loss: 0.43112, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42162, val loss: 0.42371, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.41653, val loss: 0.41874, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.40892, val loss: 0.41179, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.40425, val loss: 0.40724, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.39723, val loss: 0.40086, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.39282, val loss: 0.39652, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.38881, val loss: 0.39264, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.38253, val loss: 0.38692, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.37654, val loss: 0.38153, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.37305, val loss: 0.37814, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.36950, val loss: 0.37462, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.36435, val loss: 0.36994, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35950, val loss: 0.36553, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.35573, val loss: 0.36167, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.35112, val loss: 0.35783, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.34684, val loss: 0.35392, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.34340, val loss: 0.35033, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.33928, val loss: 0.34693, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.33545, val loss: 0.34344, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.33230, val loss: 0.34019, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.32860, val loss: 0.33717, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.32518, val loss: 0.33409, in 0.016s 1 tree, 35 leaves, max depth = 15, train loss: 0.32229, val loss: 0.33111, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31903, val loss: 0.32846, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.31597, val loss: 0.32568, in 0.000s 1 tree, 35 leaves, max depth = 15, train loss: 0.31331, val loss: 0.32288, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.31034, val loss: 0.32045, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.30762, val loss: 0.31803, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30517, val loss: 0.31549, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30249, val loss: 0.31333, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.30005, val loss: 0.31112, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29758, val loss: 0.30914, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.29532, val loss: 0.30713, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.29314, val loss: 0.30490, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.29092, val loss: 0.30313, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.28879, val loss: 0.30147, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.28684, val loss: 0.29974, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.28480, val loss: 0.29760, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.28288, val loss: 0.29610, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.28091, val loss: 0.29396, in 0.016s Fit 64 trees in 1.455 s, (2178 total leaves) Time spent computing histograms: 0.449s Time spent finding best splits: 0.078s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 64, Fold 3: Log loss = 0.28106817183611726, Average precision = 0.962650523022376, ROC-AUC = 0.9594392721104487, Elapsed Time = 1.4647454000005382 seconds Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 64, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.67371, val loss: 0.67286, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65519, val loss: 0.65356, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.63791, val loss: 0.63547, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.62194, val loss: 0.61865, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.60657, val loss: 0.60256, in 0.024s 1 tree, 35 leaves, max depth = 9, train loss: 0.59283, val loss: 0.58818, in 0.008s 1 tree, 35 leaves, max depth = 8, train loss: 0.57919, val loss: 0.57384, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56703, val loss: 0.56108, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.55477, val loss: 0.54821, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.54347, val loss: 0.53618, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.53250, val loss: 0.52470, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.52236, val loss: 0.51411, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.51274, val loss: 0.50389, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50352, val loss: 0.49421, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.49501, val loss: 0.48519, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.48663, val loss: 0.47632, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.47934, val loss: 0.46856, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.47182, val loss: 0.46059, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46462, val loss: 0.45294, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45500, val loss: 0.44317, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.44866, val loss: 0.43636, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.44246, val loss: 0.42976, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.43658, val loss: 0.42350, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.43099, val loss: 0.41756, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.42282, val loss: 0.40928, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.41514, val loss: 0.40148, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.41018, val loss: 0.39619, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.40546, val loss: 0.39115, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40090, val loss: 0.38635, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39415, val loss: 0.37953, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.39022, val loss: 0.37528, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.38642, val loss: 0.37128, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.38031, val loss: 0.36511, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37457, val loss: 0.35932, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37109, val loss: 0.35562, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.36576, val loss: 0.35022, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.36254, val loss: 0.34679, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.35785, val loss: 0.34195, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35343, val loss: 0.33732, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.34988, val loss: 0.33374, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.34577, val loss: 0.32950, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.34181, val loss: 0.32578, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.33808, val loss: 0.32192, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.33489, val loss: 0.31869, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.33135, val loss: 0.31538, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.32839, val loss: 0.31242, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.32511, val loss: 0.30929, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.32201, val loss: 0.30603, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31927, val loss: 0.30326, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.31632, val loss: 0.30052, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.31373, val loss: 0.29792, in 0.031s 1 tree, 30 leaves, max depth = 12, train loss: 0.31096, val loss: 0.29505, in 0.062s 1 tree, 35 leaves, max depth = 11, train loss: 0.30832, val loss: 0.29253, in 0.031s 1 tree, 30 leaves, max depth = 12, train loss: 0.30578, val loss: 0.28990, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30334, val loss: 0.28764, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.30102, val loss: 0.28516, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.29868, val loss: 0.28268, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.29651, val loss: 0.28049, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.29436, val loss: 0.27846, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.29230, val loss: 0.27632, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.29032, val loss: 0.27449, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.28843, val loss: 0.27247, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.28643, val loss: 0.27037, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.28456, val loss: 0.26849, in 0.016s Fit 64 trees in 1.549 s, (2190 total leaves) Time spent computing histograms: 0.481s Time spent finding best splits: 0.091s Time spent applying splits: 0.075s Time spent predicting: 0.000s Trial 64, Fold 4: Log loss = 0.2846748268270564, Average precision = 0.961753461255784, ROC-AUC = 0.9574160806979644, Elapsed Time = 1.5648371000006591 seconds Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 64, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.156 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 9, train loss: 0.67336, val loss: 0.67251, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.65449, val loss: 0.65294, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.63686, val loss: 0.63464, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.62050, val loss: 0.61768, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.60502, val loss: 0.60161, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.59048, val loss: 0.58651, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.57735, val loss: 0.57275, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56455, val loss: 0.55937, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.55294, val loss: 0.54722, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.54156, val loss: 0.53519, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.53058, val loss: 0.52378, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.52078, val loss: 0.51351, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.51087, val loss: 0.50317, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.50159, val loss: 0.49354, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49327, val loss: 0.48483, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.48484, val loss: 0.47605, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.47680, val loss: 0.46769, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46921, val loss: 0.45980, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46224, val loss: 0.45240, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.45581, val loss: 0.44566, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44929, val loss: 0.43896, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44306, val loss: 0.43249, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43715, val loss: 0.42632, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.42850, val loss: 0.41774, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.42036, val loss: 0.40963, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.41563, val loss: 0.40467, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.41076, val loss: 0.39963, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.40592, val loss: 0.39467, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.39874, val loss: 0.38757, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.39432, val loss: 0.38297, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.39036, val loss: 0.37890, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.38391, val loss: 0.37252, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.37781, val loss: 0.36650, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.37432, val loss: 0.36300, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.37078, val loss: 0.35933, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.36546, val loss: 0.35393, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.36019, val loss: 0.34877, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35545, val loss: 0.34395, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.35233, val loss: 0.34085, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.34795, val loss: 0.33641, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.34444, val loss: 0.33296, in 0.031s 1 tree, 35 leaves, max depth = 13, train loss: 0.34027, val loss: 0.32921, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.33641, val loss: 0.32521, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.33318, val loss: 0.32201, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.32945, val loss: 0.31868, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.32598, val loss: 0.31515, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.32300, val loss: 0.31225, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31972, val loss: 0.30933, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31655, val loss: 0.30652, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.31356, val loss: 0.30349, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.31089, val loss: 0.30084, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30804, val loss: 0.29834, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.30546, val loss: 0.29573, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.30283, val loss: 0.29306, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30026, val loss: 0.29082, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.29787, val loss: 0.28839, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.29551, val loss: 0.28635, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.29332, val loss: 0.28411, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.29109, val loss: 0.28195, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.28942, val loss: 0.28039, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.28734, val loss: 0.27855, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.28539, val loss: 0.27650, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.28347, val loss: 0.27481, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.28168, val loss: 0.27291, in 0.016s Fit 64 trees in 1.422 s, (2177 total leaves) Time spent computing histograms: 0.448s Time spent finding best splits: 0.075s Time spent applying splits: 0.049s Time spent predicting: 0.000s Trial 64, Fold 5: Log loss = 0.29058164076700016, Average precision = 0.9577833376287552, ROC-AUC = 0.9529413819370902, Elapsed Time = 1.4403724000003422 seconds
Optimization Progress: 65%|######5 | 65/100 [12:47<07:13, 12.38s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 65, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 115 leaves, max depth = 18, train loss: 0.67687, val loss: 0.67681, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.66007, val loss: 0.65980, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.64443, val loss: 0.64402, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.63097, val loss: 0.63039, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.61752, val loss: 0.61694, in 0.016s 1 tree, 72 leaves, max depth = 13, train loss: 0.60427, val loss: 0.60342, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.59202, val loss: 0.59098, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.58010, val loss: 0.57915, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.56880, val loss: 0.56784, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.55798, val loss: 0.55692, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.54781, val loss: 0.54680, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.53822, val loss: 0.53699, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.52978, val loss: 0.52840, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.52149, val loss: 0.52004, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.51184, val loss: 0.51034, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.50448, val loss: 0.50306, in 0.031s 1 tree, 115 leaves, max depth = 22, train loss: 0.49611, val loss: 0.49450, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.48963, val loss: 0.48785, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.48263, val loss: 0.48089, in 0.016s 1 tree, 89 leaves, max depth = 12, train loss: 0.47609, val loss: 0.47437, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.47035, val loss: 0.46859, in 0.031s 1 tree, 115 leaves, max depth = 25, train loss: 0.46423, val loss: 0.46236, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.45837, val loss: 0.45643, in 0.031s 1 tree, 115 leaves, max depth = 23, train loss: 0.45276, val loss: 0.45079, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.44807, val loss: 0.44620, in 0.016s 1 tree, 115 leaves, max depth = 22, train loss: 0.44371, val loss: 0.44173, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.43875, val loss: 0.43666, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.43283, val loss: 0.43075, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.42786, val loss: 0.42569, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.42320, val loss: 0.42105, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.41891, val loss: 0.41674, in 0.031s 1 tree, 115 leaves, max depth = 12, train loss: 0.41526, val loss: 0.41306, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.41108, val loss: 0.40893, in 0.031s 1 tree, 100 leaves, max depth = 17, train loss: 0.40730, val loss: 0.40504, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.40350, val loss: 0.40138, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.40042, val loss: 0.39852, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.39707, val loss: 0.39527, in 0.031s 1 tree, 11 leaves, max depth = 6, train loss: 0.39106, val loss: 0.38942, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.38544, val loss: 0.38394, in 0.000s 1 tree, 115 leaves, max depth = 17, train loss: 0.38192, val loss: 0.38042, in 0.031s 1 tree, 55 leaves, max depth = 9, train loss: 0.37570, val loss: 0.37469, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.37017, val loss: 0.36960, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.36543, val loss: 0.36500, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.36107, val loss: 0.36080, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.35653, val loss: 0.35650, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.35239, val loss: 0.35246, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.34975, val loss: 0.34997, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.34743, val loss: 0.34773, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.34548, val loss: 0.34567, in 0.016s 1 tree, 115 leaves, max depth = 21, train loss: 0.34175, val loss: 0.34196, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.34007, val loss: 0.34033, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.33643, val loss: 0.33681, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.33359, val loss: 0.33411, in 0.031s 1 tree, 115 leaves, max depth = 12, train loss: 0.33150, val loss: 0.33211, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.32837, val loss: 0.32916, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.32616, val loss: 0.32688, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.32435, val loss: 0.32515, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.32154, val loss: 0.32258, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.32024, val loss: 0.32131, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.31797, val loss: 0.31915, in 0.031s 1 tree, 11 leaves, max depth = 6, train loss: 0.31683, val loss: 0.31798, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.31440, val loss: 0.31568, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.31289, val loss: 0.31414, in 0.031s 1 tree, 21 leaves, max depth = 6, train loss: 0.31018, val loss: 0.31167, in 0.016s 1 tree, 115 leaves, max depth = 12, train loss: 0.30856, val loss: 0.31020, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.30728, val loss: 0.30893, in 0.031s 1 tree, 115 leaves, max depth = 23, train loss: 0.30541, val loss: 0.30701, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.30426, val loss: 0.30589, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.30154, val loss: 0.30336, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.29875, val loss: 0.30082, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.29594, val loss: 0.29845, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.29468, val loss: 0.29721, in 0.031s 1 tree, 115 leaves, max depth = 12, train loss: 0.29335, val loss: 0.29599, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.29186, val loss: 0.29438, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.29075, val loss: 0.29335, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.28945, val loss: 0.29215, in 0.031s Fit 76 trees in 1.954 s, (6956 total leaves) Time spent computing histograms: 0.599s Time spent finding best splits: 0.140s Time spent applying splits: 0.128s Time spent predicting: 0.000s Trial 65, Fold 1: Log loss = 0.300041432985021, Average precision = 0.961748428061056, ROC-AUC = 0.9546362463734865, Elapsed Time = 1.9627108999993652 seconds Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 65, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.188 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 115 leaves, max depth = 16, train loss: 0.67707, val loss: 0.67651, in 0.031s 1 tree, 88 leaves, max depth = 12, train loss: 0.66096, val loss: 0.65998, in 0.031s 1 tree, 115 leaves, max depth = 25, train loss: 0.64653, val loss: 0.64512, in 0.016s 1 tree, 115 leaves, max depth = 21, train loss: 0.63142, val loss: 0.63009, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.61789, val loss: 0.61627, in 0.031s 1 tree, 115 leaves, max depth = 22, train loss: 0.60439, val loss: 0.60271, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.59069, val loss: 0.58892, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.57942, val loss: 0.57720, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.56878, val loss: 0.56631, in 0.031s 1 tree, 115 leaves, max depth = 24, train loss: 0.55812, val loss: 0.55562, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.54770, val loss: 0.54533, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.53832, val loss: 0.53583, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.53004, val loss: 0.52742, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.52158, val loss: 0.51886, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.51199, val loss: 0.50913, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.50468, val loss: 0.50165, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.49718, val loss: 0.49402, in 0.031s 1 tree, 110 leaves, max depth = 15, train loss: 0.49132, val loss: 0.48778, in 0.016s 1 tree, 73 leaves, max depth = 14, train loss: 0.48463, val loss: 0.48094, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.47554, val loss: 0.47188, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.46928, val loss: 0.46563, in 0.031s 1 tree, 49 leaves, max depth = 11, train loss: 0.46414, val loss: 0.46020, in 0.016s 1 tree, 18 leaves, max depth = 8, train loss: 0.45613, val loss: 0.45224, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.44977, val loss: 0.44581, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.44458, val loss: 0.44071, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.43903, val loss: 0.43527, in 0.031s 1 tree, 11 leaves, max depth = 5, train loss: 0.43219, val loss: 0.42845, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.42594, val loss: 0.42229, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.42097, val loss: 0.41745, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.41692, val loss: 0.41331, in 0.016s 1 tree, 115 leaves, max depth = 11, train loss: 0.41201, val loss: 0.40849, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.40791, val loss: 0.40454, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.40409, val loss: 0.40080, in 0.031s 1 tree, 23 leaves, max depth = 9, train loss: 0.39816, val loss: 0.39500, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.39222, val loss: 0.38920, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.38828, val loss: 0.38551, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.38479, val loss: 0.38204, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.38104, val loss: 0.37849, in 0.031s 1 tree, 8 leaves, max depth = 4, train loss: 0.37631, val loss: 0.37381, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.37181, val loss: 0.36936, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.36942, val loss: 0.36701, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.36535, val loss: 0.36303, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.36301, val loss: 0.36074, in 0.031s 1 tree, 39 leaves, max depth = 10, train loss: 0.35886, val loss: 0.35699, in 0.000s 1 tree, 115 leaves, max depth = 15, train loss: 0.35488, val loss: 0.35320, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.35108, val loss: 0.34946, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.34741, val loss: 0.34594, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.34497, val loss: 0.34350, in 0.031s 1 tree, 115 leaves, max depth = 28, train loss: 0.34319, val loss: 0.34181, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.34008, val loss: 0.33890, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.33787, val loss: 0.33679, in 0.031s 1 tree, 13 leaves, max depth = 5, train loss: 0.33444, val loss: 0.33343, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.33182, val loss: 0.33094, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.32892, val loss: 0.32820, in 0.031s 1 tree, 38 leaves, max depth = 15, train loss: 0.32730, val loss: 0.32664, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.32476, val loss: 0.32422, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.32288, val loss: 0.32241, in 0.031s 1 tree, 33 leaves, max depth = 8, train loss: 0.31900, val loss: 0.31882, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.31549, val loss: 0.31553, in 0.016s 1 tree, 115 leaves, max depth = 21, train loss: 0.31430, val loss: 0.31435, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.31255, val loss: 0.31266, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.31004, val loss: 0.31036, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.30876, val loss: 0.30911, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.30668, val loss: 0.30726, in 0.016s 1 tree, 115 leaves, max depth = 22, train loss: 0.30471, val loss: 0.30549, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.30320, val loss: 0.30422, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.30182, val loss: 0.30299, in 0.016s 1 tree, 115 leaves, max depth = 21, train loss: 0.30042, val loss: 0.30185, in 0.047s 1 tree, 115 leaves, max depth = 18, train loss: 0.29909, val loss: 0.30073, in 0.031s 1 tree, 43 leaves, max depth = 13, train loss: 0.29584, val loss: 0.29779, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.29373, val loss: 0.29584, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.29251, val loss: 0.29477, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.29055, val loss: 0.29304, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.28844, val loss: 0.29103, in 0.031s 1 tree, 72 leaves, max depth = 18, train loss: 0.28595, val loss: 0.28876, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.28407, val loss: 0.28700, in 0.031s Fit 76 trees in 2.157 s, (6852 total leaves) Time spent computing histograms: 0.655s Time spent finding best splits: 0.155s Time spent applying splits: 0.142s Time spent predicting: 0.000s Trial 65, Fold 2: Log loss = 0.2915758691707463, Average precision = 0.9600215453753159, ROC-AUC = 0.9564426191546374, Elapsed Time = 2.174914799999897 seconds Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 65, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 115 leaves, max depth = 17, train loss: 0.67692, val loss: 0.67678, in 0.016s 1 tree, 94 leaves, max depth = 12, train loss: 0.66093, val loss: 0.66069, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.64646, val loss: 0.64617, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.63116, val loss: 0.63095, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.61823, val loss: 0.61797, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.60477, val loss: 0.60445, in 0.031s 1 tree, 24 leaves, max depth = 9, train loss: 0.59355, val loss: 0.59302, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.58153, val loss: 0.58117, in 0.031s 1 tree, 98 leaves, max depth = 13, train loss: 0.57012, val loss: 0.57005, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.55933, val loss: 0.55945, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.55026, val loss: 0.55033, in 0.016s 1 tree, 115 leaves, max depth = 22, train loss: 0.54040, val loss: 0.54063, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.53122, val loss: 0.53153, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.52175, val loss: 0.52198, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.51487, val loss: 0.51475, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.50686, val loss: 0.50690, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.49910, val loss: 0.49911, in 0.031s 1 tree, 115 leaves, max depth = 22, train loss: 0.49177, val loss: 0.49192, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.48464, val loss: 0.48472, in 0.031s 1 tree, 20 leaves, max depth = 8, train loss: 0.47537, val loss: 0.47608, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.46817, val loss: 0.46896, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.46215, val loss: 0.46311, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45388, val loss: 0.45540, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.44750, val loss: 0.44911, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.44111, val loss: 0.44285, in 0.016s 1 tree, 62 leaves, max depth = 17, train loss: 0.43617, val loss: 0.43780, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.43013, val loss: 0.43160, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.42574, val loss: 0.42711, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.42021, val loss: 0.42167, in 0.031s 1 tree, 20 leaves, max depth = 9, train loss: 0.41348, val loss: 0.41550, in 0.016s 1 tree, 115 leaves, max depth = 23, train loss: 0.40917, val loss: 0.41138, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.40524, val loss: 0.40755, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.40107, val loss: 0.40341, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.39649, val loss: 0.39889, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.39173, val loss: 0.39406, in 0.031s 1 tree, 9 leaves, max depth = 4, train loss: 0.38630, val loss: 0.38895, in 0.016s 1 tree, 12 leaves, max depth = 7, train loss: 0.38108, val loss: 0.38420, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.37639, val loss: 0.37970, in 0.000s 1 tree, 115 leaves, max depth = 16, train loss: 0.37357, val loss: 0.37692, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.36944, val loss: 0.37270, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.36522, val loss: 0.36839, in 0.031s 1 tree, 15 leaves, max depth = 5, train loss: 0.36091, val loss: 0.36449, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.35772, val loss: 0.36139, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.35563, val loss: 0.35948, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.35305, val loss: 0.35703, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.35105, val loss: 0.35521, in 0.031s 1 tree, 51 leaves, max depth = 12, train loss: 0.34614, val loss: 0.35104, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.34375, val loss: 0.34896, in 0.031s 1 tree, 9 leaves, max depth = 4, train loss: 0.34035, val loss: 0.34576, in 0.016s 1 tree, 115 leaves, max depth = 12, train loss: 0.33811, val loss: 0.34385, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.33609, val loss: 0.34204, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.33317, val loss: 0.33938, in 0.031s 1 tree, 115 leaves, max depth = 24, train loss: 0.33123, val loss: 0.33764, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.32828, val loss: 0.33469, in 0.031s 1 tree, 115 leaves, max depth = 23, train loss: 0.32690, val loss: 0.33343, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.32501, val loss: 0.33152, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.32243, val loss: 0.32881, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.32085, val loss: 0.32720, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.31956, val loss: 0.32597, in 0.031s 1 tree, 9 leaves, max depth = 4, train loss: 0.31638, val loss: 0.32318, in 0.016s 1 tree, 115 leaves, max depth = 22, train loss: 0.31384, val loss: 0.32059, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.31154, val loss: 0.31833, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.31009, val loss: 0.31705, in 0.031s 1 tree, 14 leaves, max depth = 6, train loss: 0.30725, val loss: 0.31474, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.30550, val loss: 0.31328, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.30286, val loss: 0.31112, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.30095, val loss: 0.30913, in 0.031s 1 tree, 17 leaves, max depth = 9, train loss: 0.29840, val loss: 0.30703, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.29698, val loss: 0.30573, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.29378, val loss: 0.30311, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.29221, val loss: 0.30159, in 0.031s 1 tree, 115 leaves, max depth = 12, train loss: 0.29104, val loss: 0.30044, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.28962, val loss: 0.29914, in 0.016s 1 tree, 115 leaves, max depth = 20, train loss: 0.28821, val loss: 0.29791, in 0.047s 1 tree, 79 leaves, max depth = 16, train loss: 0.28519, val loss: 0.29536, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.28289, val loss: 0.29348, in 0.016s Fit 76 trees in 2.174 s, (6738 total leaves) Time spent computing histograms: 0.666s Time spent finding best splits: 0.156s Time spent applying splits: 0.144s Time spent predicting: 0.000s Trial 65, Fold 3: Log loss = 0.2863868354714503, Average precision = 0.9625752619952467, ROC-AUC = 0.957905905991467, Elapsed Time = 2.191066100000171 seconds Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 65, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 115 leaves, max depth = 16, train loss: 0.67700, val loss: 0.67664, in 0.031s 1 tree, 103 leaves, max depth = 15, train loss: 0.66083, val loss: 0.66018, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.64673, val loss: 0.64585, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.63219, val loss: 0.63065, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.61877, val loss: 0.61671, in 0.031s 1 tree, 84 leaves, max depth = 18, train loss: 0.60697, val loss: 0.60409, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.59452, val loss: 0.59094, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.58223, val loss: 0.57821, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.57096, val loss: 0.56675, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.56004, val loss: 0.55525, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.55078, val loss: 0.54576, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.54119, val loss: 0.53597, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.53304, val loss: 0.52757, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.52468, val loss: 0.51881, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.51619, val loss: 0.50992, in 0.031s 1 tree, 46 leaves, max depth = 9, train loss: 0.50827, val loss: 0.50141, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.50160, val loss: 0.49462, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.49462, val loss: 0.48719, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.48746, val loss: 0.47977, in 0.031s 1 tree, 70 leaves, max depth = 15, train loss: 0.48087, val loss: 0.47305, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.47341, val loss: 0.46540, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46797, val loss: 0.45952, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.46283, val loss: 0.45429, in 0.031s 1 tree, 10 leaves, max depth = 6, train loss: 0.45438, val loss: 0.44607, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.44681, val loss: 0.43794, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.43938, val loss: 0.43028, in 0.016s 1 tree, 115 leaves, max depth = 26, train loss: 0.43502, val loss: 0.42585, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.42983, val loss: 0.42075, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.42444, val loss: 0.41508, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.42018, val loss: 0.41074, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.41557, val loss: 0.40585, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.41016, val loss: 0.40034, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.40644, val loss: 0.39643, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.40225, val loss: 0.39202, in 0.031s 1 tree, 67 leaves, max depth = 11, train loss: 0.39893, val loss: 0.38866, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.39485, val loss: 0.38439, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.39095, val loss: 0.38026, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.38728, val loss: 0.37639, in 0.031s 1 tree, 15 leaves, max depth = 6, train loss: 0.38183, val loss: 0.37067, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.37765, val loss: 0.36655, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.37419, val loss: 0.36296, in 0.031s 1 tree, 11 leaves, max depth = 4, train loss: 0.36939, val loss: 0.35793, in 0.016s 1 tree, 115 leaves, max depth = 21, train loss: 0.36660, val loss: 0.35495, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.36349, val loss: 0.35164, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.36091, val loss: 0.34899, in 0.031s 1 tree, 115 leaves, max depth = 22, train loss: 0.35894, val loss: 0.34715, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.35552, val loss: 0.34399, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.35315, val loss: 0.34177, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.35045, val loss: 0.33920, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.34781, val loss: 0.33645, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.34567, val loss: 0.33415, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.34272, val loss: 0.33122, in 0.016s 1 tree, 97 leaves, max depth = 13, train loss: 0.34072, val loss: 0.32911, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.33863, val loss: 0.32712, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.33700, val loss: 0.32544, in 0.031s 1 tree, 115 leaves, max depth = 24, train loss: 0.33485, val loss: 0.32324, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.33272, val loss: 0.32107, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.33104, val loss: 0.31945, in 0.031s 1 tree, 115 leaves, max depth = 21, train loss: 0.32875, val loss: 0.31721, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.32665, val loss: 0.31512, in 0.031s 1 tree, 115 leaves, max depth = 22, train loss: 0.32518, val loss: 0.31360, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.32164, val loss: 0.31029, in 0.016s 1 tree, 111 leaves, max depth = 16, train loss: 0.32009, val loss: 0.30874, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.31660, val loss: 0.30546, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.31458, val loss: 0.30344, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.31270, val loss: 0.30158, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.31076, val loss: 0.29983, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.30958, val loss: 0.29861, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.30837, val loss: 0.29728, in 0.031s 1 tree, 13 leaves, max depth = 8, train loss: 0.30562, val loss: 0.29465, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.30433, val loss: 0.29347, in 0.031s 1 tree, 13 leaves, max depth = 7, train loss: 0.30135, val loss: 0.29047, in 0.016s 1 tree, 111 leaves, max depth = 16, train loss: 0.30018, val loss: 0.28929, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.29827, val loss: 0.28750, in 0.016s 1 tree, 40 leaves, max depth = 15, train loss: 0.29544, val loss: 0.28480, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.29345, val loss: 0.28287, in 0.016s Fit 76 trees in 2.206 s, (7119 total leaves) Time spent computing histograms: 0.695s Time spent finding best splits: 0.161s Time spent applying splits: 0.148s Time spent predicting: 0.031s Trial 65, Fold 4: Log loss = 0.30077750899317207, Average precision = 0.9625300286328604, ROC-AUC = 0.9559317111001429, Elapsed Time = 2.2293044999987615 seconds Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 65, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.174 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 115 leaves, max depth = 16, train loss: 0.67682, val loss: 0.67611, in 0.031s 1 tree, 108 leaves, max depth = 13, train loss: 0.66060, val loss: 0.65933, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.64577, val loss: 0.64381, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.63060, val loss: 0.62798, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.61703, val loss: 0.61361, in 0.016s 1 tree, 88 leaves, max depth = 14, train loss: 0.60372, val loss: 0.59954, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.59027, val loss: 0.58544, in 0.031s 1 tree, 57 leaves, max depth = 10, train loss: 0.57904, val loss: 0.57387, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.56793, val loss: 0.56215, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.55718, val loss: 0.55096, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.54702, val loss: 0.54074, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.53633, val loss: 0.52978, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.52723, val loss: 0.52053, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.51887, val loss: 0.51178, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.51078, val loss: 0.50331, in 0.031s 1 tree, 115 leaves, max depth = 17, train loss: 0.50347, val loss: 0.49572, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.49610, val loss: 0.48797, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.48971, val loss: 0.48121, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.48336, val loss: 0.47455, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.47426, val loss: 0.46515, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.46799, val loss: 0.45860, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.46252, val loss: 0.45289, in 0.031s 1 tree, 49 leaves, max depth = 11, train loss: 0.45732, val loss: 0.44725, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.45184, val loss: 0.44139, in 0.031s 1 tree, 115 leaves, max depth = 26, train loss: 0.44538, val loss: 0.43482, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.43897, val loss: 0.42840, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.43426, val loss: 0.42334, in 0.016s 1 tree, 115 leaves, max depth = 23, train loss: 0.43002, val loss: 0.41897, in 0.031s 1 tree, 22 leaves, max depth = 10, train loss: 0.42279, val loss: 0.41166, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41607, val loss: 0.40482, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.41054, val loss: 0.39948, in 0.031s 1 tree, 63 leaves, max depth = 10, train loss: 0.40649, val loss: 0.39534, in 0.016s 1 tree, 115 leaves, max depth = 13, train loss: 0.40264, val loss: 0.39150, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.39843, val loss: 0.38709, in 0.031s 1 tree, 115 leaves, max depth = 23, train loss: 0.39550, val loss: 0.38397, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.39263, val loss: 0.38096, in 0.031s 1 tree, 115 leaves, max depth = 26, train loss: 0.38998, val loss: 0.37803, in 0.031s 1 tree, 115 leaves, max depth = 20, train loss: 0.38744, val loss: 0.37524, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.38431, val loss: 0.37205, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.37922, val loss: 0.36689, in 0.031s 1 tree, 12 leaves, max depth = 6, train loss: 0.37382, val loss: 0.36187, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.36901, val loss: 0.35703, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.36562, val loss: 0.35361, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.36298, val loss: 0.35095, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.35916, val loss: 0.34717, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.35554, val loss: 0.34361, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.35208, val loss: 0.34023, in 0.031s 1 tree, 115 leaves, max depth = 16, train loss: 0.34926, val loss: 0.33739, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.34697, val loss: 0.33514, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.34479, val loss: 0.33297, in 0.016s 1 tree, 115 leaves, max depth = 15, train loss: 0.34157, val loss: 0.32988, in 0.031s 1 tree, 29 leaves, max depth = 11, train loss: 0.33985, val loss: 0.32815, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.33773, val loss: 0.32598, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.33565, val loss: 0.32420, in 0.031s 1 tree, 115 leaves, max depth = 18, train loss: 0.33276, val loss: 0.32150, in 0.016s 1 tree, 115 leaves, max depth = 12, train loss: 0.33095, val loss: 0.31982, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.32963, val loss: 0.31831, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.32812, val loss: 0.31691, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.32550, val loss: 0.31447, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.32386, val loss: 0.31280, in 0.031s 1 tree, 71 leaves, max depth = 16, train loss: 0.32004, val loss: 0.30925, in 0.016s 1 tree, 26 leaves, max depth = 12, train loss: 0.31646, val loss: 0.30579, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.31285, val loss: 0.30255, in 0.016s 1 tree, 115 leaves, max depth = 21, train loss: 0.31177, val loss: 0.30152, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.31032, val loss: 0.30007, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.30868, val loss: 0.29841, in 0.016s 1 tree, 115 leaves, max depth = 19, train loss: 0.30738, val loss: 0.29712, in 0.031s 1 tree, 13 leaves, max depth = 6, train loss: 0.30621, val loss: 0.29591, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.30287, val loss: 0.29292, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.30115, val loss: 0.29134, in 0.016s 1 tree, 115 leaves, max depth = 17, train loss: 0.29902, val loss: 0.28937, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.29592, val loss: 0.28654, in 0.031s 1 tree, 115 leaves, max depth = 22, train loss: 0.29479, val loss: 0.28540, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.29332, val loss: 0.28408, in 0.016s 1 tree, 115 leaves, max depth = 18, train loss: 0.29197, val loss: 0.28295, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.29111, val loss: 0.28216, in 0.016s Fit 76 trees in 2.142 s, (7070 total leaves) Time spent computing histograms: 0.664s Time spent finding best splits: 0.156s Time spent applying splits: 0.144s Time spent predicting: 0.016s Trial 65, Fold 5: Log loss = 0.3062555495030684, Average precision = 0.9591346466491077, ROC-AUC = 0.9539096668710402, Elapsed Time = 2.1642406999999366 seconds
Optimization Progress: 66%|######6 | 66/100 [13:04<07:52, 13.90s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 66, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.206 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 16, train loss: 0.65572, val loss: 0.65450, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.62421, val loss: 0.62186, in 0.016s 1 tree, 68 leaves, max depth = 19, train loss: 0.59754, val loss: 0.59454, in 0.016s 1 tree, 68 leaves, max depth = 19, train loss: 0.57469, val loss: 0.57110, in 0.016s 1 tree, 68 leaves, max depth = 15, train loss: 0.55484, val loss: 0.55033, in 0.000s 1 tree, 107 leaves, max depth = 18, train loss: 0.53642, val loss: 0.53322, in 0.016s 1 tree, 67 leaves, max depth = 18, train loss: 0.52055, val loss: 0.51683, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.50540, val loss: 0.50285, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.49249, val loss: 0.48932, in 0.016s 1 tree, 107 leaves, max depth = 18, train loss: 0.47987, val loss: 0.47776, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.46924, val loss: 0.46663, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.45859, val loss: 0.45695, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.44930, val loss: 0.44856, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.44082, val loss: 0.43960, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.43316, val loss: 0.43122, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.42538, val loss: 0.42429, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.41903, val loss: 0.41746, in 0.000s 1 tree, 107 leaves, max depth = 13, train loss: 0.41233, val loss: 0.41156, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40685, val loss: 0.40550, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.40102, val loss: 0.40048, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39630, val loss: 0.39522, in 0.000s 1 tree, 107 leaves, max depth = 13, train loss: 0.39119, val loss: 0.39094, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.38623, val loss: 0.38562, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.38193, val loss: 0.38099, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.37791, val loss: 0.37649, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.37343, val loss: 0.37285, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.36951, val loss: 0.36970, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36598, val loss: 0.36571, in 0.000s 1 tree, 68 leaves, max depth = 12, train loss: 0.36221, val loss: 0.36159, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.35873, val loss: 0.35888, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.35557, val loss: 0.35549, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35248, val loss: 0.35198, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.34978, val loss: 0.34889, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.34705, val loss: 0.34604, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.34384, val loss: 0.34366, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34142, val loss: 0.34082, in 0.000s 1 tree, 86 leaves, max depth = 17, train loss: 0.33886, val loss: 0.33801, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.33596, val loss: 0.33592, in 0.016s 1 tree, 86 leaves, max depth = 15, train loss: 0.33371, val loss: 0.33340, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33152, val loss: 0.33086, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32959, val loss: 0.32860, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.32690, val loss: 0.32672, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.32465, val loss: 0.32419, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32288, val loss: 0.32207, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.32097, val loss: 0.32013, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.31851, val loss: 0.31845, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.31671, val loss: 0.31651, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.31454, val loss: 0.31507, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.31287, val loss: 0.31308, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.31129, val loss: 0.31145, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.30933, val loss: 0.31021, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30780, val loss: 0.30834, in 0.016s 1 tree, 86 leaves, max depth = 15, train loss: 0.30623, val loss: 0.30665, in 0.000s 1 tree, 107 leaves, max depth = 14, train loss: 0.30443, val loss: 0.30553, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.30294, val loss: 0.30382, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30152, val loss: 0.30207, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.29989, val loss: 0.30112, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29861, val loss: 0.29952, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.29723, val loss: 0.29826, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29609, val loss: 0.29684, in 0.000s 1 tree, 85 leaves, max depth = 19, train loss: 0.29481, val loss: 0.29542, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.29323, val loss: 0.29450, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.29213, val loss: 0.29350, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29104, val loss: 0.29212, in 0.000s 1 tree, 107 leaves, max depth = 15, train loss: 0.28966, val loss: 0.29138, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28868, val loss: 0.29012, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.28753, val loss: 0.28883, in 0.016s 1 tree, 132 leaves, max depth = 24, train loss: 0.28595, val loss: 0.28798, in 0.016s 1 tree, 64 leaves, max depth = 15, train loss: 0.28497, val loss: 0.28696, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28404, val loss: 0.28575, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.28243, val loss: 0.28485, in 0.016s 1 tree, 140 leaves, max depth = 20, train loss: 0.28082, val loss: 0.28362, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27996, val loss: 0.28251, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.27896, val loss: 0.28156, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27753, val loss: 0.28080, in 0.016s 1 tree, 131 leaves, max depth = 24, train loss: 0.27623, val loss: 0.28017, in 0.031s 1 tree, 4 leaves, max depth = 3, train loss: 0.27541, val loss: 0.27910, in 0.000s 1 tree, 40 leaves, max depth = 10, train loss: 0.27456, val loss: 0.27836, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27383, val loss: 0.27738, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.27293, val loss: 0.27635, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.27138, val loss: 0.27547, in 0.016s 1 tree, 140 leaves, max depth = 17, train loss: 0.27018, val loss: 0.27490, in 0.031s 1 tree, 140 leaves, max depth = 17, train loss: 0.26887, val loss: 0.27393, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.26804, val loss: 0.27291, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.26732, val loss: 0.27195, in 0.000s Fit 85 trees in 1.393 s, (5840 total leaves) Time spent computing histograms: 0.450s Time spent finding best splits: 0.109s Time spent applying splits: 0.125s Time spent predicting: 0.000s Trial 66, Fold 1: Log loss = 0.2807773485256574, Average precision = 0.9522178452405305, ROC-AUC = 0.9524693001008493, Elapsed Time = 1.4055117999996583 seconds Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 66, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 15, train loss: 0.65638, val loss: 0.65475, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.62534, val loss: 0.62225, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.59839, val loss: 0.59387, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.57553, val loss: 0.56976, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.55565, val loss: 0.54871, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.53740, val loss: 0.53119, in 0.016s 1 tree, 67 leaves, max depth = 20, train loss: 0.52158, val loss: 0.51443, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.50660, val loss: 0.50012, in 0.016s 1 tree, 68 leaves, max depth = 12, train loss: 0.49338, val loss: 0.48599, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.48096, val loss: 0.47419, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.47029, val loss: 0.46280, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.45981, val loss: 0.45289, in 0.016s 1 tree, 68 leaves, max depth = 17, train loss: 0.45088, val loss: 0.44338, in 0.000s 1 tree, 68 leaves, max depth = 15, train loss: 0.44292, val loss: 0.43475, in 0.016s 1 tree, 68 leaves, max depth = 18, train loss: 0.43616, val loss: 0.42749, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.42762, val loss: 0.41952, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.42217, val loss: 0.41385, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.41477, val loss: 0.40700, in 0.016s 1 tree, 68 leaves, max depth = 14, train loss: 0.40943, val loss: 0.40129, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.40301, val loss: 0.39541, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.39738, val loss: 0.39024, in 0.000s 1 tree, 106 leaves, max depth = 14, train loss: 0.39249, val loss: 0.38583, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.38798, val loss: 0.38112, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.38358, val loss: 0.37632, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.37970, val loss: 0.37226, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.37539, val loss: 0.36839, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.37166, val loss: 0.36510, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36824, val loss: 0.36151, in 0.016s 1 tree, 67 leaves, max depth = 11, train loss: 0.36446, val loss: 0.35744, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.36117, val loss: 0.35458, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.35795, val loss: 0.35107, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35495, val loss: 0.34791, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35233, val loss: 0.34513, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.34955, val loss: 0.34213, in 0.016s 1 tree, 107 leaves, max depth = 18, train loss: 0.34648, val loss: 0.33949, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34413, val loss: 0.33700, in 0.000s 1 tree, 86 leaves, max depth = 16, train loss: 0.34166, val loss: 0.33427, in 0.016s 1 tree, 107 leaves, max depth = 18, train loss: 0.33894, val loss: 0.33197, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.33661, val loss: 0.32957, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33448, val loss: 0.32730, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.33225, val loss: 0.32487, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33036, val loss: 0.32284, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.32843, val loss: 0.32074, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.32585, val loss: 0.31858, in 0.029s 1 tree, 4 leaves, max depth = 3, train loss: 0.32412, val loss: 0.31673, in 0.007s 1 tree, 107 leaves, max depth = 18, train loss: 0.32184, val loss: 0.31484, in 0.019s 1 tree, 67 leaves, max depth = 13, train loss: 0.32009, val loss: 0.31302, in 0.014s 1 tree, 106 leaves, max depth = 17, train loss: 0.31808, val loss: 0.31138, in 0.014s 1 tree, 4 leaves, max depth = 3, train loss: 0.31646, val loss: 0.30964, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.31474, val loss: 0.30775, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.31286, val loss: 0.30621, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.31137, val loss: 0.30461, in 0.016s 1 tree, 86 leaves, max depth = 16, train loss: 0.30985, val loss: 0.30298, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.30814, val loss: 0.30158, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.30675, val loss: 0.30017, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30537, val loss: 0.29869, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.30406, val loss: 0.29725, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30283, val loss: 0.29591, in 0.000s 1 tree, 107 leaves, max depth = 17, train loss: 0.30122, val loss: 0.29464, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30010, val loss: 0.29341, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.29813, val loss: 0.29203, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.29687, val loss: 0.29064, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.29578, val loss: 0.28960, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29473, val loss: 0.28845, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.29333, val loss: 0.28733, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29238, val loss: 0.28628, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.29129, val loss: 0.28509, in 0.016s 1 tree, 131 leaves, max depth = 21, train loss: 0.28970, val loss: 0.28436, in 0.016s 1 tree, 65 leaves, max depth = 18, train loss: 0.28867, val loss: 0.28342, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28777, val loss: 0.28242, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.28655, val loss: 0.28151, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.28494, val loss: 0.28081, in 0.031s 1 tree, 4 leaves, max depth = 3, train loss: 0.28409, val loss: 0.27987, in 0.000s 1 tree, 83 leaves, max depth = 17, train loss: 0.28304, val loss: 0.27882, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.28152, val loss: 0.27780, in 0.016s 1 tree, 86 leaves, max depth = 21, train loss: 0.28056, val loss: 0.27673, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27976, val loss: 0.27584, in 0.000s 1 tree, 140 leaves, max depth = 17, train loss: 0.27823, val loss: 0.27522, in 0.031s 1 tree, 131 leaves, max depth = 21, train loss: 0.27700, val loss: 0.27475, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27626, val loss: 0.27392, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.27544, val loss: 0.27315, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.27406, val loss: 0.27262, in 0.031s 1 tree, 86 leaves, max depth = 21, train loss: 0.27322, val loss: 0.27167, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27199, val loss: 0.27124, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27128, val loss: 0.27043, in 0.016s Fit 85 trees in 1.445 s, (5776 total leaves) Time spent computing histograms: 0.489s Time spent finding best splits: 0.110s Time spent applying splits: 0.127s Time spent predicting: 0.000s Trial 66, Fold 2: Log loss = 0.2800543717383729, Average precision = 0.9508008089697524, ROC-AUC = 0.9531672190791882, Elapsed Time = 1.45176309999988 seconds Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 66, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 65 leaves, max depth = 13, train loss: 0.65601, val loss: 0.65491, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.62471, val loss: 0.62261, in 0.016s 1 tree, 65 leaves, max depth = 13, train loss: 0.59805, val loss: 0.59502, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.57547, val loss: 0.57137, in 0.000s 1 tree, 66 leaves, max depth = 13, train loss: 0.55579, val loss: 0.55095, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.53741, val loss: 0.53400, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.52169, val loss: 0.51748, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.50658, val loss: 0.50363, in 0.016s 1 tree, 66 leaves, max depth = 17, train loss: 0.49376, val loss: 0.49006, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.48118, val loss: 0.47860, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.47049, val loss: 0.46743, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.45989, val loss: 0.45783, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.45098, val loss: 0.44844, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.44313, val loss: 0.44007, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.43639, val loss: 0.43281, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.42773, val loss: 0.42502, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.42206, val loss: 0.41978, in 0.000s 1 tree, 106 leaves, max depth = 12, train loss: 0.41456, val loss: 0.41311, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.40969, val loss: 0.40860, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.40315, val loss: 0.40285, in 0.000s 1 tree, 106 leaves, max depth = 12, train loss: 0.39742, val loss: 0.39788, in 0.016s 1 tree, 106 leaves, max depth = 12, train loss: 0.39241, val loss: 0.39358, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.38754, val loss: 0.38822, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.38330, val loss: 0.38357, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.37922, val loss: 0.37978, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.37483, val loss: 0.37605, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.37099, val loss: 0.37285, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36740, val loss: 0.36951, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.36374, val loss: 0.36541, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.36033, val loss: 0.36262, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.35724, val loss: 0.35919, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35410, val loss: 0.35626, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.35135, val loss: 0.35370, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34895, val loss: 0.35144, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.34628, val loss: 0.34854, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.34308, val loss: 0.34598, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.34066, val loss: 0.34293, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.33783, val loss: 0.34070, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.33553, val loss: 0.33832, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33330, val loss: 0.33623, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33133, val loss: 0.33438, in 0.000s 1 tree, 83 leaves, max depth = 14, train loss: 0.32915, val loss: 0.33179, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.32729, val loss: 0.32954, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.32460, val loss: 0.32744, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.32278, val loss: 0.32572, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.32039, val loss: 0.32389, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.31874, val loss: 0.32211, in 0.000s 1 tree, 106 leaves, max depth = 15, train loss: 0.31662, val loss: 0.32052, in 0.031s 1 tree, 4 leaves, max depth = 3, train loss: 0.31492, val loss: 0.31893, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.31326, val loss: 0.31689, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.31133, val loss: 0.31547, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30977, val loss: 0.31399, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.30838, val loss: 0.31250, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.30662, val loss: 0.31123, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.30519, val loss: 0.30943, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30374, val loss: 0.30805, in 0.000s 1 tree, 106 leaves, max depth = 13, train loss: 0.30213, val loss: 0.30692, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30083, val loss: 0.30569, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.29952, val loss: 0.30404, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29835, val loss: 0.30292, in 0.000s 1 tree, 129 leaves, max depth = 19, train loss: 0.29654, val loss: 0.30210, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.29534, val loss: 0.30085, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.29392, val loss: 0.29988, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29281, val loss: 0.29883, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.29169, val loss: 0.29740, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.29069, val loss: 0.29644, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.28934, val loss: 0.29554, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28843, val loss: 0.29467, in 0.016s 1 tree, 64 leaves, max depth = 16, train loss: 0.28739, val loss: 0.29361, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.28617, val loss: 0.29282, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.28518, val loss: 0.29153, in 0.000s 1 tree, 130 leaves, max depth = 20, train loss: 0.28374, val loss: 0.29104, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28285, val loss: 0.29020, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.28192, val loss: 0.28939, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28112, val loss: 0.28862, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.27949, val loss: 0.28740, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.27847, val loss: 0.28630, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.27708, val loss: 0.28525, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27632, val loss: 0.28451, in 0.016s 1 tree, 140 leaves, max depth = 17, train loss: 0.27491, val loss: 0.28359, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.27401, val loss: 0.28259, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.27277, val loss: 0.28166, in 0.016s 1 tree, 131 leaves, max depth = 18, train loss: 0.27160, val loss: 0.28135, in 0.031s 1 tree, 83 leaves, max depth = 21, train loss: 0.27085, val loss: 0.28032, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27010, val loss: 0.27961, in 0.016s Fit 85 trees in 1.454 s, (5765 total leaves) Time spent computing histograms: 0.497s Time spent finding best splits: 0.108s Time spent applying splits: 0.125s Time spent predicting: 0.016s Trial 66, Fold 3: Log loss = 0.2744742454659397, Average precision = 0.9557721692213328, ROC-AUC = 0.9553627860417409, Elapsed Time = 1.4555007999988447 seconds Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 66, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 67 leaves, max depth = 13, train loss: 0.65633, val loss: 0.65431, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.62527, val loss: 0.62136, in 0.000s 1 tree, 67 leaves, max depth = 15, train loss: 0.59856, val loss: 0.59275, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.57590, val loss: 0.56845, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.55621, val loss: 0.54711, in 0.000s 1 tree, 101 leaves, max depth = 14, train loss: 0.53808, val loss: 0.52926, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.52227, val loss: 0.51210, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.50739, val loss: 0.49748, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.49449, val loss: 0.48334, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.48211, val loss: 0.47120, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.47129, val loss: 0.45916, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.46088, val loss: 0.44898, in 0.031s 1 tree, 67 leaves, max depth = 13, train loss: 0.45197, val loss: 0.43904, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.44410, val loss: 0.43012, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.43733, val loss: 0.42244, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.42888, val loss: 0.41423, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.42340, val loss: 0.40837, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.41607, val loss: 0.40125, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.41067, val loss: 0.39502, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.40432, val loss: 0.38885, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.39876, val loss: 0.38346, in 0.000s 1 tree, 104 leaves, max depth = 13, train loss: 0.39390, val loss: 0.37877, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.38935, val loss: 0.37387, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.38507, val loss: 0.36895, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.38117, val loss: 0.36474, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.37690, val loss: 0.36070, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.37317, val loss: 0.35713, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36973, val loss: 0.35340, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.36603, val loss: 0.34919, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.36274, val loss: 0.34606, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.35961, val loss: 0.34245, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.35659, val loss: 0.33918, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.35395, val loss: 0.33632, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.35126, val loss: 0.33324, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.34821, val loss: 0.33045, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.34585, val loss: 0.32788, in 0.000s 1 tree, 86 leaves, max depth = 17, train loss: 0.34338, val loss: 0.32537, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.34062, val loss: 0.32287, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.33836, val loss: 0.32036, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33622, val loss: 0.31803, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.33404, val loss: 0.31583, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.33214, val loss: 0.31374, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.33025, val loss: 0.31185, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.32763, val loss: 0.30951, in 0.000s 1 tree, 4 leaves, max depth = 3, train loss: 0.32590, val loss: 0.30762, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.32355, val loss: 0.30553, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.32190, val loss: 0.30399, in 0.000s 1 tree, 104 leaves, max depth = 14, train loss: 0.31988, val loss: 0.30222, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.31825, val loss: 0.30042, in 0.000s 1 tree, 85 leaves, max depth = 18, train loss: 0.31654, val loss: 0.29874, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.31507, val loss: 0.29701, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.31360, val loss: 0.29541, in 0.000s 1 tree, 104 leaves, max depth = 16, train loss: 0.31167, val loss: 0.29378, in 0.016s 1 tree, 86 leaves, max depth = 15, train loss: 0.31021, val loss: 0.29233, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.30851, val loss: 0.29084, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30712, val loss: 0.28930, in 0.000s 1 tree, 85 leaves, max depth = 18, train loss: 0.30580, val loss: 0.28806, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30456, val loss: 0.28669, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.30295, val loss: 0.28536, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30182, val loss: 0.28411, in 0.016s 1 tree, 67 leaves, max depth = 13, train loss: 0.30057, val loss: 0.28289, in 0.000s 1 tree, 83 leaves, max depth = 16, train loss: 0.29856, val loss: 0.28111, in 0.016s [63/85] 1 tree, 85 leaves, max depth = 18, train loss: 0.29740, val loss: 0.28004, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29635, val loss: 0.27889, in 0.000s 1 tree, 103 leaves, max depth = 15, train loss: 0.29495, val loss: 0.27771, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.29400, val loss: 0.27664, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.29294, val loss: 0.27581, in 0.000s 1 tree, 140 leaves, max depth = 19, train loss: 0.29124, val loss: 0.27474, in 0.016s 1 tree, 85 leaves, max depth = 18, train loss: 0.29019, val loss: 0.27380, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28928, val loss: 0.27279, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.28759, val loss: 0.27131, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.28592, val loss: 0.27038, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28509, val loss: 0.26946, in 0.016s 1 tree, 86 leaves, max depth = 17, train loss: 0.28409, val loss: 0.26859, in 0.000s 1 tree, 129 leaves, max depth = 23, train loss: 0.28256, val loss: 0.26746, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.28172, val loss: 0.26646, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.28093, val loss: 0.26559, in 0.000s 1 tree, 140 leaves, max depth = 20, train loss: 0.27946, val loss: 0.26469, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.27862, val loss: 0.26397, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27731, val loss: 0.26317, in 0.016s 1 tree, 85 leaves, max depth = 17, train loss: 0.27655, val loss: 0.26251, in 0.016s 1 tree, 129 leaves, max depth = 22, train loss: 0.27523, val loss: 0.26151, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.27410, val loss: 0.26084, in 0.016s 1 tree, 140 leaves, max depth = 20, train loss: 0.27280, val loss: 0.26014, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.27198, val loss: 0.25924, in 0.000s Fit 85 trees in 1.299 s, (5883 total leaves) Time spent computing histograms: 0.435s Time spent finding best splits: 0.093s Time spent applying splits: 0.107s Time spent predicting: 0.016s Trial 66, Fold 4: Log loss = 0.2763670443208256, Average precision = 0.9559006771424874, ROC-AUC = 0.9546079165981148, Elapsed Time = 1.2988741000008304 seconds Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 66, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 64 leaves, max depth = 14, train loss: 0.65604, val loss: 0.65344, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.62471, val loss: 0.61997, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.59802, val loss: 0.59130, in 0.016s 1 tree, 66 leaves, max depth = 15, train loss: 0.57510, val loss: 0.56637, in 0.000s 1 tree, 67 leaves, max depth = 14, train loss: 0.55541, val loss: 0.54499, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.53731, val loss: 0.52774, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.52132, val loss: 0.51023, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.50645, val loss: 0.49620, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.49308, val loss: 0.48144, in 0.016s 1 tree, 105 leaves, max depth = 22, train loss: 0.48073, val loss: 0.46989, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.46990, val loss: 0.45785, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.45949, val loss: 0.44824, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.45047, val loss: 0.43812, in 0.000s 1 tree, 67 leaves, max depth = 16, train loss: 0.44241, val loss: 0.42900, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.43540, val loss: 0.42099, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.42689, val loss: 0.41333, in 0.000s 1 tree, 67 leaves, max depth = 13, train loss: 0.42117, val loss: 0.40674, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.41382, val loss: 0.40021, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40852, val loss: 0.39513, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40214, val loss: 0.38953, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.39658, val loss: 0.38469, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.39173, val loss: 0.38051, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38719, val loss: 0.37617, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.38276, val loss: 0.37099, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37885, val loss: 0.36726, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.37460, val loss: 0.36370, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.37089, val loss: 0.36062, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36745, val loss: 0.35734, in 0.000s 1 tree, 67 leaves, max depth = 12, train loss: 0.36360, val loss: 0.35295, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.36035, val loss: 0.34910, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.35744, val loss: 0.34567, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35447, val loss: 0.34284, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35188, val loss: 0.34037, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.34842, val loss: 0.33763, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.34543, val loss: 0.33528, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34307, val loss: 0.33303, in 0.000s 1 tree, 86 leaves, max depth = 13, train loss: 0.34050, val loss: 0.33033, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.33823, val loss: 0.32759, in 0.000s 1 tree, 83 leaves, max depth = 15, train loss: 0.33619, val loss: 0.32532, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.33341, val loss: 0.32319, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33126, val loss: 0.32113, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.32937, val loss: 0.31933, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.32726, val loss: 0.31710, in 0.000s 1 tree, 105 leaves, max depth = 18, train loss: 0.32470, val loss: 0.31517, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.32298, val loss: 0.31353, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.32071, val loss: 0.31185, in 0.016s 1 tree, 67 leaves, max depth = 15, train loss: 0.31894, val loss: 0.30969, in 0.000s 1 tree, 86 leaves, max depth = 18, train loss: 0.31725, val loss: 0.30792, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31566, val loss: 0.30640, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.31357, val loss: 0.30488, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.31193, val loss: 0.30315, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31047, val loss: 0.30175, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.30857, val loss: 0.30041, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.30720, val loss: 0.29869, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.30553, val loss: 0.29754, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30415, val loss: 0.29622, in 0.000s 1 tree, 84 leaves, max depth = 15, train loss: 0.30272, val loss: 0.29471, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30150, val loss: 0.29353, in 0.000s 1 tree, 105 leaves, max depth = 17, train loss: 0.29992, val loss: 0.29247, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29881, val loss: 0.29140, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.29749, val loss: 0.29003, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.29607, val loss: 0.28907, in 0.000s 1 tree, 63 leaves, max depth = 13, train loss: 0.29491, val loss: 0.28767, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29386, val loss: 0.28666, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.29257, val loss: 0.28581, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.29143, val loss: 0.28463, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.29046, val loss: 0.28369, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.28862, val loss: 0.28234, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.28758, val loss: 0.28125, in 0.000s 1 tree, 140 leaves, max depth = 21, train loss: 0.28593, val loss: 0.28015, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28501, val loss: 0.27926, in 0.016s 1 tree, 128 leaves, max depth = 24, train loss: 0.28349, val loss: 0.27819, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.28266, val loss: 0.27738, in 0.000s 1 tree, 84 leaves, max depth = 19, train loss: 0.28166, val loss: 0.27645, in 0.016s 1 tree, 140 leaves, max depth = 18, train loss: 0.28017, val loss: 0.27547, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.27930, val loss: 0.27437, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.27852, val loss: 0.27361, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.27692, val loss: 0.27319, in 0.031s 1 tree, 140 leaves, max depth = 21, train loss: 0.27561, val loss: 0.27234, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.27474, val loss: 0.27145, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.27336, val loss: 0.27049, in 0.016s 1 tree, 129 leaves, max depth = 25, train loss: 0.27214, val loss: 0.26968, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.27138, val loss: 0.26886, in 0.000s 1 tree, 140 leaves, max depth = 18, train loss: 0.27032, val loss: 0.26821, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.26952, val loss: 0.26741, in 0.016s Fit 85 trees in 1.252 s, (5901 total leaves) Time spent computing histograms: 0.417s Time spent finding best splits: 0.093s Time spent applying splits: 0.108s Time spent predicting: 0.000s Trial 66, Fold 5: Log loss = 0.2834021484954921, Average precision = 0.9528156004014571, ROC-AUC = 0.9512490199357152, Elapsed Time = 1.2532468999997946 seconds
Optimization Progress: 67%|######7 | 67/100 [13:19<07:45, 14.10s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 67, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 123 leaves, max depth = 13, train loss: 0.66207, val loss: 0.66244, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.63449, val loss: 0.63467, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.60773, val loss: 0.60835, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.58359, val loss: 0.58395, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.56303, val loss: 0.56339, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.54270, val loss: 0.54312, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.52582, val loss: 0.52646, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.50930, val loss: 0.51002, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.49468, val loss: 0.49593, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.48021, val loss: 0.48142, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.46803, val loss: 0.46919, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.45569, val loss: 0.45687, in 0.016s 1 tree, 123 leaves, max depth = 11, train loss: 0.44522, val loss: 0.44654, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.43531, val loss: 0.43667, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.42583, val loss: 0.42725, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.41772, val loss: 0.41933, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.40947, val loss: 0.41105, in 0.031s 1 tree, 104 leaves, max depth = 11, train loss: 0.39700, val loss: 0.39893, in 0.016s 1 tree, 105 leaves, max depth = 11, train loss: 0.38572, val loss: 0.38798, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.37518, val loss: 0.37794, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.36601, val loss: 0.36909, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.35880, val loss: 0.36205, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.35203, val loss: 0.35549, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.34422, val loss: 0.34772, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.33818, val loss: 0.34185, in 0.031s 1 tree, 95 leaves, max depth = 13, train loss: 0.33114, val loss: 0.33547, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.32573, val loss: 0.33026, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.32070, val loss: 0.32544, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.31428, val loss: 0.31945, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.31043, val loss: 0.31588, in 0.031s 1 tree, 95 leaves, max depth = 13, train loss: 0.30478, val loss: 0.31085, in 0.016s 1 tree, 95 leaves, max depth = 12, train loss: 0.29960, val loss: 0.30626, in 0.016s 1 tree, 119 leaves, max depth = 12, train loss: 0.29464, val loss: 0.30153, in 0.016s 1 tree, 110 leaves, max depth = 13, train loss: 0.28987, val loss: 0.29714, in 0.031s 1 tree, 99 leaves, max depth = 14, train loss: 0.28567, val loss: 0.29347, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.28188, val loss: 0.28973, in 0.016s Fit 36 trees in 1.033 s, (4093 total leaves) Time spent computing histograms: 0.265s Time spent finding best splits: 0.084s Time spent applying splits: 0.062s Time spent predicting: 0.000s Trial 67, Fold 1: Log loss = 0.2964510256821385, Average precision = 0.9613911346311143, ROC-AUC = 0.9561273288548583, Elapsed Time = 1.038752000000386 seconds Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 67, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.126 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 123 leaves, max depth = 12, train loss: 0.66203, val loss: 0.66200, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.63411, val loss: 0.63440, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.60761, val loss: 0.60824, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.58491, val loss: 0.58556, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.56388, val loss: 0.56467, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.54320, val loss: 0.54416, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.52518, val loss: 0.52621, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.50826, val loss: 0.50943, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.49372, val loss: 0.49497, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.47973, val loss: 0.48136, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.46730, val loss: 0.46911, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.45483, val loss: 0.45681, in 0.031s 1 tree, 123 leaves, max depth = 17, train loss: 0.44335, val loss: 0.44553, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.43279, val loss: 0.43515, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.42320, val loss: 0.42612, in 0.016s 1 tree, 109 leaves, max depth = 15, train loss: 0.40966, val loss: 0.41290, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.40109, val loss: 0.40465, in 0.031s 1 tree, 99 leaves, max depth = 15, train loss: 0.38931, val loss: 0.39323, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.37861, val loss: 0.38285, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.37151, val loss: 0.37609, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.36222, val loss: 0.36724, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.35376, val loss: 0.35919, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.34692, val loss: 0.35271, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.34065, val loss: 0.34669, in 0.031s 1 tree, 123 leaves, max depth = 11, train loss: 0.33477, val loss: 0.34122, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.32789, val loss: 0.33499, in 0.016s 1 tree, 70 leaves, max depth = 15, train loss: 0.32153, val loss: 0.32879, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.31624, val loss: 0.32395, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.31134, val loss: 0.31945, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.30695, val loss: 0.31533, in 0.031s 1 tree, 71 leaves, max depth = 15, train loss: 0.30162, val loss: 0.31016, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.29791, val loss: 0.30681, in 0.031s 1 tree, 104 leaves, max depth = 15, train loss: 0.29285, val loss: 0.30221, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.28917, val loss: 0.29880, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.28464, val loss: 0.29472, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.28049, val loss: 0.29097, in 0.031s Fit 36 trees in 1.158 s, (4182 total leaves) Time spent computing histograms: 0.288s Time spent finding best splits: 0.099s Time spent applying splits: 0.074s Time spent predicting: 0.000s Trial 67, Fold 2: Log loss = 0.29018273364600816, Average precision = 0.9635144684162199, ROC-AUC = 0.9609087841026591, Elapsed Time = 1.16494640000019 seconds Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 67, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 123 leaves, max depth = 13, train loss: 0.66171, val loss: 0.66191, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.63371, val loss: 0.63444, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.60740, val loss: 0.60833, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.58465, val loss: 0.58606, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.56365, val loss: 0.56558, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.54340, val loss: 0.54561, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.52618, val loss: 0.52882, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.50951, val loss: 0.51254, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.49496, val loss: 0.49841, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.48059, val loss: 0.48437, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.46811, val loss: 0.47231, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.45585, val loss: 0.46034, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.44493, val loss: 0.44967, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.43462, val loss: 0.43979, in 0.016s 1 tree, 100 leaves, max depth = 19, train loss: 0.42047, val loss: 0.42671, in 0.016s 1 tree, 94 leaves, max depth = 13, train loss: 0.40758, val loss: 0.41484, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.39909, val loss: 0.40653, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.39109, val loss: 0.39888, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.38368, val loss: 0.39186, in 0.031s 1 tree, 72 leaves, max depth = 12, train loss: 0.37343, val loss: 0.38254, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.36748, val loss: 0.37696, in 0.016s 1 tree, 113 leaves, max depth = 17, train loss: 0.35843, val loss: 0.36889, in 0.031s 1 tree, 58 leaves, max depth = 12, train loss: 0.35048, val loss: 0.36154, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.34415, val loss: 0.35510, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.33696, val loss: 0.34898, in 0.016s 1 tree, 52 leaves, max depth = 8, train loss: 0.33028, val loss: 0.34283, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.32409, val loss: 0.33759, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.31884, val loss: 0.33219, in 0.031s 1 tree, 57 leaves, max depth = 10, train loss: 0.31333, val loss: 0.32719, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.30845, val loss: 0.32230, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.30394, val loss: 0.31774, in 0.016s 1 tree, 69 leaves, max depth = 11, train loss: 0.29919, val loss: 0.31350, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.29456, val loss: 0.30967, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.29132, val loss: 0.30675, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.28832, val loss: 0.30402, in 0.016s 1 tree, 121 leaves, max depth = 16, train loss: 0.28397, val loss: 0.30020, in 0.031s Fit 36 trees in 1.094 s, (3926 total leaves) Time spent computing histograms: 0.266s Time spent finding best splits: 0.089s Time spent applying splits: 0.066s Time spent predicting: 0.000s Trial 67, Fold 3: Log loss = 0.2912220947073553, Average precision = 0.9607902861811848, ROC-AUC = 0.9576986882530265, Elapsed Time = 1.1027567000001 seconds Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 67, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 123 leaves, max depth = 13, train loss: 0.66161, val loss: 0.66116, in 0.016s 1 tree, 123 leaves, max depth = 18, train loss: 0.63456, val loss: 0.63290, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.60786, val loss: 0.60554, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.58402, val loss: 0.58078, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.56375, val loss: 0.55993, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.54400, val loss: 0.53941, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.52610, val loss: 0.52098, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.50937, val loss: 0.50376, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.49516, val loss: 0.48910, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.48216, val loss: 0.47566, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.47015, val loss: 0.46367, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.45797, val loss: 0.45109, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.44776, val loss: 0.44061, in 0.016s 1 tree, 98 leaves, max depth = 13, train loss: 0.43317, val loss: 0.42574, in 0.031s 1 tree, 113 leaves, max depth = 15, train loss: 0.41970, val loss: 0.41204, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.41075, val loss: 0.40270, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.40205, val loss: 0.39378, in 0.016s 1 tree, 113 leaves, max depth = 14, train loss: 0.39073, val loss: 0.38230, in 0.016s 1 tree, 115 leaves, max depth = 14, train loss: 0.38041, val loss: 0.37183, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.37319, val loss: 0.36440, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.36649, val loss: 0.35743, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.36030, val loss: 0.35119, in 0.031s 1 tree, 108 leaves, max depth = 13, train loss: 0.35195, val loss: 0.34273, in 0.016s 1 tree, 123 leaves, max depth = 11, train loss: 0.34559, val loss: 0.33650, in 0.016s 1 tree, 64 leaves, max depth = 12, train loss: 0.33860, val loss: 0.32911, in 0.016s 1 tree, 89 leaves, max depth = 12, train loss: 0.33201, val loss: 0.32276, in 0.031s 1 tree, 68 leaves, max depth = 14, train loss: 0.32607, val loss: 0.31646, in 0.000s 1 tree, 123 leaves, max depth = 13, train loss: 0.32076, val loss: 0.31136, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.31576, val loss: 0.30658, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.31028, val loss: 0.30104, in 0.016s 1 tree, 66 leaves, max depth = 13, train loss: 0.30556, val loss: 0.29607, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.30132, val loss: 0.29154, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.29680, val loss: 0.28723, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.29245, val loss: 0.28286, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.28842, val loss: 0.27881, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.28530, val loss: 0.27583, in 0.016s Fit 36 trees in 1.174 s, (4074 total leaves) Time spent computing histograms: 0.285s Time spent finding best splits: 0.092s Time spent applying splits: 0.068s Time spent predicting: 0.000s Trial 67, Fold 4: Log loss = 0.2932094147428679, Average precision = 0.9616622060698489, ROC-AUC = 0.9566776587957301, Elapsed Time = 1.1851515000016661 seconds Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 67, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 123 leaves, max depth = 12, train loss: 0.66160, val loss: 0.66026, in 0.016s 1 tree, 123 leaves, max depth = 12, train loss: 0.63372, val loss: 0.63151, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.60714, val loss: 0.60408, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.58399, val loss: 0.58006, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.56306, val loss: 0.55834, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.54263, val loss: 0.53737, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.52525, val loss: 0.51995, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.50810, val loss: 0.50242, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.49365, val loss: 0.48802, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.47948, val loss: 0.47342, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.46704, val loss: 0.46063, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.45467, val loss: 0.44833, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.44370, val loss: 0.43713, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.43318, val loss: 0.42656, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.42340, val loss: 0.41686, in 0.016s 1 tree, 84 leaves, max depth = 13, train loss: 0.40973, val loss: 0.40342, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.40123, val loss: 0.39513, in 0.016s 1 tree, 109 leaves, max depth = 18, train loss: 0.38941, val loss: 0.38343, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.38191, val loss: 0.37592, in 0.016s 1 tree, 111 leaves, max depth = 17, train loss: 0.37151, val loss: 0.36565, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.36552, val loss: 0.35979, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.35676, val loss: 0.35088, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.35002, val loss: 0.34431, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.34232, val loss: 0.33649, in 0.016s 1 tree, 69 leaves, max depth = 13, train loss: 0.33537, val loss: 0.32936, in 0.016s 1 tree, 94 leaves, max depth = 14, train loss: 0.32863, val loss: 0.32315, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.32405, val loss: 0.31905, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.31874, val loss: 0.31396, in 0.031s 1 tree, 91 leaves, max depth = 14, train loss: 0.31288, val loss: 0.30869, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.30754, val loss: 0.30385, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.30285, val loss: 0.29930, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.29802, val loss: 0.29431, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.29356, val loss: 0.28997, in 0.031s 1 tree, 86 leaves, max depth = 13, train loss: 0.28917, val loss: 0.28608, in 0.016s 1 tree, 69 leaves, max depth = 15, train loss: 0.28511, val loss: 0.28198, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.28138, val loss: 0.27808, in 0.016s Fit 36 trees in 1.096 s, (3866 total leaves) Time spent computing histograms: 0.256s Time spent finding best splits: 0.086s Time spent applying splits: 0.065s Time spent predicting: 0.000s Trial 67, Fold 5: Log loss = 0.29708795787730236, Average precision = 0.9595966341554678, ROC-AUC = 0.9554665477583932, Elapsed Time = 1.1053009999995993 seconds
Optimization Progress: 68%|######8 | 68/100 [13:32<07:17, 13.68s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 68, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.205 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 11, train loss: 0.68570, val loss: 0.68549, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.67859, val loss: 0.67818, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.67171, val loss: 0.67115, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.66511, val loss: 0.66436, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.65863, val loss: 0.65769, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.65235, val loss: 0.65121, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.64632, val loss: 0.64499, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.64047, val loss: 0.63894, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.63476, val loss: 0.63302, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.62910, val loss: 0.62719, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.62373, val loss: 0.62162, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.61854, val loss: 0.61624, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.61345, val loss: 0.61097, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.60855, val loss: 0.60588, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.60375, val loss: 0.60090, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.59912, val loss: 0.59609, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.59456, val loss: 0.59141, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.59013, val loss: 0.58686, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58582, val loss: 0.58234, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.58155, val loss: 0.57792, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.57740, val loss: 0.57362, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.57337, val loss: 0.56944, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.56945, val loss: 0.56537, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.56563, val loss: 0.56141, in 0.000s 1 tree, 28 leaves, max depth = 13, train loss: 0.56200, val loss: 0.55766, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.55846, val loss: 0.55402, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.55504, val loss: 0.55044, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.55171, val loss: 0.54699, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.54848, val loss: 0.54362, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.54498, val loss: 0.54036, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.54187, val loss: 0.53713, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.53884, val loss: 0.53395, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.53580, val loss: 0.53077, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.53248, val loss: 0.52769, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.52956, val loss: 0.52463, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.52678, val loss: 0.52176, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.52407, val loss: 0.51896, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.52093, val loss: 0.51605, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.51833, val loss: 0.51336, in 0.018s 1 tree, 31 leaves, max depth = 10, train loss: 0.51581, val loss: 0.51071, in 0.007s 1 tree, 28 leaves, max depth = 11, train loss: 0.51334, val loss: 0.50816, in 0.008s 1 tree, 28 leaves, max depth = 10, train loss: 0.51087, val loss: 0.50557, in 0.008s 1 tree, 28 leaves, max depth = 11, train loss: 0.50854, val loss: 0.50315, in 0.008s 1 tree, 51 leaves, max depth = 10, train loss: 0.50559, val loss: 0.50043, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.50335, val loss: 0.49810, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.50117, val loss: 0.49583, in 0.000s 1 tree, 51 leaves, max depth = 11, train loss: 0.49833, val loss: 0.49322, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.49616, val loss: 0.49093, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.49405, val loss: 0.48869, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.49133, val loss: 0.48618, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.48935, val loss: 0.48407, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.48671, val loss: 0.48164, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.48415, val loss: 0.47928, in 0.031s 1 tree, 30 leaves, max depth = 12, train loss: 0.48228, val loss: 0.47731, in 0.000s 1 tree, 52 leaves, max depth = 10, train loss: 0.47979, val loss: 0.47504, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.47737, val loss: 0.47282, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47554, val loss: 0.47085, in 0.000s 1 tree, 32 leaves, max depth = 10, train loss: 0.47372, val loss: 0.46893, in 0.016s 1 tree, 52 leaves, max depth = 10, train loss: 0.47139, val loss: 0.46680, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.46972, val loss: 0.46503, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.46810, val loss: 0.46330, in 0.000s 1 tree, 52 leaves, max depth = 10, train loss: 0.46585, val loss: 0.46125, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.46425, val loss: 0.45954, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.46264, val loss: 0.45784, in 0.000s 1 tree, 50 leaves, max depth = 10, train loss: 0.46048, val loss: 0.45587, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.45901, val loss: 0.45430, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.45750, val loss: 0.45270, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.45610, val loss: 0.45120, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.45402, val loss: 0.44932, in 0.000s 1 tree, 51 leaves, max depth = 10, train loss: 0.45199, val loss: 0.44749, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.45059, val loss: 0.44597, in 0.000s Fit 71 trees in 0.957 s, (2372 total leaves) Time spent computing histograms: 0.332s Time spent finding best splits: 0.045s Time spent applying splits: 0.052s Time spent predicting: 0.000s Trial 68, Fold 1: Log loss = 0.4511051676766809, Average precision = 0.9082653287694907, ROC-AUC = 0.915245410996879, Elapsed Time = 0.974603800001205 seconds Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 68, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0.163 s 0.040 GB of training data: 0.006 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 12, train loss: 0.68579, val loss: 0.68545, in 0.007s 1 tree, 32 leaves, max depth = 12, train loss: 0.67871, val loss: 0.67806, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.67182, val loss: 0.67084, in 0.008s 1 tree, 30 leaves, max depth = 9, train loss: 0.66519, val loss: 0.66391, in 0.008s 1 tree, 31 leaves, max depth = 17, train loss: 0.65876, val loss: 0.65716, in 0.009s 1 tree, 32 leaves, max depth = 11, train loss: 0.65251, val loss: 0.65062, in 0.007s 1 tree, 30 leaves, max depth = 14, train loss: 0.64645, val loss: 0.64428, in 0.010s 1 tree, 31 leaves, max depth = 17, train loss: 0.64058, val loss: 0.63811, in 0.010s 1 tree, 30 leaves, max depth = 10, train loss: 0.63485, val loss: 0.63207, in 0.009s 1 tree, 31 leaves, max depth = 10, train loss: 0.62923, val loss: 0.62612, in 0.009s 1 tree, 32 leaves, max depth = 11, train loss: 0.62384, val loss: 0.62048, in 0.011s 1 tree, 30 leaves, max depth = 14, train loss: 0.61863, val loss: 0.61498, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.61354, val loss: 0.60959, in 0.008s 1 tree, 29 leaves, max depth = 9, train loss: 0.60864, val loss: 0.60443, in 0.008s 1 tree, 30 leaves, max depth = 10, train loss: 0.60383, val loss: 0.59933, in 0.009s 1 tree, 30 leaves, max depth = 14, train loss: 0.59918, val loss: 0.59444, in 0.009s 1 tree, 29 leaves, max depth = 8, train loss: 0.59462, val loss: 0.58962, in 0.008s 1 tree, 29 leaves, max depth = 8, train loss: 0.59019, val loss: 0.58494, in 0.009s 1 tree, 6 leaves, max depth = 4, train loss: 0.58587, val loss: 0.58039, in 0.007s 1 tree, 31 leaves, max depth = 10, train loss: 0.58163, val loss: 0.57589, in 0.008s 1 tree, 29 leaves, max depth = 8, train loss: 0.57756, val loss: 0.57158, in 0.008s 1 tree, 31 leaves, max depth = 10, train loss: 0.57355, val loss: 0.56733, in 0.008s 1 tree, 31 leaves, max depth = 10, train loss: 0.56965, val loss: 0.56318, in 0.009s 1 tree, 31 leaves, max depth = 10, train loss: 0.56586, val loss: 0.55915, in 0.009s 1 tree, 29 leaves, max depth = 8, train loss: 0.56222, val loss: 0.55529, in 0.010s 1 tree, 51 leaves, max depth = 11, train loss: 0.55854, val loss: 0.55174, in 0.012s 1 tree, 32 leaves, max depth = 11, train loss: 0.55506, val loss: 0.54807, in 0.010s 1 tree, 31 leaves, max depth = 11, train loss: 0.55169, val loss: 0.54448, in 0.008s 1 tree, 29 leaves, max depth = 14, train loss: 0.54841, val loss: 0.54100, in 0.008s 1 tree, 50 leaves, max depth = 13, train loss: 0.54493, val loss: 0.53765, in 0.011s 1 tree, 31 leaves, max depth = 11, train loss: 0.54178, val loss: 0.53430, in 0.009s 1 tree, 32 leaves, max depth = 11, train loss: 0.53870, val loss: 0.53104, in 0.010s 1 tree, 31 leaves, max depth = 10, train loss: 0.53564, val loss: 0.52777, in 0.009s 1 tree, 50 leaves, max depth = 13, train loss: 0.53235, val loss: 0.52460, in 0.011s 1 tree, 29 leaves, max depth = 8, train loss: 0.52945, val loss: 0.52151, in 0.009s 1 tree, 29 leaves, max depth = 8, train loss: 0.52663, val loss: 0.51850, in 0.009s 1 tree, 29 leaves, max depth = 8, train loss: 0.52388, val loss: 0.51557, in 0.009s 1 tree, 50 leaves, max depth = 12, train loss: 0.52076, val loss: 0.51257, in 0.010s 1 tree, 29 leaves, max depth = 8, train loss: 0.51812, val loss: 0.50974, in 0.009s 1 tree, 32 leaves, max depth = 11, train loss: 0.51556, val loss: 0.50703, in 0.008s 1 tree, 29 leaves, max depth = 8, train loss: 0.51306, val loss: 0.50435, in 0.008s 1 tree, 31 leaves, max depth = 10, train loss: 0.51058, val loss: 0.50168, in 0.009s 1 tree, 29 leaves, max depth = 8, train loss: 0.50821, val loss: 0.49914, in 0.009s 1 tree, 49 leaves, max depth = 12, train loss: 0.50528, val loss: 0.49634, in 0.010s 1 tree, 31 leaves, max depth = 10, train loss: 0.50302, val loss: 0.49393, in 0.010s 1 tree, 28 leaves, max depth = 8, train loss: 0.50080, val loss: 0.49154, in 0.008s 1 tree, 49 leaves, max depth = 12, train loss: 0.49799, val loss: 0.48885, in 0.010s 1 tree, 31 leaves, max depth = 9, train loss: 0.49580, val loss: 0.48650, in 0.008s 1 tree, 31 leaves, max depth = 9, train loss: 0.49367, val loss: 0.48420, in 0.009s 1 tree, 49 leaves, max depth = 14, train loss: 0.49097, val loss: 0.48162, in 0.012s 1 tree, 32 leaves, max depth = 12, train loss: 0.48897, val loss: 0.47950, in 0.007s 1 tree, 50 leaves, max depth = 9, train loss: 0.48636, val loss: 0.47700, in 0.010s 1 tree, 50 leaves, max depth = 15, train loss: 0.48381, val loss: 0.47458, in 0.012s 1 tree, 31 leaves, max depth = 11, train loss: 0.48191, val loss: 0.47253, in 0.010s 1 tree, 50 leaves, max depth = 9, train loss: 0.47945, val loss: 0.47018, in 0.011s 1 tree, 50 leaves, max depth = 9, train loss: 0.47705, val loss: 0.46789, in 0.008s 1 tree, 6 leaves, max depth = 4, train loss: 0.47520, val loss: 0.46590, in 0.006s 1 tree, 31 leaves, max depth = 11, train loss: 0.47343, val loss: 0.46401, in 0.008s 1 tree, 49 leaves, max depth = 10, train loss: 0.47112, val loss: 0.46181, in 0.010s 1 tree, 31 leaves, max depth = 15, train loss: 0.46943, val loss: 0.45999, in 0.011s 1 tree, 31 leaves, max depth = 15, train loss: 0.46778, val loss: 0.45820, in 0.011s 1 tree, 49 leaves, max depth = 10, train loss: 0.46555, val loss: 0.45609, in 0.011s 1 tree, 29 leaves, max depth = 9, train loss: 0.46394, val loss: 0.45435, in 0.009s 1 tree, 29 leaves, max depth = 10, train loss: 0.46238, val loss: 0.45265, in 0.010s 1 tree, 50 leaves, max depth = 12, train loss: 0.46022, val loss: 0.45060, in 0.011s 1 tree, 29 leaves, max depth = 9, train loss: 0.45872, val loss: 0.44896, in 0.008s 1 tree, 29 leaves, max depth = 9, train loss: 0.45725, val loss: 0.44737, in 0.009s 1 tree, 31 leaves, max depth = 15, train loss: 0.45584, val loss: 0.44583, in 0.009s 1 tree, 50 leaves, max depth = 12, train loss: 0.45377, val loss: 0.44386, in 0.010s 1 tree, 50 leaves, max depth = 12, train loss: 0.45175, val loss: 0.44195, in 0.017s 1 tree, 31 leaves, max depth = 10, train loss: 0.45034, val loss: 0.44041, in 0.011s Fit 71 trees in 0.993 s, (2415 total leaves) Time spent computing histograms: 0.344s Time spent finding best splits: 0.048s Time spent applying splits: 0.057s Time spent predicting: 0.012s Trial 68, Fold 2: Log loss = 0.4508887269438503, Average precision = 0.9074085799523012, ROC-AUC = 0.9224698560611556, Elapsed Time = 1.001351099999738 seconds Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 68, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.151 s 0.005 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 11, train loss: 0.68578, val loss: 0.68551, in 0.008s 1 tree, 30 leaves, max depth = 10, train loss: 0.67874, val loss: 0.67825, in 0.008s 1 tree, 31 leaves, max depth = 9, train loss: 0.67189, val loss: 0.67115, in 0.009s 1 tree, 28 leaves, max depth = 11, train loss: 0.66532, val loss: 0.66432, in 0.008s 1 tree, 32 leaves, max depth = 13, train loss: 0.65894, val loss: 0.65768, in 0.008s 1 tree, 30 leaves, max depth = 10, train loss: 0.65272, val loss: 0.65125, in 0.008s 1 tree, 31 leaves, max depth = 14, train loss: 0.64672, val loss: 0.64500, in 0.009s 1 tree, 31 leaves, max depth = 13, train loss: 0.64089, val loss: 0.63895, in 0.008s 1 tree, 30 leaves, max depth = 8, train loss: 0.63521, val loss: 0.63305, in 0.008s 1 tree, 30 leaves, max depth = 8, train loss: 0.62962, val loss: 0.62726, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.62426, val loss: 0.62172, in 0.007s 1 tree, 31 leaves, max depth = 13, train loss: 0.61909, val loss: 0.61633, in 0.009s 1 tree, 31 leaves, max depth = 14, train loss: 0.61407, val loss: 0.61108, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.60920, val loss: 0.60604, in 0.007s 1 tree, 28 leaves, max depth = 11, train loss: 0.60446, val loss: 0.60110, in 0.008s 1 tree, 31 leaves, max depth = 14, train loss: 0.59986, val loss: 0.59628, in 0.009s 1 tree, 30 leaves, max depth = 9, train loss: 0.59535, val loss: 0.59157, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.59097, val loss: 0.58703, in 0.009s 1 tree, 6 leaves, max depth = 4, train loss: 0.58668, val loss: 0.58256, in 0.007s 1 tree, 30 leaves, max depth = 8, train loss: 0.58245, val loss: 0.57818, in 0.007s 1 tree, 30 leaves, max depth = 8, train loss: 0.57833, val loss: 0.57392, in 0.008s 1 tree, 30 leaves, max depth = 8, train loss: 0.57433, val loss: 0.56977, in 0.008s 1 tree, 30 leaves, max depth = 8, train loss: 0.57045, val loss: 0.56574, in 0.009s 1 tree, 30 leaves, max depth = 8, train loss: 0.56666, val loss: 0.56181, in 0.007s 1 tree, 29 leaves, max depth = 10, train loss: 0.56307, val loss: 0.55807, in 0.008s 1 tree, 52 leaves, max depth = 11, train loss: 0.55934, val loss: 0.55458, in 0.012s 1 tree, 29 leaves, max depth = 10, train loss: 0.55589, val loss: 0.55098, in 0.008s 1 tree, 29 leaves, max depth = 10, train loss: 0.55255, val loss: 0.54748, in 0.008s 1 tree, 28 leaves, max depth = 12, train loss: 0.54931, val loss: 0.54407, in 0.008s 1 tree, 51 leaves, max depth = 12, train loss: 0.54580, val loss: 0.54078, in 0.012s 1 tree, 29 leaves, max depth = 10, train loss: 0.54268, val loss: 0.53750, in 0.007s 1 tree, 29 leaves, max depth = 10, train loss: 0.53962, val loss: 0.53431, in 0.009s 1 tree, 30 leaves, max depth = 8, train loss: 0.53656, val loss: 0.53113, in 0.007s 1 tree, 51 leaves, max depth = 12, train loss: 0.53324, val loss: 0.52802, in 0.010s 1 tree, 30 leaves, max depth = 8, train loss: 0.53030, val loss: 0.52496, in 0.008s 1 tree, 30 leaves, max depth = 8, train loss: 0.52750, val loss: 0.52201, in 0.007s 1 tree, 31 leaves, max depth = 9, train loss: 0.52477, val loss: 0.51913, in 0.008s 1 tree, 51 leaves, max depth = 13, train loss: 0.52161, val loss: 0.51618, in 0.009s 1 tree, 30 leaves, max depth = 8, train loss: 0.51900, val loss: 0.51343, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.51646, val loss: 0.51076, in 0.010s 1 tree, 31 leaves, max depth = 9, train loss: 0.51397, val loss: 0.50813, in 0.010s 1 tree, 30 leaves, max depth = 8, train loss: 0.51150, val loss: 0.50554, in 0.011s 1 tree, 31 leaves, max depth = 9, train loss: 0.50914, val loss: 0.50305, in 0.009s 1 tree, 53 leaves, max depth = 12, train loss: 0.50618, val loss: 0.50029, in 0.011s 1 tree, 28 leaves, max depth = 14, train loss: 0.50395, val loss: 0.49791, in 0.009s 1 tree, 31 leaves, max depth = 9, train loss: 0.50174, val loss: 0.49557, in 0.009s [47/71] 1 tree, 53 leaves, max depth = 12, train loss: 0.49890, val loss: 0.49292, in 0.012s 1 tree, 30 leaves, max depth = 8, train loss: 0.49672, val loss: 0.49064, in 0.009s 1 tree, 30 leaves, max depth = 8, train loss: 0.49460, val loss: 0.48842, in 0.009s 1 tree, 52 leaves, max depth = 11, train loss: 0.49187, val loss: 0.48588, in 0.010s 1 tree, 30 leaves, max depth = 10, train loss: 0.48989, val loss: 0.48379, in 0.010s 1 tree, 52 leaves, max depth = 11, train loss: 0.48724, val loss: 0.48133, in 0.012s 1 tree, 52 leaves, max depth = 11, train loss: 0.48467, val loss: 0.47893, in 0.010s 1 tree, 29 leaves, max depth = 13, train loss: 0.48280, val loss: 0.47694, in 0.009s 1 tree, 52 leaves, max depth = 11, train loss: 0.48030, val loss: 0.47463, in 0.012s 1 tree, 52 leaves, max depth = 11, train loss: 0.47787, val loss: 0.47238, in 0.010s 1 tree, 6 leaves, max depth = 4, train loss: 0.47604, val loss: 0.47044, in 0.007s 1 tree, 29 leaves, max depth = 10, train loss: 0.47429, val loss: 0.46858, in 0.009s 1 tree, 52 leaves, max depth = 11, train loss: 0.47195, val loss: 0.46642, in 0.010s 1 tree, 31 leaves, max depth = 13, train loss: 0.47028, val loss: 0.46464, in 0.009s 1 tree, 31 leaves, max depth = 9, train loss: 0.46864, val loss: 0.46287, in 0.009s 1 tree, 52 leaves, max depth = 10, train loss: 0.46638, val loss: 0.46079, in 0.013s 1 tree, 29 leaves, max depth = 10, train loss: 0.46479, val loss: 0.45910, in 0.009s 1 tree, 31 leaves, max depth = 9, train loss: 0.46325, val loss: 0.45744, in 0.009s 1 tree, 52 leaves, max depth = 13, train loss: 0.46107, val loss: 0.45543, in 0.012s 1 tree, 31 leaves, max depth = 9, train loss: 0.45958, val loss: 0.45382, in 0.009s 1 tree, 29 leaves, max depth = 9, train loss: 0.45807, val loss: 0.45219, in 0.009s 1 tree, 31 leaves, max depth = 9, train loss: 0.45666, val loss: 0.45067, in 0.010s 1 tree, 51 leaves, max depth = 11, train loss: 0.45456, val loss: 0.44873, in 0.011s 1 tree, 52 leaves, max depth = 13, train loss: 0.45252, val loss: 0.44685, in 0.010s 1 tree, 30 leaves, max depth = 9, train loss: 0.45112, val loss: 0.44537, in 0.009s Fit 71 trees in 0.947 s, (2430 total leaves) Time spent computing histograms: 0.345s Time spent finding best splits: 0.045s Time spent applying splits: 0.055s Time spent predicting: 0.006s Trial 68, Fold 3: Log loss = 0.4462839684151748, Average precision = 0.9177517585201269, ROC-AUC = 0.9285210913648141, Elapsed Time = 0.9534422999986418 seconds Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 68, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.176 s 0.006 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 11, train loss: 0.68579, val loss: 0.68541, in 0.009s 1 tree, 31 leaves, max depth = 11, train loss: 0.67875, val loss: 0.67795, in 0.009s 1 tree, 29 leaves, max depth = 10, train loss: 0.67191, val loss: 0.67074, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.66531, val loss: 0.66379, in 0.009s 1 tree, 29 leaves, max depth = 11, train loss: 0.65891, val loss: 0.65701, in 0.010s 1 tree, 31 leaves, max depth = 11, train loss: 0.65268, val loss: 0.65040, in 0.010s 1 tree, 30 leaves, max depth = 11, train loss: 0.64666, val loss: 0.64402, in 0.009s 1 tree, 30 leaves, max depth = 10, train loss: 0.64082, val loss: 0.63784, in 0.009s 1 tree, 29 leaves, max depth = 11, train loss: 0.63512, val loss: 0.63179, in 0.010s 1 tree, 30 leaves, max depth = 10, train loss: 0.62952, val loss: 0.62582, in 0.008s 1 tree, 31 leaves, max depth = 11, train loss: 0.62416, val loss: 0.62010, in 0.009s 1 tree, 29 leaves, max depth = 11, train loss: 0.61897, val loss: 0.61457, in 0.007s 1 tree, 29 leaves, max depth = 11, train loss: 0.61390, val loss: 0.60916, in 0.009s 1 tree, 29 leaves, max depth = 10, train loss: 0.60902, val loss: 0.60394, in 0.008s 1 tree, 30 leaves, max depth = 12, train loss: 0.60423, val loss: 0.59882, in 0.011s 1 tree, 30 leaves, max depth = 11, train loss: 0.59961, val loss: 0.59387, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.59508, val loss: 0.58904, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.59068, val loss: 0.58433, in 0.009s 1 tree, 6 leaves, max depth = 4, train loss: 0.58638, val loss: 0.57974, in 0.008s 1 tree, 29 leaves, max depth = 12, train loss: 0.58216, val loss: 0.57520, in 0.010s 1 tree, 29 leaves, max depth = 12, train loss: 0.57805, val loss: 0.57078, in 0.009s 1 tree, 29 leaves, max depth = 12, train loss: 0.57406, val loss: 0.56648, in 0.008s 1 tree, 29 leaves, max depth = 12, train loss: 0.57018, val loss: 0.56230, in 0.009s 1 tree, 29 leaves, max depth = 12, train loss: 0.56641, val loss: 0.55822, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.56280, val loss: 0.55433, in 0.011s 1 tree, 50 leaves, max depth = 12, train loss: 0.55909, val loss: 0.55066, in 0.010s 1 tree, 30 leaves, max depth = 11, train loss: 0.55563, val loss: 0.54690, in 0.009s 1 tree, 30 leaves, max depth = 11, train loss: 0.55226, val loss: 0.54327, in 0.011s 1 tree, 30 leaves, max depth = 12, train loss: 0.54899, val loss: 0.53972, in 0.009s 1 tree, 50 leaves, max depth = 11, train loss: 0.54550, val loss: 0.53627, in 0.013s 1 tree, 31 leaves, max depth = 11, train loss: 0.54235, val loss: 0.53287, in 0.010s 1 tree, 31 leaves, max depth = 11, train loss: 0.53928, val loss: 0.52952, in 0.009s 1 tree, 29 leaves, max depth = 12, train loss: 0.53624, val loss: 0.52620, in 0.010s 1 tree, 51 leaves, max depth = 11, train loss: 0.53293, val loss: 0.52293, in 0.011s 1 tree, 29 leaves, max depth = 9, train loss: 0.53000, val loss: 0.51974, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.52720, val loss: 0.51669, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.52447, val loss: 0.51372, in 0.010s 1 tree, 51 leaves, max depth = 12, train loss: 0.52134, val loss: 0.51062, in 0.012s 1 tree, 29 leaves, max depth = 10, train loss: 0.51872, val loss: 0.50776, in 0.010s 1 tree, 30 leaves, max depth = 11, train loss: 0.51617, val loss: 0.50495, in 0.009s 1 tree, 29 leaves, max depth = 10, train loss: 0.51368, val loss: 0.50223, in 0.010s 1 tree, 29 leaves, max depth = 9, train loss: 0.51122, val loss: 0.49952, in 0.010s 1 tree, 29 leaves, max depth = 10, train loss: 0.50887, val loss: 0.49693, in 0.142s 1 tree, 52 leaves, max depth = 12, train loss: 0.50592, val loss: 0.49404, in 0.015s 1 tree, 31 leaves, max depth = 12, train loss: 0.50366, val loss: 0.49157, in 0.040s 1 tree, 29 leaves, max depth = 10, train loss: 0.50146, val loss: 0.48914, in 0.011s 1 tree, 52 leaves, max depth = 13, train loss: 0.49864, val loss: 0.48637, in 0.012s 1 tree, 30 leaves, max depth = 9, train loss: 0.49647, val loss: 0.48397, in 0.012s 1 tree, 30 leaves, max depth = 9, train loss: 0.49436, val loss: 0.48162, in 0.012s 1 tree, 52 leaves, max depth = 13, train loss: 0.49165, val loss: 0.47896, in 0.013s 1 tree, 30 leaves, max depth = 11, train loss: 0.48966, val loss: 0.47675, in 0.011s 1 tree, 52 leaves, max depth = 13, train loss: 0.48703, val loss: 0.47417, in 0.012s 1 tree, 52 leaves, max depth = 13, train loss: 0.48447, val loss: 0.47166, in 0.012s 1 tree, 29 leaves, max depth = 11, train loss: 0.48250, val loss: 0.46949, in 0.011s 1 tree, 52 leaves, max depth = 13, train loss: 0.48003, val loss: 0.46706, in 0.013s 1 tree, 52 leaves, max depth = 13, train loss: 0.47761, val loss: 0.46470, in 0.015s 1 tree, 6 leaves, max depth = 4, train loss: 0.47576, val loss: 0.46266, in 0.008s 1 tree, 31 leaves, max depth = 10, train loss: 0.47400, val loss: 0.46069, in 0.010s 1 tree, 53 leaves, max depth = 11, train loss: 0.47168, val loss: 0.45842, in 0.013s 1 tree, 30 leaves, max depth = 11, train loss: 0.46992, val loss: 0.45646, in 0.011s 1 tree, 30 leaves, max depth = 12, train loss: 0.46827, val loss: 0.45461, in 0.011s 1 tree, 52 leaves, max depth = 10, train loss: 0.46603, val loss: 0.45243, in 0.013s 1 tree, 31 leaves, max depth = 10, train loss: 0.46444, val loss: 0.45063, in 0.011s 1 tree, 30 leaves, max depth = 9, train loss: 0.46282, val loss: 0.44883, in 0.010s 1 tree, 52 leaves, max depth = 12, train loss: 0.46066, val loss: 0.44673, in 0.014s 1 tree, 30 leaves, max depth = 12, train loss: 0.45917, val loss: 0.44505, in 0.011s 1 tree, 30 leaves, max depth = 11, train loss: 0.45765, val loss: 0.44334, in 0.010s 1 tree, 30 leaves, max depth = 9, train loss: 0.45617, val loss: 0.44169, in 0.010s 1 tree, 51 leaves, max depth = 13, train loss: 0.45409, val loss: 0.43967, in 0.014s 1 tree, 52 leaves, max depth = 13, train loss: 0.45207, val loss: 0.43770, in 0.012s 1 tree, 30 leaves, max depth = 9, train loss: 0.45068, val loss: 0.43611, in 0.012s Fit 71 trees in 1.244 s, (2413 total leaves) Time spent computing histograms: 0.453s Time spent finding best splits: 0.147s Time spent applying splits: 0.067s Time spent predicting: 0.007s Trial 68, Fold 4: Log loss = 0.44999522667030717, Average precision = 0.9131729718295871, ROC-AUC = 0.9238388222446356, Elapsed Time = 1.253656900000351 seconds Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 68, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.288 s 0.009 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 10, train loss: 0.68566, val loss: 0.68519, in 0.014s 1 tree, 30 leaves, max depth = 11, train loss: 0.67855, val loss: 0.67757, in 0.020s 1 tree, 28 leaves, max depth = 10, train loss: 0.67162, val loss: 0.67019, in 0.015s 1 tree, 29 leaves, max depth = 11, train loss: 0.66493, val loss: 0.66307, in 0.013s 1 tree, 29 leaves, max depth = 13, train loss: 0.65846, val loss: 0.65615, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.65216, val loss: 0.64938, in 0.014s 1 tree, 30 leaves, max depth = 10, train loss: 0.64607, val loss: 0.64282, in 0.011s 1 tree, 29 leaves, max depth = 13, train loss: 0.64016, val loss: 0.63648, in 0.010s 1 tree, 28 leaves, max depth = 10, train loss: 0.63439, val loss: 0.63030, in 0.013s 1 tree, 29 leaves, max depth = 8, train loss: 0.62874, val loss: 0.62423, in 0.012s 1 tree, 29 leaves, max depth = 11, train loss: 0.62332, val loss: 0.61836, in 0.013s 1 tree, 29 leaves, max depth = 13, train loss: 0.61807, val loss: 0.61271, in 0.010s 1 tree, 30 leaves, max depth = 10, train loss: 0.61297, val loss: 0.60718, in 0.013s 1 tree, 29 leaves, max depth = 11, train loss: 0.60804, val loss: 0.60183, in 0.015s 1 tree, 30 leaves, max depth = 11, train loss: 0.60322, val loss: 0.59665, in 0.013s 1 tree, 29 leaves, max depth = 11, train loss: 0.59854, val loss: 0.59160, in 0.044s 1 tree, 27 leaves, max depth = 10, train loss: 0.59397, val loss: 0.58665, in 0.033s 1 tree, 29 leaves, max depth = 11, train loss: 0.58954, val loss: 0.58181, in 0.021s 1 tree, 6 leaves, max depth = 4, train loss: 0.58520, val loss: 0.57711, in 0.011s 1 tree, 30 leaves, max depth = 8, train loss: 0.58094, val loss: 0.57249, in 0.012s 1 tree, 30 leaves, max depth = 8, train loss: 0.57680, val loss: 0.56798, in 0.013s 1 tree, 30 leaves, max depth = 8, train loss: 0.57278, val loss: 0.56360, in 0.013s 1 tree, 30 leaves, max depth = 8, train loss: 0.56887, val loss: 0.55934, in 0.010s 1 tree, 30 leaves, max depth = 8, train loss: 0.56507, val loss: 0.55518, in 0.017s 1 tree, 29 leaves, max depth = 11, train loss: 0.56142, val loss: 0.55118, in 0.015s 1 tree, 27 leaves, max depth = 9, train loss: 0.55787, val loss: 0.54728, in 0.014s 1 tree, 29 leaves, max depth = 11, train loss: 0.55443, val loss: 0.54348, in 0.012s 1 tree, 30 leaves, max depth = 11, train loss: 0.55107, val loss: 0.53981, in 0.011s 1 tree, 30 leaves, max depth = 12, train loss: 0.54781, val loss: 0.53623, in 0.013s 1 tree, 49 leaves, max depth = 14, train loss: 0.54429, val loss: 0.53288, in 0.013s 1 tree, 30 leaves, max depth = 11, train loss: 0.54116, val loss: 0.52941, in 0.013s 1 tree, 29 leaves, max depth = 11, train loss: 0.53811, val loss: 0.52602, in 0.012s 1 tree, 30 leaves, max depth = 8, train loss: 0.53508, val loss: 0.52268, in 0.012s 1 tree, 49 leaves, max depth = 13, train loss: 0.53175, val loss: 0.51952, in 0.015s 1 tree, 30 leaves, max depth = 8, train loss: 0.52884, val loss: 0.51630, in 0.013s 1 tree, 27 leaves, max depth = 9, train loss: 0.52605, val loss: 0.51321, in 0.013s 1 tree, 30 leaves, max depth = 11, train loss: 0.52334, val loss: 0.51021, in 0.012s 1 tree, 49 leaves, max depth = 14, train loss: 0.52019, val loss: 0.50722, in 0.018s 1 tree, 30 leaves, max depth = 11, train loss: 0.51758, val loss: 0.50434, in 0.015s 1 tree, 30 leaves, max depth = 11, train loss: 0.51505, val loss: 0.50152, in 0.014s 1 tree, 27 leaves, max depth = 9, train loss: 0.51257, val loss: 0.49877, in 0.011s 1 tree, 30 leaves, max depth = 8, train loss: 0.51011, val loss: 0.49603, in 0.014s 1 tree, 30 leaves, max depth = 11, train loss: 0.50777, val loss: 0.49342, in 0.011s 1 tree, 49 leaves, max depth = 13, train loss: 0.50480, val loss: 0.49063, in 0.017s 1 tree, 30 leaves, max depth = 11, train loss: 0.50255, val loss: 0.48812, in 0.014s 1 tree, 27 leaves, max depth = 9, train loss: 0.50035, val loss: 0.48566, in 0.012s 1 tree, 50 leaves, max depth = 13, train loss: 0.49750, val loss: 0.48298, in 0.018s 1 tree, 30 leaves, max depth = 8, train loss: 0.49534, val loss: 0.48055, in 0.013s 1 tree, 30 leaves, max depth = 8, train loss: 0.49324, val loss: 0.47819, in 0.010s 1 tree, 51 leaves, max depth = 13, train loss: 0.49050, val loss: 0.47562, in 0.015s 1 tree, 31 leaves, max depth = 10, train loss: 0.48852, val loss: 0.47339, in 0.011s 1 tree, 52 leaves, max depth = 12, train loss: 0.48587, val loss: 0.47090, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.48329, val loss: 0.46849, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.48140, val loss: 0.46637, in 0.015s 1 tree, 52 leaves, max depth = 12, train loss: 0.47890, val loss: 0.46403, in 0.018s 1 tree, 52 leaves, max depth = 12, train loss: 0.47647, val loss: 0.46176, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47463, val loss: 0.45969, in 0.010s 1 tree, 30 leaves, max depth = 10, train loss: 0.47288, val loss: 0.45772, in 0.014s 1 tree, 51 leaves, max depth = 12, train loss: 0.47053, val loss: 0.45552, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.46885, val loss: 0.45362, in 0.012s 1 tree, 29 leaves, max depth = 10, train loss: 0.46721, val loss: 0.45176, in 0.012s 1 tree, 53 leaves, max depth = 12, train loss: 0.46494, val loss: 0.44966, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.46336, val loss: 0.44786, in 0.012s 1 tree, 29 leaves, max depth = 9, train loss: 0.46181, val loss: 0.44611, in 0.012s 1 tree, 53 leaves, max depth = 12, train loss: 0.45962, val loss: 0.44408, in 0.017s 1 tree, 29 leaves, max depth = 10, train loss: 0.45814, val loss: 0.44239, in 0.013s 1 tree, 30 leaves, max depth = 12, train loss: 0.45669, val loss: 0.44075, in 0.013s 1 tree, 29 leaves, max depth = 12, train loss: 0.45528, val loss: 0.43914, in 0.012s 1 tree, 52 leaves, max depth = 12, train loss: 0.45318, val loss: 0.43721, in 0.015s 1 tree, 51 leaves, max depth = 12, train loss: 0.45112, val loss: 0.43532, in 0.017s 1 tree, 30 leaves, max depth = 9, train loss: 0.44973, val loss: 0.43373, in 0.012s Fit 71 trees in 1.562 s, (2361 total leaves) Time spent computing histograms: 0.499s Time spent finding best splits: 0.097s Time spent applying splits: 0.103s Time spent predicting: 0.010s Trial 68, Fold 5: Log loss = 0.45655774610222777, Average precision = 0.9056527493890548, ROC-AUC = 0.9152489456180443, Elapsed Time = 1.5716627000001608 seconds
Optimization Progress: 69%|######9 | 69/100 [13:45<06:56, 13.42s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 69, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.210 s 0.040 GB of training data: 0.006 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 11, train loss: 0.63986, val loss: 0.63959, in 0.022s 1 tree, 81 leaves, max depth = 14, train loss: 0.59513, val loss: 0.59500, in 0.027s 1 tree, 81 leaves, max depth = 13, train loss: 0.55742, val loss: 0.55748, in 0.027s 1 tree, 81 leaves, max depth = 10, train loss: 0.52598, val loss: 0.52567, in 0.025s 1 tree, 81 leaves, max depth = 14, train loss: 0.49873, val loss: 0.49862, in 0.027s 1 tree, 81 leaves, max depth = 11, train loss: 0.47577, val loss: 0.47540, in 0.024s 1 tree, 81 leaves, max depth = 12, train loss: 0.45490, val loss: 0.45454, in 0.022s 1 tree, 81 leaves, max depth = 13, train loss: 0.42895, val loss: 0.42968, in 0.030s 1 tree, 81 leaves, max depth = 14, train loss: 0.41344, val loss: 0.41434, in 0.029s 1 tree, 81 leaves, max depth = 15, train loss: 0.39295, val loss: 0.39481, in 0.040s 1 tree, 81 leaves, max depth = 12, train loss: 0.38000, val loss: 0.38182, in 0.025s 1 tree, 81 leaves, max depth = 14, train loss: 0.36856, val loss: 0.37059, in 0.027s 1 tree, 81 leaves, max depth = 16, train loss: 0.35323, val loss: 0.35628, in 0.021s 1 tree, 81 leaves, max depth = 12, train loss: 0.34423, val loss: 0.34735, in 0.021s 1 tree, 81 leaves, max depth = 23, train loss: 0.33261, val loss: 0.33615, in 0.020s 1 tree, 81 leaves, max depth = 18, train loss: 0.32270, val loss: 0.32651, in 0.019s 1 tree, 81 leaves, max depth = 14, train loss: 0.31290, val loss: 0.31784, in 0.019s 1 tree, 81 leaves, max depth = 15, train loss: 0.30479, val loss: 0.30985, in 0.021s 1 tree, 81 leaves, max depth = 18, train loss: 0.29754, val loss: 0.30281, in 0.020s 1 tree, 81 leaves, max depth = 17, train loss: 0.29065, val loss: 0.29640, in 0.020s 1 tree, 81 leaves, max depth = 15, train loss: 0.28404, val loss: 0.28983, in 0.025s 1 tree, 81 leaves, max depth = 16, train loss: 0.27746, val loss: 0.28413, in 0.020s 1 tree, 81 leaves, max depth = 16, train loss: 0.27221, val loss: 0.27917, in 0.019s 1 tree, 81 leaves, max depth = 15, train loss: 0.26767, val loss: 0.27498, in 0.019s 1 tree, 81 leaves, max depth = 16, train loss: 0.26246, val loss: 0.27045, in 0.021s 1 tree, 81 leaves, max depth = 16, train loss: 0.25901, val loss: 0.26722, in 0.022s 1 tree, 81 leaves, max depth = 16, train loss: 0.25492, val loss: 0.26337, in 0.027s 1 tree, 81 leaves, max depth = 14, train loss: 0.25056, val loss: 0.25908, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.24661, val loss: 0.25568, in 0.025s 1 tree, 81 leaves, max depth = 20, train loss: 0.24311, val loss: 0.25264, in 0.022s 1 tree, 81 leaves, max depth = 16, train loss: 0.23997, val loss: 0.24940, in 0.028s 1 tree, 81 leaves, max depth = 13, train loss: 0.23714, val loss: 0.24698, in 0.022s 1 tree, 81 leaves, max depth = 16, train loss: 0.23463, val loss: 0.24430, in 0.027s 1 tree, 81 leaves, max depth = 15, train loss: 0.23237, val loss: 0.24190, in 0.024s 1 tree, 81 leaves, max depth = 20, train loss: 0.23021, val loss: 0.23974, in 0.023s 1 tree, 81 leaves, max depth = 17, train loss: 0.22807, val loss: 0.23791, in 0.023s 1 tree, 81 leaves, max depth = 16, train loss: 0.22619, val loss: 0.23637, in 0.024s 1 tree, 81 leaves, max depth = 16, train loss: 0.22420, val loss: 0.23447, in 0.025s 1 tree, 81 leaves, max depth = 19, train loss: 0.22252, val loss: 0.23257, in 0.023s 1 tree, 81 leaves, max depth = 12, train loss: 0.21952, val loss: 0.22950, in 0.024s 1 tree, 81 leaves, max depth = 17, train loss: 0.21691, val loss: 0.22687, in 0.027s 1 tree, 81 leaves, max depth = 19, train loss: 0.21515, val loss: 0.22530, in 0.018s 1 tree, 81 leaves, max depth = 15, train loss: 0.21288, val loss: 0.22376, in 0.037s 1 tree, 81 leaves, max depth = 14, train loss: 0.21158, val loss: 0.22261, in 0.033s 1 tree, 81 leaves, max depth = 14, train loss: 0.20955, val loss: 0.22117, in 0.027s 1 tree, 81 leaves, max depth = 14, train loss: 0.20827, val loss: 0.22013, in 0.039s 1 tree, 81 leaves, max depth = 17, train loss: 0.20628, val loss: 0.21827, in 0.030s 1 tree, 81 leaves, max depth = 14, train loss: 0.20490, val loss: 0.21743, in 0.031s 1 tree, 81 leaves, max depth = 18, train loss: 0.20398, val loss: 0.21654, in 0.023s 1 tree, 81 leaves, max depth = 12, train loss: 0.20227, val loss: 0.21475, in 0.023s 1 tree, 81 leaves, max depth = 17, train loss: 0.20127, val loss: 0.21394, in 0.027s 1 tree, 81 leaves, max depth = 16, train loss: 0.19967, val loss: 0.21238, in 0.027s 1 tree, 81 leaves, max depth = 21, train loss: 0.19852, val loss: 0.21186, in 0.027s 1 tree, 81 leaves, max depth = 18, train loss: 0.19679, val loss: 0.21099, in 0.030s 1 tree, 81 leaves, max depth = 16, train loss: 0.19580, val loss: 0.21047, in 0.024s 1 tree, 81 leaves, max depth = 16, train loss: 0.19387, val loss: 0.20913, in 0.025s 1 tree, 81 leaves, max depth = 14, train loss: 0.19295, val loss: 0.20858, in 0.028s 1 tree, 30 leaves, max depth = 9, train loss: 0.19227, val loss: 0.20795, in 0.018s 1 tree, 81 leaves, max depth = 18, train loss: 0.19154, val loss: 0.20746, in 0.030s 1 tree, 81 leaves, max depth = 10, train loss: 0.19028, val loss: 0.20628, in 0.031s 1 tree, 30 leaves, max depth = 8, train loss: 0.18957, val loss: 0.20565, in 0.014s 1 tree, 81 leaves, max depth = 15, train loss: 0.18883, val loss: 0.20507, in 0.039s 1 tree, 81 leaves, max depth = 12, train loss: 0.18778, val loss: 0.20461, in 0.026s 1 tree, 81 leaves, max depth = 14, train loss: 0.18668, val loss: 0.20367, in 0.031s Fit 64 trees in 2.035 s, (5082 total leaves) Time spent computing histograms: 0.551s Time spent finding best splits: 0.174s Time spent applying splits: 0.132s Time spent predicting: 0.006s Trial 69, Fold 1: Log loss = 0.21489149959789555, Average precision = 0.9714608550870261, ROC-AUC = 0.9659503556014396, Elapsed Time = 2.049052999998821 seconds Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 69, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.222 s 0.003 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 14, train loss: 0.63988, val loss: 0.63921, in 0.037s 1 tree, 81 leaves, max depth = 13, train loss: 0.59507, val loss: 0.59392, in 0.032s 1 tree, 81 leaves, max depth = 10, train loss: 0.55679, val loss: 0.55522, in 0.033s 1 tree, 81 leaves, max depth = 11, train loss: 0.52476, val loss: 0.52314, in 0.034s 1 tree, 81 leaves, max depth = 11, train loss: 0.49713, val loss: 0.49523, in 0.023s 1 tree, 81 leaves, max depth = 11, train loss: 0.47379, val loss: 0.47188, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.45414, val loss: 0.45221, in 0.031s 1 tree, 81 leaves, max depth = 11, train loss: 0.43616, val loss: 0.43414, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.41255, val loss: 0.41119, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.39259, val loss: 0.39175, in 0.047s 1 tree, 81 leaves, max depth = 13, train loss: 0.37987, val loss: 0.37892, in 0.016s 1 tree, 81 leaves, max depth = 11, train loss: 0.36864, val loss: 0.36777, in 0.031s 1 tree, 81 leaves, max depth = 17, train loss: 0.35364, val loss: 0.35340, in 0.031s 1 tree, 81 leaves, max depth = 10, train loss: 0.34474, val loss: 0.34508, in 0.031s 1 tree, 81 leaves, max depth = 21, train loss: 0.33334, val loss: 0.33414, in 0.031s 1 tree, 81 leaves, max depth = 23, train loss: 0.32359, val loss: 0.32474, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.31404, val loss: 0.31600, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.30572, val loss: 0.30832, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.29856, val loss: 0.30171, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.29125, val loss: 0.29508, in 0.031s 1 tree, 81 leaves, max depth = 20, train loss: 0.28513, val loss: 0.28928, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.27927, val loss: 0.28393, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.27337, val loss: 0.27867, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.26848, val loss: 0.27419, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.26391, val loss: 0.27003, in 0.031s 1 tree, 81 leaves, max depth = 18, train loss: 0.26033, val loss: 0.26694, in 0.031s 1 tree, 81 leaves, max depth = 19, train loss: 0.25651, val loss: 0.26342, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.25359, val loss: 0.26103, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.24960, val loss: 0.25753, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.24629, val loss: 0.25437, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.24297, val loss: 0.25137, in 0.031s 1 tree, 81 leaves, max depth = 18, train loss: 0.23972, val loss: 0.24892, in 0.016s 1 tree, 81 leaves, max depth = 19, train loss: 0.23699, val loss: 0.24639, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.23396, val loss: 0.24376, in 0.031s 1 tree, 81 leaves, max depth = 16, train loss: 0.23131, val loss: 0.24219, in 0.031s 1 tree, 81 leaves, max depth = 18, train loss: 0.22825, val loss: 0.23950, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.22577, val loss: 0.23760, in 0.031s 1 tree, 81 leaves, max depth = 18, train loss: 0.22390, val loss: 0.23597, in 0.031s 1 tree, 81 leaves, max depth = 17, train loss: 0.22187, val loss: 0.23433, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.22006, val loss: 0.23325, in 0.016s 1 tree, 81 leaves, max depth = 19, train loss: 0.21850, val loss: 0.23180, in 0.034s 1 tree, 81 leaves, max depth = 17, train loss: 0.21685, val loss: 0.23056, in 0.033s 1 tree, 81 leaves, max depth = 14, train loss: 0.21446, val loss: 0.22850, in 0.026s 1 tree, 81 leaves, max depth = 15, train loss: 0.21299, val loss: 0.22776, in 0.029s 1 tree, 81 leaves, max depth = 16, train loss: 0.21151, val loss: 0.22703, in 0.028s 1 tree, 81 leaves, max depth = 18, train loss: 0.21039, val loss: 0.22598, in 0.033s 1 tree, 81 leaves, max depth = 13, train loss: 0.20842, val loss: 0.22441, in 0.030s 1 tree, 81 leaves, max depth = 13, train loss: 0.20726, val loss: 0.22383, in 0.028s 1 tree, 81 leaves, max depth = 15, train loss: 0.20551, val loss: 0.22234, in 0.023s 1 tree, 81 leaves, max depth = 17, train loss: 0.20365, val loss: 0.22172, in 0.023s 1 tree, 81 leaves, max depth = 17, train loss: 0.20250, val loss: 0.22130, in 0.027s 1 tree, 81 leaves, max depth = 15, train loss: 0.20148, val loss: 0.22055, in 0.024s 1 tree, 81 leaves, max depth = 17, train loss: 0.20064, val loss: 0.21992, in 0.024s 1 tree, 81 leaves, max depth = 16, train loss: 0.19963, val loss: 0.21960, in 0.025s 1 tree, 81 leaves, max depth = 13, train loss: 0.19879, val loss: 0.21923, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.19732, val loss: 0.21807, in 0.029s 1 tree, 81 leaves, max depth = 14, train loss: 0.19537, val loss: 0.21622, in 0.028s 1 tree, 56 leaves, max depth = 9, train loss: 0.19453, val loss: 0.21554, in 0.025s 1 tree, 81 leaves, max depth = 15, train loss: 0.19318, val loss: 0.21534, in 0.028s 1 tree, 81 leaves, max depth = 18, train loss: 0.19241, val loss: 0.21489, in 0.032s 1 tree, 81 leaves, max depth = 16, train loss: 0.19155, val loss: 0.21452, in 0.030s 1 tree, 81 leaves, max depth = 17, train loss: 0.19096, val loss: 0.21420, in 0.021s 1 tree, 81 leaves, max depth = 17, train loss: 0.18936, val loss: 0.21371, in 0.030s 1 tree, 81 leaves, max depth = 17, train loss: 0.18869, val loss: 0.21347, in 0.033s Fit 64 trees in 2.289 s, (5159 total leaves) Time spent computing histograms: 0.620s Time spent finding best splits: 0.196s Time spent applying splits: 0.144s Time spent predicting: 0.004s Trial 69, Fold 2: Log loss = 0.2075985488626582, Average precision = 0.9717443660603746, ROC-AUC = 0.9680514764907778, Elapsed Time = 2.299709499999153 seconds Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 69, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.194 s 0.007 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 13, train loss: 0.63987, val loss: 0.63947, in 0.028s 1 tree, 81 leaves, max depth = 12, train loss: 0.59499, val loss: 0.59463, in 0.024s 1 tree, 81 leaves, max depth = 14, train loss: 0.55746, val loss: 0.55734, in 0.038s 1 tree, 81 leaves, max depth = 13, train loss: 0.52609, val loss: 0.52627, in 0.034s 1 tree, 81 leaves, max depth = 14, train loss: 0.49853, val loss: 0.49911, in 0.033s 1 tree, 81 leaves, max depth = 12, train loss: 0.47531, val loss: 0.47643, in 0.034s 1 tree, 81 leaves, max depth = 12, train loss: 0.45509, val loss: 0.45697, in 0.029s 1 tree, 81 leaves, max depth = 13, train loss: 0.42915, val loss: 0.43298, in 0.022s 1 tree, 81 leaves, max depth = 16, train loss: 0.40738, val loss: 0.41291, in 0.028s 1 tree, 81 leaves, max depth = 13, train loss: 0.39386, val loss: 0.39996, in 0.032s 1 tree, 81 leaves, max depth = 13, train loss: 0.38112, val loss: 0.38758, in 0.033s 1 tree, 81 leaves, max depth = 14, train loss: 0.36952, val loss: 0.37628, in 0.028s 1 tree, 81 leaves, max depth = 15, train loss: 0.35433, val loss: 0.36264, in 0.025s 1 tree, 81 leaves, max depth = 15, train loss: 0.34193, val loss: 0.35138, in 0.023s 1 tree, 81 leaves, max depth = 14, train loss: 0.33192, val loss: 0.34097, in 0.026s 1 tree, 81 leaves, max depth = 16, train loss: 0.32182, val loss: 0.33202, in 0.024s 1 tree, 81 leaves, max depth = 19, train loss: 0.31197, val loss: 0.32389, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.30416, val loss: 0.31588, in 0.025s 1 tree, 81 leaves, max depth = 16, train loss: 0.29680, val loss: 0.30944, in 0.029s 1 tree, 81 leaves, max depth = 17, train loss: 0.29015, val loss: 0.30255, in 0.028s 1 tree, 81 leaves, max depth = 20, train loss: 0.28313, val loss: 0.29674, in 0.030s 1 tree, 81 leaves, max depth = 15, train loss: 0.27703, val loss: 0.29162, in 0.026s 1 tree, 81 leaves, max depth = 13, train loss: 0.27141, val loss: 0.28524, in 0.030s 1 tree, 81 leaves, max depth = 16, train loss: 0.26667, val loss: 0.28114, in 0.017s 1 tree, 81 leaves, max depth = 18, train loss: 0.26201, val loss: 0.27745, in 0.039s 1 tree, 81 leaves, max depth = 16, train loss: 0.25798, val loss: 0.27417, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.25373, val loss: 0.26940, in 0.030s 1 tree, 81 leaves, max depth = 16, train loss: 0.25023, val loss: 0.26653, in 0.032s 1 tree, 81 leaves, max depth = 16, train loss: 0.24674, val loss: 0.26283, in 0.020s 1 tree, 81 leaves, max depth = 15, train loss: 0.24304, val loss: 0.25892, in 0.029s 1 tree, 81 leaves, max depth = 19, train loss: 0.23963, val loss: 0.25649, in 0.040s 1 tree, 81 leaves, max depth = 16, train loss: 0.23667, val loss: 0.25422, in 0.038s 1 tree, 81 leaves, max depth = 20, train loss: 0.23400, val loss: 0.25249, in 0.027s 1 tree, 81 leaves, max depth = 18, train loss: 0.23133, val loss: 0.24988, in 0.034s 1 tree, 81 leaves, max depth = 17, train loss: 0.22905, val loss: 0.24810, in 0.028s 1 tree, 81 leaves, max depth = 18, train loss: 0.22699, val loss: 0.24627, in 0.016s 1 tree, 71 leaves, max depth = 15, train loss: 0.22465, val loss: 0.24366, in 0.028s 1 tree, 81 leaves, max depth = 21, train loss: 0.22287, val loss: 0.24230, in 0.023s 1 tree, 81 leaves, max depth = 19, train loss: 0.22131, val loss: 0.24100, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.21964, val loss: 0.23973, in 0.027s 1 tree, 81 leaves, max depth = 15, train loss: 0.21708, val loss: 0.23710, in 0.027s 1 tree, 81 leaves, max depth = 18, train loss: 0.21526, val loss: 0.23609, in 0.034s 1 tree, 81 leaves, max depth = 13, train loss: 0.21387, val loss: 0.23514, in 0.036s 1 tree, 81 leaves, max depth = 15, train loss: 0.21127, val loss: 0.23212, in 0.033s 1 tree, 81 leaves, max depth = 18, train loss: 0.20922, val loss: 0.23101, in 0.024s 1 tree, 81 leaves, max depth = 18, train loss: 0.20691, val loss: 0.22871, in 0.030s 1 tree, 81 leaves, max depth = 16, train loss: 0.20583, val loss: 0.22773, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.20476, val loss: 0.22706, in 0.028s 1 tree, 81 leaves, max depth = 14, train loss: 0.20317, val loss: 0.22557, in 0.033s 1 tree, 81 leaves, max depth = 22, train loss: 0.20185, val loss: 0.22498, in 0.027s 1 tree, 81 leaves, max depth = 11, train loss: 0.20030, val loss: 0.22357, in 0.034s 1 tree, 81 leaves, max depth = 17, train loss: 0.19919, val loss: 0.22303, in 0.032s 1 tree, 81 leaves, max depth = 15, train loss: 0.19814, val loss: 0.22239, in 0.027s 1 tree, 35 leaves, max depth = 8, train loss: 0.19735, val loss: 0.22186, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.19656, val loss: 0.22135, in 0.028s 1 tree, 81 leaves, max depth = 12, train loss: 0.19521, val loss: 0.22019, in 0.147s 1 tree, 81 leaves, max depth = 15, train loss: 0.19336, val loss: 0.21974, in 0.063s 1 tree, 81 leaves, max depth = 14, train loss: 0.19216, val loss: 0.21892, in 0.031s 1 tree, 81 leaves, max depth = 19, train loss: 0.19054, val loss: 0.21887, in 0.047s 1 tree, 81 leaves, max depth = 23, train loss: 0.18960, val loss: 0.21862, in 0.031s 1 tree, 81 leaves, max depth = 18, train loss: 0.18879, val loss: 0.21838, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.18776, val loss: 0.21756, in 0.031s 1 tree, 53 leaves, max depth = 9, train loss: 0.18709, val loss: 0.21695, in 0.047s 1 tree, 81 leaves, max depth = 12, train loss: 0.18606, val loss: 0.21668, in 0.031s Fit 64 trees in 2.523 s, (5100 total leaves) Time spent computing histograms: 0.699s Time spent finding best splits: 0.223s Time spent applying splits: 0.220s Time spent predicting: 0.021s Trial 69, Fold 3: Log loss = 0.20688421374573782, Average precision = 0.9722179513099881, ROC-AUC = 0.9679817744700523, Elapsed Time = 2.536510100000669 seconds Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 69, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.176 s 0.004 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 12, train loss: 0.64013, val loss: 0.63816, in 0.021s 1 tree, 81 leaves, max depth = 13, train loss: 0.59548, val loss: 0.59200, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.55797, val loss: 0.55333, in 0.023s 1 tree, 81 leaves, max depth = 11, train loss: 0.52682, val loss: 0.52062, in 0.020s 1 tree, 81 leaves, max depth = 11, train loss: 0.49932, val loss: 0.49204, in 0.019s 1 tree, 81 leaves, max depth = 13, train loss: 0.47554, val loss: 0.46745, in 0.024s 1 tree, 81 leaves, max depth = 11, train loss: 0.45514, val loss: 0.44620, in 0.023s 1 tree, 81 leaves, max depth = 15, train loss: 0.42986, val loss: 0.42069, in 0.022s 1 tree, 81 leaves, max depth = 14, train loss: 0.40805, val loss: 0.39863, in 0.023s 1 tree, 81 leaves, max depth = 17, train loss: 0.39410, val loss: 0.38447, in 0.022s 1 tree, 81 leaves, max depth = 11, train loss: 0.38141, val loss: 0.37133, in 0.024s 1 tree, 81 leaves, max depth = 12, train loss: 0.37016, val loss: 0.35979, in 0.020s 1 tree, 81 leaves, max depth = 13, train loss: 0.35523, val loss: 0.34493, in 0.021s 1 tree, 81 leaves, max depth = 13, train loss: 0.34655, val loss: 0.33580, in 0.025s 1 tree, 81 leaves, max depth = 16, train loss: 0.33509, val loss: 0.32404, in 0.022s 1 tree, 81 leaves, max depth = 16, train loss: 0.32458, val loss: 0.31422, in 0.024s 1 tree, 81 leaves, max depth = 14, train loss: 0.31574, val loss: 0.30489, in 0.024s 1 tree, 81 leaves, max depth = 18, train loss: 0.30767, val loss: 0.29685, in 0.021s 1 tree, 81 leaves, max depth = 12, train loss: 0.30169, val loss: 0.29087, in 0.028s 1 tree, 81 leaves, max depth = 14, train loss: 0.29413, val loss: 0.28403, in 0.020s 1 tree, 81 leaves, max depth = 14, train loss: 0.28754, val loss: 0.27801, in 0.020s 1 tree, 81 leaves, max depth = 15, train loss: 0.28177, val loss: 0.27206, in 0.025s 1 tree, 81 leaves, max depth = 18, train loss: 0.27651, val loss: 0.26732, in 0.026s 1 tree, 81 leaves, max depth = 15, train loss: 0.27194, val loss: 0.26260, in 0.020s 1 tree, 81 leaves, max depth = 12, train loss: 0.26621, val loss: 0.25690, in 0.025s 1 tree, 81 leaves, max depth = 16, train loss: 0.26250, val loss: 0.25309, in 0.027s 1 tree, 81 leaves, max depth = 12, train loss: 0.25767, val loss: 0.24828, in 0.020s 1 tree, 81 leaves, max depth = 15, train loss: 0.25414, val loss: 0.24517, in 0.020s 1 tree, 81 leaves, max depth = 20, train loss: 0.25106, val loss: 0.24234, in 0.024s 1 tree, 81 leaves, max depth = 14, train loss: 0.24816, val loss: 0.23982, in 0.024s 1 tree, 81 leaves, max depth = 14, train loss: 0.24521, val loss: 0.23772, in 0.020s 1 tree, 81 leaves, max depth = 14, train loss: 0.24270, val loss: 0.23559, in 0.021s 1 tree, 81 leaves, max depth = 15, train loss: 0.23787, val loss: 0.23138, in 0.026s 1 tree, 81 leaves, max depth = 16, train loss: 0.23379, val loss: 0.22801, in 0.022s 1 tree, 81 leaves, max depth = 18, train loss: 0.23169, val loss: 0.22607, in 0.022s 1 tree, 81 leaves, max depth = 12, train loss: 0.22965, val loss: 0.22473, in 0.023s 1 tree, 81 leaves, max depth = 21, train loss: 0.22772, val loss: 0.22313, in 0.020s 1 tree, 81 leaves, max depth = 15, train loss: 0.22468, val loss: 0.22049, in 0.020s 1 tree, 81 leaves, max depth = 14, train loss: 0.22302, val loss: 0.21942, in 0.020s 1 tree, 81 leaves, max depth = 22, train loss: 0.22104, val loss: 0.21805, in 0.021s 1 tree, 81 leaves, max depth = 15, train loss: 0.21846, val loss: 0.21560, in 0.021s 1 tree, 81 leaves, max depth = 14, train loss: 0.21666, val loss: 0.21421, in 0.019s 1 tree, 81 leaves, max depth = 13, train loss: 0.21530, val loss: 0.21346, in 0.020s 1 tree, 81 leaves, max depth = 14, train loss: 0.21313, val loss: 0.21137, in 0.021s 1 tree, 81 leaves, max depth = 24, train loss: 0.21162, val loss: 0.21031, in 0.026s 1 tree, 81 leaves, max depth = 20, train loss: 0.21024, val loss: 0.20949, in 0.025s 1 tree, 81 leaves, max depth = 13, train loss: 0.20834, val loss: 0.20770, in 0.022s 1 tree, 81 leaves, max depth = 13, train loss: 0.20694, val loss: 0.20664, in 0.022s 1 tree, 81 leaves, max depth = 13, train loss: 0.20569, val loss: 0.20631, in 0.019s 1 tree, 81 leaves, max depth = 21, train loss: 0.20445, val loss: 0.20555, in 0.023s 1 tree, 81 leaves, max depth = 15, train loss: 0.20286, val loss: 0.20421, in 0.020s 1 tree, 81 leaves, max depth = 16, train loss: 0.20194, val loss: 0.20386, in 0.022s 1 tree, 81 leaves, max depth = 19, train loss: 0.20099, val loss: 0.20321, in 0.025s 1 tree, 81 leaves, max depth = 15, train loss: 0.19999, val loss: 0.20291, in 0.024s 1 tree, 81 leaves, max depth = 13, train loss: 0.19860, val loss: 0.20164, in 0.020s 1 tree, 81 leaves, max depth = 14, train loss: 0.19761, val loss: 0.20094, in 0.028s 1 tree, 53 leaves, max depth = 8, train loss: 0.19671, val loss: 0.20033, in 0.019s 1 tree, 81 leaves, max depth = 13, train loss: 0.19599, val loss: 0.20006, in 0.019s 1 tree, 81 leaves, max depth = 13, train loss: 0.19485, val loss: 0.19904, in 0.020s 1 tree, 81 leaves, max depth = 19, train loss: 0.19356, val loss: 0.19818, in 0.026s 1 tree, 81 leaves, max depth = 14, train loss: 0.19205, val loss: 0.19753, in 0.024s 1 tree, 81 leaves, max depth = 14, train loss: 0.19033, val loss: 0.19751, in 0.026s 1 tree, 81 leaves, max depth = 20, train loss: 0.18934, val loss: 0.19672, in 0.023s 1 tree, 81 leaves, max depth = 24, train loss: 0.18852, val loss: 0.19627, in 0.022s Fit 64 trees in 1.816 s, (5156 total leaves) Time spent computing histograms: 0.491s Time spent finding best splits: 0.147s Time spent applying splits: 0.110s Time spent predicting: 0.009s Trial 69, Fold 4: Log loss = 0.21187220792729713, Average precision = 0.9714045250282974, ROC-AUC = 0.9661044973544972, Elapsed Time = 1.8256971999999223 seconds Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 69, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.160 s 0.006 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 12, train loss: 0.63928, val loss: 0.63710, in 0.022s 1 tree, 81 leaves, max depth = 15, train loss: 0.59393, val loss: 0.59046, in 0.023s 1 tree, 81 leaves, max depth = 16, train loss: 0.55585, val loss: 0.55107, in 0.023s 1 tree, 81 leaves, max depth = 11, train loss: 0.52421, val loss: 0.51861, in 0.023s 1 tree, 81 leaves, max depth = 16, train loss: 0.49657, val loss: 0.49014, in 0.025s 1 tree, 81 leaves, max depth = 11, train loss: 0.47331, val loss: 0.46640, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.45361, val loss: 0.44604, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.43550, val loss: 0.42753, in 0.012s 1 tree, 81 leaves, max depth = 19, train loss: 0.41168, val loss: 0.40405, in 0.034s 1 tree, 81 leaves, max depth = 13, train loss: 0.39753, val loss: 0.38956, in 0.027s 1 tree, 81 leaves, max depth = 16, train loss: 0.37903, val loss: 0.37142, in 0.023s 1 tree, 81 leaves, max depth = 14, train loss: 0.36771, val loss: 0.36019, in 0.027s 1 tree, 81 leaves, max depth = 17, train loss: 0.35250, val loss: 0.34534, in 0.026s 1 tree, 81 leaves, max depth = 12, train loss: 0.34387, val loss: 0.33703, in 0.027s 1 tree, 81 leaves, max depth = 16, train loss: 0.33226, val loss: 0.32538, in 0.020s 1 tree, 81 leaves, max depth = 17, train loss: 0.32308, val loss: 0.31651, in 0.020s 1 tree, 81 leaves, max depth = 17, train loss: 0.31296, val loss: 0.30761, in 0.023s 1 tree, 81 leaves, max depth = 18, train loss: 0.30456, val loss: 0.29907, in 0.021s 1 tree, 81 leaves, max depth = 15, train loss: 0.29730, val loss: 0.29187, in 0.024s 1 tree, 81 leaves, max depth = 13, train loss: 0.28990, val loss: 0.28469, in 0.026s 1 tree, 81 leaves, max depth = 13, train loss: 0.28360, val loss: 0.27901, in 0.021s 1 tree, 81 leaves, max depth = 16, train loss: 0.27702, val loss: 0.27318, in 0.012s 1 tree, 81 leaves, max depth = 15, train loss: 0.27144, val loss: 0.26784, in 0.035s 1 tree, 81 leaves, max depth = 18, train loss: 0.26602, val loss: 0.26314, in 0.023s 1 tree, 81 leaves, max depth = 13, train loss: 0.26151, val loss: 0.25900, in 0.022s 1 tree, 81 leaves, max depth = 15, train loss: 0.25723, val loss: 0.25492, in 0.022s 1 tree, 81 leaves, max depth = 13, train loss: 0.25302, val loss: 0.25158, in 0.011s 1 tree, 81 leaves, max depth = 21, train loss: 0.24793, val loss: 0.24690, in 0.040s 1 tree, 81 leaves, max depth = 17, train loss: 0.24435, val loss: 0.24390, in 0.025s 1 tree, 81 leaves, max depth = 14, train loss: 0.24120, val loss: 0.24083, in 0.025s 1 tree, 81 leaves, max depth = 17, train loss: 0.23831, val loss: 0.23855, in 0.022s 1 tree, 81 leaves, max depth = 18, train loss: 0.23579, val loss: 0.23619, in 0.023s 1 tree, 81 leaves, max depth = 13, train loss: 0.23344, val loss: 0.23381, in 0.020s 1 tree, 81 leaves, max depth = 11, train loss: 0.23032, val loss: 0.23083, in 0.026s 1 tree, 81 leaves, max depth = 15, train loss: 0.22802, val loss: 0.22963, in 0.026s 1 tree, 81 leaves, max depth = 18, train loss: 0.22587, val loss: 0.22783, in 0.028s 1 tree, 81 leaves, max depth = 15, train loss: 0.22397, val loss: 0.22687, in 0.023s 1 tree, 81 leaves, max depth = 15, train loss: 0.22150, val loss: 0.22485, in 0.024s 1 tree, 81 leaves, max depth = 13, train loss: 0.21941, val loss: 0.22347, in 0.024s 1 tree, 81 leaves, max depth = 19, train loss: 0.21783, val loss: 0.22193, in 0.025s 1 tree, 81 leaves, max depth = 22, train loss: 0.21644, val loss: 0.22093, in 0.022s 1 tree, 81 leaves, max depth = 15, train loss: 0.21492, val loss: 0.22032, in 0.022s 1 tree, 81 leaves, max depth = 13, train loss: 0.21273, val loss: 0.21836, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.21047, val loss: 0.21671, in 0.021s 1 tree, 81 leaves, max depth = 26, train loss: 0.20886, val loss: 0.21569, in 0.027s 1 tree, 81 leaves, max depth = 15, train loss: 0.20679, val loss: 0.21467, in 0.023s 1 tree, 81 leaves, max depth = 19, train loss: 0.20570, val loss: 0.21368, in 0.027s 1 tree, 81 leaves, max depth = 17, train loss: 0.20363, val loss: 0.21201, in 0.026s 1 tree, 81 leaves, max depth = 13, train loss: 0.20257, val loss: 0.21179, in 0.027s 1 tree, 81 leaves, max depth = 13, train loss: 0.20149, val loss: 0.21103, in 0.024s 1 tree, 81 leaves, max depth = 15, train loss: 0.20053, val loss: 0.21078, in 0.022s 1 tree, 81 leaves, max depth = 18, train loss: 0.19876, val loss: 0.20915, in 0.027s 1 tree, 81 leaves, max depth = 12, train loss: 0.19783, val loss: 0.20852, in 0.023s 1 tree, 81 leaves, max depth = 18, train loss: 0.19679, val loss: 0.20808, in 0.022s 1 tree, 81 leaves, max depth = 16, train loss: 0.19527, val loss: 0.20672, in 0.023s 1 tree, 81 leaves, max depth = 20, train loss: 0.19429, val loss: 0.20622, in 0.026s 1 tree, 81 leaves, max depth = 16, train loss: 0.19327, val loss: 0.20577, in 0.024s 1 tree, 81 leaves, max depth = 20, train loss: 0.19191, val loss: 0.20489, in 0.028s 1 tree, 81 leaves, max depth = 16, train loss: 0.19065, val loss: 0.20367, in 0.025s 1 tree, 81 leaves, max depth = 13, train loss: 0.18912, val loss: 0.20332, in 0.024s 1 tree, 81 leaves, max depth = 20, train loss: 0.18829, val loss: 0.20310, in 0.023s 1 tree, 81 leaves, max depth = 15, train loss: 0.18746, val loss: 0.20285, in 0.023s 1 tree, 81 leaves, max depth = 17, train loss: 0.18682, val loss: 0.20251, in 0.027s 1 tree, 81 leaves, max depth = 14, train loss: 0.18526, val loss: 0.20241, in 0.029s Fit 64 trees in 1.926 s, (5184 total leaves) Time spent computing histograms: 0.525s Time spent finding best splits: 0.164s Time spent applying splits: 0.124s Time spent predicting: 0.023s Trial 69, Fold 5: Log loss = 0.21520133387638787, Average precision = 0.970336942697105, ROC-AUC = 0.9663292644408524, Elapsed Time = 1.9375400000008085 seconds
Optimization Progress: 70%|####### | 70/100 [14:04<07:36, 15.20s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 70, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.166 s 0.040 GB of training data: 0.004 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 36 leaves, max depth = 13, train loss: 0.67807, val loss: 0.67763, in 0.010s 1 tree, 36 leaves, max depth = 13, train loss: 0.66411, val loss: 0.66325, in 0.002s 1 tree, 36 leaves, max depth = 13, train loss: 0.65101, val loss: 0.64973, in 0.017s 1 tree, 36 leaves, max depth = 13, train loss: 0.63869, val loss: 0.63702, in 0.012s 1 tree, 36 leaves, max depth = 13, train loss: 0.62711, val loss: 0.62506, in 0.004s 1 tree, 36 leaves, max depth = 13, train loss: 0.61620, val loss: 0.61378, in 0.014s 1 tree, 36 leaves, max depth = 13, train loss: 0.60593, val loss: 0.60315, in 0.001s 1 tree, 36 leaves, max depth = 13, train loss: 0.59625, val loss: 0.59312, in 0.017s 1 tree, 36 leaves, max depth = 13, train loss: 0.58711, val loss: 0.58364, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.57849, val loss: 0.57469, in 0.017s 1 tree, 36 leaves, max depth = 13, train loss: 0.57035, val loss: 0.56623, in 0.014s 1 tree, 36 leaves, max depth = 13, train loss: 0.56266, val loss: 0.55823, in 0.002s 1 tree, 35 leaves, max depth = 11, train loss: 0.55554, val loss: 0.55084, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.54832, val loss: 0.54407, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.54161, val loss: 0.53707, in 0.018s 1 tree, 48 leaves, max depth = 11, train loss: 0.53493, val loss: 0.53081, in 0.015s 1 tree, 36 leaves, max depth = 12, train loss: 0.52874, val loss: 0.52433, in 0.002s 1 tree, 48 leaves, max depth = 11, train loss: 0.52254, val loss: 0.51855, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.51691, val loss: 0.51263, in 0.011s 1 tree, 48 leaves, max depth = 12, train loss: 0.51114, val loss: 0.50726, in 0.006s 1 tree, 36 leaves, max depth = 12, train loss: 0.50583, val loss: 0.50167, in 0.015s 1 tree, 48 leaves, max depth = 12, train loss: 0.50046, val loss: 0.49669, in 0.001s 1 tree, 37 leaves, max depth = 11, train loss: 0.49553, val loss: 0.49150, in 0.006s 1 tree, 37 leaves, max depth = 11, train loss: 0.49086, val loss: 0.48657, in 0.013s 1 tree, 48 leaves, max depth = 10, train loss: 0.48592, val loss: 0.48200, in 0.004s 1 tree, 48 leaves, max depth = 10, train loss: 0.48124, val loss: 0.47768, in 0.017s 1 tree, 48 leaves, max depth = 10, train loss: 0.47680, val loss: 0.47359, in 0.013s 1 tree, 33 leaves, max depth = 10, train loss: 0.47269, val loss: 0.46924, in 0.004s 1 tree, 48 leaves, max depth = 10, train loss: 0.46854, val loss: 0.46542, in 0.017s 1 tree, 36 leaves, max depth = 9, train loss: 0.46465, val loss: 0.46128, in 0.013s 1 tree, 30 leaves, max depth = 11, train loss: 0.46098, val loss: 0.45739, in 0.004s 1 tree, 48 leaves, max depth = 10, train loss: 0.45713, val loss: 0.45387, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.45348, val loss: 0.45053, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.45002, val loss: 0.44738, in 0.007s 1 tree, 31 leaves, max depth = 10, train loss: 0.44673, val loss: 0.44386, in 0.014s 1 tree, 48 leaves, max depth = 10, train loss: 0.44346, val loss: 0.44090, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.44039, val loss: 0.43764, in 0.018s 1 tree, 48 leaves, max depth = 9, train loss: 0.43733, val loss: 0.43489, in 0.015s 1 tree, 34 leaves, max depth = 10, train loss: 0.43435, val loss: 0.43175, in 0.000s 1 tree, 48 leaves, max depth = 10, train loss: 0.43148, val loss: 0.42917, in 0.018s 1 tree, 32 leaves, max depth = 10, train loss: 0.42877, val loss: 0.42624, in 0.012s 1 tree, 48 leaves, max depth = 10, train loss: 0.42604, val loss: 0.42378, in 0.004s 1 tree, 29 leaves, max depth = 10, train loss: 0.42348, val loss: 0.42104, in 0.014s 1 tree, 48 leaves, max depth = 10, train loss: 0.42092, val loss: 0.41876, in 0.001s 1 tree, 48 leaves, max depth = 9, train loss: 0.41848, val loss: 0.41658, in 0.018s 1 tree, 32 leaves, max depth = 10, train loss: 0.41611, val loss: 0.41401, in 0.014s 1 tree, 48 leaves, max depth = 11, train loss: 0.41381, val loss: 0.41199, in 0.003s 1 tree, 33 leaves, max depth = 10, train loss: 0.41160, val loss: 0.40962, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40945, val loss: 0.40727, in 0.011s Fit 49 trees in 0.799 s, (1922 total leaves) Time spent computing histograms: 0.239s Time spent finding best splits: 0.050s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 70, Fold 1: Log loss = 0.4112736509614114, Average precision = 0.9467322043099218, ROC-AUC = 0.9422860864962266, Elapsed Time = 0.8045333999998547 seconds Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 70, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.160 s 0.005 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 11, train loss: 0.67829, val loss: 0.67754, in 0.001s 1 tree, 35 leaves, max depth = 13, train loss: 0.66451, val loss: 0.66308, in 0.018s 1 tree, 34 leaves, max depth = 12, train loss: 0.65148, val loss: 0.64937, in 0.012s 1 tree, 35 leaves, max depth = 13, train loss: 0.63932, val loss: 0.63659, in 0.005s 1 tree, 34 leaves, max depth = 12, train loss: 0.62780, val loss: 0.62444, in 0.013s 1 tree, 34 leaves, max depth = 12, train loss: 0.61696, val loss: 0.61298, in 0.003s 1 tree, 32 leaves, max depth = 15, train loss: 0.60683, val loss: 0.60238, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.59719, val loss: 0.59217, in 0.009s 1 tree, 33 leaves, max depth = 13, train loss: 0.58819, val loss: 0.58270, in 0.008s 1 tree, 33 leaves, max depth = 12, train loss: 0.57960, val loss: 0.57358, in 0.013s 1 tree, 32 leaves, max depth = 15, train loss: 0.57157, val loss: 0.56514, in 0.004s 1 tree, 35 leaves, max depth = 12, train loss: 0.56387, val loss: 0.55697, in 0.015s 1 tree, 32 leaves, max depth = 15, train loss: 0.55670, val loss: 0.54942, in 0.011s 1 tree, 48 leaves, max depth = 12, train loss: 0.54937, val loss: 0.54231, in 0.006s 1 tree, 35 leaves, max depth = 9, train loss: 0.54266, val loss: 0.53516, in 0.015s 1 tree, 48 leaves, max depth = 13, train loss: 0.53587, val loss: 0.52858, in 0.010s 1 tree, 32 leaves, max depth = 15, train loss: 0.52977, val loss: 0.52214, in 0.007s 1 tree, 48 leaves, max depth = 13, train loss: 0.52347, val loss: 0.51604, in 0.014s 1 tree, 32 leaves, max depth = 13, train loss: 0.51783, val loss: 0.51007, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.51196, val loss: 0.50441, in 0.017s 1 tree, 48 leaves, max depth = 11, train loss: 0.50641, val loss: 0.49906, in 0.014s 1 tree, 35 leaves, max depth = 14, train loss: 0.50130, val loss: 0.49363, in 0.004s 1 tree, 48 leaves, max depth = 12, train loss: 0.49612, val loss: 0.48863, in 0.015s 1 tree, 35 leaves, max depth = 12, train loss: 0.49137, val loss: 0.48358, in 0.013s 1 tree, 48 leaves, max depth = 13, train loss: 0.48653, val loss: 0.47893, in 0.004s 1 tree, 48 leaves, max depth = 13, train loss: 0.48194, val loss: 0.47452, in 0.017s 1 tree, 34 leaves, max depth = 12, train loss: 0.47761, val loss: 0.46990, in 0.013s 1 tree, 48 leaves, max depth = 11, train loss: 0.47332, val loss: 0.46579, in 0.013s 1 tree, 34 leaves, max depth = 12, train loss: 0.46929, val loss: 0.46148, in 0.008s 1 tree, 48 leaves, max depth = 12, train loss: 0.46526, val loss: 0.45764, in 0.015s 1 tree, 35 leaves, max depth = 15, train loss: 0.46150, val loss: 0.45359, in 0.010s 1 tree, 48 leaves, max depth = 13, train loss: 0.45771, val loss: 0.44999, in 0.008s 1 tree, 34 leaves, max depth = 11, train loss: 0.45420, val loss: 0.44624, in 0.014s 1 tree, 48 leaves, max depth = 13, train loss: 0.45065, val loss: 0.44287, in 0.004s 1 tree, 48 leaves, max depth = 13, train loss: 0.44727, val loss: 0.43966, in 0.007s 1 tree, 33 leaves, max depth = 11, train loss: 0.44403, val loss: 0.43620, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.44085, val loss: 0.43320, in 0.011s 1 tree, 33 leaves, max depth = 11, train loss: 0.43782, val loss: 0.42994, in 0.008s 1 tree, 48 leaves, max depth = 10, train loss: 0.43482, val loss: 0.42711, in 0.013s 1 tree, 34 leaves, max depth = 11, train loss: 0.43198, val loss: 0.42407, in 0.003s 1 tree, 35 leaves, max depth = 11, train loss: 0.42923, val loss: 0.42104, in 0.015s 1 tree, 48 leaves, max depth = 12, train loss: 0.42641, val loss: 0.41838, in 0.012s 1 tree, 33 leaves, max depth = 11, train loss: 0.42389, val loss: 0.41566, in 0.008s 1 tree, 48 leaves, max depth = 11, train loss: 0.42123, val loss: 0.41314, in 0.012s 1 tree, 48 leaves, max depth = 12, train loss: 0.41870, val loss: 0.41077, in 0.002s 1 tree, 48 leaves, max depth = 11, train loss: 0.41630, val loss: 0.40851, in 0.010s 1 tree, 34 leaves, max depth = 13, train loss: 0.41399, val loss: 0.40599, in 0.010s 1 tree, 48 leaves, max depth = 11, train loss: 0.41172, val loss: 0.40387, in 0.002s 1 tree, 3 leaves, max depth = 2, train loss: 0.40962, val loss: 0.40170, in 0.015s Fit 49 trees in 0.820 s, (1908 total leaves) Time spent computing histograms: 0.247s Time spent finding best splits: 0.052s Time spent applying splits: 0.044s Time spent predicting: 0.012s Trial 70, Fold 2: Log loss = 0.4112498194579391, Average precision = 0.9438457631997765, ROC-AUC = 0.9436594615806474, Elapsed Time = 0.8273012000008748 seconds Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 70, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.178 s 0.005 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67784, in 0.011s 1 tree, 33 leaves, max depth = 13, train loss: 0.66472, val loss: 0.66374, in 0.007s 1 tree, 34 leaves, max depth = 9, train loss: 0.65189, val loss: 0.65043, in 0.011s 1 tree, 36 leaves, max depth = 12, train loss: 0.63984, val loss: 0.63795, in 0.008s 1 tree, 32 leaves, max depth = 9, train loss: 0.62838, val loss: 0.62609, in 0.010s 1 tree, 33 leaves, max depth = 13, train loss: 0.61774, val loss: 0.61502, in 0.007s 1 tree, 34 leaves, max depth = 9, train loss: 0.60766, val loss: 0.60453, in 0.012s 1 tree, 33 leaves, max depth = 13, train loss: 0.59822, val loss: 0.59468, in 0.011s 1 tree, 34 leaves, max depth = 9, train loss: 0.58910, val loss: 0.58524, in 0.010s 1 tree, 32 leaves, max depth = 9, train loss: 0.58050, val loss: 0.57632, in 0.009s 1 tree, 33 leaves, max depth = 9, train loss: 0.57252, val loss: 0.56797, in 0.008s 1 tree, 34 leaves, max depth = 11, train loss: 0.56498, val loss: 0.56012, in 0.010s 1 tree, 35 leaves, max depth = 9, train loss: 0.55785, val loss: 0.55266, in 0.008s 1 tree, 48 leaves, max depth = 12, train loss: 0.55044, val loss: 0.54574, in 0.013s 1 tree, 32 leaves, max depth = 12, train loss: 0.54391, val loss: 0.53887, in 0.010s 1 tree, 48 leaves, max depth = 12, train loss: 0.53704, val loss: 0.53248, in 0.007s 1 tree, 33 leaves, max depth = 9, train loss: 0.53085, val loss: 0.52602, in 0.013s 1 tree, 48 leaves, max depth = 11, train loss: 0.52448, val loss: 0.52010, in 0.012s 1 tree, 35 leaves, max depth = 12, train loss: 0.51887, val loss: 0.51423, in 0.007s 1 tree, 48 leaves, max depth = 11, train loss: 0.51294, val loss: 0.50873, in 0.013s 1 tree, 48 leaves, max depth = 11, train loss: 0.50733, val loss: 0.50354, in 0.012s 1 tree, 35 leaves, max depth = 11, train loss: 0.50224, val loss: 0.49820, in 0.007s 1 tree, 48 leaves, max depth = 11, train loss: 0.49701, val loss: 0.49337, in 0.014s 1 tree, 35 leaves, max depth = 11, train loss: 0.49229, val loss: 0.48840, in 0.003s 1 tree, 48 leaves, max depth = 12, train loss: 0.48739, val loss: 0.48389, in 0.017s 1 tree, 48 leaves, max depth = 10, train loss: 0.48275, val loss: 0.47963, in 0.011s 1 tree, 35 leaves, max depth = 11, train loss: 0.47846, val loss: 0.47508, in 0.005s 1 tree, 48 leaves, max depth = 10, train loss: 0.47411, val loss: 0.47110, in 0.015s 1 tree, 35 leaves, max depth = 11, train loss: 0.47011, val loss: 0.46688, in 0.010s 1 tree, 48 leaves, max depth = 12, train loss: 0.46603, val loss: 0.46314, in 0.010s 1 tree, 35 leaves, max depth = 13, train loss: 0.46232, val loss: 0.45918, in 0.009s 1 tree, 48 leaves, max depth = 12, train loss: 0.45849, val loss: 0.45568, in 0.005s 1 tree, 48 leaves, max depth = 12, train loss: 0.45486, val loss: 0.45237, in 0.019s 1 tree, 35 leaves, max depth = 11, train loss: 0.45143, val loss: 0.44873, in 0.012s 1 tree, 48 leaves, max depth = 12, train loss: 0.44801, val loss: 0.44561, in 0.012s 1 tree, 35 leaves, max depth = 11, train loss: 0.44481, val loss: 0.44220, in 0.008s 1 tree, 48 leaves, max depth = 11, train loss: 0.44158, val loss: 0.43927, in 0.017s [38/49] 1 tree, 35 leaves, max depth = 11, train loss: 0.43859, val loss: 0.43608, in 0.012s 1 tree, 48 leaves, max depth = 10, train loss: 0.43554, val loss: 0.43332, in 0.014s 1 tree, 35 leaves, max depth = 11, train loss: 0.43274, val loss: 0.43032, in 0.009s 1 tree, 35 leaves, max depth = 11, train loss: 0.43009, val loss: 0.42748, in 0.012s 1 tree, 48 leaves, max depth = 12, train loss: 0.42723, val loss: 0.42488, in 0.014s 1 tree, 35 leaves, max depth = 10, train loss: 0.42473, val loss: 0.42215, in 0.007s 1 tree, 48 leaves, max depth = 11, train loss: 0.42204, val loss: 0.41971, in 0.014s 1 tree, 48 leaves, max depth = 11, train loss: 0.41947, val loss: 0.41740, in 0.013s 1 tree, 48 leaves, max depth = 12, train loss: 0.41704, val loss: 0.41523, in 0.007s 1 tree, 34 leaves, max depth = 12, train loss: 0.41477, val loss: 0.41275, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.41247, val loss: 0.41068, in 0.014s 1 tree, 3 leaves, max depth = 2, train loss: 0.41030, val loss: 0.40867, in 0.002s Fit 49 trees in 0.842 s, (1918 total leaves) Time spent computing histograms: 0.252s Time spent finding best splits: 0.054s Time spent applying splits: 0.047s Time spent predicting: 0.005s Trial 70, Fold 3: Log loss = 0.4076260721292932, Average precision = 0.9485255964042099, ROC-AUC = 0.9468514340611185, Elapsed Time = 0.8560482000011689 seconds Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 70, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.179 s 0.006 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 11, train loss: 0.67831, val loss: 0.67743, in 0.011s 1 tree, 34 leaves, max depth = 11, train loss: 0.66449, val loss: 0.66276, in 0.012s 1 tree, 34 leaves, max depth = 11, train loss: 0.65152, val loss: 0.64897, in 0.006s 1 tree, 34 leaves, max depth = 11, train loss: 0.63933, val loss: 0.63598, in 0.013s 1 tree, 34 leaves, max depth = 11, train loss: 0.62787, val loss: 0.62375, in 0.010s 1 tree, 34 leaves, max depth = 11, train loss: 0.61708, val loss: 0.61222, in 0.010s 1 tree, 34 leaves, max depth = 11, train loss: 0.60692, val loss: 0.60133, in 0.009s 1 tree, 34 leaves, max depth = 11, train loss: 0.59734, val loss: 0.59104, in 0.006s 1 tree, 34 leaves, max depth = 11, train loss: 0.58831, val loss: 0.58132, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.57978, val loss: 0.57213, in 0.012s 1 tree, 34 leaves, max depth = 11, train loss: 0.57174, val loss: 0.56343, in 0.012s 1 tree, 33 leaves, max depth = 10, train loss: 0.56414, val loss: 0.55520, in 0.010s 1 tree, 35 leaves, max depth = 12, train loss: 0.55698, val loss: 0.54751, in 0.012s 1 tree, 48 leaves, max depth = 10, train loss: 0.54957, val loss: 0.54023, in 0.013s 1 tree, 33 leaves, max depth = 11, train loss: 0.54295, val loss: 0.53302, in 0.009s 1 tree, 48 leaves, max depth = 10, train loss: 0.53609, val loss: 0.52630, in 0.014s 1 tree, 34 leaves, max depth = 11, train loss: 0.53002, val loss: 0.51973, in 0.011s 1 tree, 48 leaves, max depth = 10, train loss: 0.52365, val loss: 0.51350, in 0.014s 1 tree, 34 leaves, max depth = 15, train loss: 0.51805, val loss: 0.50737, in 0.010s 1 tree, 48 leaves, max depth = 10, train loss: 0.51212, val loss: 0.50156, in 0.014s 1 tree, 48 leaves, max depth = 10, train loss: 0.50651, val loss: 0.49607, in 0.013s 1 tree, 34 leaves, max depth = 11, train loss: 0.50144, val loss: 0.49057, in 0.011s 1 tree, 48 leaves, max depth = 11, train loss: 0.49621, val loss: 0.48545, in 0.015s 1 tree, 34 leaves, max depth = 13, train loss: 0.49147, val loss: 0.48021, in 0.000s 1 tree, 48 leaves, max depth = 11, train loss: 0.48658, val loss: 0.47544, in 0.018s 1 tree, 48 leaves, max depth = 11, train loss: 0.48194, val loss: 0.47091, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.47766, val loss: 0.46621, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.47332, val loss: 0.46198, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.46934, val loss: 0.45760, in 0.020s 1 tree, 48 leaves, max depth = 11, train loss: 0.46528, val loss: 0.45364, in 0.017s 1 tree, 32 leaves, max depth = 14, train loss: 0.46154, val loss: 0.44948, in 0.015s 1 tree, 48 leaves, max depth = 11, train loss: 0.45773, val loss: 0.44577, in 0.013s 1 tree, 48 leaves, max depth = 11, train loss: 0.45411, val loss: 0.44225, in 0.006s 1 tree, 34 leaves, max depth = 14, train loss: 0.45068, val loss: 0.43842, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.44727, val loss: 0.43511, in 0.019s 1 tree, 34 leaves, max depth = 14, train loss: 0.44406, val loss: 0.43152, in 0.014s 1 tree, 48 leaves, max depth = 10, train loss: 0.44085, val loss: 0.42841, in 0.013s 1 tree, 32 leaves, max depth = 13, train loss: 0.43786, val loss: 0.42504, in 0.008s 1 tree, 48 leaves, max depth = 9, train loss: 0.43483, val loss: 0.42210, in 0.019s 1 tree, 35 leaves, max depth = 14, train loss: 0.43202, val loss: 0.41892, in 0.010s 1 tree, 33 leaves, max depth = 12, train loss: 0.42933, val loss: 0.41587, in 0.013s 1 tree, 48 leaves, max depth = 11, train loss: 0.42649, val loss: 0.41313, in 0.014s 1 tree, 32 leaves, max depth = 13, train loss: 0.42400, val loss: 0.41030, in 0.000s 1 tree, 48 leaves, max depth = 10, train loss: 0.42132, val loss: 0.40771, in 0.020s 1 tree, 48 leaves, max depth = 11, train loss: 0.41877, val loss: 0.40522, in 0.021s 1 tree, 48 leaves, max depth = 10, train loss: 0.41633, val loss: 0.40287, in 0.018s 1 tree, 31 leaves, max depth = 10, train loss: 0.41407, val loss: 0.40030, in 0.015s 1 tree, 48 leaves, max depth = 9, train loss: 0.41177, val loss: 0.39807, in 0.009s 1 tree, 3 leaves, max depth = 2, train loss: 0.40968, val loss: 0.39584, in 0.016s Fit 49 trees in 0.948 s, (1906 total leaves) Time spent computing histograms: 0.290s Time spent finding best splits: 0.062s Time spent applying splits: 0.055s Time spent predicting: 0.003s Trial 70, Fold 4: Log loss = 0.4089359936187368, Average precision = 0.9485610339411467, ROC-AUC = 0.9454596069062046, Elapsed Time = 0.9537225000003673 seconds Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 70, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.200 s 0.040 GB of training data: 0.019 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 10, train loss: 0.67829, val loss: 0.67728, in 0.010s 1 tree, 35 leaves, max depth = 12, train loss: 0.66450, val loss: 0.66250, in 0.012s 1 tree, 33 leaves, max depth = 11, train loss: 0.65137, val loss: 0.64845, in 0.007s 1 tree, 33 leaves, max depth = 11, train loss: 0.63903, val loss: 0.63522, in 0.022s 1 tree, 33 leaves, max depth = 11, train loss: 0.62742, val loss: 0.62276, in 0.013s 1 tree, 36 leaves, max depth = 12, train loss: 0.61664, val loss: 0.61110, in 0.011s 1 tree, 33 leaves, max depth = 11, train loss: 0.60633, val loss: 0.59999, in 0.012s 1 tree, 33 leaves, max depth = 11, train loss: 0.59662, val loss: 0.58949, in 0.010s 1 tree, 35 leaves, max depth = 12, train loss: 0.58758, val loss: 0.57966, in 0.014s 1 tree, 33 leaves, max depth = 11, train loss: 0.57892, val loss: 0.57027, in 0.005s 1 tree, 33 leaves, max depth = 11, train loss: 0.57075, val loss: 0.56137, in 0.019s 1 tree, 33 leaves, max depth = 11, train loss: 0.56303, val loss: 0.55295, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.55582, val loss: 0.54504, in 0.023s 1 tree, 48 leaves, max depth = 12, train loss: 0.54855, val loss: 0.53805, in 0.014s 1 tree, 33 leaves, max depth = 11, train loss: 0.54181, val loss: 0.53067, in 0.013s 1 tree, 48 leaves, max depth = 12, train loss: 0.53507, val loss: 0.52421, in 0.008s 1 tree, 34 leaves, max depth = 10, train loss: 0.52893, val loss: 0.51749, in 0.019s 1 tree, 48 leaves, max depth = 12, train loss: 0.52266, val loss: 0.51151, in 0.015s 1 tree, 34 leaves, max depth = 10, train loss: 0.51698, val loss: 0.50528, in 0.013s 1 tree, 48 leaves, max depth = 12, train loss: 0.51115, val loss: 0.49973, in 0.015s 1 tree, 34 leaves, max depth = 12, train loss: 0.50590, val loss: 0.49394, in 0.010s 1 tree, 48 leaves, max depth = 12, train loss: 0.50045, val loss: 0.48877, in 0.008s 1 tree, 48 leaves, max depth = 12, train loss: 0.49529, val loss: 0.48389, in 0.019s 1 tree, 35 leaves, max depth = 10, train loss: 0.49051, val loss: 0.47859, in 0.017s 1 tree, 48 leaves, max depth = 12, train loss: 0.48569, val loss: 0.47403, in 0.014s 1 tree, 35 leaves, max depth = 10, train loss: 0.48124, val loss: 0.46910, in 0.014s 1 tree, 48 leaves, max depth = 13, train loss: 0.47673, val loss: 0.46486, in 0.014s 1 tree, 36 leaves, max depth = 10, train loss: 0.47260, val loss: 0.46021, in 0.004s 1 tree, 48 leaves, max depth = 12, train loss: 0.46836, val loss: 0.45625, in 0.021s 1 tree, 36 leaves, max depth = 10, train loss: 0.46451, val loss: 0.45193, in 0.013s 1 tree, 33 leaves, max depth = 10, train loss: 0.46079, val loss: 0.44774, in 0.013s 1 tree, 48 leaves, max depth = 12, train loss: 0.45686, val loss: 0.44408, in 0.013s 1 tree, 48 leaves, max depth = 12, train loss: 0.45312, val loss: 0.44062, in 0.003s 1 tree, 48 leaves, max depth = 12, train loss: 0.44957, val loss: 0.43733, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.44627, val loss: 0.43360, in 0.011s 1 tree, 48 leaves, max depth = 12, train loss: 0.44292, val loss: 0.43050, in 0.015s 1 tree, 34 leaves, max depth = 12, train loss: 0.43985, val loss: 0.42702, in 0.012s 1 tree, 48 leaves, max depth = 13, train loss: 0.43670, val loss: 0.42411, in 0.012s 1 tree, 33 leaves, max depth = 9, train loss: 0.43381, val loss: 0.42083, in 0.013s 1 tree, 48 leaves, max depth = 12, train loss: 0.43083, val loss: 0.41810, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.42813, val loss: 0.41500, in 0.014s 1 tree, 48 leaves, max depth = 12, train loss: 0.42531, val loss: 0.41244, in 0.007s 1 tree, 33 leaves, max depth = 9, train loss: 0.42277, val loss: 0.40953, in 0.023s 1 tree, 48 leaves, max depth = 13, train loss: 0.42011, val loss: 0.40711, in 0.015s 1 tree, 48 leaves, max depth = 12, train loss: 0.41758, val loss: 0.40481, in 0.015s 1 tree, 33 leaves, max depth = 9, train loss: 0.41522, val loss: 0.40210, in 0.012s 1 tree, 48 leaves, max depth = 13, train loss: 0.41282, val loss: 0.39994, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.41054, val loss: 0.39788, in 0.001s 1 tree, 3 leaves, max depth = 2, train loss: 0.40843, val loss: 0.39584, in 0.016s Fit 49 trees in 1.039 s, (1913 total leaves) Time spent computing histograms: 0.307s Time spent finding best splits: 0.070s Time spent applying splits: 0.058s Time spent predicting: 0.004s Trial 70, Fold 5: Log loss = 0.41464192296993685, Average precision = 0.9462843445442741, ROC-AUC = 0.94220047191721, Elapsed Time = 1.0569494000010309 seconds
Optimization Progress: 71%|#######1 | 71/100 [14:16<06:52, 14.21s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 71, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.186 s 0.040 GB of training data: 0.006 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 14, train loss: 0.68366, val loss: 0.68339, in 0.011s 1 tree, 41 leaves, max depth = 14, train loss: 0.67456, val loss: 0.67401, in 0.012s 1 tree, 41 leaves, max depth = 14, train loss: 0.66581, val loss: 0.66499, in 0.012s 1 tree, 41 leaves, max depth = 14, train loss: 0.65740, val loss: 0.65632, in 0.011s 1 tree, 41 leaves, max depth = 14, train loss: 0.64931, val loss: 0.64798, in 0.011s 1 tree, 41 leaves, max depth = 14, train loss: 0.64153, val loss: 0.63996, in 0.011s 1 tree, 41 leaves, max depth = 17, train loss: 0.63405, val loss: 0.63223, in 0.005s 1 tree, 41 leaves, max depth = 14, train loss: 0.62685, val loss: 0.62479, in 0.015s 1 tree, 41 leaves, max depth = 13, train loss: 0.61991, val loss: 0.61762, in 0.011s 1 tree, 41 leaves, max depth = 13, train loss: 0.61324, val loss: 0.61071, in 0.011s 1 tree, 41 leaves, max depth = 13, train loss: 0.60680, val loss: 0.60405, in 0.010s 1 tree, 41 leaves, max depth = 13, train loss: 0.60060, val loss: 0.59763, in 0.010s 1 tree, 41 leaves, max depth = 17, train loss: 0.59463, val loss: 0.59144, in 0.010s 1 tree, 41 leaves, max depth = 17, train loss: 0.58887, val loss: 0.58546, in 0.012s 1 tree, 41 leaves, max depth = 17, train loss: 0.58332, val loss: 0.57970, in 0.002s 1 tree, 41 leaves, max depth = 17, train loss: 0.57796, val loss: 0.57414, in 0.020s 1 tree, 41 leaves, max depth = 16, train loss: 0.57279, val loss: 0.56876, in 0.012s 1 tree, 41 leaves, max depth = 13, train loss: 0.56781, val loss: 0.56358, in 0.011s 1 tree, 41 leaves, max depth = 17, train loss: 0.56300, val loss: 0.55858, in 0.015s 1 tree, 41 leaves, max depth = 17, train loss: 0.55836, val loss: 0.55374, in 0.010s 1 tree, 41 leaves, max depth = 8, train loss: 0.55361, val loss: 0.54929, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.54920, val loss: 0.54469, in 0.015s 1 tree, 41 leaves, max depth = 8, train loss: 0.54468, val loss: 0.54046, in 0.013s 1 tree, 41 leaves, max depth = 18, train loss: 0.54050, val loss: 0.53609, in 0.011s 1 tree, 41 leaves, max depth = 8, train loss: 0.53619, val loss: 0.53206, in 0.009s 1 tree, 41 leaves, max depth = 18, train loss: 0.53221, val loss: 0.52790, in 0.010s 1 tree, 41 leaves, max depth = 10, train loss: 0.52811, val loss: 0.52406, in 0.010s 1 tree, 41 leaves, max depth = 13, train loss: 0.52432, val loss: 0.52010, in 0.012s 1 tree, 41 leaves, max depth = 10, train loss: 0.52040, val loss: 0.51643, in 0.012s 1 tree, 41 leaves, max depth = 9, train loss: 0.51661, val loss: 0.51290, in 0.009s 1 tree, 41 leaves, max depth = 15, train loss: 0.51306, val loss: 0.50917, in 0.011s 1 tree, 41 leaves, max depth = 9, train loss: 0.50944, val loss: 0.50581, in 0.011s 1 tree, 41 leaves, max depth = 15, train loss: 0.50605, val loss: 0.50225, in 0.009s 1 tree, 41 leaves, max depth = 9, train loss: 0.50260, val loss: 0.49903, in 0.012s 1 tree, 41 leaves, max depth = 15, train loss: 0.49937, val loss: 0.49563, in 0.010s 1 tree, 41 leaves, max depth = 9, train loss: 0.49606, val loss: 0.49256, in 0.025s 1 tree, 41 leaves, max depth = 15, train loss: 0.49298, val loss: 0.48931, in 0.042s 1 tree, 41 leaves, max depth = 9, train loss: 0.48981, val loss: 0.48639, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.48675, val loss: 0.48354, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.48387, val loss: 0.48049, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.48093, val loss: 0.47778, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.47818, val loss: 0.47487, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47536, val loss: 0.47226, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.47274, val loss: 0.46948, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47003, val loss: 0.46699, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.46752, val loss: 0.46432, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.46492, val loss: 0.46194, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.46241, val loss: 0.45963, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.46003, val loss: 0.45710, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.45761, val loss: 0.45490, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.45534, val loss: 0.45247, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.45302, val loss: 0.45035, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.45085, val loss: 0.44806, in 0.020s 1 tree, 41 leaves, max depth = 10, train loss: 0.44862, val loss: 0.44605, in 0.013s 1 tree, 41 leaves, max depth = 14, train loss: 0.44652, val loss: 0.44383, in 0.013s 1 tree, 41 leaves, max depth = 10, train loss: 0.44437, val loss: 0.44189, in 0.003s 1 tree, 41 leaves, max depth = 10, train loss: 0.44229, val loss: 0.44001, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.44031, val loss: 0.43788, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43831, val loss: 0.43608, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.43639, val loss: 0.43406, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43446, val loss: 0.43233, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.43263, val loss: 0.43036, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.43078, val loss: 0.42869, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.42902, val loss: 0.42680, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42724, val loss: 0.42518, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42551, val loss: 0.42364, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.42382, val loss: 0.42185, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42216, val loss: 0.42035, in 0.043s 1 tree, 41 leaves, max depth = 11, train loss: 0.42054, val loss: 0.41861, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.41893, val loss: 0.41718, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.41739, val loss: 0.41551, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.41584, val loss: 0.41411, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.41434, val loss: 0.41253, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.41284, val loss: 0.41120, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41145, val loss: 0.40966, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.41001, val loss: 0.40838, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40867, val loss: 0.40689, in 0.016s 1 tree, 41 leaves, max depth = 16, train loss: 0.40726, val loss: 0.40540, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40597, val loss: 0.40396, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.40458, val loss: 0.40275, in 0.016s Fit 80 trees in 1.451 s, (3172 total leaves) Time spent computing histograms: 0.504s Time spent finding best splits: 0.151s Time spent applying splits: 0.113s Time spent predicting: 0.002s Trial 71, Fold 1: Log loss = 0.4071347963646409, Average precision = 0.9452412166113221, ROC-AUC = 0.9425571788235519, Elapsed Time = 1.457269799999267 seconds Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 71, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.204 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 9, train loss: 0.68378, val loss: 0.68335, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.67479, val loss: 0.67395, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.66615, val loss: 0.66490, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.65784, val loss: 0.65620, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.64986, val loss: 0.64783, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.64218, val loss: 0.63978, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.63479, val loss: 0.63202, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.62768, val loss: 0.62455, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.62083, val loss: 0.61736, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.61424, val loss: 0.61042, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.60789, val loss: 0.60373, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.60178, val loss: 0.59729, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.59588, val loss: 0.59107, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.59020, val loss: 0.58507, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.58472, val loss: 0.57928, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.57944, val loss: 0.57369, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.57435, val loss: 0.56830, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.56943, val loss: 0.56309, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.56469, val loss: 0.55807, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.56012, val loss: 0.55321, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.55543, val loss: 0.54868, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.55109, val loss: 0.54406, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.54663, val loss: 0.53973, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.54248, val loss: 0.53535, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.53822, val loss: 0.53124, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.53428, val loss: 0.52706, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.53022, val loss: 0.52315, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52646, val loss: 0.51917, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.52259, val loss: 0.51544, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.51901, val loss: 0.51165, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51531, val loss: 0.50808, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51174, val loss: 0.50465, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.50838, val loss: 0.50108, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.50496, val loss: 0.49780, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.50175, val loss: 0.49439, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49848, val loss: 0.49126, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.49542, val loss: 0.48800, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49229, val loss: 0.48500, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.48937, val loss: 0.48189, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48636, val loss: 0.47902, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48346, val loss: 0.47624, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.48070, val loss: 0.47330, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.47792, val loss: 0.47064, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.47528, val loss: 0.46782, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.47261, val loss: 0.46527, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.47009, val loss: 0.46258, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.46752, val loss: 0.46014, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.46511, val loss: 0.45756, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.46265, val loss: 0.45521, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.46027, val loss: 0.45294, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.45798, val loss: 0.45050, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.45569, val loss: 0.44831, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.45351, val loss: 0.44596, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.45130, val loss: 0.44387, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.44920, val loss: 0.44162, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.44708, val loss: 0.43961, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.44507, val loss: 0.43745, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.44303, val loss: 0.43551, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.44110, val loss: 0.43344, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43914, val loss: 0.43158, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43724, val loss: 0.42978, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.43540, val loss: 0.42781, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43357, val loss: 0.42609, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.43180, val loss: 0.42419, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.43003, val loss: 0.42252, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.42834, val loss: 0.42070, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.42664, val loss: 0.41910, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.42501, val loss: 0.41735, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.42337, val loss: 0.41580, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.42177, val loss: 0.41431, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.42022, val loss: 0.41263, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.41868, val loss: 0.41120, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.41719, val loss: 0.40959, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.41571, val loss: 0.40820, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.41427, val loss: 0.40665, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41292, val loss: 0.40525, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.41149, val loss: 0.40393, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41019, val loss: 0.40258, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.40881, val loss: 0.40129, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.40745, val loss: 0.39983, in 0.016s Fit 80 trees in 1.360 s, (3208 total leaves) Time spent computing histograms: 0.462s Time spent finding best splits: 0.115s Time spent applying splits: 0.082s Time spent predicting: 0.016s Trial 71, Fold 2: Log loss = 0.4097584221749652, Average precision = 0.9431351239372479, ROC-AUC = 0.9423233982034884, Elapsed Time = 1.3551755999997113 seconds Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 71, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 13, train loss: 0.68386, val loss: 0.68359, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.67498, val loss: 0.67438, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.66631, val loss: 0.66543, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.65797, val loss: 0.65681, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.64995, val loss: 0.64852, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.64236, val loss: 0.64064, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.63494, val loss: 0.63296, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.62779, val loss: 0.62556, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.62091, val loss: 0.61844, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.61428, val loss: 0.61157, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.60800, val loss: 0.60502, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.60184, val loss: 0.59864, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.59591, val loss: 0.59248, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.59019, val loss: 0.58655, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.58468, val loss: 0.58082, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.57945, val loss: 0.57535, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.57431, val loss: 0.57001, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.56936, val loss: 0.56486, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.56458, val loss: 0.55988, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.56004, val loss: 0.55512, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.55532, val loss: 0.55073, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.55094, val loss: 0.54616, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.54644, val loss: 0.54198, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.54234, val loss: 0.53767, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.53806, val loss: 0.53368, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.53417, val loss: 0.52958, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.53008, val loss: 0.52579, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52614, val loss: 0.52212, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.52249, val loss: 0.51827, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.51873, val loss: 0.51478, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.51523, val loss: 0.51113, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.51163, val loss: 0.50780, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.50829, val loss: 0.50432, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.50485, val loss: 0.50114, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.50163, val loss: 0.49774, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.49833, val loss: 0.49470, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49533, val loss: 0.49151, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.49217, val loss: 0.48860, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.48912, val loss: 0.48580, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48629, val loss: 0.48278, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.48337, val loss: 0.48010, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.48063, val loss: 0.47723, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.47783, val loss: 0.47467, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.47522, val loss: 0.47193, in 0.031s 1 tree, 41 leaves, max depth = 13, train loss: 0.47253, val loss: 0.46948, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.47006, val loss: 0.46683, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.46747, val loss: 0.46449, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.46497, val loss: 0.46222, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.46261, val loss: 0.45972, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.46021, val loss: 0.45754, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.45797, val loss: 0.45514, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.45566, val loss: 0.45304, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.45352, val loss: 0.45073, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.45130, val loss: 0.44873, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.44921, val loss: 0.44653, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.44708, val loss: 0.44462, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.44500, val loss: 0.44275, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.44303, val loss: 0.44065, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.44103, val loss: 0.43886, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.43913, val loss: 0.43684, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.43722, val loss: 0.43513, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.43543, val loss: 0.43318, in 0.000s 1 tree, 41 leaves, max depth = 14, train loss: 0.43358, val loss: 0.43154, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.43183, val loss: 0.42968, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.43006, val loss: 0.42810, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.42833, val loss: 0.42658, in 0.000s 1 tree, 41 leaves, max depth = 14, train loss: 0.42670, val loss: 0.42479, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.42503, val loss: 0.42330, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42343, val loss: 0.42156, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.42183, val loss: 0.42013, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.42028, val loss: 0.41848, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.41873, val loss: 0.41710, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41732, val loss: 0.41579, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.41582, val loss: 0.41446, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.41436, val loss: 0.41287, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41301, val loss: 0.41161, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.41157, val loss: 0.41034, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41026, val loss: 0.40912, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.40888, val loss: 0.40761, in 0.016s [80/80] 1 tree, 41 leaves, max depth = 12, train loss: 0.40750, val loss: 0.40640, in 0.000s Fit 80 trees in 1.283 s, (3172 total leaves) Time spent computing histograms: 0.451s Time spent finding best splits: 0.112s Time spent applying splits: 0.083s Time spent predicting: 0.047s Trial 71, Fold 3: Log loss = 0.4058957659864714, Average precision = 0.9473637724315495, ROC-AUC = 0.9462533317361866, Elapsed Time = 1.2974756000003254 seconds Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 71, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 13, train loss: 0.68375, val loss: 0.68320, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.67473, val loss: 0.67364, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.66606, val loss: 0.66444, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.65774, val loss: 0.65560, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.64973, val loss: 0.64708, in 0.000s 1 tree, 41 leaves, max depth = 13, train loss: 0.64202, val loss: 0.63888, in 0.062s 1 tree, 41 leaves, max depth = 13, train loss: 0.63461, val loss: 0.63097, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.62748, val loss: 0.62336, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.62062, val loss: 0.61602, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.61400, val loss: 0.60894, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.60763, val loss: 0.60212, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.60150, val loss: 0.59553, in 0.031s 1 tree, 41 leaves, max depth = 14, train loss: 0.59558, val loss: 0.58918, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.58988, val loss: 0.58304, in 0.000s 1 tree, 41 leaves, max depth = 14, train loss: 0.58439, val loss: 0.57712, in 0.031s 1 tree, 41 leaves, max depth = 14, train loss: 0.57909, val loss: 0.57140, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.57397, val loss: 0.56588, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.56904, val loss: 0.56054, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.56428, val loss: 0.55538, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.55969, val loss: 0.55040, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.55499, val loss: 0.54579, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.55063, val loss: 0.54104, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.54615, val loss: 0.53666, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.54201, val loss: 0.53214, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53775, val loss: 0.52796, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.53381, val loss: 0.52366, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52974, val loss: 0.51968, in 0.016s 1 tree, 41 leaves, max depth = 16, train loss: 0.52601, val loss: 0.51560, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52212, val loss: 0.51180, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.51837, val loss: 0.50814, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.51487, val loss: 0.50429, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.51129, val loss: 0.50080, in 0.000s 1 tree, 41 leaves, max depth = 17, train loss: 0.50796, val loss: 0.49713, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.50453, val loss: 0.49379, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.50135, val loss: 0.49029, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49807, val loss: 0.48709, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.49504, val loss: 0.48374, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.49189, val loss: 0.48069, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.48886, val loss: 0.47773, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.48600, val loss: 0.47456, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.48309, val loss: 0.47173, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.48036, val loss: 0.46870, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47757, val loss: 0.46599, in 0.016s 1 tree, 41 leaves, max depth = 17, train loss: 0.47496, val loss: 0.46308, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47228, val loss: 0.46048, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.46977, val loss: 0.45768, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.46720, val loss: 0.45519, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46471, val loss: 0.45277, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.46235, val loss: 0.45013, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.45996, val loss: 0.44781, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.45770, val loss: 0.44528, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.45540, val loss: 0.44305, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.45322, val loss: 0.44064, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.45101, val loss: 0.43850, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.44894, val loss: 0.43616, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.44682, val loss: 0.43410, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.44476, val loss: 0.43211, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.44278, val loss: 0.42991, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.44080, val loss: 0.42799, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.43891, val loss: 0.42585, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.43700, val loss: 0.42401, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.43519, val loss: 0.42195, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.43335, val loss: 0.42018, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.43160, val loss: 0.41822, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.42983, val loss: 0.41652, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42812, val loss: 0.41486, in 0.016s 1 tree, 41 leaves, max depth = 15, train loss: 0.42646, val loss: 0.41297, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42481, val loss: 0.41137, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.42321, val loss: 0.40957, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42162, val loss: 0.40804, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.42008, val loss: 0.40628, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.41855, val loss: 0.40481, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.41707, val loss: 0.40311, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.41559, val loss: 0.40169, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41424, val loss: 0.40024, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.41280, val loss: 0.39887, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.41140, val loss: 0.39728, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41010, val loss: 0.39588, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.40872, val loss: 0.39457, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.40747, val loss: 0.39322, in 0.016s Fit 80 trees in 1.456 s, (3172 total leaves) Time spent computing histograms: 0.502s Time spent finding best splits: 0.142s Time spent applying splits: 0.116s Time spent predicting: 0.016s Trial 71, Fold 4: Log loss = 0.40726675348936964, Average precision = 0.9476603377576511, ROC-AUC = 0.9445157531992818, Elapsed Time = 1.45156870000028 seconds Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 71, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 41 leaves, max depth = 12, train loss: 0.68366, val loss: 0.68305, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.67454, val loss: 0.67334, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.66579, val loss: 0.66400, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.65737, val loss: 0.65502, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.64928, val loss: 0.64637, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.64150, val loss: 0.63804, in 0.000s 1 tree, 41 leaves, max depth = 14, train loss: 0.63401, val loss: 0.63002, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.62681, val loss: 0.62229, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.61987, val loss: 0.61484, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.61319, val loss: 0.60765, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.60675, val loss: 0.60072, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.60059, val loss: 0.59405, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.59461, val loss: 0.58760, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.58884, val loss: 0.58137, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.58328, val loss: 0.57535, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.57795, val loss: 0.56955, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.57278, val loss: 0.56394, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.56778, val loss: 0.55851, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.56297, val loss: 0.55327, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.55834, val loss: 0.54821, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.55385, val loss: 0.54331, in 0.000s 1 tree, 41 leaves, max depth = 9, train loss: 0.54930, val loss: 0.53893, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.54506, val loss: 0.53427, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.54072, val loss: 0.53011, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.53668, val loss: 0.52567, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.53254, val loss: 0.52171, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.52871, val loss: 0.51748, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.52476, val loss: 0.51370, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.52094, val loss: 0.51005, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.51734, val loss: 0.50608, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.51370, val loss: 0.50260, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.51026, val loss: 0.49881, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.50678, val loss: 0.49549, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.50351, val loss: 0.49187, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.50018, val loss: 0.48870, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.49705, val loss: 0.48524, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.49387, val loss: 0.48221, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.49089, val loss: 0.47890, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.48783, val loss: 0.47602, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.48488, val loss: 0.47323, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.48207, val loss: 0.47010, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.47924, val loss: 0.46743, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.47655, val loss: 0.46443, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.47383, val loss: 0.46188, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.47126, val loss: 0.45900, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46865, val loss: 0.45656, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.46619, val loss: 0.45380, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46369, val loss: 0.45146, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.46127, val loss: 0.44920, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.45894, val loss: 0.44661, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.45662, val loss: 0.44443, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.45438, val loss: 0.44192, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.45215, val loss: 0.43983, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.45001, val loss: 0.43741, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.44786, val loss: 0.43541, in 0.006s 1 tree, 41 leaves, max depth = 12, train loss: 0.44581, val loss: 0.43312, in 0.011s 1 tree, 41 leaves, max depth = 10, train loss: 0.44375, val loss: 0.43120, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.44178, val loss: 0.42898, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43979, val loss: 0.42713, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43786, val loss: 0.42535, in 0.000s 1 tree, 41 leaves, max depth = 11, train loss: 0.43599, val loss: 0.42322, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43413, val loss: 0.42152, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.43233, val loss: 0.41948, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43055, val loss: 0.41784, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.42882, val loss: 0.41588, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.42710, val loss: 0.41430, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.42544, val loss: 0.41241, in 0.016s 1 tree, 41 leaves, max depth = 8, train loss: 0.42378, val loss: 0.41089, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.42219, val loss: 0.40907, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.42058, val loss: 0.40761, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.41903, val loss: 0.40621, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.41751, val loss: 0.40446, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.41601, val loss: 0.40311, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.41455, val loss: 0.40143, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.41310, val loss: 0.40012, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41175, val loss: 0.39882, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.41035, val loss: 0.39721, in 0.000s 1 tree, 41 leaves, max depth = 10, train loss: 0.40896, val loss: 0.39596, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40766, val loss: 0.39472, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.40631, val loss: 0.39350, in 0.000s Fit 80 trees in 1.252 s, (3208 total leaves) Time spent computing histograms: 0.449s Time spent finding best splits: 0.105s Time spent applying splits: 0.077s Time spent predicting: 0.016s Trial 71, Fold 5: Log loss = 0.41181279095167456, Average precision = 0.9466656914645775, ROC-AUC = 0.9421432473106294, Elapsed Time = 1.260043800000858 seconds
Optimization Progress: 72%|#######2 | 72/100 [14:31<06:44, 14.46s/it]
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 72, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.174 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 8, train loss: 0.67697, val loss: 0.67687, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.65917, val loss: 0.65903, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.64329, val loss: 0.64282, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.62763, val loss: 0.62691, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.61362, val loss: 0.61266, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.60063, val loss: 0.59951, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.58836, val loss: 0.58702, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.57685, val loss: 0.57542, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.56646, val loss: 0.56492, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55606, val loss: 0.55439, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.54542, val loss: 0.54376, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.53592, val loss: 0.53421, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.52668, val loss: 0.52488, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.51739, val loss: 0.51547, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.50938, val loss: 0.50740, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.50126, val loss: 0.49913, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.49289, val loss: 0.49065, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.48516, val loss: 0.48289, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.47817, val loss: 0.47575, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.47160, val loss: 0.46908, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.46499, val loss: 0.46230, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.45913, val loss: 0.45651, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.45384, val loss: 0.45113, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.44845, val loss: 0.44584, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44262, val loss: 0.44005, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.43719, val loss: 0.43446, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.43284, val loss: 0.43001, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.42792, val loss: 0.42508, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.42329, val loss: 0.42046, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.41898, val loss: 0.41610, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.41219, val loss: 0.40948, in 0.016s 1 tree, 19 leaves, max depth = 8, train loss: 0.40554, val loss: 0.40305, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.40193, val loss: 0.39945, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39788, val loss: 0.39530, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.39181, val loss: 0.38928, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.38608, val loss: 0.38364, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38279, val loss: 0.38022, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.38020, val loss: 0.37756, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37624, val loss: 0.37367, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.37110, val loss: 0.36872, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.36653, val loss: 0.36431, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.36350, val loss: 0.36140, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.35978, val loss: 0.35774, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.35539, val loss: 0.35340, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.35191, val loss: 0.35002, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.34756, val loss: 0.34595, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.34337, val loss: 0.34205, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.33970, val loss: 0.33845, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33604, val loss: 0.33497, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33410, val loss: 0.33311, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.33240, val loss: 0.33134, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33050, val loss: 0.32953, in 0.016s 1 tree, 31 leaves, max depth = 6, train loss: 0.32764, val loss: 0.32686, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.32615, val loss: 0.32539, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.32309, val loss: 0.32252, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.32040, val loss: 0.31991, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.31752, val loss: 0.31711, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.31494, val loss: 0.31461, in 0.016s 1 tree, 14 leaves, max depth = 4, train loss: 0.31222, val loss: 0.31195, in 0.000s 1 tree, 31 leaves, max depth = 7, train loss: 0.30977, val loss: 0.30961, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.30706, val loss: 0.30715, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.30468, val loss: 0.30474, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.30229, val loss: 0.30265, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30077, val loss: 0.30114, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.29940, val loss: 0.29982, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29807, val loss: 0.29855, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29615, val loss: 0.29673, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.29389, val loss: 0.29459, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29276, val loss: 0.29356, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29084, val loss: 0.29178, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28918, val loss: 0.29010, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.28762, val loss: 0.28855, in 0.016s Fit 72 trees in 1.518 s, (2004 total leaves) Time spent computing histograms: 0.493s Time spent finding best splits: 0.063s Time spent applying splits: 0.048s Time spent predicting: 0.000s Trial 72, Fold 1: Log loss = 0.29415414771195725, Average precision = 0.961117044484153, ROC-AUC = 0.955106737189506, Elapsed Time = 1.520083399998839 seconds Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 72, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 12, train loss: 0.67712, val loss: 0.67641, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.66005, val loss: 0.65885, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.64408, val loss: 0.64277, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.62828, val loss: 0.62687, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.61428, val loss: 0.61290, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.60131, val loss: 0.59976, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.58898, val loss: 0.58732, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.57744, val loss: 0.57545, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.56386, val loss: 0.56174, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55307, val loss: 0.55081, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.54280, val loss: 0.54049, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.53334, val loss: 0.53082, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.52391, val loss: 0.52131, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.51428, val loss: 0.51165, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50609, val loss: 0.50330, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.49819, val loss: 0.49535, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.49135, val loss: 0.48834, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.48198, val loss: 0.47897, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.47499, val loss: 0.47195, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.46864, val loss: 0.46550, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.46255, val loss: 0.45939, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.45669, val loss: 0.45352, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.45090, val loss: 0.44779, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.44490, val loss: 0.44177, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.43918, val loss: 0.43596, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.43335, val loss: 0.43019, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42869, val loss: 0.42539, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42351, val loss: 0.42020, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.41865, val loss: 0.41531, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.41473, val loss: 0.41117, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.40794, val loss: 0.40451, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.40284, val loss: 0.39943, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.39809, val loss: 0.39475, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39402, val loss: 0.39078, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38959, val loss: 0.38634, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.38391, val loss: 0.38077, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38071, val loss: 0.37760, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.37797, val loss: 0.37484, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.37242, val loss: 0.36973, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.36747, val loss: 0.36485, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.36423, val loss: 0.36155, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.36126, val loss: 0.35872, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.35811, val loss: 0.35565, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35325, val loss: 0.35115, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.34984, val loss: 0.34780, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.34553, val loss: 0.34368, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.34243, val loss: 0.34065, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34025, val loss: 0.33850, in 0.016s 1 tree, 16 leaves, max depth = 9, train loss: 0.33640, val loss: 0.33477, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33429, val loss: 0.33270, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33209, val loss: 0.33059, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.33039, val loss: 0.32883, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.32773, val loss: 0.32623, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.32618, val loss: 0.32468, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.32291, val loss: 0.32154, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.32128, val loss: 0.31998, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.31821, val loss: 0.31701, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.31689, val loss: 0.31573, in 0.016s 1 tree, 19 leaves, max depth = 10, train loss: 0.31383, val loss: 0.31284, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.31127, val loss: 0.31055, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30995, val loss: 0.30924, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.30830, val loss: 0.30766, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30628, val loss: 0.30561, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.30413, val loss: 0.30355, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30095, val loss: 0.30056, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.29854, val loss: 0.29839, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.29608, val loss: 0.29601, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.29377, val loss: 0.29395, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.29159, val loss: 0.29202, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.29052, val loss: 0.29105, in 0.016s 1 tree, 14 leaves, max depth = 9, train loss: 0.28840, val loss: 0.28899, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.28733, val loss: 0.28799, in 0.016s Fit 72 trees in 1.565 s, (2024 total leaves) Time spent computing histograms: 0.514s Time spent finding best splits: 0.065s Time spent applying splits: 0.048s Time spent predicting: 0.000s Trial 72, Fold 2: Log loss = 0.2910875895115242, Average precision = 0.9600590605027294, ROC-AUC = 0.9561806266030257, Elapsed Time = 1.5716917000008834 seconds Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 72, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.141 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 11, train loss: 0.67712, val loss: 0.67691, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.65932, val loss: 0.65910, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.64390, val loss: 0.64366, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.62823, val loss: 0.62796, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.61429, val loss: 0.61401, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.60112, val loss: 0.60090, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.59001, val loss: 0.58937, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.57862, val loss: 0.57777, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.56516, val loss: 0.56479, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.55414, val loss: 0.55369, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.54410, val loss: 0.54346, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.53448, val loss: 0.53385, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.52503, val loss: 0.52421, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.51582, val loss: 0.51505, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50822, val loss: 0.50743, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.50017, val loss: 0.49955, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.49241, val loss: 0.49172, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.48508, val loss: 0.48451, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.47734, val loss: 0.47684, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.47086, val loss: 0.47041, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.46221, val loss: 0.46222, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.45627, val loss: 0.45636, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.44987, val loss: 0.45001, in 0.031s 1 tree, 9 leaves, max depth = 5, train loss: 0.44457, val loss: 0.44475, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.43940, val loss: 0.43959, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.43239, val loss: 0.43305, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.42637, val loss: 0.42688, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.42126, val loss: 0.42181, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.41720, val loss: 0.41780, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41356, val loss: 0.41415, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.40887, val loss: 0.40949, in 0.000s 1 tree, 31 leaves, max depth = 7, train loss: 0.40381, val loss: 0.40428, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.39948, val loss: 0.40012, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.39361, val loss: 0.39479, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38899, val loss: 0.38996, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38554, val loss: 0.38652, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38128, val loss: 0.38204, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.37775, val loss: 0.37861, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.37474, val loss: 0.37571, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.37147, val loss: 0.37236, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36830, val loss: 0.36934, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36503, val loss: 0.36616, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.36256, val loss: 0.36362, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35975, val loss: 0.36095, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.35525, val loss: 0.35684, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.35084, val loss: 0.35292, in 0.016s 1 tree, 30 leaves, max depth = 7, train loss: 0.34676, val loss: 0.34927, in 0.016s 1 tree, 22 leaves, max depth = 11, train loss: 0.34244, val loss: 0.34561, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.33939, val loss: 0.34244, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33726, val loss: 0.34041, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33445, val loss: 0.33750, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.33093, val loss: 0.33432, in 0.000s 1 tree, 14 leaves, max depth = 5, train loss: 0.32757, val loss: 0.33127, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.32594, val loss: 0.32977, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.32387, val loss: 0.32789, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.32132, val loss: 0.32524, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.31816, val loss: 0.32245, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31573, val loss: 0.31988, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.31407, val loss: 0.31835, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.31210, val loss: 0.31619, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.30956, val loss: 0.31378, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.30811, val loss: 0.31242, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.30674, val loss: 0.31142, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.30570, val loss: 0.31046, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30407, val loss: 0.30906, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.30206, val loss: 0.30700, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.30100, val loss: 0.30585, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29924, val loss: 0.30419, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.29655, val loss: 0.30200, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.29526, val loss: 0.30082, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.29414, val loss: 0.29998, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.29122, val loss: 0.29764, in 0.016s Fit 72 trees in 1.438 s, (1999 total leaves) Time spent computing histograms: 0.464s Time spent finding best splits: 0.057s Time spent applying splits: 0.042s Time spent predicting: 0.016s Trial 72, Fold 3: Log loss = 0.29083142577126364, Average precision = 0.9623127350153663, ROC-AUC = 0.9575925963825833, Elapsed Time = 1.439354000000094 seconds Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 72, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 12, train loss: 0.67709, val loss: 0.67659, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.65937, val loss: 0.65814, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.64409, val loss: 0.64209, in 0.028s 1 tree, 31 leaves, max depth = 11, train loss: 0.62851, val loss: 0.62581, in 0.004s 1 tree, 31 leaves, max depth = 7, train loss: 0.61465, val loss: 0.61120, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.60179, val loss: 0.59760, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.58943, val loss: 0.58453, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.57794, val loss: 0.57272, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.56433, val loss: 0.55877, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.55333, val loss: 0.54733, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.54345, val loss: 0.53691, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.53302, val loss: 0.52598, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.52488, val loss: 0.51730, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.51605, val loss: 0.50820, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.50749, val loss: 0.49922, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.49906, val loss: 0.49025, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.49157, val loss: 0.48232, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.48484, val loss: 0.47526, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.47795, val loss: 0.46807, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47195, val loss: 0.46160, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.46522, val loss: 0.45448, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.45704, val loss: 0.44617, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.45148, val loss: 0.44022, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.44646, val loss: 0.43496, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44141, val loss: 0.42975, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.43599, val loss: 0.42389, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.43032, val loss: 0.41792, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.42332, val loss: 0.41068, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.41902, val loss: 0.40591, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.41363, val loss: 0.40045, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.40920, val loss: 0.39581, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.40423, val loss: 0.39076, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.40008, val loss: 0.38631, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.39611, val loss: 0.38200, in 0.016s 1 tree, 10 leaves, max depth = 4, train loss: 0.39013, val loss: 0.37592, in 0.016s 1 tree, 21 leaves, max depth = 12, train loss: 0.38560, val loss: 0.37141, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38133, val loss: 0.36717, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37775, val loss: 0.36328, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37464, val loss: 0.36004, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37136, val loss: 0.35652, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.36838, val loss: 0.35326, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.36351, val loss: 0.34834, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.36105, val loss: 0.34581, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35835, val loss: 0.34290, in 0.031s 1 tree, 12 leaves, max depth = 5, train loss: 0.35445, val loss: 0.33864, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.35028, val loss: 0.33441, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.34754, val loss: 0.33147, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34515, val loss: 0.32891, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34211, val loss: 0.32588, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33955, val loss: 0.32326, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33768, val loss: 0.32141, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.33495, val loss: 0.31868, in 0.016s 1 tree, 14 leaves, max depth = 8, train loss: 0.33159, val loss: 0.31529, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.32815, val loss: 0.31178, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.32629, val loss: 0.30975, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.32417, val loss: 0.30756, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.32101, val loss: 0.30431, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31858, val loss: 0.30186, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.31529, val loss: 0.29868, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.31359, val loss: 0.29691, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31037, val loss: 0.29386, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.30728, val loss: 0.29096, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.30571, val loss: 0.28932, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.30427, val loss: 0.28771, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30231, val loss: 0.28576, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30096, val loss: 0.28442, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.29857, val loss: 0.28195, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29675, val loss: 0.28018, in 0.016s 1 tree, 15 leaves, max depth = 6, train loss: 0.29412, val loss: 0.27754, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29292, val loss: 0.27634, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.29158, val loss: 0.27498, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.28908, val loss: 0.27262, in 0.016s Fit 72 trees in 1.471 s, (1976 total leaves) Time spent computing histograms: 0.477s Time spent finding best splits: 0.058s Time spent applying splits: 0.043s Time spent predicting: 0.000s Trial 72, Fold 4: Log loss = 0.29002303537082896, Average precision = 0.961783253739625, ROC-AUC = 0.9564205151040437, Elapsed Time = 1.4727536999998847 seconds Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 72, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 9, train loss: 0.67688, val loss: 0.67615, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.65903, val loss: 0.65764, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.64330, val loss: 0.64111, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.62756, val loss: 0.62463, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.61339, val loss: 0.60985, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.60040, val loss: 0.59614, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.58793, val loss: 0.58305, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.57600, val loss: 0.57038, in 0.000s 1 tree, 12 leaves, max depth = 8, train loss: 0.56271, val loss: 0.55670, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55226, val loss: 0.54563, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.54236, val loss: 0.53534, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.53167, val loss: 0.52422, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.52345, val loss: 0.51559, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.51453, val loss: 0.50638, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.50615, val loss: 0.49729, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.49755, val loss: 0.48826, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.49016, val loss: 0.48036, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.48341, val loss: 0.47333, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.47628, val loss: 0.46573, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.46975, val loss: 0.45870, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.46301, val loss: 0.45159, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.45717, val loss: 0.44546, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.45215, val loss: 0.44003, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44590, val loss: 0.43360, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.44010, val loss: 0.42758, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.43443, val loss: 0.42177, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42927, val loss: 0.41637, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.42440, val loss: 0.41135, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.41972, val loss: 0.40648, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41568, val loss: 0.40221, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41051, val loss: 0.39697, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.40393, val loss: 0.39034, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39755, val loss: 0.38400, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.39356, val loss: 0.37991, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38906, val loss: 0.37536, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.38553, val loss: 0.37194, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.38139, val loss: 0.36773, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.37885, val loss: 0.36498, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37322, val loss: 0.35978, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.36834, val loss: 0.35483, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.36464, val loss: 0.35110, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.36168, val loss: 0.34809, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.35860, val loss: 0.34504, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.35401, val loss: 0.34044, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.35067, val loss: 0.33723, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.34625, val loss: 0.33289, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34357, val loss: 0.33028, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.34144, val loss: 0.32820, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33947, val loss: 0.32617, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.33723, val loss: 0.32400, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33527, val loss: 0.32201, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.33313, val loss: 0.31990, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.33157, val loss: 0.31812, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33008, val loss: 0.31666, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.32637, val loss: 0.31306, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.32485, val loss: 0.31142, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.32156, val loss: 0.30811, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31908, val loss: 0.30572, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.31592, val loss: 0.30259, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.31304, val loss: 0.29981, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.31167, val loss: 0.29847, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.30940, val loss: 0.29626, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.30764, val loss: 0.29474, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.30617, val loss: 0.29335, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.30450, val loss: 0.29182, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.30324, val loss: 0.29062, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30046, val loss: 0.28809, in 0.016s 1 tree, 16 leaves, max depth = 6, train loss: 0.29801, val loss: 0.28582, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.29633, val loss: 0.28421, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.29520, val loss: 0.28311, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.29394, val loss: 0.28197, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.29297, val loss: 0.28096, in 0.016s Fit 72 trees in 1.455 s, (2002 total leaves) Time spent computing histograms: 0.480s Time spent finding best splits: 0.057s Time spent applying splits: 0.042s Time spent predicting: 0.000s Trial 72, Fold 5: Log loss = 0.3028119526249657, Average precision = 0.9591369312082461, ROC-AUC = 0.9534296490347993, Elapsed Time = 1.4715311999989353 seconds
Optimization Progress: 73%|#######3 | 73/100 [14:46<06:34, 14.60s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 73, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 50 leaves, max depth = 10, train loss: 0.68290, val loss: 0.68280, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.67228, val loss: 0.67213, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.66279, val loss: 0.66251, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.65306, val loss: 0.65280, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.64407, val loss: 0.64377, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.63495, val loss: 0.63466, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.62648, val loss: 0.62613, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.61796, val loss: 0.61761, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.61008, val loss: 0.60968, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.60261, val loss: 0.60209, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.59486, val loss: 0.59435, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.58763, val loss: 0.58711, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.58076, val loss: 0.58021, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.57382, val loss: 0.57311, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.56699, val loss: 0.56628, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.56066, val loss: 0.55988, in 0.031s 1 tree, 83 leaves, max depth = 13, train loss: 0.55430, val loss: 0.55345, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.54815, val loss: 0.54724, in 0.016s 1 tree, 80 leaves, max depth = 11, train loss: 0.54213, val loss: 0.54109, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.53620, val loss: 0.53513, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.53070, val loss: 0.52957, in 0.016s 1 tree, 76 leaves, max depth = 10, train loss: 0.52550, val loss: 0.52437, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.52000, val loss: 0.51885, in 0.016s 1 tree, 76 leaves, max depth = 10, train loss: 0.51510, val loss: 0.51395, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.51024, val loss: 0.50906, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.50552, val loss: 0.50431, in 0.016s 1 tree, 81 leaves, max depth = 11, train loss: 0.50074, val loss: 0.49943, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.49628, val loss: 0.49495, in 0.016s 1 tree, 76 leaves, max depth = 10, train loss: 0.49202, val loss: 0.49070, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.48747, val loss: 0.48613, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.48305, val loss: 0.48169, in 0.031s 1 tree, 80 leaves, max depth = 12, train loss: 0.47878, val loss: 0.47737, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.47490, val loss: 0.47347, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.47112, val loss: 0.46971, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.46727, val loss: 0.46576, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.46339, val loss: 0.46188, in 0.031s 1 tree, 79 leaves, max depth = 12, train loss: 0.45967, val loss: 0.45813, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.45614, val loss: 0.45453, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.45271, val loss: 0.45103, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.44931, val loss: 0.44757, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.44616, val loss: 0.44441, in 0.031s 1 tree, 78 leaves, max depth = 12, train loss: 0.44286, val loss: 0.44111, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.43971, val loss: 0.43788, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.43665, val loss: 0.43475, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.43199, val loss: 0.43026, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.42755, val loss: 0.42594, in 0.031s 1 tree, 77 leaves, max depth = 10, train loss: 0.42490, val loss: 0.42332, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.42215, val loss: 0.42050, in 0.016s 1 tree, 61 leaves, max depth = 10, train loss: 0.41797, val loss: 0.41643, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.41536, val loss: 0.41372, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.41130, val loss: 0.40985, in 0.016s 1 tree, 81 leaves, max depth = 11, train loss: 0.40879, val loss: 0.40729, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.40622, val loss: 0.40472, in 0.016s 1 tree, 77 leaves, max depth = 10, train loss: 0.40397, val loss: 0.40252, in 0.016s 1 tree, 57 leaves, max depth = 14, train loss: 0.40018, val loss: 0.39892, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.39650, val loss: 0.39543, in 0.031s 1 tree, 58 leaves, max depth = 11, train loss: 0.39292, val loss: 0.39205, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.38946, val loss: 0.38877, in 0.016s 1 tree, 77 leaves, max depth = 11, train loss: 0.38718, val loss: 0.38650, in 0.016s Fit 59 trees in 1.314 s, (4358 total leaves) Time spent computing histograms: 0.388s Time spent finding best splits: 0.071s Time spent applying splits: 0.075s Time spent predicting: 0.000s Trial 73, Fold 1: Log loss = 0.3897518471591272, Average precision = 0.9534496063506044, ROC-AUC = 0.9484036620017876, Elapsed Time = 1.3255394999996497 seconds Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 73, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 51 leaves, max depth = 10, train loss: 0.68289, val loss: 0.68265, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.67227, val loss: 0.67181, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.66281, val loss: 0.66226, in 0.031s 1 tree, 75 leaves, max depth = 12, train loss: 0.65294, val loss: 0.65220, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.64388, val loss: 0.64302, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.63463, val loss: 0.63359, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.62617, val loss: 0.62503, in 0.031s 1 tree, 80 leaves, max depth = 12, train loss: 0.61761, val loss: 0.61623, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.60979, val loss: 0.60827, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.60233, val loss: 0.60072, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.59451, val loss: 0.59275, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.58718, val loss: 0.58529, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.58032, val loss: 0.57831, in 0.031s 1 tree, 58 leaves, max depth = 13, train loss: 0.57336, val loss: 0.57121, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.56641, val loss: 0.56412, in 0.031s 1 tree, 80 leaves, max depth = 14, train loss: 0.56016, val loss: 0.55776, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.55370, val loss: 0.55123, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.54745, val loss: 0.54490, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.54144, val loss: 0.53877, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.53556, val loss: 0.53283, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.53019, val loss: 0.52735, in 0.031s 1 tree, 75 leaves, max depth = 13, train loss: 0.52459, val loss: 0.52167, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.51921, val loss: 0.51623, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.51434, val loss: 0.51120, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.50943, val loss: 0.50625, in 0.031s 1 tree, 77 leaves, max depth = 13, train loss: 0.50466, val loss: 0.50146, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.49988, val loss: 0.49662, in 0.031s 1 tree, 80 leaves, max depth = 14, train loss: 0.49549, val loss: 0.49215, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.49126, val loss: 0.48778, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.48678, val loss: 0.48318, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.48238, val loss: 0.47871, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.47815, val loss: 0.47437, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.47432, val loss: 0.47046, in 0.031s 1 tree, 77 leaves, max depth = 14, train loss: 0.47057, val loss: 0.46663, in 0.016s 1 tree, 83 leaves, max depth = 11, train loss: 0.46678, val loss: 0.46278, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.46292, val loss: 0.45882, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.45924, val loss: 0.45504, in 0.031s 1 tree, 58 leaves, max depth = 12, train loss: 0.45572, val loss: 0.45143, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.45229, val loss: 0.44791, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.44897, val loss: 0.44455, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.44586, val loss: 0.44147, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.44268, val loss: 0.43830, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.43964, val loss: 0.43516, in 0.031s 1 tree, 79 leaves, max depth = 12, train loss: 0.43651, val loss: 0.43195, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.43346, val loss: 0.42883, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.42896, val loss: 0.42444, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.42618, val loss: 0.42161, in 0.016s 1 tree, 83 leaves, max depth = 11, train loss: 0.42343, val loss: 0.41884, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.41913, val loss: 0.41462, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.41654, val loss: 0.41195, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.41243, val loss: 0.40790, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.41000, val loss: 0.40541, in 0.031s 1 tree, 80 leaves, max depth = 13, train loss: 0.40758, val loss: 0.40302, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.40517, val loss: 0.40054, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.40133, val loss: 0.39677, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.39756, val loss: 0.39308, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.39395, val loss: 0.38953, in 0.031s 1 tree, 58 leaves, max depth = 12, train loss: 0.39046, val loss: 0.38610, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.38825, val loss: 0.38384, in 0.016s Fit 59 trees in 1.548 s, (4366 total leaves) Time spent computing histograms: 0.469s Time spent finding best splits: 0.086s Time spent applying splits: 0.089s Time spent predicting: 0.000s Trial 73, Fold 2: Log loss = 0.39035312161219826, Average precision = 0.9510917369187883, ROC-AUC = 0.9489883885629515, Elapsed Time = 1.5692022999992332 seconds Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 73, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 54 leaves, max depth = 11, train loss: 0.68288, val loss: 0.68273, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.67249, val loss: 0.67227, in 0.016s 1 tree, 80 leaves, max depth = 12, train loss: 0.66301, val loss: 0.66264, in 0.031s 1 tree, 77 leaves, max depth = 13, train loss: 0.65325, val loss: 0.65285, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.64416, val loss: 0.64374, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.63505, val loss: 0.63459, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.62650, val loss: 0.62612, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.61798, val loss: 0.61757, in 0.031s 1 tree, 79 leaves, max depth = 12, train loss: 0.61010, val loss: 0.60971, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.60263, val loss: 0.60226, in 0.031s 1 tree, 78 leaves, max depth = 13, train loss: 0.59489, val loss: 0.59449, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.58766, val loss: 0.58718, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.58074, val loss: 0.58028, in 0.031s 1 tree, 58 leaves, max depth = 12, train loss: 0.57378, val loss: 0.57327, in 0.031s 1 tree, 80 leaves, max depth = 12, train loss: 0.56684, val loss: 0.56634, in 0.031s 1 tree, 80 leaves, max depth = 14, train loss: 0.56054, val loss: 0.56007, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.55409, val loss: 0.55364, in 0.031s 1 tree, 83 leaves, max depth = 12, train loss: 0.54783, val loss: 0.54741, in 0.031s 1 tree, 82 leaves, max depth = 12, train loss: 0.54181, val loss: 0.54139, in 0.031s 1 tree, 78 leaves, max depth = 13, train loss: 0.53588, val loss: 0.53546, in 0.032s 1 tree, 77 leaves, max depth = 13, train loss: 0.53035, val loss: 0.53000, in 0.031s 1 tree, 77 leaves, max depth = 14, train loss: 0.52520, val loss: 0.52487, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.51973, val loss: 0.51941, in 0.031s 1 tree, 77 leaves, max depth = 14, train loss: 0.51488, val loss: 0.51457, in 0.031s 1 tree, 79 leaves, max depth = 14, train loss: 0.51008, val loss: 0.50981, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.50529, val loss: 0.50508, in 0.031s 1 tree, 82 leaves, max depth = 10, train loss: 0.50049, val loss: 0.50031, in 0.031s 1 tree, 80 leaves, max depth = 14, train loss: 0.49606, val loss: 0.49591, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.49185, val loss: 0.49171, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.48735, val loss: 0.48722, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.48298, val loss: 0.48286, in 0.031s 1 tree, 75 leaves, max depth = 13, train loss: 0.47873, val loss: 0.47862, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.47490, val loss: 0.47484, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.47116, val loss: 0.47112, in 0.031s 1 tree, 81 leaves, max depth = 10, train loss: 0.46730, val loss: 0.46729, in 0.031s 1 tree, 77 leaves, max depth = 14, train loss: 0.46343, val loss: 0.46345, in 0.031s 1 tree, 77 leaves, max depth = 13, train loss: 0.45971, val loss: 0.45973, in 0.031s 1 tree, 55 leaves, max depth = 13, train loss: 0.45617, val loss: 0.45620, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.45275, val loss: 0.45278, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.44934, val loss: 0.44942, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.44629, val loss: 0.44641, in 0.031s 1 tree, 83 leaves, max depth = 10, train loss: 0.44310, val loss: 0.44325, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.44001, val loss: 0.44016, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.43526, val loss: 0.43578, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.43222, val loss: 0.43276, in 0.016s 1 tree, 84 leaves, max depth = 12, train loss: 0.42930, val loss: 0.42988, in 0.031s 1 tree, 57 leaves, max depth = 13, train loss: 0.42483, val loss: 0.42577, in 0.031s 1 tree, 58 leaves, max depth = 10, train loss: 0.42212, val loss: 0.42307, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.41783, val loss: 0.41914, in 0.031s 1 tree, 84 leaves, max depth = 10, train loss: 0.41521, val loss: 0.41655, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.41254, val loss: 0.41391, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.40994, val loss: 0.41134, in 0.031s 1 tree, 83 leaves, max depth = 10, train loss: 0.40751, val loss: 0.40896, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.40530, val loss: 0.40678, in 0.031s 1 tree, 83 leaves, max depth = 13, train loss: 0.40307, val loss: 0.40463, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.40082, val loss: 0.40241, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.39698, val loss: 0.39893, in 0.031s 1 tree, 58 leaves, max depth = 13, train loss: 0.39330, val loss: 0.39556, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.39107, val loss: 0.39337, in 0.031s Fit 59 trees in 1.924 s, (4403 total leaves) Time spent computing histograms: 0.573s Time spent finding best splits: 0.113s Time spent applying splits: 0.115s Time spent predicting: 0.000s Trial 73, Fold 3: Log loss = 0.3888056946075886, Average precision = 0.9533842900230023, ROC-AUC = 0.9508368767516446, Elapsed Time = 1.926594299999124 seconds Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 73, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.187 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 57 leaves, max depth = 12, train loss: 0.68285, val loss: 0.68242, in 0.016s 1 tree, 73 leaves, max depth = 11, train loss: 0.67245, val loss: 0.67155, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.66300, val loss: 0.66154, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.65329, val loss: 0.65139, in 0.032s 1 tree, 56 leaves, max depth = 14, train loss: 0.64452, val loss: 0.64220, in 0.016s 1 tree, 74 leaves, max depth = 11, train loss: 0.63547, val loss: 0.63273, in 0.031s 1 tree, 75 leaves, max depth = 11, train loss: 0.62672, val loss: 0.62358, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.61845, val loss: 0.61489, in 0.031s 1 tree, 76 leaves, max depth = 16, train loss: 0.61065, val loss: 0.60675, in 0.016s 1 tree, 73 leaves, max depth = 11, train loss: 0.60273, val loss: 0.59845, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.59550, val loss: 0.59081, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.58842, val loss: 0.58342, in 0.031s 1 tree, 58 leaves, max depth = 12, train loss: 0.58140, val loss: 0.57597, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.57426, val loss: 0.56851, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.56756, val loss: 0.56143, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.56130, val loss: 0.55482, in 0.031s 1 tree, 76 leaves, max depth = 12, train loss: 0.55479, val loss: 0.54802, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.54887, val loss: 0.54181, in 0.031s 1 tree, 56 leaves, max depth = 11, train loss: 0.54308, val loss: 0.53560, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.53743, val loss: 0.52957, in 0.031s 1 tree, 76 leaves, max depth = 12, train loss: 0.53170, val loss: 0.52352, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.52625, val loss: 0.51771, in 0.031s 1 tree, 76 leaves, max depth = 12, train loss: 0.52096, val loss: 0.51208, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.51576, val loss: 0.50658, in 0.031s 1 tree, 84 leaves, max depth = 11, train loss: 0.51079, val loss: 0.50132, in 0.016s 1 tree, 84 leaves, max depth = 11, train loss: 0.50589, val loss: 0.49613, in 0.031s 1 tree, 78 leaves, max depth = 11, train loss: 0.50104, val loss: 0.49104, in 0.016s 1 tree, 74 leaves, max depth = 11, train loss: 0.49639, val loss: 0.48612, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.49210, val loss: 0.48169, in 0.031s 1 tree, 39 leaves, max depth = 10, train loss: 0.48646, val loss: 0.47593, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.48215, val loss: 0.47136, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.47805, val loss: 0.46696, in 0.031s 1 tree, 59 leaves, max depth = 11, train loss: 0.47406, val loss: 0.46274, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.47040, val loss: 0.45892, in 0.031s 1 tree, 78 leaves, max depth = 11, train loss: 0.46648, val loss: 0.45479, in 0.016s 1 tree, 74 leaves, max depth = 15, train loss: 0.46291, val loss: 0.45104, in 0.031s 1 tree, 57 leaves, max depth = 11, train loss: 0.45781, val loss: 0.44585, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.45430, val loss: 0.44212, in 0.031s 1 tree, 61 leaves, max depth = 11, train loss: 0.45090, val loss: 0.43850, in 0.016s 1 tree, 85 leaves, max depth = 11, train loss: 0.44754, val loss: 0.43491, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.44449, val loss: 0.43166, in 0.031s 1 tree, 85 leaves, max depth = 11, train loss: 0.44130, val loss: 0.42826, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.43824, val loss: 0.42501, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.43513, val loss: 0.42172, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.43210, val loss: 0.41852, in 0.031s 1 tree, 82 leaves, max depth = 12, train loss: 0.42924, val loss: 0.41545, in 0.031s 1 tree, 84 leaves, max depth = 11, train loss: 0.42649, val loss: 0.41248, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.42368, val loss: 0.40952, in 0.031s 1 tree, 73 leaves, max depth = 11, train loss: 0.42103, val loss: 0.40675, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.41695, val loss: 0.40260, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.41289, val loss: 0.39850, in 0.016s 1 tree, 60 leaves, max depth = 11, train loss: 0.41044, val loss: 0.39589, in 0.031s 1 tree, 82 leaves, max depth = 11, train loss: 0.40805, val loss: 0.39330, in 0.031s 1 tree, 76 leaves, max depth = 10, train loss: 0.40583, val loss: 0.39100, in 0.031s 1 tree, 57 leaves, max depth = 10, train loss: 0.40205, val loss: 0.38719, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.39840, val loss: 0.38350, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.39483, val loss: 0.37990, in 0.031s 1 tree, 56 leaves, max depth = 13, train loss: 0.39140, val loss: 0.37643, in 0.016s 1 tree, 79 leaves, max depth = 11, train loss: 0.38917, val loss: 0.37406, in 0.031s Fit 59 trees in 1.813 s, (4181 total leaves) Time spent computing histograms: 0.544s Time spent finding best splits: 0.102s Time spent applying splits: 0.103s Time spent predicting: 0.000s Trial 73, Fold 4: Log loss = 0.38931738228668655, Average precision = 0.9534384907358634, ROC-AUC = 0.9483126277149486, Elapsed Time = 1.8212827000006655 seconds Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 73, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.159 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 54 leaves, max depth = 10, train loss: 0.68275, val loss: 0.68220, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.67212, val loss: 0.67113, in 0.031s 1 tree, 82 leaves, max depth = 11, train loss: 0.66268, val loss: 0.66117, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.65277, val loss: 0.65085, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.64361, val loss: 0.64126, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.63436, val loss: 0.63162, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.62582, val loss: 0.62266, in 0.031s 1 tree, 79 leaves, max depth = 12, train loss: 0.61717, val loss: 0.61365, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.60930, val loss: 0.60541, in 0.031s 1 tree, 86 leaves, max depth = 12, train loss: 0.60181, val loss: 0.59755, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.59400, val loss: 0.58943, in 0.016s 1 tree, 75 leaves, max depth = 15, train loss: 0.58665, val loss: 0.58168, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.57974, val loss: 0.57444, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.57282, val loss: 0.56718, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.56587, val loss: 0.55994, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.55959, val loss: 0.55336, in 0.016s 1 tree, 86 leaves, max depth = 11, train loss: 0.55318, val loss: 0.54671, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.54695, val loss: 0.54022, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.54096, val loss: 0.53393, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.53510, val loss: 0.52782, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.52970, val loss: 0.52215, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.52411, val loss: 0.51634, in 0.031s 1 tree, 83 leaves, max depth = 10, train loss: 0.51877, val loss: 0.51080, in 0.016s 1 tree, 82 leaves, max depth = 10, train loss: 0.51391, val loss: 0.50582, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.50876, val loss: 0.50046, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.50392, val loss: 0.49539, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.49903, val loss: 0.49029, in 0.031s 1 tree, 78 leaves, max depth = 13, train loss: 0.49429, val loss: 0.48534, in 0.016s 1 tree, 87 leaves, max depth = 12, train loss: 0.49009, val loss: 0.48093, in 0.031s 1 tree, 70 leaves, max depth = 13, train loss: 0.48590, val loss: 0.47661, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.48174, val loss: 0.47224, in 0.031s 1 tree, 82 leaves, max depth = 10, train loss: 0.47761, val loss: 0.46793, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.47360, val loss: 0.46367, in 0.016s 1 tree, 84 leaves, max depth = 10, train loss: 0.46992, val loss: 0.45991, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.46597, val loss: 0.45579, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.46213, val loss: 0.45179, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.45842, val loss: 0.44793, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.45494, val loss: 0.44423, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.45155, val loss: 0.44063, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.44821, val loss: 0.43713, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.44500, val loss: 0.43377, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.44172, val loss: 0.43034, in 0.031s 1 tree, 83 leaves, max depth = 10, train loss: 0.43866, val loss: 0.42716, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.43555, val loss: 0.42392, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.43288, val loss: 0.42107, in 0.031s 1 tree, 75 leaves, max depth = 12, train loss: 0.43012, val loss: 0.41827, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.42561, val loss: 0.41380, in 0.031s 1 tree, 80 leaves, max depth = 12, train loss: 0.42277, val loss: 0.41084, in 0.016s 1 tree, 76 leaves, max depth = 13, train loss: 0.42006, val loss: 0.40799, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.41750, val loss: 0.40526, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.41328, val loss: 0.40107, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.41083, val loss: 0.39845, in 0.031s 1 tree, 85 leaves, max depth = 10, train loss: 0.40844, val loss: 0.39597, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.40620, val loss: 0.39369, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.40400, val loss: 0.39138, in 0.031s 1 tree, 59 leaves, max depth = 12, train loss: 0.40180, val loss: 0.38902, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.39793, val loss: 0.38519, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.39420, val loss: 0.38151, in 0.031s 1 tree, 79 leaves, max depth = 12, train loss: 0.39198, val loss: 0.37922, in 0.016s Fit 59 trees in 1.612 s, (4358 total leaves) Time spent computing histograms: 0.474s Time spent finding best splits: 0.085s Time spent applying splits: 0.089s Time spent predicting: 0.000s Trial 73, Fold 5: Log loss = 0.39799173642544305, Average precision = 0.9482297461164175, ROC-AUC = 0.9457495122902849, Elapsed Time = 1.61448879999989 seconds
Optimization Progress: 74%|#######4 | 74/100 [15:01<06:24, 14.80s/it]
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 74, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 7, train loss: 0.67330, val loss: 0.67326, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.65458, val loss: 0.65471, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.63709, val loss: 0.63734, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.62070, val loss: 0.62092, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.60535, val loss: 0.60569, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.59092, val loss: 0.59126, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57792, val loss: 0.57821, in 0.016s 1 tree, 31 leaves, max depth = 6, train loss: 0.56526, val loss: 0.56537, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.55357, val loss: 0.55364, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.54238, val loss: 0.54229, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.53134, val loss: 0.53123, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.52148, val loss: 0.52133, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.51214, val loss: 0.51196, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.50265, val loss: 0.50242, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.49430, val loss: 0.49403, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.48587, val loss: 0.48543, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.47779, val loss: 0.47734, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.47006, val loss: 0.46957, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.46002, val loss: 0.45979, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.45315, val loss: 0.45278, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44395, val loss: 0.44386, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.43768, val loss: 0.43745, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.43160, val loss: 0.43132, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.42587, val loss: 0.42548, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.42033, val loss: 0.41994, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.41508, val loss: 0.41469, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.41018, val loss: 0.40968, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.40275, val loss: 0.40253, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39812, val loss: 0.39797, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.39139, val loss: 0.39144, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.38713, val loss: 0.38719, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.38081, val loss: 0.38121, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37694, val loss: 0.37747, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37130, val loss: 0.37198, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.36599, val loss: 0.36683, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36251, val loss: 0.36348, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35765, val loss: 0.35867, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35308, val loss: 0.35415, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.34934, val loss: 0.35048, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.34577, val loss: 0.34696, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.34144, val loss: 0.34299, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33745, val loss: 0.33907, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.33418, val loss: 0.33590, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33026, val loss: 0.33231, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.32671, val loss: 0.32882, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.32335, val loss: 0.32551, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.31987, val loss: 0.32237, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.31677, val loss: 0.31920, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.31400, val loss: 0.31652, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.31115, val loss: 0.31359, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.30826, val loss: 0.31076, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.30524, val loss: 0.30805, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30281, val loss: 0.30571, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.30001, val loss: 0.30321, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.29734, val loss: 0.30084, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29492, val loss: 0.29833, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29276, val loss: 0.29626, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29043, val loss: 0.29397, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.28807, val loss: 0.29188, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28633, val loss: 0.29023, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.28424, val loss: 0.28818, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28233, val loss: 0.28632, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.28033, val loss: 0.28424, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27826, val loss: 0.28244, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27641, val loss: 0.28062, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27454, val loss: 0.27866, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.27282, val loss: 0.27699, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27105, val loss: 0.27515, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.26937, val loss: 0.27340, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26762, val loss: 0.27187, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.26609, val loss: 0.27038, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26447, val loss: 0.26896, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26305, val loss: 0.26762, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26155, val loss: 0.26632, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.26021, val loss: 0.26505, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25889, val loss: 0.26374, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25751, val loss: 0.26253, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.25620, val loss: 0.26140, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.25484, val loss: 0.25996, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.25367, val loss: 0.25878, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.25238, val loss: 0.25742, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25122, val loss: 0.25644, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.25036, val loss: 0.25543, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.24924, val loss: 0.25449, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.24821, val loss: 0.25341, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.24741, val loss: 0.25247, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.24636, val loss: 0.25162, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.24542, val loss: 0.25063, in 0.016s Fit 88 trees in 1.767 s, (2728 total leaves) Time spent computing histograms: 0.579s Time spent finding best splits: 0.109s Time spent applying splits: 0.065s Time spent predicting: 0.000s Trial 74, Fold 1: Log loss = 0.25379233630043185, Average precision = 0.9658598545976502, ROC-AUC = 0.9608860820751064, Elapsed Time = 1.7715054000000237 seconds Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 74, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.189 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 8, train loss: 0.67327, val loss: 0.67289, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.65441, val loss: 0.65373, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.63766, val loss: 0.63673, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.62144, val loss: 0.62018, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.60567, val loss: 0.60419, in 0.000s 1 tree, 31 leaves, max depth = 7, train loss: 0.59106, val loss: 0.58943, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.57804, val loss: 0.57624, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.56509, val loss: 0.56316, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55273, val loss: 0.55058, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.54132, val loss: 0.53894, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.53024, val loss: 0.52770, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.52047, val loss: 0.51781, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.51056, val loss: 0.50776, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.50119, val loss: 0.49827, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.49290, val loss: 0.48989, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.48453, val loss: 0.48150, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.47650, val loss: 0.47337, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.46887, val loss: 0.46569, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.45897, val loss: 0.45590, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.45211, val loss: 0.44902, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.44547, val loss: 0.44235, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.43914, val loss: 0.43587, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.43315, val loss: 0.42976, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.42744, val loss: 0.42394, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.42201, val loss: 0.41840, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.41406, val loss: 0.41059, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.40908, val loss: 0.40553, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.40198, val loss: 0.39860, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.39736, val loss: 0.39391, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.39061, val loss: 0.38733, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.38661, val loss: 0.38337, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.38038, val loss: 0.37725, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.37449, val loss: 0.37148, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.37068, val loss: 0.36760, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.36706, val loss: 0.36397, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.36200, val loss: 0.35901, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.35722, val loss: 0.35433, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.35341, val loss: 0.35056, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.34905, val loss: 0.34629, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.34530, val loss: 0.34266, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.34104, val loss: 0.33875, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.33712, val loss: 0.33494, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.33386, val loss: 0.33173, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.32999, val loss: 0.32819, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.32649, val loss: 0.32480, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.32320, val loss: 0.32160, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.32007, val loss: 0.31860, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.31709, val loss: 0.31574, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.31440, val loss: 0.31310, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.31117, val loss: 0.31019, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.30811, val loss: 0.30744, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.30520, val loss: 0.30480, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.30255, val loss: 0.30224, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.29988, val loss: 0.29985, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.29746, val loss: 0.29750, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.29500, val loss: 0.29531, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.29279, val loss: 0.29315, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.29057, val loss: 0.29096, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28833, val loss: 0.28894, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.28634, val loss: 0.28704, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28418, val loss: 0.28497, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.28216, val loss: 0.28318, in 0.031s 1 tree, 31 leaves, max depth = 15, train loss: 0.28036, val loss: 0.28145, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.27837, val loss: 0.27953, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27654, val loss: 0.27793, in 0.031s 1 tree, 31 leaves, max depth = 13, train loss: 0.27491, val loss: 0.27639, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.27306, val loss: 0.27463, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27139, val loss: 0.27316, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.26992, val loss: 0.27173, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.26819, val loss: 0.27009, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.26665, val loss: 0.26873, in 0.016s 1 tree, 31 leaves, max depth = 17, train loss: 0.26533, val loss: 0.26743, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.26391, val loss: 0.26614, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.26239, val loss: 0.26467, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.26119, val loss: 0.26352, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.26004, val loss: 0.26242, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.25877, val loss: 0.26129, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25730, val loss: 0.25990, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25590, val loss: 0.25857, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25472, val loss: 0.25757, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25373, val loss: 0.25663, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.25248, val loss: 0.25544, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.25139, val loss: 0.25452, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.25045, val loss: 0.25366, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.24945, val loss: 0.25277, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.24855, val loss: 0.25187, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.24768, val loss: 0.25099, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.24656, val loss: 0.24991, in 0.016s Fit 88 trees in 1.924 s, (2728 total leaves) Time spent computing histograms: 0.620s Time spent finding best splits: 0.115s Time spent applying splits: 0.070s Time spent predicting: 0.016s Trial 74, Fold 2: Log loss = 0.25000810147832486, Average precision = 0.9661793912567854, ROC-AUC = 0.9633919848640689, Elapsed Time = 1.929179999999178 seconds Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 74, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 7, train loss: 0.67339, val loss: 0.67326, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.65476, val loss: 0.65461, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.63732, val loss: 0.63712, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.62100, val loss: 0.62075, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.60552, val loss: 0.60522, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.59113, val loss: 0.59081, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57803, val loss: 0.57775, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.56524, val loss: 0.56497, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.55353, val loss: 0.55332, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.54227, val loss: 0.54194, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.53130, val loss: 0.53103, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.52147, val loss: 0.52127, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.51219, val loss: 0.51203, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50361, val loss: 0.50338, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.49525, val loss: 0.49508, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.48691, val loss: 0.48681, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.47885, val loss: 0.47884, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.47120, val loss: 0.47125, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.46117, val loss: 0.46190, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.45430, val loss: 0.45516, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.44767, val loss: 0.44861, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.44139, val loss: 0.44233, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.43536, val loss: 0.43637, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.42967, val loss: 0.43074, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.42140, val loss: 0.42312, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.41612, val loss: 0.41794, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.40851, val loss: 0.41096, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.40152, val loss: 0.40454, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.39683, val loss: 0.39994, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.39236, val loss: 0.39554, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.38833, val loss: 0.39161, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.38200, val loss: 0.38588, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.37803, val loss: 0.38201, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.37217, val loss: 0.37672, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.36664, val loss: 0.37175, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.36309, val loss: 0.36824, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35820, val loss: 0.36380, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.35358, val loss: 0.35962, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34985, val loss: 0.35578, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34561, val loss: 0.35193, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34212, val loss: 0.34834, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33802, val loss: 0.34484, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33424, val loss: 0.34142, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33107, val loss: 0.33812, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.32736, val loss: 0.33502, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.32399, val loss: 0.33196, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.32059, val loss: 0.32914, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.31774, val loss: 0.32614, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.31475, val loss: 0.32346, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.31209, val loss: 0.32068, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.30928, val loss: 0.31821, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.30676, val loss: 0.31561, in 0.031s 1 tree, 31 leaves, max depth = 7, train loss: 0.30392, val loss: 0.31328, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.30122, val loss: 0.31108, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29874, val loss: 0.30901, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.29631, val loss: 0.30705, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.29405, val loss: 0.30499, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.29192, val loss: 0.30306, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28967, val loss: 0.30059, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28754, val loss: 0.29888, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28554, val loss: 0.29721, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.28365, val loss: 0.29553, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28162, val loss: 0.29328, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27980, val loss: 0.29183, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27809, val loss: 0.29031, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.27625, val loss: 0.28836, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.27469, val loss: 0.28696, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.27288, val loss: 0.28496, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.27122, val loss: 0.28320, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26967, val loss: 0.28196, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.26828, val loss: 0.28072, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.26685, val loss: 0.27961, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.26531, val loss: 0.27802, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.26378, val loss: 0.27627, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.26248, val loss: 0.27524, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.26125, val loss: 0.27418, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.26003, val loss: 0.27324, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.25890, val loss: 0.27223, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.25776, val loss: 0.27140, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.25671, val loss: 0.27043, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25565, val loss: 0.26968, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.25389, val loss: 0.26769, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.25293, val loss: 0.26687, in 0.016s 1 tree, 31 leaves, max depth = 17, train loss: 0.25198, val loss: 0.26595, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25071, val loss: 0.26451, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.24954, val loss: 0.26330, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.24865, val loss: 0.26257, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.24781, val loss: 0.26191, in 0.016s Fit 88 trees in 1.923 s, (2728 total leaves) Time spent computing histograms: 0.631s Time spent finding best splits: 0.118s Time spent applying splits: 0.071s Time spent predicting: 0.000s Trial 74, Fold 3: Log loss = 0.24984248592210537, Average precision = 0.9660236678991468, ROC-AUC = 0.9624181211536054, Elapsed Time = 1.928146400001424 seconds Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 74, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 8, train loss: 0.67358, val loss: 0.67269, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.65502, val loss: 0.65336, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.63839, val loss: 0.63595, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.62249, val loss: 0.61927, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.60716, val loss: 0.60328, in 0.016s 1 tree, 31 leaves, max depth = 6, train loss: 0.59294, val loss: 0.58834, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.57999, val loss: 0.57478, in 0.016s 1 tree, 31 leaves, max depth = 6, train loss: 0.56735, val loss: 0.56147, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.55506, val loss: 0.54864, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.54374, val loss: 0.53667, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.53273, val loss: 0.52513, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.52299, val loss: 0.51489, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.51305, val loss: 0.50451, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.50375, val loss: 0.49478, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.49549, val loss: 0.48607, in 0.016s 1 tree, 31 leaves, max depth = 6, train loss: 0.48724, val loss: 0.47734, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.47923, val loss: 0.46890, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.47174, val loss: 0.46096, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.46455, val loss: 0.45340, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.45807, val loss: 0.44661, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.45199, val loss: 0.44016, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.44568, val loss: 0.43350, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.43966, val loss: 0.42719, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.43100, val loss: 0.41841, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.42566, val loss: 0.41265, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.41772, val loss: 0.40460, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41268, val loss: 0.39926, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.40552, val loss: 0.39195, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.40101, val loss: 0.38719, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.39424, val loss: 0.38033, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.39030, val loss: 0.37620, in 0.031s 1 tree, 31 leaves, max depth = 7, train loss: 0.38619, val loss: 0.37182, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38008, val loss: 0.36564, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37426, val loss: 0.35973, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37058, val loss: 0.35589, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.36550, val loss: 0.35065, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.36070, val loss: 0.34558, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35689, val loss: 0.34176, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.35252, val loss: 0.33715, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.34896, val loss: 0.33359, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34471, val loss: 0.32952, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34075, val loss: 0.32540, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33746, val loss: 0.32208, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33366, val loss: 0.31843, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33005, val loss: 0.31504, in 0.031s 1 tree, 31 leaves, max depth = 7, train loss: 0.32768, val loss: 0.31246, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.32478, val loss: 0.30953, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.32151, val loss: 0.30611, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31832, val loss: 0.30312, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.31534, val loss: 0.30001, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.31241, val loss: 0.29728, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.30971, val loss: 0.29439, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.30710, val loss: 0.29169, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.30447, val loss: 0.28925, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.30202, val loss: 0.28668, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.29961, val loss: 0.28445, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.29729, val loss: 0.28214, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.29514, val loss: 0.27985, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29291, val loss: 0.27753, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.29086, val loss: 0.27538, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28876, val loss: 0.27320, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28672, val loss: 0.27130, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28479, val loss: 0.26955, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.28300, val loss: 0.26762, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28121, val loss: 0.26597, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.27958, val loss: 0.26426, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.27773, val loss: 0.26234, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.27597, val loss: 0.26059, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.27468, val loss: 0.25921, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.27312, val loss: 0.25780, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.27165, val loss: 0.25646, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27001, val loss: 0.25472, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.26863, val loss: 0.25326, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.26728, val loss: 0.25205, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.26601, val loss: 0.25068, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26477, val loss: 0.24955, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.26328, val loss: 0.24793, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.26213, val loss: 0.24669, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.26103, val loss: 0.24552, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25962, val loss: 0.24398, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.25833, val loss: 0.24270, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.25724, val loss: 0.24170, in 0.031s 1 tree, 31 leaves, max depth = 15, train loss: 0.25622, val loss: 0.24067, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.25525, val loss: 0.23977, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.25430, val loss: 0.23881, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25335, val loss: 0.23798, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.25249, val loss: 0.23704, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.25164, val loss: 0.23640, in 0.016s Fit 88 trees in 1.924 s, (2728 total leaves) Time spent computing histograms: 0.620s Time spent finding best splits: 0.116s Time spent applying splits: 0.072s Time spent predicting: 0.000s Trial 74, Fold 4: Log loss = 0.25293808784018346, Average precision = 0.9665306166848716, ROC-AUC = 0.9624423419911252, Elapsed Time = 1.941979100000026 seconds Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 74, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 8, train loss: 0.67313, val loss: 0.67229, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.65420, val loss: 0.65272, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.63646, val loss: 0.63431, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.61988, val loss: 0.61715, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.60432, val loss: 0.60101, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.58974, val loss: 0.58590, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.57671, val loss: 0.57228, in 0.031s 1 tree, 31 leaves, max depth = 6, train loss: 0.56408, val loss: 0.55913, in 0.047s 1 tree, 31 leaves, max depth = 12, train loss: 0.55185, val loss: 0.54647, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.54054, val loss: 0.53457, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.52958, val loss: 0.52320, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.51975, val loss: 0.51295, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.51048, val loss: 0.50328, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.50171, val loss: 0.49433, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.49336, val loss: 0.48563, in 0.016s 1 tree, 31 leaves, max depth = 6, train loss: 0.48508, val loss: 0.47700, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47696, val loss: 0.46855, in 0.031s 1 tree, 31 leaves, max depth = 12, train loss: 0.46926, val loss: 0.46053, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.45928, val loss: 0.45052, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.45252, val loss: 0.44346, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.44338, val loss: 0.43430, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.43721, val loss: 0.42788, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.43116, val loss: 0.42166, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.42556, val loss: 0.41586, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.42007, val loss: 0.41022, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.41486, val loss: 0.40489, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.40997, val loss: 0.39979, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.40257, val loss: 0.39246, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.39798, val loss: 0.38776, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.39395, val loss: 0.38369, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.38728, val loss: 0.37711, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.38094, val loss: 0.37081, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37703, val loss: 0.36680, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.37145, val loss: 0.36118, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.36590, val loss: 0.35569, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.36233, val loss: 0.35206, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.35746, val loss: 0.34716, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.35360, val loss: 0.34330, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.34917, val loss: 0.33878, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.34560, val loss: 0.33520, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.34141, val loss: 0.33095, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.33804, val loss: 0.32763, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.33484, val loss: 0.32447, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33088, val loss: 0.32089, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.32722, val loss: 0.31717, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.32364, val loss: 0.31386, in 0.021s 1 tree, 31 leaves, max depth = 14, train loss: 0.32075, val loss: 0.31101, in 0.026s 1 tree, 31 leaves, max depth = 10, train loss: 0.31739, val loss: 0.30799, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.31423, val loss: 0.30478, in 0.016s 1 tree, 31 leaves, max depth = 7, train loss: 0.31202, val loss: 0.30265, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.30900, val loss: 0.29995, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.30619, val loss: 0.29709, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.30342, val loss: 0.29463, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.30084, val loss: 0.29200, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.29829, val loss: 0.28975, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.29594, val loss: 0.28733, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.29361, val loss: 0.28508, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.29135, val loss: 0.28280, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28911, val loss: 0.28075, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.28697, val loss: 0.27863, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.28493, val loss: 0.27655, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.28300, val loss: 0.27468, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.28100, val loss: 0.27287, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.27917, val loss: 0.27099, in 0.031s 1 tree, 31 leaves, max depth = 11, train loss: 0.27772, val loss: 0.26963, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.27591, val loss: 0.26806, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.27426, val loss: 0.26630, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.27254, val loss: 0.26463, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.27081, val loss: 0.26294, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.26914, val loss: 0.26128, in 0.031s 1 tree, 31 leaves, max depth = 8, train loss: 0.26755, val loss: 0.25986, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.26607, val loss: 0.25843, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.26464, val loss: 0.25697, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.26319, val loss: 0.25570, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.26188, val loss: 0.25436, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.26040, val loss: 0.25290, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25900, val loss: 0.25150, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.25771, val loss: 0.25036, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.25602, val loss: 0.24875, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.25484, val loss: 0.24756, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25366, val loss: 0.24653, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.25255, val loss: 0.24542, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.25142, val loss: 0.24442, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.25037, val loss: 0.24338, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.24941, val loss: 0.24240, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.24840, val loss: 0.24150, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.24748, val loss: 0.24063, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.24654, val loss: 0.23985, in 0.016s Fit 88 trees in 2.002 s, (2728 total leaves) Time spent computing histograms: 0.666s Time spent finding best splits: 0.127s Time spent applying splits: 0.079s Time spent predicting: 0.031s Trial 74, Fold 5: Log loss = 0.25782377744800283, Average precision = 0.963778447082425, ROC-AUC = 0.9593320327740928, Elapsed Time = 2.021543599999859 seconds
Optimization Progress: 75%|#######5 | 75/100 [15:18<06:27, 15.51s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 75, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.173 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 8, train loss: 0.67221, val loss: 0.67213, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.65231, val loss: 0.65222, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.63380, val loss: 0.63384, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.61653, val loss: 0.61666, in 0.031s 1 tree, 33 leaves, max depth = 9, train loss: 0.60101, val loss: 0.60101, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.58590, val loss: 0.58598, in 0.016s 1 tree, 34 leaves, max depth = 7, train loss: 0.57252, val loss: 0.57234, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.55908, val loss: 0.55882, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.54714, val loss: 0.54684, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.53593, val loss: 0.53559, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.52466, val loss: 0.52433, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.51472, val loss: 0.51434, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.50466, val loss: 0.50433, in 0.031s 1 tree, 38 leaves, max depth = 11, train loss: 0.49495, val loss: 0.49458, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.48608, val loss: 0.48554, in 0.031s 1 tree, 35 leaves, max depth = 7, train loss: 0.47756, val loss: 0.47681, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.46934, val loss: 0.46859, in 0.031s 1 tree, 39 leaves, max depth = 11, train loss: 0.46160, val loss: 0.46082, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.45427, val loss: 0.45348, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.44735, val loss: 0.44655, in 0.031s 1 tree, 46 leaves, max depth = 11, train loss: 0.43763, val loss: 0.43720, in 0.016s 1 tree, 38 leaves, max depth = 8, train loss: 0.43135, val loss: 0.43082, in 0.031s 1 tree, 45 leaves, max depth = 11, train loss: 0.42528, val loss: 0.42480, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.41953, val loss: 0.41910, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.41121, val loss: 0.41102, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.40601, val loss: 0.40585, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.39828, val loss: 0.39846, in 0.031s 1 tree, 44 leaves, max depth = 10, train loss: 0.39346, val loss: 0.39374, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.38637, val loss: 0.38701, in 0.031s 1 tree, 43 leaves, max depth = 10, train loss: 0.37974, val loss: 0.38077, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.37344, val loss: 0.37480, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.36937, val loss: 0.37080, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.36572, val loss: 0.36725, in 0.016s 1 tree, 43 leaves, max depth = 8, train loss: 0.36211, val loss: 0.36360, in 0.031s 1 tree, 24 leaves, max depth = 10, train loss: 0.35687, val loss: 0.35843, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.35196, val loss: 0.35359, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.34735, val loss: 0.34907, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.34352, val loss: 0.34529, in 0.031s 1 tree, 41 leaves, max depth = 13, train loss: 0.33988, val loss: 0.34172, in 0.016s 1 tree, 71 leaves, max depth = 16, train loss: 0.33543, val loss: 0.33774, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.33109, val loss: 0.33377, in 0.031s 1 tree, 32 leaves, max depth = 15, train loss: 0.32734, val loss: 0.33011, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.32389, val loss: 0.32662, in 0.016s 1 tree, 72 leaves, max depth = 15, train loss: 0.32009, val loss: 0.32325, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.31674, val loss: 0.31996, in 0.016s Fit 45 trees in 1.283 s, (1877 total leaves) Time spent computing histograms: 0.363s Time spent finding best splits: 0.072s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 75, Fold 1: Log loss = 0.3221012483428208, Average precision = 0.958427671986638, ROC-AUC = 0.9527340445362127, Elapsed Time = 1.290885200000048 seconds Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 75, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 7, train loss: 0.67199, val loss: 0.67151, in 0.016s 1 tree, 27 leaves, max depth = 7, train loss: 0.65196, val loss: 0.65107, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.63425, val loss: 0.63317, in 0.031s 1 tree, 26 leaves, max depth = 6, train loss: 0.61718, val loss: 0.61572, in 0.016s 1 tree, 29 leaves, max depth = 6, train loss: 0.60087, val loss: 0.59909, in 0.016s 1 tree, 28 leaves, max depth = 7, train loss: 0.58555, val loss: 0.58349, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.57237, val loss: 0.57017, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.55885, val loss: 0.55651, in 0.016s 1 tree, 29 leaves, max depth = 7, train loss: 0.54694, val loss: 0.54439, in 0.031s 1 tree, 34 leaves, max depth = 10, train loss: 0.53509, val loss: 0.53228, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.52378, val loss: 0.52076, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.51382, val loss: 0.51084, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.50354, val loss: 0.50043, in 0.031s 1 tree, 38 leaves, max depth = 10, train loss: 0.49384, val loss: 0.49062, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.48543, val loss: 0.48212, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.47674, val loss: 0.47332, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.46590, val loss: 0.46258, in 0.031s 1 tree, 39 leaves, max depth = 10, train loss: 0.45874, val loss: 0.45537, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.45137, val loss: 0.44806, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.44426, val loss: 0.44085, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.43753, val loss: 0.43403, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.43111, val loss: 0.42754, in 0.031s 1 tree, 39 leaves, max depth = 13, train loss: 0.42506, val loss: 0.42147, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.41930, val loss: 0.41569, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.41100, val loss: 0.40755, in 0.031s 1 tree, 40 leaves, max depth = 13, train loss: 0.40574, val loss: 0.40218, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.39807, val loss: 0.39464, in 0.016s 1 tree, 40 leaves, max depth = 13, train loss: 0.39328, val loss: 0.38985, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.38641, val loss: 0.38324, in 0.031s 1 tree, 38 leaves, max depth = 12, train loss: 0.38197, val loss: 0.37877, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.37567, val loss: 0.37272, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.37166, val loss: 0.36871, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.36811, val loss: 0.36518, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.36244, val loss: 0.35977, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.35727, val loss: 0.35469, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.35242, val loss: 0.34991, in 0.016s 1 tree, 43 leaves, max depth = 15, train loss: 0.34846, val loss: 0.34609, in 0.016s 1 tree, 72 leaves, max depth = 17, train loss: 0.34377, val loss: 0.34176, in 0.016s 1 tree, 19 leaves, max depth = 11, train loss: 0.33950, val loss: 0.33755, in 0.016s 1 tree, 68 leaves, max depth = 16, train loss: 0.33520, val loss: 0.33361, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.33221, val loss: 0.33060, in 0.031s 1 tree, 50 leaves, max depth = 17, train loss: 0.32874, val loss: 0.32719, in 0.031s 1 tree, 73 leaves, max depth = 15, train loss: 0.32487, val loss: 0.32367, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.32132, val loss: 0.32022, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.31874, val loss: 0.31763, in 0.016s Fit 45 trees in 1.236 s, (1739 total leaves) Time spent computing histograms: 0.354s Time spent finding best splits: 0.065s Time spent applying splits: 0.042s Time spent predicting: 0.000s Trial 75, Fold 2: Log loss = 0.322476233779029, Average precision = 0.9562086952003208, ROC-AUC = 0.9535934786085698, Elapsed Time = 1.2553203999996185 seconds Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 75, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 26 leaves, max depth = 6, train loss: 0.67231, val loss: 0.67222, in 0.016s 1 tree, 37 leaves, max depth = 12, train loss: 0.65264, val loss: 0.65255, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.63430, val loss: 0.63415, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.61687, val loss: 0.61675, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.60140, val loss: 0.60140, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.58629, val loss: 0.58624, in 0.031s 1 tree, 31 leaves, max depth = 7, train loss: 0.57286, val loss: 0.57294, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.55950, val loss: 0.55951, in 0.016s 1 tree, 37 leaves, max depth = 8, train loss: 0.54730, val loss: 0.54720, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.53530, val loss: 0.53520, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.52462, val loss: 0.52459, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.51393, val loss: 0.51393, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.50383, val loss: 0.50381, in 0.031s 1 tree, 41 leaves, max depth = 11, train loss: 0.49431, val loss: 0.49436, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.48528, val loss: 0.48538, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.47689, val loss: 0.47704, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.46917, val loss: 0.46947, in 0.031s 1 tree, 43 leaves, max depth = 12, train loss: 0.46147, val loss: 0.46178, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.45418, val loss: 0.45454, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.44727, val loss: 0.44771, in 0.016s 1 tree, 38 leaves, max depth = 8, train loss: 0.44084, val loss: 0.44139, in 0.031s 1 tree, 47 leaves, max depth = 12, train loss: 0.43136, val loss: 0.43266, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.42539, val loss: 0.42674, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.41674, val loss: 0.41879, in 0.031s 1 tree, 42 leaves, max depth = 8, train loss: 0.41142, val loss: 0.41361, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.40353, val loss: 0.40639, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.39854, val loss: 0.40152, in 0.031s 1 tree, 45 leaves, max depth = 11, train loss: 0.39147, val loss: 0.39508, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.38689, val loss: 0.39058, in 0.031s 1 tree, 45 leaves, max depth = 13, train loss: 0.38278, val loss: 0.38655, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.37624, val loss: 0.38063, in 0.031s 1 tree, 45 leaves, max depth = 13, train loss: 0.37221, val loss: 0.37668, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.36870, val loss: 0.37325, in 0.016s 1 tree, 59 leaves, max depth = 16, train loss: 0.36293, val loss: 0.36818, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.35765, val loss: 0.36339, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35269, val loss: 0.35889, in 0.016s 1 tree, 71 leaves, max depth = 17, train loss: 0.34778, val loss: 0.35476, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.34334, val loss: 0.35070, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.33964, val loss: 0.34681, in 0.016s 1 tree, 67 leaves, max depth = 17, train loss: 0.33527, val loss: 0.34316, in 0.031s 1 tree, 41 leaves, max depth = 14, train loss: 0.33183, val loss: 0.33954, in 0.016s 1 tree, 33 leaves, max depth = 15, train loss: 0.32798, val loss: 0.33607, in 0.016s 1 tree, 65 leaves, max depth = 17, train loss: 0.32410, val loss: 0.33283, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.32061, val loss: 0.32968, in 0.031s 1 tree, 40 leaves, max depth = 14, train loss: 0.31753, val loss: 0.32644, in 0.016s Fit 45 trees in 1.267 s, (1891 total leaves) Time spent computing histograms: 0.357s Time spent finding best splits: 0.070s Time spent applying splits: 0.047s Time spent predicting: 0.016s Trial 75, Fold 3: Log loss = 0.3175289258925027, Average precision = 0.9590985162914907, ROC-AUC = 0.9555419383421137, Elapsed Time = 1.26342300000033 seconds Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 75, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 23 leaves, max depth = 8, train loss: 0.67229, val loss: 0.67146, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.65289, val loss: 0.65121, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.63548, val loss: 0.63305, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.61860, val loss: 0.61545, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.60247, val loss: 0.59873, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.58739, val loss: 0.58307, in 0.016s 1 tree, 22 leaves, max depth = 7, train loss: 0.57442, val loss: 0.56933, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.56103, val loss: 0.55538, in 0.031s 1 tree, 32 leaves, max depth = 11, train loss: 0.54925, val loss: 0.54302, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.53743, val loss: 0.53066, in 0.031s 1 tree, 40 leaves, max depth = 10, train loss: 0.52599, val loss: 0.51872, in 0.016s 1 tree, 36 leaves, max depth = 8, train loss: 0.51608, val loss: 0.50829, in 0.031s 1 tree, 42 leaves, max depth = 10, train loss: 0.50584, val loss: 0.49763, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.49620, val loss: 0.48754, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.48781, val loss: 0.47875, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.47914, val loss: 0.46970, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.46828, val loss: 0.45867, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.46102, val loss: 0.45115, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.45357, val loss: 0.44334, in 0.031s 1 tree, 47 leaves, max depth = 11, train loss: 0.44402, val loss: 0.43363, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.43727, val loss: 0.42651, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.43084, val loss: 0.41973, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.42472, val loss: 0.41334, in 0.031s 1 tree, 46 leaves, max depth = 11, train loss: 0.41892, val loss: 0.40726, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.41371, val loss: 0.40164, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.40849, val loss: 0.39614, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.40086, val loss: 0.38843, in 0.031s 1 tree, 44 leaves, max depth = 11, train loss: 0.39605, val loss: 0.38338, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.38905, val loss: 0.37632, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.38247, val loss: 0.36968, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.37626, val loss: 0.36341, in 0.016s 1 tree, 29 leaves, max depth = 7, train loss: 0.37240, val loss: 0.35922, in 0.031s 1 tree, 50 leaves, max depth = 11, train loss: 0.36881, val loss: 0.35557, in 0.016s 1 tree, 53 leaves, max depth = 8, train loss: 0.36513, val loss: 0.35163, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.35993, val loss: 0.34623, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.35505, val loss: 0.34119, in 0.016s 1 tree, 42 leaves, max depth = 13, train loss: 0.35112, val loss: 0.33725, in 0.031s 1 tree, 65 leaves, max depth = 14, train loss: 0.34635, val loss: 0.33285, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.34205, val loss: 0.32836, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.33843, val loss: 0.32471, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.33451, val loss: 0.32055, in 0.016s 1 tree, 63 leaves, max depth = 14, train loss: 0.33042, val loss: 0.31680, in 0.031s 1 tree, 48 leaves, max depth = 12, train loss: 0.32693, val loss: 0.31321, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.32379, val loss: 0.31002, in 0.031s 1 tree, 50 leaves, max depth = 12, train loss: 0.32061, val loss: 0.30675, in 0.016s Fit 45 trees in 1.235 s, (1829 total leaves) Time spent computing histograms: 0.357s Time spent finding best splits: 0.066s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 75, Fold 4: Log loss = 0.32137275120798536, Average precision = 0.9595918254177039, ROC-AUC = 0.9550634133779696, Elapsed Time = 1.250694600001225 seconds Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 75, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 24 leaves, max depth = 6, train loss: 0.67198, val loss: 0.67111, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.65189, val loss: 0.65033, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.63318, val loss: 0.63101, in 0.031s 1 tree, 31 leaves, max depth = 7, train loss: 0.61603, val loss: 0.61320, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.59974, val loss: 0.59635, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.58526, val loss: 0.58123, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.57084, val loss: 0.56627, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.55811, val loss: 0.55296, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.54535, val loss: 0.53974, in 0.000s 1 tree, 28 leaves, max depth = 6, train loss: 0.53368, val loss: 0.52742, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.52238, val loss: 0.51571, in 0.031s 1 tree, 39 leaves, max depth = 13, train loss: 0.51176, val loss: 0.50473, in 0.016s 1 tree, 34 leaves, max depth = 7, train loss: 0.50191, val loss: 0.49446, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.49242, val loss: 0.48461, in 0.016s 1 tree, 39 leaves, max depth = 8, train loss: 0.48368, val loss: 0.47551, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.47519, val loss: 0.46673, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.46769, val loss: 0.45902, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.46004, val loss: 0.45116, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.45277, val loss: 0.44365, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.44591, val loss: 0.43656, in 0.016s 1 tree, 37 leaves, max depth = 7, train loss: 0.43958, val loss: 0.42997, in 0.031s 1 tree, 39 leaves, max depth = 13, train loss: 0.43341, val loss: 0.42367, in 0.016s 1 tree, 38 leaves, max depth = 13, train loss: 0.42749, val loss: 0.41766, in 0.016s 1 tree, 34 leaves, max depth = 13, train loss: 0.41856, val loss: 0.40882, in 0.016s 1 tree, 40 leaves, max depth = 8, train loss: 0.41037, val loss: 0.40068, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.40518, val loss: 0.39539, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.39750, val loss: 0.38779, in 0.031s 1 tree, 47 leaves, max depth = 13, train loss: 0.39028, val loss: 0.38066, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.38349, val loss: 0.37397, in 0.016s 1 tree, 39 leaves, max depth = 13, train loss: 0.37935, val loss: 0.36974, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.37513, val loss: 0.36546, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.37127, val loss: 0.36156, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.36545, val loss: 0.35582, in 0.031s 1 tree, 49 leaves, max depth = 8, train loss: 0.36189, val loss: 0.35224, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.35665, val loss: 0.34691, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.35172, val loss: 0.34191, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.34782, val loss: 0.33804, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.34334, val loss: 0.33346, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.33966, val loss: 0.32987, in 0.016s 1 tree, 72 leaves, max depth = 15, train loss: 0.33530, val loss: 0.32588, in 0.016s 1 tree, 30 leaves, max depth = 14, train loss: 0.33133, val loss: 0.32180, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.32796, val loss: 0.31851, in 0.031s 1 tree, 48 leaves, max depth = 14, train loss: 0.32464, val loss: 0.31509, in 0.016s 1 tree, 72 leaves, max depth = 14, train loss: 0.32081, val loss: 0.31166, in 0.016s 1 tree, 52 leaves, max depth = 15, train loss: 0.31830, val loss: 0.30920, in 0.031s Fit 45 trees in 1.174 s, (1794 total leaves) Time spent computing histograms: 0.325s Time spent finding best splits: 0.061s Time spent applying splits: 0.041s Time spent predicting: 0.000s Trial 75, Fold 5: Log loss = 0.32681020784542797, Average precision = 0.956019732699627, ROC-AUC = 0.9517103840365644, Elapsed Time = 1.1787164000015764 seconds
Optimization Progress: 76%|#######6 | 76/100 [15:31<05:55, 14.81s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 76, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.141 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 9, train loss: 0.68558, val loss: 0.68534, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.68143, val loss: 0.68087, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.67412, val loss: 0.67336, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66707, val loss: 0.66605, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.66021, val loss: 0.65900, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.65357, val loss: 0.65217, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.64716, val loss: 0.64551, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.64100, val loss: 0.63918, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63732, val loss: 0.63523, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.63129, val loss: 0.62903, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.62557, val loss: 0.62311, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.62004, val loss: 0.61740, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.61461, val loss: 0.61180, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.60943, val loss: 0.60643, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.60439, val loss: 0.60124, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.59949, val loss: 0.59618, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.59496, val loss: 0.59147, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.59031, val loss: 0.58665, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58579, val loss: 0.58192, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58130, val loss: 0.57726, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.57693, val loss: 0.57275, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57270, val loss: 0.56836, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56892, val loss: 0.56444, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.56491, val loss: 0.56028, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56112, val loss: 0.55633, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.55741, val loss: 0.55248, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.55383, val loss: 0.54875, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.55037, val loss: 0.54517, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.54701, val loss: 0.54167, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.54328, val loss: 0.53817, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.54005, val loss: 0.53482, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53688, val loss: 0.53148, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.53378, val loss: 0.52825, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53079, val loss: 0.52508, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.52780, val loss: 0.52194, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.52495, val loss: 0.51897, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.52222, val loss: 0.51614, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51882, val loss: 0.51297, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.51616, val loss: 0.51020, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.51362, val loss: 0.50756, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51110, val loss: 0.50493, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.50868, val loss: 0.50237, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.50635, val loss: 0.49993, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.50317, val loss: 0.49698, in 0.016s [45/70] 1 tree, 6 leaves, max depth = 4, train loss: 0.50089, val loss: 0.49455, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49866, val loss: 0.49222, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.49561, val loss: 0.48939, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49401, val loss: 0.48783, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.49192, val loss: 0.48562, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48901, val loss: 0.48293, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48702, val loss: 0.48084, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.48420, val loss: 0.47824, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.48229, val loss: 0.47624, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.48043, val loss: 0.47430, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.47858, val loss: 0.47235, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.47589, val loss: 0.46987, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47411, val loss: 0.46796, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.47238, val loss: 0.46613, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46979, val loss: 0.46375, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.46817, val loss: 0.46204, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46655, val loss: 0.46033, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46497, val loss: 0.45866, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.46344, val loss: 0.45705, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46190, val loss: 0.45543, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46046, val loss: 0.45388, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45873, val loss: 0.45231, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45737, val loss: 0.45086, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45601, val loss: 0.44941, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45362, val loss: 0.44722, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45225, val loss: 0.44575, in 0.016s Fit 70 trees in 0.704 s, (1102 total leaves) Time spent computing histograms: 0.265s Time spent finding best splits: 0.022s Time spent applying splits: 0.022s Time spent predicting: 0.000s Trial 76, Fold 1: Log loss = 0.45140575375698305, Average precision = 0.9059768387995848, ROC-AUC = 0.9121660859465738, Elapsed Time = 0.7139700999996421 seconds Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 76, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 9, train loss: 0.68557, val loss: 0.68520, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.68137, val loss: 0.68058, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.67405, val loss: 0.67292, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66698, val loss: 0.66551, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66013, val loss: 0.65834, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.65353, val loss: 0.65141, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64711, val loss: 0.64468, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.64092, val loss: 0.63820, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.63722, val loss: 0.63434, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63122, val loss: 0.62801, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.62550, val loss: 0.62199, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61998, val loss: 0.61618, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.61457, val loss: 0.61045, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.60940, val loss: 0.60499, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.60434, val loss: 0.59968, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.59942, val loss: 0.59451, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.59492, val loss: 0.58979, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.59029, val loss: 0.58490, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58577, val loss: 0.58012, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58130, val loss: 0.57538, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57699, val loss: 0.57078, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.57278, val loss: 0.56630, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56899, val loss: 0.56233, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56501, val loss: 0.55809, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.56124, val loss: 0.55407, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.55752, val loss: 0.55013, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.55395, val loss: 0.54635, in 0.016s 1 tree, 17 leaves, max depth = 12, train loss: 0.55049, val loss: 0.54269, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.54712, val loss: 0.53911, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.54346, val loss: 0.53557, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.54023, val loss: 0.53214, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53707, val loss: 0.52878, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.53399, val loss: 0.52547, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53099, val loss: 0.52228, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.52804, val loss: 0.51910, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.52521, val loss: 0.51606, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.52249, val loss: 0.51317, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.51911, val loss: 0.50990, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51646, val loss: 0.50706, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.51392, val loss: 0.50434, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51142, val loss: 0.50166, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.50900, val loss: 0.49908, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.50667, val loss: 0.49658, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.50351, val loss: 0.49353, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.50124, val loss: 0.49109, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.49903, val loss: 0.48872, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.49601, val loss: 0.48580, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.49432, val loss: 0.48427, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.49224, val loss: 0.48203, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.48935, val loss: 0.47926, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48736, val loss: 0.47712, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.48457, val loss: 0.47444, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48265, val loss: 0.47238, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.48079, val loss: 0.47037, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.47895, val loss: 0.46838, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47629, val loss: 0.46582, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.47451, val loss: 0.46390, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.47279, val loss: 0.46203, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47023, val loss: 0.45955, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.46860, val loss: 0.45781, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.46702, val loss: 0.45610, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46545, val loss: 0.45440, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46393, val loss: 0.45275, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.46241, val loss: 0.45112, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.46097, val loss: 0.44955, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45927, val loss: 0.44798, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.45791, val loss: 0.44649, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.45658, val loss: 0.44506, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.45421, val loss: 0.44279, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45286, val loss: 0.44131, in 0.016s Fit 70 trees in 0.783 s, (1091 total leaves) Time spent computing histograms: 0.287s Time spent finding best splits: 0.025s Time spent applying splits: 0.025s Time spent predicting: 0.000s Trial 76, Fold 2: Log loss = 0.4535027898029579, Average precision = 0.9029478982459576, ROC-AUC = 0.9163226291457116, Elapsed Time = 0.7876867999984825 seconds Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 76, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 10, train loss: 0.68561, val loss: 0.68537, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.68139, val loss: 0.68104, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.67412, val loss: 0.67350, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66709, val loss: 0.66623, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.66028, val loss: 0.65919, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.65369, val loss: 0.65237, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64730, val loss: 0.64574, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.64116, val loss: 0.63936, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.63744, val loss: 0.63559, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.63147, val loss: 0.62942, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.62578, val loss: 0.62352, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.62028, val loss: 0.61780, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.61490, val loss: 0.61222, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.60976, val loss: 0.60689, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.60475, val loss: 0.60165, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.59988, val loss: 0.59658, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.59539, val loss: 0.59192, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.59077, val loss: 0.58712, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58627, val loss: 0.58244, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.58183, val loss: 0.57783, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.57752, val loss: 0.57336, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.57333, val loss: 0.56901, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.56958, val loss: 0.56511, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56562, val loss: 0.56099, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.56185, val loss: 0.55707, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55817, val loss: 0.55322, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.55462, val loss: 0.54952, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.55118, val loss: 0.54590, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.54785, val loss: 0.54241, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.54415, val loss: 0.53893, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.54095, val loss: 0.53556, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53780, val loss: 0.53227, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.53474, val loss: 0.52906, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.53176, val loss: 0.52594, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.52881, val loss: 0.52287, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.52600, val loss: 0.51992, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.52330, val loss: 0.51707, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51992, val loss: 0.51390, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.51729, val loss: 0.51114, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.51478, val loss: 0.50848, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.51230, val loss: 0.50586, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.50991, val loss: 0.50334, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.50760, val loss: 0.50090, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.50443, val loss: 0.49794, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.50217, val loss: 0.49556, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.49997, val loss: 0.49323, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49694, val loss: 0.49040, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49529, val loss: 0.48891, in 0.016s [49/70] 1 tree, 17 leaves, max depth = 9, train loss: 0.49322, val loss: 0.48673, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.49033, val loss: 0.48404, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.48836, val loss: 0.48195, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.48556, val loss: 0.47935, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.48367, val loss: 0.47733, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48183, val loss: 0.47537, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48000, val loss: 0.47343, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.47732, val loss: 0.47094, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47556, val loss: 0.46907, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.47385, val loss: 0.46726, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.47128, val loss: 0.46488, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.46967, val loss: 0.46316, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.46808, val loss: 0.46146, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46653, val loss: 0.45980, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46502, val loss: 0.45818, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46351, val loss: 0.45656, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.46209, val loss: 0.45504, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.46038, val loss: 0.45349, in 0.005s 1 tree, 17 leaves, max depth = 10, train loss: 0.45903, val loss: 0.45204, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.45769, val loss: 0.45061, in 0.012s 1 tree, 17 leaves, max depth = 5, train loss: 0.45531, val loss: 0.44842, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.45397, val loss: 0.44700, in 0.016s Fit 70 trees in 0.815 s, (1102 total leaves) Time spent computing histograms: 0.292s Time spent finding best splits: 0.025s Time spent applying splits: 0.025s Time spent predicting: 0.016s Trial 76, Fold 3: Log loss = 0.44810020855286986, Average precision = 0.912123486339555, ROC-AUC = 0.9214749358332289, Elapsed Time = 0.8128985000003013 seconds Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 76, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 9, train loss: 0.68560, val loss: 0.68517, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.68135, val loss: 0.68064, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.67408, val loss: 0.67298, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66705, val loss: 0.66557, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.66023, val loss: 0.65837, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.65363, val loss: 0.65139, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64724, val loss: 0.64464, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.64107, val loss: 0.63812, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.63732, val loss: 0.63425, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.63137, val loss: 0.62789, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.62567, val loss: 0.62181, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.62015, val loss: 0.61596, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.61476, val loss: 0.61022, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.60960, val loss: 0.60470, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.60455, val loss: 0.59931, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.59966, val loss: 0.59409, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.59520, val loss: 0.58922, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.59057, val loss: 0.58424, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.58607, val loss: 0.57943, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.58164, val loss: 0.57466, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.57734, val loss: 0.57002, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.57316, val loss: 0.56551, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.56936, val loss: 0.56142, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.56541, val loss: 0.55714, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.56164, val loss: 0.55305, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55794, val loss: 0.54908, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.55439, val loss: 0.54521, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.55093, val loss: 0.54148, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.54758, val loss: 0.53784, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.54386, val loss: 0.53419, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.54064, val loss: 0.53071, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53749, val loss: 0.52730, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.53441, val loss: 0.52395, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53144, val loss: 0.52072, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.52850, val loss: 0.51749, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.52567, val loss: 0.51440, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.52296, val loss: 0.51144, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.51956, val loss: 0.50811, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51692, val loss: 0.50525, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.51439, val loss: 0.50248, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51189, val loss: 0.49975, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.50949, val loss: 0.49709, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.50717, val loss: 0.49452, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.50399, val loss: 0.49140, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.50173, val loss: 0.48891, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49952, val loss: 0.48649, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49647, val loss: 0.48349, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49482, val loss: 0.48186, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.49275, val loss: 0.47954, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.48984, val loss: 0.47669, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.48786, val loss: 0.47449, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.48505, val loss: 0.47174, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48315, val loss: 0.46962, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.48129, val loss: 0.46756, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.47946, val loss: 0.46553, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47677, val loss: 0.46290, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.47501, val loss: 0.46094, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47330, val loss: 0.45902, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.47071, val loss: 0.45650, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.46909, val loss: 0.45467, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.46750, val loss: 0.45286, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46594, val loss: 0.45112, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.46442, val loss: 0.44942, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46289, val loss: 0.44770, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46147, val loss: 0.44607, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45979, val loss: 0.44445, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.45843, val loss: 0.44290, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.45709, val loss: 0.44137, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45471, val loss: 0.43906, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.45338, val loss: 0.43753, in 0.016s Fit 70 trees in 0.799 s, (1102 total leaves) Time spent computing histograms: 0.296s Time spent finding best splits: 0.025s Time spent applying splits: 0.025s Time spent predicting: 0.000s Trial 76, Fold 4: Log loss = 0.45288470105915773, Average precision = 0.9060052309612978, ROC-AUC = 0.9150917640101958, Elapsed Time = 0.8077116000004025 seconds Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 76, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 8, train loss: 0.68551, val loss: 0.68497, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.68135, val loss: 0.68038, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.67399, val loss: 0.67253, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.66686, val loss: 0.66493, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.65997, val loss: 0.65758, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.65331, val loss: 0.65043, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64684, val loss: 0.64352, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.64060, val loss: 0.63680, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.63693, val loss: 0.63286, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63092, val loss: 0.62638, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.62516, val loss: 0.62017, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61959, val loss: 0.61417, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.61416, val loss: 0.60834, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.60895, val loss: 0.60267, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.60384, val loss: 0.59713, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.59889, val loss: 0.59174, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.59436, val loss: 0.58683, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.58968, val loss: 0.58174, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.58512, val loss: 0.57681, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.58065, val loss: 0.57194, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.57631, val loss: 0.56720, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.57209, val loss: 0.56260, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56826, val loss: 0.55842, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.56427, val loss: 0.55405, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.56046, val loss: 0.54987, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.55674, val loss: 0.54579, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.55314, val loss: 0.54183, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.54964, val loss: 0.53800, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.54624, val loss: 0.53425, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.54261, val loss: 0.53077, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.53935, val loss: 0.52719, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53616, val loss: 0.52369, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.53308, val loss: 0.52027, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.53006, val loss: 0.51694, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.52709, val loss: 0.51365, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.52426, val loss: 0.51050, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.52151, val loss: 0.50745, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.51819, val loss: 0.50430, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.51552, val loss: 0.50136, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.51296, val loss: 0.49851, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.51044, val loss: 0.49572, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.50801, val loss: 0.49298, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.50565, val loss: 0.49033, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.50255, val loss: 0.48739, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.50025, val loss: 0.48483, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.49803, val loss: 0.48235, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49505, val loss: 0.47954, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.49339, val loss: 0.47796, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.49129, val loss: 0.47558, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48845, val loss: 0.47291, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.48644, val loss: 0.47063, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48369, val loss: 0.46805, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.48176, val loss: 0.46587, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47988, val loss: 0.46374, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.47802, val loss: 0.46166, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47539, val loss: 0.45922, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.47360, val loss: 0.45719, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.47187, val loss: 0.45524, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46934, val loss: 0.45289, in 0.000s 1 tree, 17 leaves, max depth = 11, train loss: 0.46770, val loss: 0.45100, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.46608, val loss: 0.44916, in 0.000s 1 tree, 17 leaves, max depth = 10, train loss: 0.46450, val loss: 0.44737, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.46296, val loss: 0.44563, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.46142, val loss: 0.44387, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45998, val loss: 0.44220, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45832, val loss: 0.44076, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45694, val loss: 0.43914, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.45558, val loss: 0.43758, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.45325, val loss: 0.43543, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.45190, val loss: 0.43387, in 0.016s Fit 70 trees in 0.845 s, (1091 total leaves) Time spent computing histograms: 0.309s Time spent finding best splits: 0.026s Time spent applying splits: 0.026s Time spent predicting: 0.000s Trial 76, Fold 5: Log loss = 0.45875741501839024, Average precision = 0.9023385615786321, ROC-AUC = 0.910490608104342, Elapsed Time = 0.8457624999991822 seconds
Optimization Progress: 77%|#######7 | 77/100 [15:42<05:12, 13.58s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 77, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 215 leaves, max depth = 15, train loss: 0.67801, val loss: 0.67814, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.66343, val loss: 0.66381, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.65008, val loss: 0.65061, in 0.016s 1 tree, 239 leaves, max depth = 21, train loss: 0.63755, val loss: 0.63810, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.62487, val loss: 0.62569, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.61277, val loss: 0.61378, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.60123, val loss: 0.60250, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.59020, val loss: 0.59172, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.57966, val loss: 0.58142, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.56959, val loss: 0.57159, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.55995, val loss: 0.56216, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.55080, val loss: 0.55313, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.54175, val loss: 0.54411, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.53367, val loss: 0.53603, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.52583, val loss: 0.52832, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.51793, val loss: 0.52064, in 0.016s 1 tree, 239 leaves, max depth = 21, train loss: 0.51083, val loss: 0.51353, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.50352, val loss: 0.50642, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.49649, val loss: 0.49962, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.48982, val loss: 0.49308, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.48341, val loss: 0.48677, in 0.031s 1 tree, 239 leaves, max depth = 23, train loss: 0.47743, val loss: 0.48093, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.47138, val loss: 0.47510, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.46547, val loss: 0.46914, in 0.016s 1 tree, 197 leaves, max depth = 19, train loss: 0.45788, val loss: 0.46181, in 0.031s 1 tree, 239 leaves, max depth = 23, train loss: 0.45269, val loss: 0.45677, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.44748, val loss: 0.45154, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.44224, val loss: 0.44640, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.43731, val loss: 0.44168, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.43254, val loss: 0.43712, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.42795, val loss: 0.43272, in 0.031s 1 tree, 197 leaves, max depth = 15, train loss: 0.42147, val loss: 0.42648, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.41716, val loss: 0.42240, in 0.031s 1 tree, 239 leaves, max depth = 14, train loss: 0.41300, val loss: 0.41844, in 0.031s 1 tree, 239 leaves, max depth = 14, train loss: 0.40898, val loss: 0.41464, in 0.031s 1 tree, 197 leaves, max depth = 15, train loss: 0.40306, val loss: 0.40893, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.39914, val loss: 0.40512, in 0.031s 1 tree, 201 leaves, max depth = 15, train loss: 0.39360, val loss: 0.39982, in 0.031s 1 tree, 196 leaves, max depth = 15, train loss: 0.38825, val loss: 0.39467, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.38463, val loss: 0.39116, in 0.031s 1 tree, 195 leaves, max depth = 15, train loss: 0.37959, val loss: 0.38632, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.37622, val loss: 0.38299, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.37292, val loss: 0.37981, in 0.031s 1 tree, 231 leaves, max depth = 16, train loss: 0.36828, val loss: 0.37534, in 0.031s 1 tree, 197 leaves, max depth = 15, train loss: 0.36378, val loss: 0.37104, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.36085, val loss: 0.36833, in 0.031s 1 tree, 234 leaves, max depth = 15, train loss: 0.35668, val loss: 0.36432, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.35377, val loss: 0.36160, in 0.031s 1 tree, 102 leaves, max depth = 14, train loss: 0.35003, val loss: 0.35787, in 0.016s 1 tree, 102 leaves, max depth = 14, train loss: 0.34645, val loss: 0.35430, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.34374, val loss: 0.35181, in 0.031s 1 tree, 98 leaves, max depth = 14, train loss: 0.34034, val loss: 0.34843, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.33713, val loss: 0.34524, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.33425, val loss: 0.34246, in 0.094s 1 tree, 148 leaves, max depth = 13, train loss: 0.33110, val loss: 0.33959, in 0.031s 1 tree, 99 leaves, max depth = 14, train loss: 0.32811, val loss: 0.33661, in 0.016s 1 tree, 239 leaves, max depth = 22, train loss: 0.32540, val loss: 0.33403, in 0.047s 1 tree, 149 leaves, max depth = 13, train loss: 0.32249, val loss: 0.33139, in 0.031s 1 tree, 149 leaves, max depth = 13, train loss: 0.31970, val loss: 0.32887, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.31704, val loss: 0.32621, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.31454, val loss: 0.32388, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.31209, val loss: 0.32166, in 0.031s 1 tree, 146 leaves, max depth = 13, train loss: 0.30955, val loss: 0.31937, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.30713, val loss: 0.31697, in 0.031s 1 tree, 145 leaves, max depth = 13, train loss: 0.30475, val loss: 0.31484, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.30249, val loss: 0.31260, in 0.016s 1 tree, 239 leaves, max depth = 20, train loss: 0.30025, val loss: 0.31055, in 0.031s 1 tree, 146 leaves, max depth = 13, train loss: 0.29804, val loss: 0.30859, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.29596, val loss: 0.30651, in 0.016s 1 tree, 239 leaves, max depth = 19, train loss: 0.29382, val loss: 0.30451, in 0.031s 1 tree, 100 leaves, max depth = 15, train loss: 0.29185, val loss: 0.30254, in 0.016s 1 tree, 239 leaves, max depth = 19, train loss: 0.28975, val loss: 0.30054, in 0.031s 1 tree, 145 leaves, max depth = 13, train loss: 0.28778, val loss: 0.29880, in 0.016s 1 tree, 239 leaves, max depth = 20, train loss: 0.28584, val loss: 0.29698, in 0.047s 1 tree, 101 leaves, max depth = 16, train loss: 0.28404, val loss: 0.29522, in 0.016s 1 tree, 138 leaves, max depth = 14, train loss: 0.28220, val loss: 0.29360, in 0.016s 1 tree, 139 leaves, max depth = 14, train loss: 0.28043, val loss: 0.29204, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.27854, val loss: 0.29027, in 0.031s 1 tree, 142 leaves, max depth = 16, train loss: 0.27688, val loss: 0.28881, in 0.016s 1 tree, 96 leaves, max depth = 17, train loss: 0.27527, val loss: 0.28720, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.27375, val loss: 0.28571, in 0.031s 1 tree, 137 leaves, max depth = 16, train loss: 0.27220, val loss: 0.28436, in 0.016s Fit 82 trees in 2.581 s, (16428 total leaves) Time spent computing histograms: 0.714s Time spent finding best splits: 0.357s Time spent applying splits: 0.265s Time spent predicting: 0.016s Trial 77, Fold 1: Log loss = 0.2906058467959607, Average precision = 0.9604337413844874, ROC-AUC = 0.9551242125626724, Elapsed Time = 2.6012752999995428 seconds Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 77, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 227 leaves, max depth = 17, train loss: 0.67806, val loss: 0.67807, in 0.016s 1 tree, 239 leaves, max depth = 15, train loss: 0.66349, val loss: 0.66363, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.65034, val loss: 0.65058, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.63797, val loss: 0.63838, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.62525, val loss: 0.62581, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.61313, val loss: 0.61383, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.60156, val loss: 0.60240, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.59052, val loss: 0.59148, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.57996, val loss: 0.58105, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.56987, val loss: 0.57111, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.56001, val loss: 0.56144, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.55076, val loss: 0.55232, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.54190, val loss: 0.54357, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.53340, val loss: 0.53516, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.52524, val loss: 0.52710, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.51758, val loss: 0.51954, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.51022, val loss: 0.51237, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.50278, val loss: 0.50511, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.49563, val loss: 0.49816, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.48876, val loss: 0.49149, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.48215, val loss: 0.48509, in 0.016s 1 tree, 239 leaves, max depth = 20, train loss: 0.47622, val loss: 0.47933, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.47008, val loss: 0.47341, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.46418, val loss: 0.46771, in 0.047s 1 tree, 239 leaves, max depth = 16, train loss: 0.45849, val loss: 0.46219, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.45301, val loss: 0.45688, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.44773, val loss: 0.45172, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.44265, val loss: 0.44677, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.43774, val loss: 0.44203, in 0.031s 1 tree, 193 leaves, max depth = 18, train loss: 0.43098, val loss: 0.43546, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.42637, val loss: 0.43097, in 0.031s 1 tree, 193 leaves, max depth = 18, train loss: 0.42002, val loss: 0.42481, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.41577, val loss: 0.42080, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.41170, val loss: 0.41690, in 0.047s 1 tree, 197 leaves, max depth = 17, train loss: 0.40582, val loss: 0.41122, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.40188, val loss: 0.40751, in 0.031s 1 tree, 194 leaves, max depth = 18, train loss: 0.39636, val loss: 0.40219, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.39264, val loss: 0.39859, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.38905, val loss: 0.39519, in 0.031s 1 tree, 197 leaves, max depth = 18, train loss: 0.38392, val loss: 0.39027, in 0.031s 1 tree, 196 leaves, max depth = 17, train loss: 0.37901, val loss: 0.38556, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.37567, val loss: 0.38251, in 0.047s 1 tree, 200 leaves, max depth = 17, train loss: 0.37103, val loss: 0.37806, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.36785, val loss: 0.37512, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.36484, val loss: 0.37233, in 0.031s 1 tree, 202 leaves, max depth = 17, train loss: 0.36051, val loss: 0.36819, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.35764, val loss: 0.36547, in 0.031s 1 tree, 202 leaves, max depth = 17, train loss: 0.35356, val loss: 0.36158, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.34987, val loss: 0.35799, in 0.016s 1 tree, 92 leaves, max depth = 15, train loss: 0.34634, val loss: 0.35454, in 0.016s 1 tree, 95 leaves, max depth = 15, train loss: 0.34296, val loss: 0.35127, in 0.016s 1 tree, 239 leaves, max depth = 23, train loss: 0.33998, val loss: 0.34853, in 0.031s 1 tree, 239 leaves, max depth = 23, train loss: 0.33710, val loss: 0.34591, in 0.047s 1 tree, 98 leaves, max depth = 20, train loss: 0.33396, val loss: 0.34286, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.33094, val loss: 0.33996, in 0.031s 1 tree, 239 leaves, max depth = 24, train loss: 0.32823, val loss: 0.33750, in 0.031s 1 tree, 142 leaves, max depth = 16, train loss: 0.32527, val loss: 0.33478, in 0.031s 1 tree, 239 leaves, max depth = 22, train loss: 0.32272, val loss: 0.33241, in 0.032s 1 tree, 239 leaves, max depth = 22, train loss: 0.32026, val loss: 0.33015, in 0.047s 1 tree, 142 leaves, max depth = 16, train loss: 0.31749, val loss: 0.32760, in 0.016s 1 tree, 141 leaves, max depth = 16, train loss: 0.31483, val loss: 0.32516, in 0.016s 1 tree, 98 leaves, max depth = 15, train loss: 0.31228, val loss: 0.32271, in 0.016s 1 tree, 143 leaves, max depth = 16, train loss: 0.30978, val loss: 0.32044, in 0.031s 1 tree, 94 leaves, max depth = 17, train loss: 0.30740, val loss: 0.31815, in 0.031s 1 tree, 239 leaves, max depth = 22, train loss: 0.30513, val loss: 0.31610, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.30287, val loss: 0.31392, in 0.016s 1 tree, 143 leaves, max depth = 15, train loss: 0.30062, val loss: 0.31188, in 0.031s 1 tree, 141 leaves, max depth = 15, train loss: 0.29845, val loss: 0.30990, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.29639, val loss: 0.30793, in 0.031s 1 tree, 144 leaves, max depth = 15, train loss: 0.29436, val loss: 0.30610, in 0.016s 1 tree, 96 leaves, max depth = 16, train loss: 0.29242, val loss: 0.30427, in 0.031s 1 tree, 144 leaves, max depth = 15, train loss: 0.29051, val loss: 0.30255, in 0.016s 1 tree, 239 leaves, max depth = 22, train loss: 0.28849, val loss: 0.30080, in 0.047s 1 tree, 239 leaves, max depth = 22, train loss: 0.28647, val loss: 0.29903, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.28468, val loss: 0.29730, in 0.016s 1 tree, 144 leaves, max depth = 15, train loss: 0.28293, val loss: 0.29573, in 0.031s 1 tree, 97 leaves, max depth = 18, train loss: 0.28128, val loss: 0.29417, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.27939, val loss: 0.29253, in 0.031s 1 tree, 98 leaves, max depth = 19, train loss: 0.27782, val loss: 0.29104, in 0.016s 1 tree, 239 leaves, max depth = 21, train loss: 0.27600, val loss: 0.28946, in 0.031s 1 tree, 239 leaves, max depth = 22, train loss: 0.27429, val loss: 0.28797, in 0.031s 1 tree, 144 leaves, max depth = 15, train loss: 0.27275, val loss: 0.28661, in 0.016s Fit 82 trees in 2.893 s, (16380 total leaves) Time spent computing histograms: 0.770s Time spent finding best splits: 0.387s Time spent applying splits: 0.297s Time spent predicting: 0.016s Trial 77, Fold 2: Log loss = 0.2863839142277258, Average precision = 0.9617003319210659, ROC-AUC = 0.9584315015048827, Elapsed Time = 2.906086900000446 seconds Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 77, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 205 leaves, max depth = 17, train loss: 0.67810, val loss: 0.67818, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.66363, val loss: 0.66398, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.64983, val loss: 0.65037, in 0.047s 1 tree, 239 leaves, max depth = 14, train loss: 0.63677, val loss: 0.63750, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.62483, val loss: 0.62579, in 0.031s 1 tree, 239 leaves, max depth = 14, train loss: 0.61269, val loss: 0.61381, in 0.031s 1 tree, 239 leaves, max depth = 14, train loss: 0.60112, val loss: 0.60237, in 0.031s 1 tree, 239 leaves, max depth = 14, train loss: 0.59015, val loss: 0.59158, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.57957, val loss: 0.58115, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.56945, val loss: 0.57120, in 0.016s 1 tree, 239 leaves, max depth = 17, train loss: 0.55977, val loss: 0.56168, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.55094, val loss: 0.55307, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.54203, val loss: 0.54431, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.53345, val loss: 0.53595, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.52524, val loss: 0.52788, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.51736, val loss: 0.52031, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.51018, val loss: 0.51340, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.50288, val loss: 0.50621, in 0.047s 1 tree, 239 leaves, max depth = 16, train loss: 0.49587, val loss: 0.49932, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.48947, val loss: 0.49322, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.48301, val loss: 0.48699, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.47675, val loss: 0.48088, in 0.031s 1 tree, 191 leaves, max depth = 17, train loss: 0.46881, val loss: 0.47342, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.46295, val loss: 0.46772, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.45731, val loss: 0.46222, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.45188, val loss: 0.45693, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.44665, val loss: 0.45186, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.44161, val loss: 0.44695, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.43674, val loss: 0.44223, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.43206, val loss: 0.43770, in 0.047s 1 tree, 195 leaves, max depth = 17, train loss: 0.42551, val loss: 0.43161, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.42111, val loss: 0.42735, in 0.031s 1 tree, 195 leaves, max depth = 17, train loss: 0.41494, val loss: 0.42166, in 0.016s 1 tree, 239 leaves, max depth = 16, train loss: 0.41080, val loss: 0.41772, in 0.047s 1 tree, 239 leaves, max depth = 14, train loss: 0.40681, val loss: 0.41400, in 0.031s 1 tree, 228 leaves, max depth = 16, train loss: 0.40115, val loss: 0.40883, in 0.031s 1 tree, 199 leaves, max depth = 19, train loss: 0.39570, val loss: 0.40379, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.39201, val loss: 0.40036, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.38835, val loss: 0.39693, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.38491, val loss: 0.39369, in 0.031s 1 tree, 203 leaves, max depth = 18, train loss: 0.37992, val loss: 0.38914, in 0.031s 1 tree, 202 leaves, max depth = 19, train loss: 0.37513, val loss: 0.38475, in 0.031s 1 tree, 200 leaves, max depth = 19, train loss: 0.37053, val loss: 0.38055, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.36736, val loss: 0.37762, in 0.031s 1 tree, 202 leaves, max depth = 21, train loss: 0.36305, val loss: 0.37369, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.36008, val loss: 0.37087, in 0.047s 1 tree, 210 leaves, max depth = 20, train loss: 0.35598, val loss: 0.36718, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.35319, val loss: 0.36477, in 0.031s 1 tree, 202 leaves, max depth = 19, train loss: 0.34930, val loss: 0.36125, in 0.031s 1 tree, 91 leaves, max depth = 20, train loss: 0.34581, val loss: 0.35802, in 0.016s 1 tree, 94 leaves, max depth = 13, train loss: 0.34244, val loss: 0.35493, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.33951, val loss: 0.35203, in 0.031s 1 tree, 94 leaves, max depth = 13, train loss: 0.33633, val loss: 0.34912, in 0.031s 1 tree, 137 leaves, max depth = 17, train loss: 0.33321, val loss: 0.34654, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.33026, val loss: 0.34386, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.32761, val loss: 0.34126, in 0.031s 1 tree, 139 leaves, max depth = 17, train loss: 0.32473, val loss: 0.33889, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.32215, val loss: 0.33635, in 0.047s 1 tree, 93 leaves, max depth = 16, train loss: 0.31946, val loss: 0.33389, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.31700, val loss: 0.33148, in 0.031s 1 tree, 95 leaves, max depth = 16, train loss: 0.31445, val loss: 0.32918, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.31211, val loss: 0.32687, in 0.016s 1 tree, 137 leaves, max depth = 17, train loss: 0.30959, val loss: 0.32483, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.30735, val loss: 0.32263, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.30519, val loss: 0.32056, in 0.031s 1 tree, 138 leaves, max depth = 17, train loss: 0.30284, val loss: 0.31866, in 0.031s 1 tree, 95 leaves, max depth = 22, train loss: 0.30062, val loss: 0.31665, in 0.016s 1 tree, 91 leaves, max depth = 13, train loss: 0.29847, val loss: 0.31469, in 0.031s 1 tree, 139 leaves, max depth = 16, train loss: 0.29632, val loss: 0.31296, in 0.031s 1 tree, 138 leaves, max depth = 16, train loss: 0.29425, val loss: 0.31131, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.29228, val loss: 0.30941, in 0.031s 1 tree, 97 leaves, max depth = 15, train loss: 0.29035, val loss: 0.30767, in 0.031s 1 tree, 145 leaves, max depth = 17, train loss: 0.28843, val loss: 0.30616, in 0.031s 1 tree, 97 leaves, max depth = 15, train loss: 0.28662, val loss: 0.30454, in 0.016s 1 tree, 144 leaves, max depth = 16, train loss: 0.28481, val loss: 0.30312, in 0.031s 1 tree, 145 leaves, max depth = 16, train loss: 0.28306, val loss: 0.30176, in 0.031s 1 tree, 239 leaves, max depth = 22, train loss: 0.28142, val loss: 0.30027, in 0.031s 1 tree, 97 leaves, max depth = 15, train loss: 0.27979, val loss: 0.29881, in 0.016s 1 tree, 141 leaves, max depth = 17, train loss: 0.27816, val loss: 0.29755, in 0.031s 1 tree, 91 leaves, max depth = 13, train loss: 0.27661, val loss: 0.29614, in 0.016s 1 tree, 87 leaves, max depth = 13, train loss: 0.27513, val loss: 0.29478, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.27343, val loss: 0.29310, in 0.031s Fit 82 trees in 2.814 s, (16290 total leaves) Time spent computing histograms: 0.755s Time spent finding best splits: 0.373s Time spent applying splits: 0.283s Time spent predicting: 0.016s Trial 77, Fold 3: Log loss = 0.28523170367129097, Average precision = 0.9624306571419454, ROC-AUC = 0.9585553236750103, Elapsed Time = 2.813362500000949 seconds Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 77, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 229 leaves, max depth = 15, train loss: 0.67817, val loss: 0.67782, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.66367, val loss: 0.66310, in 0.031s 1 tree, 239 leaves, max depth = 23, train loss: 0.65044, val loss: 0.64969, in 0.047s 1 tree, 239 leaves, max depth = 19, train loss: 0.63804, val loss: 0.63711, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.62544, val loss: 0.62441, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.61341, val loss: 0.61225, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.60195, val loss: 0.60068, in 0.016s 1 tree, 239 leaves, max depth = 17, train loss: 0.59099, val loss: 0.58955, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.58052, val loss: 0.57891, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.57052, val loss: 0.56877, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.56094, val loss: 0.55903, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.55188, val loss: 0.54974, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.54288, val loss: 0.54058, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.53464, val loss: 0.53214, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.52634, val loss: 0.52368, in 0.031s 1 tree, 222 leaves, max depth = 18, train loss: 0.51860, val loss: 0.51566, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.51145, val loss: 0.50844, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.50408, val loss: 0.50092, in 0.047s 1 tree, 239 leaves, max depth = 19, train loss: 0.49745, val loss: 0.49424, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.49062, val loss: 0.48727, in 0.047s 1 tree, 239 leaves, max depth = 16, train loss: 0.48412, val loss: 0.48057, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.47779, val loss: 0.47411, in 0.047s 1 tree, 195 leaves, max depth = 15, train loss: 0.46992, val loss: 0.46612, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.46410, val loss: 0.46027, in 0.063s 1 tree, 239 leaves, max depth = 17, train loss: 0.45840, val loss: 0.45450, in 0.047s 1 tree, 239 leaves, max depth = 16, train loss: 0.45292, val loss: 0.44892, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.44764, val loss: 0.44354, in 0.031s 1 tree, 226 leaves, max depth = 17, train loss: 0.44079, val loss: 0.43656, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.43590, val loss: 0.43157, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.43112, val loss: 0.42671, in 0.047s 1 tree, 239 leaves, max depth = 15, train loss: 0.42656, val loss: 0.42211, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.42226, val loss: 0.41775, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.41793, val loss: 0.41346, in 0.047s 1 tree, 201 leaves, max depth = 18, train loss: 0.41188, val loss: 0.40736, in 0.031s 1 tree, 198 leaves, max depth = 16, train loss: 0.40612, val loss: 0.40155, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.40230, val loss: 0.39773, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.39847, val loss: 0.39397, in 0.031s 1 tree, 198 leaves, max depth = 19, train loss: 0.39314, val loss: 0.38861, in 0.047s 1 tree, 239 leaves, max depth = 20, train loss: 0.38988, val loss: 0.38529, in 0.047s 1 tree, 239 leaves, max depth = 16, train loss: 0.38635, val loss: 0.38184, in 0.031s 1 tree, 198 leaves, max depth = 19, train loss: 0.38141, val loss: 0.37688, in 0.047s 1 tree, 239 leaves, max depth = 19, train loss: 0.37815, val loss: 0.37374, in 0.047s 1 tree, 202 leaves, max depth = 17, train loss: 0.37349, val loss: 0.36910, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.37041, val loss: 0.36614, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.36750, val loss: 0.36334, in 0.047s 1 tree, 199 leaves, max depth = 17, train loss: 0.36316, val loss: 0.35899, in 0.031s 1 tree, 236 leaves, max depth = 16, train loss: 0.35902, val loss: 0.35483, in 0.047s 1 tree, 237 leaves, max depth = 19, train loss: 0.35505, val loss: 0.35089, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.35227, val loss: 0.34822, in 0.047s 1 tree, 96 leaves, max depth = 16, train loss: 0.34870, val loss: 0.34449, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.34526, val loss: 0.34091, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.34230, val loss: 0.33817, in 0.031s 1 tree, 97 leaves, max depth = 17, train loss: 0.33906, val loss: 0.33483, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.33624, val loss: 0.33222, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.33352, val loss: 0.32970, in 0.047s 1 tree, 144 leaves, max depth = 15, train loss: 0.33043, val loss: 0.32680, in 0.031s 1 tree, 96 leaves, max depth = 16, train loss: 0.32751, val loss: 0.32374, in 0.016s 1 tree, 239 leaves, max depth = 21, train loss: 0.32526, val loss: 0.32166, in 0.047s 1 tree, 144 leaves, max depth = 15, train loss: 0.32241, val loss: 0.31900, in 0.016s 1 tree, 99 leaves, max depth = 18, train loss: 0.31971, val loss: 0.31622, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.31728, val loss: 0.31402, in 0.031s 1 tree, 142 leaves, max depth = 15, train loss: 0.31465, val loss: 0.31157, in 0.031s 1 tree, 140 leaves, max depth = 15, train loss: 0.31213, val loss: 0.30922, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.30984, val loss: 0.30714, in 0.047s 1 tree, 104 leaves, max depth = 16, train loss: 0.30749, val loss: 0.30467, in 0.016s 1 tree, 239 leaves, max depth = 21, train loss: 0.30560, val loss: 0.30295, in 0.047s 1 tree, 140 leaves, max depth = 15, train loss: 0.30329, val loss: 0.30081, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.30109, val loss: 0.29852, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.29893, val loss: 0.29652, in 0.031s 1 tree, 95 leaves, max depth = 16, train loss: 0.29688, val loss: 0.29437, in 0.031s 1 tree, 135 leaves, max depth = 15, train loss: 0.29485, val loss: 0.29248, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.29277, val loss: 0.29050, in 0.031s 1 tree, 92 leaves, max depth = 16, train loss: 0.29087, val loss: 0.28848, in 0.031s 1 tree, 143 leaves, max depth = 15, train loss: 0.28899, val loss: 0.28673, in 0.031s 1 tree, 100 leaves, max depth = 17, train loss: 0.28720, val loss: 0.28487, in 0.031s 1 tree, 136 leaves, max depth = 15, train loss: 0.28544, val loss: 0.28323, in 0.016s 1 tree, 239 leaves, max depth = 20, train loss: 0.28351, val loss: 0.28141, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.28186, val loss: 0.27965, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.28022, val loss: 0.27816, in 0.031s 1 tree, 96 leaves, max depth = 16, train loss: 0.27866, val loss: 0.27656, in 0.016s 1 tree, 239 leaves, max depth = 18, train loss: 0.27685, val loss: 0.27485, in 0.062s 1 tree, 100 leaves, max depth = 16, train loss: 0.27538, val loss: 0.27331, in 0.016s Fit 82 trees in 3.237 s, (16435 total leaves) Time spent computing histograms: 0.853s Time spent finding best splits: 0.450s Time spent applying splits: 0.337s Time spent predicting: 0.000s Trial 77, Fold 4: Log loss = 0.2879911332859797, Average precision = 0.9611189898196466, ROC-AUC = 0.9559277619214528, Elapsed Time = 3.2417513000000326 seconds Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 77, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.190 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 208 leaves, max depth = 18, train loss: 0.67793, val loss: 0.67750, in 0.047s 1 tree, 239 leaves, max depth = 16, train loss: 0.66321, val loss: 0.66250, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.64977, val loss: 0.64872, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.63720, val loss: 0.63584, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.62440, val loss: 0.62281, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.61278, val loss: 0.61096, in 0.031s 1 tree, 239 leaves, max depth = 16, train loss: 0.60110, val loss: 0.59907, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.58997, val loss: 0.58771, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.57933, val loss: 0.57687, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.56931, val loss: 0.56661, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.55970, val loss: 0.55686, in 0.047s 1 tree, 239 leaves, max depth = 23, train loss: 0.55089, val loss: 0.54786, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.54188, val loss: 0.53865, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.53324, val loss: 0.52981, in 0.031s 1 tree, 239 leaves, max depth = 23, train loss: 0.52546, val loss: 0.52186, in 0.047s 1 tree, 239 leaves, max depth = 15, train loss: 0.51795, val loss: 0.51439, in 0.047s 1 tree, 239 leaves, max depth = 17, train loss: 0.51026, val loss: 0.50656, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.50288, val loss: 0.49903, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.49593, val loss: 0.49189, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.48910, val loss: 0.48493, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.48245, val loss: 0.47822, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.47626, val loss: 0.47188, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.47009, val loss: 0.46567, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.46419, val loss: 0.45982, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.45846, val loss: 0.45407, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.45302, val loss: 0.44871, in 0.047s 1 tree, 239 leaves, max depth = 18, train loss: 0.44770, val loss: 0.44337, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.44257, val loss: 0.43823, in 0.047s 1 tree, 194 leaves, max depth = 18, train loss: 0.43561, val loss: 0.43133, in 0.031s 1 tree, 239 leaves, max depth = 15, train loss: 0.43094, val loss: 0.42677, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.42629, val loss: 0.42214, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.42181, val loss: 0.41768, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.41753, val loss: 0.41353, in 0.047s 1 tree, 239 leaves, max depth = 15, train loss: 0.41345, val loss: 0.40950, in 0.047s 1 tree, 239 leaves, max depth = 20, train loss: 0.40941, val loss: 0.40552, in 0.047s 1 tree, 195 leaves, max depth = 20, train loss: 0.40343, val loss: 0.39967, in 0.031s 1 tree, 202 leaves, max depth = 20, train loss: 0.39769, val loss: 0.39404, in 0.047s 1 tree, 197 leaves, max depth = 20, train loss: 0.39220, val loss: 0.38862, in 0.031s 1 tree, 202 leaves, max depth = 20, train loss: 0.38693, val loss: 0.38346, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.38340, val loss: 0.38000, in 0.047s 1 tree, 235 leaves, max depth = 17, train loss: 0.37851, val loss: 0.37524, in 0.047s 1 tree, 198 leaves, max depth = 20, train loss: 0.37373, val loss: 0.37053, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.37048, val loss: 0.36736, in 0.047s 1 tree, 239 leaves, max depth = 19, train loss: 0.36733, val loss: 0.36431, in 0.031s 1 tree, 204 leaves, max depth = 20, train loss: 0.36290, val loss: 0.36000, in 0.047s 1 tree, 239 leaves, max depth = 19, train loss: 0.35994, val loss: 0.35713, in 0.047s 1 tree, 230 leaves, max depth = 17, train loss: 0.35581, val loss: 0.35313, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.35300, val loss: 0.35040, in 0.047s 1 tree, 235 leaves, max depth = 17, train loss: 0.34911, val loss: 0.34666, in 0.047s 1 tree, 206 leaves, max depth = 20, train loss: 0.34529, val loss: 0.34297, in 0.031s 1 tree, 239 leaves, max depth = 21, train loss: 0.34269, val loss: 0.34056, in 0.047s 1 tree, 235 leaves, max depth = 17, train loss: 0.33915, val loss: 0.33717, in 0.031s 1 tree, 97 leaves, max depth = 18, train loss: 0.33589, val loss: 0.33391, in 0.031s 1 tree, 103 leaves, max depth = 16, train loss: 0.33277, val loss: 0.33070, in 0.016s 1 tree, 239 leaves, max depth = 19, train loss: 0.32995, val loss: 0.32810, in 0.047s 1 tree, 102 leaves, max depth = 16, train loss: 0.32699, val loss: 0.32512, in 0.016s 1 tree, 239 leaves, max depth = 19, train loss: 0.32430, val loss: 0.32263, in 0.062s 1 tree, 147 leaves, max depth = 19, train loss: 0.32146, val loss: 0.32001, in 0.016s 1 tree, 239 leaves, max depth = 19, train loss: 0.31887, val loss: 0.31765, in 0.047s 1 tree, 239 leaves, max depth = 19, train loss: 0.31637, val loss: 0.31536, in 0.031s 1 tree, 146 leaves, max depth = 19, train loss: 0.31371, val loss: 0.31296, in 0.031s 1 tree, 100 leaves, max depth = 16, train loss: 0.31113, val loss: 0.31038, in 0.031s 1 tree, 146 leaves, max depth = 17, train loss: 0.30864, val loss: 0.30814, in 0.016s 1 tree, 101 leaves, max depth = 16, train loss: 0.30623, val loss: 0.30573, in 0.031s 1 tree, 143 leaves, max depth = 17, train loss: 0.30390, val loss: 0.30364, in 0.031s 1 tree, 103 leaves, max depth = 16, train loss: 0.30165, val loss: 0.30133, in 0.031s 1 tree, 239 leaves, max depth = 19, train loss: 0.29942, val loss: 0.29931, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.29727, val loss: 0.29715, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.29522, val loss: 0.29505, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.29309, val loss: 0.29303, in 0.031s 1 tree, 239 leaves, max depth = 18, train loss: 0.29105, val loss: 0.29120, in 0.031s 1 tree, 144 leaves, max depth = 18, train loss: 0.28905, val loss: 0.28943, in 0.031s 1 tree, 144 leaves, max depth = 18, train loss: 0.28713, val loss: 0.28774, in 0.031s 1 tree, 239 leaves, max depth = 20, train loss: 0.28520, val loss: 0.28603, in 0.031s 1 tree, 145 leaves, max depth = 16, train loss: 0.28338, val loss: 0.28443, in 0.031s 1 tree, 101 leaves, max depth = 15, train loss: 0.28162, val loss: 0.28263, in 0.031s 1 tree, 145 leaves, max depth = 16, train loss: 0.27991, val loss: 0.28113, in 0.016s 1 tree, 239 leaves, max depth = 21, train loss: 0.27837, val loss: 0.27987, in 0.047s 1 tree, 145 leaves, max depth = 16, train loss: 0.27674, val loss: 0.27841, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.27495, val loss: 0.27671, in 0.031s 1 tree, 99 leaves, max depth = 14, train loss: 0.27338, val loss: 0.27511, in 0.031s 1 tree, 239 leaves, max depth = 17, train loss: 0.27195, val loss: 0.27404, in 0.031s Fit 82 trees in 3.362 s, (16999 total leaves) Time spent computing histograms: 0.884s Time spent finding best splits: 0.470s Time spent applying splits: 0.352s Time spent predicting: 0.000s Trial 77, Fold 5: Log loss = 0.29372377089913193, Average precision = 0.9590494070438371, ROC-AUC = 0.9544510711034315, Elapsed Time = 3.3814411999992444 seconds
Optimization Progress: 78%|#######8 | 78/100 [16:04<05:53, 16.07s/it]
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 78, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 10, train loss: 0.67124, val loss: 0.67051, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.65149, val loss: 0.65008, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.63349, val loss: 0.63142, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.61737, val loss: 0.61467, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.60230, val loss: 0.59900, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.58849, val loss: 0.58462, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.57593, val loss: 0.57164, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.56429, val loss: 0.55947, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.55358, val loss: 0.54825, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.54300, val loss: 0.53831, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.53351, val loss: 0.52834, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.52406, val loss: 0.51949, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.51578, val loss: 0.51077, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.50799, val loss: 0.50252, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.50079, val loss: 0.49490, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.49266, val loss: 0.48735, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.48639, val loss: 0.48068, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.47902, val loss: 0.47387, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47331, val loss: 0.46780, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.46663, val loss: 0.46165, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.46046, val loss: 0.45599, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.45475, val loss: 0.45076, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.44947, val loss: 0.44594, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.44481, val loss: 0.44097, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.43999, val loss: 0.43658, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.43553, val loss: 0.43253, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.43134, val loss: 0.42799, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.42744, val loss: 0.42379, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.42339, val loss: 0.42015, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.41964, val loss: 0.41679, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.41619, val loss: 0.41308, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.41274, val loss: 0.41001, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40952, val loss: 0.40644, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.40634, val loss: 0.40364, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40339, val loss: 0.40036, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.40033, val loss: 0.39706, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.39740, val loss: 0.39453, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.39460, val loss: 0.39151, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39201, val loss: 0.38865, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38936, val loss: 0.38569, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38692, val loss: 0.38295, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.38421, val loss: 0.38066, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.38188, val loss: 0.37816, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37963, val loss: 0.37563, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.37711, val loss: 0.37353, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37503, val loss: 0.37117, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.37269, val loss: 0.36925, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.37052, val loss: 0.36749, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36857, val loss: 0.36527, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.36648, val loss: 0.36306, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.36446, val loss: 0.36144, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.36265, val loss: 0.35937, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.36066, val loss: 0.35724, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.35882, val loss: 0.35527, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.35692, val loss: 0.35377, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35524, val loss: 0.35183, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.35359, val loss: 0.35010, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35203, val loss: 0.34830, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35058, val loss: 0.34662, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.34901, val loss: 0.34504, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34767, val loss: 0.34347, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.34582, val loss: 0.34206, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.34434, val loss: 0.34034, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.34262, val loss: 0.33904, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34134, val loss: 0.33753, in 0.000s 1 tree, 39 leaves, max depth = 12, train loss: 0.33995, val loss: 0.33592, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.33834, val loss: 0.33471, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33712, val loss: 0.33328, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.33580, val loss: 0.33196, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.33456, val loss: 0.33052, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.33303, val loss: 0.32939, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.33188, val loss: 0.32803, in 0.000s Fit 72 trees in 0.970 s, (2538 total leaves) Time spent computing histograms: 0.341s Time spent finding best splits: 0.067s Time spent applying splits: 0.053s Time spent predicting: 0.000s Trial 78, Fold 1: Log loss = 0.3356414306376848, Average precision = 0.9479986388867059, ROC-AUC = 0.9462670362484049, Elapsed Time = 0.9823266000003059 seconds Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 78, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 32 leaves, max depth = 14, train loss: 0.67170, val loss: 0.67066, in 0.000s 1 tree, 32 leaves, max depth = 14, train loss: 0.65225, val loss: 0.65025, in 0.016s 1 tree, 32 leaves, max depth = 14, train loss: 0.63453, val loss: 0.63162, in 0.016s 1 tree, 32 leaves, max depth = 14, train loss: 0.61835, val loss: 0.61458, in 0.000s 1 tree, 32 leaves, max depth = 14, train loss: 0.60356, val loss: 0.59896, in 0.016s 1 tree, 32 leaves, max depth = 14, train loss: 0.59000, val loss: 0.58464, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.57755, val loss: 0.57148, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.56614, val loss: 0.55937, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.55535, val loss: 0.54792, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.54481, val loss: 0.53772, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.53525, val loss: 0.52753, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.52584, val loss: 0.51844, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.51718, val loss: 0.51010, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.50916, val loss: 0.50152, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.50154, val loss: 0.49336, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.49392, val loss: 0.48605, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.48731, val loss: 0.47894, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.48041, val loss: 0.47233, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.47447, val loss: 0.46593, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.46821, val loss: 0.45995, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.46243, val loss: 0.45444, in 0.000s 1 tree, 32 leaves, max depth = 9, train loss: 0.45721, val loss: 0.44880, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.45194, val loss: 0.44378, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.44705, val loss: 0.43848, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.44225, val loss: 0.43392, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.43780, val loss: 0.42971, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.43360, val loss: 0.42515, in 0.000s 1 tree, 32 leaves, max depth = 9, train loss: 0.42973, val loss: 0.42093, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.42568, val loss: 0.41712, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.42194, val loss: 0.41360, in 0.000s 1 tree, 32 leaves, max depth = 14, train loss: 0.41847, val loss: 0.40981, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.41503, val loss: 0.40659, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41191, val loss: 0.40336, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.40874, val loss: 0.40040, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.40565, val loss: 0.39702, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40282, val loss: 0.39408, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.39991, val loss: 0.39137, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.39699, val loss: 0.38816, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39430, val loss: 0.38526, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39174, val loss: 0.38260, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38938, val loss: 0.38013, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.38670, val loss: 0.37767, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38433, val loss: 0.37508, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.38184, val loss: 0.37280, in 0.016s [45/72] 1 tree, 5 leaves, max depth = 3, train loss: 0.37967, val loss: 0.37053, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.37736, val loss: 0.36843, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.37524, val loss: 0.36650, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.37302, val loss: 0.36405, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37100, val loss: 0.36194, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.36902, val loss: 0.36014, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.36699, val loss: 0.35790, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36512, val loss: 0.35594, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.36327, val loss: 0.35427, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36144, val loss: 0.35228, in 0.000s 1 tree, 57 leaves, max depth = 13, train loss: 0.35971, val loss: 0.35074, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35797, val loss: 0.34890, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35628, val loss: 0.34706, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35467, val loss: 0.34536, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35317, val loss: 0.34378, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.35158, val loss: 0.34208, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35019, val loss: 0.34061, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.34872, val loss: 0.33897, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.34703, val loss: 0.33747, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34572, val loss: 0.33608, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.34435, val loss: 0.33459, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34313, val loss: 0.33330, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.34181, val loss: 0.33182, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34067, val loss: 0.33061, in 0.000s 1 tree, 57 leaves, max depth = 14, train loss: 0.33903, val loss: 0.32916, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.33779, val loss: 0.32778, in 0.000s 1 tree, 57 leaves, max depth = 14, train loss: 0.33627, val loss: 0.32644, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33508, val loss: 0.32517, in 0.016s Fit 72 trees in 1.033 s, (2557 total leaves) Time spent computing histograms: 0.360s Time spent finding best splits: 0.071s Time spent applying splits: 0.056s Time spent predicting: 0.000s Trial 78, Fold 2: Log loss = 0.3370147572962747, Average precision = 0.9455089027903654, ROC-AUC = 0.9465475254336876, Elapsed Time = 1.0398611000000528 seconds Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 78, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 10, train loss: 0.67146, val loss: 0.67075, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.65187, val loss: 0.65048, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.63402, val loss: 0.63199, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.61795, val loss: 0.61537, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.60301, val loss: 0.59986, in 0.000s 1 tree, 30 leaves, max depth = 10, train loss: 0.58933, val loss: 0.58563, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57700, val loss: 0.57273, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.56545, val loss: 0.56069, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.55483, val loss: 0.54961, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.54438, val loss: 0.53992, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.53497, val loss: 0.53005, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.52563, val loss: 0.52142, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.51739, val loss: 0.51279, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.50980, val loss: 0.50484, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.50266, val loss: 0.49730, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.49462, val loss: 0.48990, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.48837, val loss: 0.48332, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.48109, val loss: 0.47663, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47550, val loss: 0.47073, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.46887, val loss: 0.46467, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.46275, val loss: 0.45910, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.45708, val loss: 0.45396, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.45184, val loss: 0.44923, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.44721, val loss: 0.44420, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.44242, val loss: 0.43990, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.43799, val loss: 0.43593, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.43382, val loss: 0.43141, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.42997, val loss: 0.42729, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.42594, val loss: 0.42371, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.42221, val loss: 0.42040, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.41878, val loss: 0.41666, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41554, val loss: 0.41364, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41254, val loss: 0.41086, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.40914, val loss: 0.40788, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.40598, val loss: 0.40514, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.40295, val loss: 0.40181, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.40004, val loss: 0.39930, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.39727, val loss: 0.39624, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.39470, val loss: 0.39346, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39204, val loss: 0.39098, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38958, val loss: 0.38870, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.38688, val loss: 0.38640, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38459, val loss: 0.38385, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38233, val loss: 0.38175, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.37982, val loss: 0.37963, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37772, val loss: 0.37768, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.37538, val loss: 0.37573, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.37322, val loss: 0.37394, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37126, val loss: 0.37211, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.36921, val loss: 0.36984, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.36727, val loss: 0.36764, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36547, val loss: 0.36595, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.36343, val loss: 0.36429, in 0.016s [54/72] 1 tree, 31 leaves, max depth = 9, train loss: 0.36169, val loss: 0.36235, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.35978, val loss: 0.36081, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35809, val loss: 0.35923, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.35649, val loss: 0.35745, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35492, val loss: 0.35598, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35346, val loss: 0.35462, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.35195, val loss: 0.35290, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35059, val loss: 0.35163, in 0.016s [62/72] 1 tree, 56 leaves, max depth = 11, train loss: 0.34874, val loss: 0.35016, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.34731, val loss: 0.34842, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34603, val loss: 0.34722, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.34428, val loss: 0.34585, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34293, val loss: 0.34436, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34172, val loss: 0.34323, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.34044, val loss: 0.34165, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.33879, val loss: 0.34037, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33764, val loss: 0.33930, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.33644, val loss: 0.33782, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.33488, val loss: 0.33661, in 0.000s Fit 72 trees in 0.923 s, (2483 total leaves) Time spent computing histograms: 0.324s Time spent finding best splits: 0.062s Time spent applying splits: 0.048s Time spent predicting: 0.000s Trial 78, Fold 3: Log loss = 0.33187436289576167, Average precision = 0.9506772581017511, ROC-AUC = 0.9500002558243684, Elapsed Time = 0.9412186999998085 seconds Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 78, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 11, train loss: 0.67153, val loss: 0.67024, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.65196, val loss: 0.64946, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.63413, val loss: 0.63046, in 0.000s 1 tree, 31 leaves, max depth = 15, train loss: 0.61804, val loss: 0.61331, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.60312, val loss: 0.59733, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.58946, val loss: 0.58263, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.57706, val loss: 0.56932, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.56553, val loss: 0.55684, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.55494, val loss: 0.54533, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.54443, val loss: 0.53496, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.53504, val loss: 0.52471, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.52565, val loss: 0.51547, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.51702, val loss: 0.50697, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.50902, val loss: 0.49824, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.50164, val loss: 0.49015, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.49405, val loss: 0.48269, in 0.000s 1 tree, 31 leaves, max depth = 15, train loss: 0.48745, val loss: 0.47543, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.48059, val loss: 0.46870, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47464, val loss: 0.46208, in 0.000s 1 tree, 56 leaves, max depth = 13, train loss: 0.46842, val loss: 0.45600, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.46268, val loss: 0.45038, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.45747, val loss: 0.44458, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.45224, val loss: 0.43947, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44745, val loss: 0.43409, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.44268, val loss: 0.42944, in 0.031s 1 tree, 56 leaves, max depth = 12, train loss: 0.43827, val loss: 0.42513, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.43408, val loss: 0.42039, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.43020, val loss: 0.41601, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.42620, val loss: 0.41213, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.42250, val loss: 0.40853, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.41903, val loss: 0.40460, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.41563, val loss: 0.40131, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41252, val loss: 0.39795, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.40939, val loss: 0.39492, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40652, val loss: 0.39184, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.40348, val loss: 0.38835, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.40060, val loss: 0.38559, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39782, val loss: 0.38238, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.39522, val loss: 0.37940, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.39266, val loss: 0.37663, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39029, val loss: 0.37407, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.38763, val loss: 0.37154, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.38532, val loss: 0.36886, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.38286, val loss: 0.36652, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38068, val loss: 0.36416, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37866, val loss: 0.36197, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.37637, val loss: 0.35981, in 0.031s 1 tree, 56 leaves, max depth = 10, train loss: 0.37425, val loss: 0.35781, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.37236, val loss: 0.35576, in 0.016s 1 tree, 31 leaves, max depth = 8, train loss: 0.37029, val loss: 0.35337, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.36831, val loss: 0.35152, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36655, val loss: 0.34961, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.36459, val loss: 0.34735, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36278, val loss: 0.34525, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.36092, val loss: 0.34352, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35930, val loss: 0.34175, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.35767, val loss: 0.33985, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35616, val loss: 0.33821, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.35439, val loss: 0.33658, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.35298, val loss: 0.33504, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35141, val loss: 0.33327, in 0.000s 1 tree, 55 leaves, max depth = 11, train loss: 0.34976, val loss: 0.33176, in 0.000s 1 tree, 39 leaves, max depth = 11, train loss: 0.34831, val loss: 0.33017, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34698, val loss: 0.32871, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34574, val loss: 0.32736, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.34436, val loss: 0.32581, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.34278, val loss: 0.32437, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.34148, val loss: 0.32296, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.34001, val loss: 0.32163, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33882, val loss: 0.32033, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.33760, val loss: 0.31901, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.33621, val loss: 0.31775, in 0.016s Fit 72 trees in 1.142 s, (2531 total leaves) Time spent computing histograms: 0.409s Time spent finding best splits: 0.094s Time spent applying splits: 0.076s Time spent predicting: 0.016s Trial 78, Fold 4: Log loss = 0.3340373016637059, Average precision = 0.9511456838640654, ROC-AUC = 0.9483110629460336, Elapsed Time = 1.1569469999994908 seconds Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 78, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 10, train loss: 0.67150, val loss: 0.67008, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.65194, val loss: 0.64918, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.63411, val loss: 0.63007, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.61783, val loss: 0.61256, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.60295, val loss: 0.59650, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.58906, val loss: 0.58146, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.57654, val loss: 0.56791, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.56481, val loss: 0.55513, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.55403, val loss: 0.54333, in 0.000s 1 tree, 58 leaves, max depth = 12, train loss: 0.54368, val loss: 0.53336, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.53411, val loss: 0.52285, in 0.000s 1 tree, 58 leaves, max depth = 13, train loss: 0.52486, val loss: 0.51398, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.51650, val loss: 0.50477, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.50863, val loss: 0.49605, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.50137, val loss: 0.48798, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.49342, val loss: 0.48044, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.48707, val loss: 0.47334, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.47986, val loss: 0.46655, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.47418, val loss: 0.46016, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.46763, val loss: 0.45403, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.46158, val loss: 0.44837, in 0.000s 1 tree, 58 leaves, max depth = 12, train loss: 0.45600, val loss: 0.44316, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.45084, val loss: 0.43836, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.44609, val loss: 0.43302, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.44138, val loss: 0.42866, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.43705, val loss: 0.42375, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.43274, val loss: 0.41978, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.42881, val loss: 0.41530, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.42485, val loss: 0.41168, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.42118, val loss: 0.40834, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.41766, val loss: 0.40432, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.41429, val loss: 0.40127, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.41115, val loss: 0.39825, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.40798, val loss: 0.39459, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.40488, val loss: 0.39182, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40203, val loss: 0.38909, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.39918, val loss: 0.38656, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.39631, val loss: 0.38322, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.39368, val loss: 0.38015, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.39111, val loss: 0.37769, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.38873, val loss: 0.37542, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.38608, val loss: 0.37311, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.38371, val loss: 0.37031, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.38125, val loss: 0.36820, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37906, val loss: 0.36610, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.37704, val loss: 0.36417, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.37475, val loss: 0.36222, in 0.000s 1 tree, 31 leaves, max depth = 12, train loss: 0.37254, val loss: 0.35965, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37067, val loss: 0.35786, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.36853, val loss: 0.35606, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.36651, val loss: 0.35370, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.36477, val loss: 0.35204, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.36277, val loss: 0.35037, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.36091, val loss: 0.34820, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.35905, val loss: 0.34666, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35742, val loss: 0.34511, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.35573, val loss: 0.34309, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35422, val loss: 0.34165, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.35246, val loss: 0.34022, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35105, val loss: 0.33887, in 0.000s 1 tree, 38 leaves, max depth = 10, train loss: 0.34945, val loss: 0.33717, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34793, val loss: 0.33533, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.34627, val loss: 0.33399, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34494, val loss: 0.33272, in 0.016s [65/72] 1 tree, 39 leaves, max depth = 10, train loss: 0.34352, val loss: 0.33121, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34228, val loss: 0.33003, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.34094, val loss: 0.32839, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.33978, val loss: 0.32729, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.33817, val loss: 0.32601, in 0.000s 1 tree, 39 leaves, max depth = 10, train loss: 0.33689, val loss: 0.32465, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.33539, val loss: 0.32347, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.33421, val loss: 0.32220, in 0.000s Fit 72 trees in 0.940 s, (2570 total leaves) Time spent computing histograms: 0.325s Time spent finding best splits: 0.064s Time spent applying splits: 0.051s Time spent predicting: 0.000s Trial 78, Fold 5: Log loss = 0.3390389330580322, Average precision = 0.9488684476965955, ROC-AUC = 0.9455521988740873, Elapsed Time = 0.9489164000005985 seconds
Optimization Progress: 79%|#######9 | 79/100 [16:17<05:15, 15.03s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 79, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 59 leaves, max depth = 13, train loss: 0.68276, val loss: 0.68246, in 0.000s 1 tree, 58 leaves, max depth = 13, train loss: 0.67273, val loss: 0.67213, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.66312, val loss: 0.66230, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.65404, val loss: 0.65296, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.64525, val loss: 0.64390, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.63684, val loss: 0.63521, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.62886, val loss: 0.62696, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.62123, val loss: 0.61907, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.61382, val loss: 0.61141, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.60659, val loss: 0.60391, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.59982, val loss: 0.59685, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.59336, val loss: 0.59016, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.58707, val loss: 0.58364, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.58111, val loss: 0.57743, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.57530, val loss: 0.57140, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.56972, val loss: 0.56561, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.56435, val loss: 0.56007, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.55918, val loss: 0.55475, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.55423, val loss: 0.54953, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.54937, val loss: 0.54445, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.54469, val loss: 0.53955, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.54019, val loss: 0.53484, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.53516, val loss: 0.53018, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.53091, val loss: 0.52573, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.52690, val loss: 0.52159, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.52219, val loss: 0.51723, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.51765, val loss: 0.51305, in 0.000s 1 tree, 59 leaves, max depth = 15, train loss: 0.51400, val loss: 0.50921, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.50968, val loss: 0.50523, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.50554, val loss: 0.50142, in 0.000s 1 tree, 59 leaves, max depth = 15, train loss: 0.50213, val loss: 0.49785, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.49882, val loss: 0.49433, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.49554, val loss: 0.49085, in 0.000s 1 tree, 97 leaves, max depth = 12, train loss: 0.49167, val loss: 0.48730, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.48856, val loss: 0.48400, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.48563, val loss: 0.48096, in 0.000s 1 tree, 59 leaves, max depth = 15, train loss: 0.48282, val loss: 0.47803, in 0.016s 1 tree, 96 leaves, max depth = 12, train loss: 0.47919, val loss: 0.47472, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.47653, val loss: 0.47194, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.47398, val loss: 0.46919, in 0.000s 1 tree, 59 leaves, max depth = 15, train loss: 0.47151, val loss: 0.46661, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.46906, val loss: 0.46399, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.46677, val loss: 0.46159, in 0.000s 1 tree, 97 leaves, max depth = 12, train loss: 0.46340, val loss: 0.45853, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.46121, val loss: 0.45620, in 0.016s 1 tree, 97 leaves, max depth = 12, train loss: 0.45799, val loss: 0.45327, in 0.016s Fit 46 trees in 0.736 s, (3000 total leaves) Time spent computing histograms: 0.204s Time spent finding best splits: 0.062s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 79, Fold 1: Log loss = 0.45842813120494297, Average precision = 0.906537808073568, ROC-AUC = 0.9138198238243407, Elapsed Time = 0.7366600000004837 seconds Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 79, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 58 leaves, max depth = 16, train loss: 0.68272, val loss: 0.68227, in 0.000s 1 tree, 59 leaves, max depth = 14, train loss: 0.67275, val loss: 0.67182, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.66317, val loss: 0.66178, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.65406, val loss: 0.65226, in 0.000s 1 tree, 59 leaves, max depth = 17, train loss: 0.64534, val loss: 0.64314, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.63693, val loss: 0.63431, in 0.000s 1 tree, 59 leaves, max depth = 16, train loss: 0.62892, val loss: 0.62593, in 0.016s 1 tree, 59 leaves, max depth = 17, train loss: 0.62126, val loss: 0.61790, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.61386, val loss: 0.61011, in 0.000s 1 tree, 59 leaves, max depth = 14, train loss: 0.60668, val loss: 0.60253, in 0.000s 1 tree, 59 leaves, max depth = 14, train loss: 0.59990, val loss: 0.59539, in 0.016s 1 tree, 59 leaves, max depth = 18, train loss: 0.59342, val loss: 0.58856, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.58714, val loss: 0.58194, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.58118, val loss: 0.57563, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.57539, val loss: 0.56951, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.56983, val loss: 0.56362, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.56448, val loss: 0.55796, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.55934, val loss: 0.55252, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.55439, val loss: 0.54726, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.54956, val loss: 0.54212, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.54491, val loss: 0.53717, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.54045, val loss: 0.53241, in 0.016s 1 tree, 96 leaves, max depth = 17, train loss: 0.53548, val loss: 0.52765, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.53127, val loss: 0.52316, in 0.000s 1 tree, 59 leaves, max depth = 14, train loss: 0.52728, val loss: 0.51891, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.52263, val loss: 0.51445, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.51815, val loss: 0.51018, in 0.016s 1 tree, 59 leaves, max depth = 17, train loss: 0.51446, val loss: 0.50627, in 0.000s 1 tree, 96 leaves, max depth = 18, train loss: 0.51021, val loss: 0.50220, in 0.016s 1 tree, 96 leaves, max depth = 18, train loss: 0.50612, val loss: 0.49830, in 0.016s 1 tree, 59 leaves, max depth = 17, train loss: 0.50269, val loss: 0.49465, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.49938, val loss: 0.49112, in 0.000s 1 tree, 59 leaves, max depth = 12, train loss: 0.49612, val loss: 0.48762, in 0.016s 1 tree, 96 leaves, max depth = 18, train loss: 0.49230, val loss: 0.48398, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.48922, val loss: 0.48066, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.48631, val loss: 0.47754, in 0.000s 1 tree, 59 leaves, max depth = 14, train loss: 0.48351, val loss: 0.47453, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.47993, val loss: 0.47113, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.47728, val loss: 0.46828, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.47473, val loss: 0.46554, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.47227, val loss: 0.46289, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.46984, val loss: 0.46026, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.46756, val loss: 0.45780, in 0.000s 1 tree, 96 leaves, max depth = 14, train loss: 0.46424, val loss: 0.45465, in 0.016s 1 tree, 59 leaves, max depth = 16, train loss: 0.46208, val loss: 0.45233, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.45889, val loss: 0.44933, in 0.016s Fit 46 trees in 0.799 s, (2993 total leaves) Time spent computing histograms: 0.224s Time spent finding best splits: 0.067s Time spent applying splits: 0.051s Time spent predicting: 0.000s Trial 79, Fold 2: Log loss = 0.45980606153601994, Average precision = 0.9046649152467294, ROC-AUC = 0.9184656113721006, Elapsed Time = 0.8067893000006734 seconds Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 79, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 58 leaves, max depth = 15, train loss: 0.68282, val loss: 0.68247, in 0.016s 1 tree, 58 leaves, max depth = 15, train loss: 0.67289, val loss: 0.67223, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.66337, val loss: 0.66234, in 0.000s 1 tree, 58 leaves, max depth = 12, train loss: 0.65434, val loss: 0.65300, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.64570, val loss: 0.64407, in 0.016s 1 tree, 58 leaves, max depth = 15, train loss: 0.63737, val loss: 0.63547, in 0.000s 1 tree, 58 leaves, max depth = 15, train loss: 0.62943, val loss: 0.62725, in 0.016s 1 tree, 58 leaves, max depth = 15, train loss: 0.62184, val loss: 0.61939, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.61447, val loss: 0.61172, in 0.000s 1 tree, 58 leaves, max depth = 14, train loss: 0.60731, val loss: 0.60432, in 0.016s 1 tree, 58 leaves, max depth = 15, train loss: 0.60056, val loss: 0.59734, in 0.016s 1 tree, 58 leaves, max depth = 15, train loss: 0.59413, val loss: 0.59067, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.58796, val loss: 0.58425, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.58203, val loss: 0.57807, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.57635, val loss: 0.57215, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.57080, val loss: 0.56634, in 0.000s 1 tree, 58 leaves, max depth = 10, train loss: 0.56547, val loss: 0.56076, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.56035, val loss: 0.55539, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.55540, val loss: 0.55024, in 0.000s 1 tree, 58 leaves, max depth = 14, train loss: 0.55058, val loss: 0.54524, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.54594, val loss: 0.54043, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.54148, val loss: 0.53581, in 0.000s 1 tree, 95 leaves, max depth = 13, train loss: 0.53645, val loss: 0.53114, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.53224, val loss: 0.52676, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.52827, val loss: 0.52258, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.52356, val loss: 0.51820, in 0.000s 1 tree, 95 leaves, max depth = 14, train loss: 0.51903, val loss: 0.51400, in 0.016s 1 tree, 58 leaves, max depth = 16, train loss: 0.51539, val loss: 0.51018, in 0.000s 1 tree, 95 leaves, max depth = 15, train loss: 0.51108, val loss: 0.50619, in 0.016s 1 tree, 95 leaves, max depth = 15, train loss: 0.50694, val loss: 0.50236, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.50355, val loss: 0.49878, in 0.016s 1 tree, 58 leaves, max depth = 16, train loss: 0.50026, val loss: 0.49534, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.49700, val loss: 0.49193, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.49313, val loss: 0.48836, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.49006, val loss: 0.48513, in 0.000s 1 tree, 58 leaves, max depth = 10, train loss: 0.48717, val loss: 0.48204, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.48439, val loss: 0.47907, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.48076, val loss: 0.47573, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.47813, val loss: 0.47291, in 0.016s 1 tree, 58 leaves, max depth = 16, train loss: 0.47558, val loss: 0.47024, in 0.000s 1 tree, 58 leaves, max depth = 10, train loss: 0.47314, val loss: 0.46762, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.47072, val loss: 0.46506, in 0.016s 1 tree, 58 leaves, max depth = 16, train loss: 0.46844, val loss: 0.46268, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.46507, val loss: 0.45958, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.46292, val loss: 0.45727, in 0.000s 1 tree, 95 leaves, max depth = 14, train loss: 0.45969, val loss: 0.45431, in 0.016s Fit 46 trees in 0.861 s, (2951 total leaves) Time spent computing histograms: 0.237s Time spent finding best splits: 0.075s Time spent applying splits: 0.056s Time spent predicting: 0.031s Trial 79, Fold 3: Log loss = 0.45530085571122997, Average precision = 0.9135555234024215, ROC-AUC = 0.9231098792749757, Elapsed Time = 0.8606184000000212 seconds Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 79, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 58 leaves, max depth = 15, train loss: 0.68277, val loss: 0.68224, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.67280, val loss: 0.67171, in 0.000s 1 tree, 59 leaves, max depth = 13, train loss: 0.66325, val loss: 0.66162, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.65418, val loss: 0.65207, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.64544, val loss: 0.64281, in 0.000s 1 tree, 59 leaves, max depth = 16, train loss: 0.63710, val loss: 0.63395, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.62914, val loss: 0.62549, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.62151, val loss: 0.61738, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.61414, val loss: 0.60952, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.60699, val loss: 0.60187, in 0.000s 1 tree, 59 leaves, max depth = 16, train loss: 0.60023, val loss: 0.59465, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.59377, val loss: 0.58774, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.58751, val loss: 0.58104, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.58157, val loss: 0.57465, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.57580, val loss: 0.56845, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.57025, val loss: 0.56248, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.56492, val loss: 0.55674, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.55979, val loss: 0.55122, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.55486, val loss: 0.54590, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.55005, val loss: 0.54068, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.54543, val loss: 0.53565, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.54099, val loss: 0.53081, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.53603, val loss: 0.52594, in 0.031s 1 tree, 59 leaves, max depth = 13, train loss: 0.53184, val loss: 0.52136, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.52786, val loss: 0.51703, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.52322, val loss: 0.51247, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.51875, val loss: 0.50809, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.51509, val loss: 0.50408, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.51085, val loss: 0.49992, in 0.031s 1 tree, 95 leaves, max depth = 16, train loss: 0.50677, val loss: 0.49592, in 0.016s 1 tree, 59 leaves, max depth = 16, train loss: 0.50336, val loss: 0.49219, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.50005, val loss: 0.48856, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.49681, val loss: 0.48498, in 0.016s 1 tree, 94 leaves, max depth = 18, train loss: 0.49301, val loss: 0.48126, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.48994, val loss: 0.47786, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.48704, val loss: 0.47465, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.48425, val loss: 0.47156, in 0.016s 1 tree, 95 leaves, max depth = 14, train loss: 0.48068, val loss: 0.46808, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.47804, val loss: 0.46515, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.47549, val loss: 0.46231, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.47304, val loss: 0.45958, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.47062, val loss: 0.45687, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.46835, val loss: 0.45432, in 0.016s 1 tree, 95 leaves, max depth = 12, train loss: 0.46504, val loss: 0.45110, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.46287, val loss: 0.44865, in 0.016s 1 tree, 95 leaves, max depth = 15, train loss: 0.45971, val loss: 0.44557, in 0.016s Fit 46 trees in 1.033 s, (2982 total leaves) Time spent computing histograms: 0.307s Time spent finding best splits: 0.098s Time spent applying splits: 0.076s Time spent predicting: 0.000s Trial 79, Fold 4: Log loss = 0.45939396332992505, Average precision = 0.9076412395230304, ROC-AUC = 0.9176424118543316, Elapsed Time = 1.0503874999994878 seconds Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 79, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.283 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 59 leaves, max depth = 11, train loss: 0.68265, val loss: 0.68201, in 0.016s 1 tree, 59 leaves, max depth = 16, train loss: 0.67260, val loss: 0.67133, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.66300, val loss: 0.66111, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.65381, val loss: 0.65130, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.64502, val loss: 0.64191, in 0.000s 1 tree, 59 leaves, max depth = 15, train loss: 0.63659, val loss: 0.63289, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.62853, val loss: 0.62427, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.62081, val loss: 0.61598, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.61337, val loss: 0.60795, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.60614, val loss: 0.60017, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.59931, val loss: 0.59281, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.59277, val loss: 0.58576, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.58650, val loss: 0.57898, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.58050, val loss: 0.57243, in 0.016s 1 tree, 59 leaves, max depth = 15, train loss: 0.57471, val loss: 0.56613, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.56915, val loss: 0.56010, in 0.000s 1 tree, 57 leaves, max depth = 14, train loss: 0.56374, val loss: 0.55423, in 0.016s 1 tree, 57 leaves, max depth = 14, train loss: 0.55855, val loss: 0.54858, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.55356, val loss: 0.54312, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.54870, val loss: 0.53780, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.54402, val loss: 0.53267, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.53953, val loss: 0.52773, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.53462, val loss: 0.52307, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.53038, val loss: 0.51839, in 0.016s 1 tree, 58 leaves, max depth = 14, train loss: 0.52635, val loss: 0.51396, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.52175, val loss: 0.50961, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.51734, val loss: 0.50543, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.51363, val loss: 0.50134, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.50943, val loss: 0.49738, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.50539, val loss: 0.49357, in 0.016s 1 tree, 59 leaves, max depth = 12, train loss: 0.50194, val loss: 0.48976, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.49860, val loss: 0.48607, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.49531, val loss: 0.48241, in 0.016s 1 tree, 97 leaves, max depth = 18, train loss: 0.49154, val loss: 0.47887, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.48842, val loss: 0.47539, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.48547, val loss: 0.47210, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.48264, val loss: 0.46892, in 0.016s 1 tree, 97 leaves, max depth = 16, train loss: 0.47910, val loss: 0.46563, in 0.016s 1 tree, 59 leaves, max depth = 13, train loss: 0.47644, val loss: 0.46266, in 0.016s 1 tree, 59 leaves, max depth = 14, train loss: 0.47385, val loss: 0.45976, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.47136, val loss: 0.45696, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.46890, val loss: 0.45418, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.46659, val loss: 0.45157, in 0.016s 1 tree, 97 leaves, max depth = 17, train loss: 0.46330, val loss: 0.44853, in 0.016s 1 tree, 59 leaves, max depth = 16, train loss: 0.46113, val loss: 0.44605, in 0.016s 1 tree, 97 leaves, max depth = 17, train loss: 0.45798, val loss: 0.44315, in 0.016s Fit 46 trees in 1.189 s, (2986 total leaves) Time spent computing histograms: 0.311s Time spent finding best splits: 0.101s Time spent applying splits: 0.076s Time spent predicting: 0.000s Trial 79, Fold 5: Log loss = 0.46532086876685613, Average precision = 0.9022929032342281, ROC-AUC = 0.9129145532579009, Elapsed Time = 1.2088435999994545 seconds
Optimization Progress: 80%|######## | 80/100 [16:28<04:39, 13.97s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 80, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.236 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.68558, val loss: 0.68559, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.67822, val loss: 0.67832, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.67105, val loss: 0.67123, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.66407, val loss: 0.66432, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65741, val loss: 0.65768, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.65121, val loss: 0.65141, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.64510, val loss: 0.64521, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.63875, val loss: 0.63893, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.63260, val loss: 0.63281, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.62659, val loss: 0.62684, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.62095, val loss: 0.62120, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.61516, val loss: 0.61547, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.60951, val loss: 0.60987, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.60391, val loss: 0.60431, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.59866, val loss: 0.59903, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.59338, val loss: 0.59381, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.58846, val loss: 0.58888, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.58334, val loss: 0.58377, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.57833, val loss: 0.57879, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.57374, val loss: 0.57422, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.56908, val loss: 0.56954, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.56438, val loss: 0.56486, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.55986, val loss: 0.56037, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.55543, val loss: 0.55598, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.55128, val loss: 0.55181, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54722, val loss: 0.54773, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.54306, val loss: 0.54360, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.53897, val loss: 0.53956, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.53497, val loss: 0.53559, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.53099, val loss: 0.53163, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.52714, val loss: 0.52774, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52350, val loss: 0.52407, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51975, val loss: 0.52033, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51608, val loss: 0.51663, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.51246, val loss: 0.51298, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.50893, val loss: 0.50944, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50570, val loss: 0.50618, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50231, val loss: 0.50280, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49917, val loss: 0.49958, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.49610, val loss: 0.49644, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49298, val loss: 0.49323, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.48982, val loss: 0.49005, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.48692, val loss: 0.48707, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.48388, val loss: 0.48403, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.48090, val loss: 0.48104, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.47818, val loss: 0.47825, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47531, val loss: 0.47537, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47250, val loss: 0.47255, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46974, val loss: 0.46978, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.46708, val loss: 0.46707, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46442, val loss: 0.46440, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.46179, val loss: 0.46176, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45924, val loss: 0.45918, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45671, val loss: 0.45665, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45425, val loss: 0.45418, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45184, val loss: 0.45175, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44944, val loss: 0.44935, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.44598, val loss: 0.44599, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.44265, val loss: 0.44275, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43934, val loss: 0.43956, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.43716, val loss: 0.43733, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43497, val loss: 0.43514, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43282, val loss: 0.43299, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.43076, val loss: 0.43088, in 0.016s Fit 64 trees in 1.533 s, (2240 total leaves) Time spent computing histograms: 0.473s Time spent finding best splits: 0.073s Time spent applying splits: 0.048s Time spent predicting: 0.000s Trial 80, Fold 1: Log loss = 0.43370266737972424, Average precision = 0.9503427459847125, ROC-AUC = 0.9456591322416752, Elapsed Time = 1.5502667000000656 seconds Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 80, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.188 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 10, train loss: 0.68564, val loss: 0.68548, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.67827, val loss: 0.67796, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.67106, val loss: 0.67061, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.66407, val loss: 0.66347, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.65724, val loss: 0.65651, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.65055, val loss: 0.64969, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.64435, val loss: 0.64342, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.63806, val loss: 0.63703, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.63215, val loss: 0.63106, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62622, val loss: 0.62498, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.62027, val loss: 0.61892, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.61477, val loss: 0.61335, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.60909, val loss: 0.60755, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.60353, val loss: 0.60189, in 0.031s 1 tree, 35 leaves, max depth = 14, train loss: 0.59838, val loss: 0.59667, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59306, val loss: 0.59132, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.58777, val loss: 0.58596, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.58260, val loss: 0.58073, in 0.016s 1 tree, 35 leaves, max depth = 14, train loss: 0.57789, val loss: 0.57597, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.57301, val loss: 0.57103, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.56819, val loss: 0.56617, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.56347, val loss: 0.56141, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.55883, val loss: 0.55672, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.55435, val loss: 0.55213, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54990, val loss: 0.54764, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.54559, val loss: 0.54325, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54134, val loss: 0.53895, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.53755, val loss: 0.53511, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.53347, val loss: 0.53097, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52946, val loss: 0.52692, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.52579, val loss: 0.52322, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.52221, val loss: 0.51960, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51845, val loss: 0.51578, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51476, val loss: 0.51204, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51115, val loss: 0.50838, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.50761, val loss: 0.50479, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.50437, val loss: 0.50152, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.50096, val loss: 0.49806, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.49782, val loss: 0.49490, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.49482, val loss: 0.49188, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.49167, val loss: 0.48870, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.48857, val loss: 0.48559, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.48572, val loss: 0.48273, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.48267, val loss: 0.47965, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47968, val loss: 0.47662, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47701, val loss: 0.47393, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.47414, val loss: 0.47104, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.47131, val loss: 0.46817, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46874, val loss: 0.46553, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46602, val loss: 0.46277, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46234, val loss: 0.45914, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45972, val loss: 0.45650, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45714, val loss: 0.45388, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.45462, val loss: 0.45132, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45214, val loss: 0.44881, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.44974, val loss: 0.44642, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.44736, val loss: 0.44398, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44399, val loss: 0.44066, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.44068, val loss: 0.43739, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43745, val loss: 0.43421, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.43525, val loss: 0.43199, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43303, val loss: 0.42975, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.43085, val loss: 0.42752, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.42872, val loss: 0.42536, in 0.016s Fit 64 trees in 1.642 s, (2240 total leaves) Time spent computing histograms: 0.537s Time spent finding best splits: 0.086s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 80, Fold 2: Log loss = 0.43118969439221216, Average precision = 0.9489102449125872, ROC-AUC = 0.947832373933251, Elapsed Time = 1.6524898000006942 seconds Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 80, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.68564, val loss: 0.68560, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67826, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.67119, val loss: 0.67112, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.66424, val loss: 0.66415, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.65747, val loss: 0.65735, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.65115, val loss: 0.65106, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.64469, val loss: 0.64458, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.63875, val loss: 0.63867, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.63288, val loss: 0.63282, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62698, val loss: 0.62688, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.62109, val loss: 0.62098, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.61556, val loss: 0.61547, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.61014, val loss: 0.61008, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.60453, val loss: 0.60448, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59937, val loss: 0.59934, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59415, val loss: 0.59412, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.58900, val loss: 0.58896, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.58402, val loss: 0.58398, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.57901, val loss: 0.57898, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.57436, val loss: 0.57435, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.56984, val loss: 0.56986, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56512, val loss: 0.56515, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.56050, val loss: 0.56055, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.55599, val loss: 0.55606, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.55162, val loss: 0.55172, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.54729, val loss: 0.54741, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.54306, val loss: 0.54320, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.53925, val loss: 0.53940, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.53518, val loss: 0.53534, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.53120, val loss: 0.53138, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.52751, val loss: 0.52772, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.52368, val loss: 0.52392, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.51993, val loss: 0.52018, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.51626, val loss: 0.51653, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.51266, val loss: 0.51295, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50913, val loss: 0.50944, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.50588, val loss: 0.50621, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.50249, val loss: 0.50283, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49830, val loss: 0.49887, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49523, val loss: 0.49582, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.49202, val loss: 0.49262, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.48887, val loss: 0.48949, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.48599, val loss: 0.48666, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.48296, val loss: 0.48363, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.47998, val loss: 0.48067, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.47729, val loss: 0.47802, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.47443, val loss: 0.47515, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.47161, val loss: 0.47235, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.46885, val loss: 0.46961, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46618, val loss: 0.46696, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.46352, val loss: 0.46430, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46091, val loss: 0.46170, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.45835, val loss: 0.45916, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45583, val loss: 0.45668, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.45337, val loss: 0.45422, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.45099, val loss: 0.45186, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.44861, val loss: 0.44951, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.44518, val loss: 0.44634, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44190, val loss: 0.44329, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.43967, val loss: 0.44111, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.43745, val loss: 0.43891, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43526, val loss: 0.43674, in 0.031s 1 tree, 35 leaves, max depth = 12, train loss: 0.43312, val loss: 0.43461, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.43101, val loss: 0.43254, in 0.016s Fit 64 trees in 1.472 s, (2240 total leaves) Time spent computing histograms: 0.485s Time spent finding best splits: 0.073s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 80, Fold 3: Log loss = 0.4285578476224689, Average precision = 0.9520868528689832, ROC-AUC = 0.9500523988500544, Elapsed Time = 1.4797304999992775 seconds Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 80, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.159 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 8, train loss: 0.68571, val loss: 0.68540, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.67843, val loss: 0.67781, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.67133, val loss: 0.67041, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.66450, val loss: 0.66324, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.65775, val loss: 0.65620, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.65150, val loss: 0.64966, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.64506, val loss: 0.64294, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.63912, val loss: 0.63671, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.63330, val loss: 0.63062, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.62743, val loss: 0.62446, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62157, val loss: 0.61833, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.61614, val loss: 0.61265, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.61053, val loss: 0.60679, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.60499, val loss: 0.60097, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.59988, val loss: 0.59562, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.59465, val loss: 0.59008, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.58942, val loss: 0.58461, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.58438, val loss: 0.57929, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.57942, val loss: 0.57407, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.57484, val loss: 0.56936, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.57009, val loss: 0.56437, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.56547, val loss: 0.55949, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.56089, val loss: 0.55468, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.55643, val loss: 0.54999, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.55233, val loss: 0.54576, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.54837, val loss: 0.54155, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.54425, val loss: 0.53720, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.54013, val loss: 0.53289, in 0.031s 1 tree, 35 leaves, max depth = 8, train loss: 0.53606, val loss: 0.52863, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.53212, val loss: 0.52448, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52846, val loss: 0.52064, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.52466, val loss: 0.51666, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.52093, val loss: 0.51273, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.51726, val loss: 0.50888, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.51368, val loss: 0.50510, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.51015, val loss: 0.50140, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50693, val loss: 0.49799, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50354, val loss: 0.49443, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.50036, val loss: 0.49112, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49738, val loss: 0.48793, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49428, val loss: 0.48465, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.49113, val loss: 0.48135, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.48829, val loss: 0.47834, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.48529, val loss: 0.47516, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.48237, val loss: 0.47206, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47948, val loss: 0.46900, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47669, val loss: 0.46602, in 0.031s 1 tree, 35 leaves, max depth = 9, train loss: 0.47398, val loss: 0.46314, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.47124, val loss: 0.46023, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46854, val loss: 0.45738, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46596, val loss: 0.45462, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.46337, val loss: 0.45187, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46082, val loss: 0.44918, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45833, val loss: 0.44652, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45585, val loss: 0.44392, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.45343, val loss: 0.44136, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.45111, val loss: 0.43890, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.44888, val loss: 0.43651, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.44664, val loss: 0.43414, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.44446, val loss: 0.43180, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.44113, val loss: 0.42842, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.43895, val loss: 0.42613, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.43682, val loss: 0.42387, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43364, val loss: 0.42064, in 0.016s Fit 64 trees in 1.472 s, (2240 total leaves) Time spent computing histograms: 0.483s Time spent finding best splits: 0.079s Time spent applying splits: 0.052s Time spent predicting: 0.000s Trial 80, Fold 4: Log loss = 0.43292715360677114, Average precision = 0.9522821749291704, ROC-AUC = 0.9482136001964754, Elapsed Time = 1.4838823999998567 seconds Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 80, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 9, train loss: 0.68554, val loss: 0.68523, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.67812, val loss: 0.67753, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.67089, val loss: 0.67003, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.66384, val loss: 0.66271, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.65696, val loss: 0.65558, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.65032, val loss: 0.64869, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.64413, val loss: 0.64223, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.63782, val loss: 0.63567, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.63186, val loss: 0.62942, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.62589, val loss: 0.62316, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.61992, val loss: 0.61696, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.61440, val loss: 0.61117, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.60869, val loss: 0.60525, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.60312, val loss: 0.59947, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.59793, val loss: 0.59403, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.59268, val loss: 0.58857, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.58747, val loss: 0.58317, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.58244, val loss: 0.57794, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.57746, val loss: 0.57278, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.57284, val loss: 0.56794, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.56804, val loss: 0.56294, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.56334, val loss: 0.55805, in 0.016s 1 tree, 35 leaves, max depth = 12, train loss: 0.55877, val loss: 0.55330, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.55429, val loss: 0.54866, in 0.016s 1 tree, 35 leaves, max depth = 8, train loss: 0.54995, val loss: 0.54414, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.54561, val loss: 0.53960, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.54136, val loss: 0.53516, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.53729, val loss: 0.53092, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.53322, val loss: 0.52666, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.52925, val loss: 0.52252, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.52555, val loss: 0.51865, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.52176, val loss: 0.51471, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.51805, val loss: 0.51086, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.51441, val loss: 0.50707, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.51082, val loss: 0.50334, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50728, val loss: 0.49964, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50403, val loss: 0.49623, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.50064, val loss: 0.49271, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.49751, val loss: 0.48938, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49441, val loss: 0.48614, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.49120, val loss: 0.48278, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.48806, val loss: 0.47952, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.48520, val loss: 0.47651, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.48216, val loss: 0.47333, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.47920, val loss: 0.47025, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.47530, val loss: 0.46632, in 0.031s 1 tree, 35 leaves, max depth = 10, train loss: 0.47261, val loss: 0.46350, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.46988, val loss: 0.46061, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46712, val loss: 0.45774, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.46440, val loss: 0.45489, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.46180, val loss: 0.45217, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45917, val loss: 0.44943, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.45661, val loss: 0.44677, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45408, val loss: 0.44414, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.45162, val loss: 0.44157, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44920, val loss: 0.43908, in 0.016s 1 tree, 35 leaves, max depth = 7, train loss: 0.44688, val loss: 0.43665, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44454, val loss: 0.43422, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.44224, val loss: 0.43183, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43998, val loss: 0.42946, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43777, val loss: 0.42716, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43559, val loss: 0.42491, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.43235, val loss: 0.42168, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.43025, val loss: 0.41950, in 0.000s Fit 64 trees in 1.455 s, (2240 total leaves) Time spent computing histograms: 0.473s Time spent finding best splits: 0.072s Time spent applying splits: 0.048s Time spent predicting: 0.000s Trial 80, Fold 5: Log loss = 0.43518271020566096, Average precision = 0.947309225647094, ROC-AUC = 0.9459746205153932, Elapsed Time = 1.4620661999997537 seconds
Optimization Progress: 81%|########1 | 81/100 [16:44<04:37, 14.61s/it]
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 81, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.147 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 7 leaves, max depth = 3, train loss: 0.68509, val loss: 0.68486, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.67748, val loss: 0.67703, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.67013, val loss: 0.66946, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.66301, val loss: 0.66212, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.65612, val loss: 0.65502, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.64945, val loss: 0.64814, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.64299, val loss: 0.64148, in 0.000s 1 tree, 7 leaves, max depth = 3, train loss: 0.63674, val loss: 0.63502, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.63068, val loss: 0.62875, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.62481, val loss: 0.62267, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.61912, val loss: 0.61678, in 0.000s Fit 11 trees in 0.331 s, (85 total leaves) Time spent computing histograms: 0.047s Time spent finding best splits: 0.004s Time spent applying splits: 0.003s Time spent predicting: 0.000s Trial 81, Fold 1: Log loss = 0.6195250216480869, Average precision = 0.8109929655039347, ROC-AUC = 0.8611422531222668, Elapsed Time = 0.33699350000097184 seconds Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 81, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 8 leaves, max depth = 4, train loss: 0.68524, val loss: 0.68485, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.67772, val loss: 0.67695, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.67039, val loss: 0.66926, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.66336, val loss: 0.66185, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.65649, val loss: 0.65464, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.64990, val loss: 0.64769, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.64346, val loss: 0.64092, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.63729, val loss: 0.63440, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.63124, val loss: 0.62804, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.62539, val loss: 0.62187, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61977, val loss: 0.61593, in 0.000s Fit 11 trees in 0.329 s, (83 total leaves) Time spent computing histograms: 0.041s Time spent finding best splits: 0.004s Time spent applying splits: 0.002s Time spent predicting: 0.016s Trial 81, Fold 2: Log loss = 0.6193558844500675, Average precision = 0.8194662883517918, ROC-AUC = 0.8683016630646799, Elapsed Time = 0.34302349999961734 seconds Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 81, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 8 leaves, max depth = 4, train loss: 0.68520, val loss: 0.68494, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.67773, val loss: 0.67717, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.67043, val loss: 0.66962, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.66344, val loss: 0.66235, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.65660, val loss: 0.65527, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.64999, val loss: 0.64842, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.64365, val loss: 0.64181, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.63744, val loss: 0.63538, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.63150, val loss: 0.62917, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.62567, val loss: 0.62313, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.62003, val loss: 0.61727, in 0.000s Fit 11 trees in 0.345 s, (86 total leaves) Time spent computing histograms: 0.045s Time spent finding best splits: 0.004s Time spent applying splits: 0.003s Time spent predicting: 0.000s Trial 81, Fold 3: Log loss = 0.6181173893012052, Average precision = 0.823732825492677, ROC-AUC = 0.8711087608710308, Elapsed Time = 0.3515162000003329 seconds Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 81, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.159 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 7 leaves, max depth = 4, train loss: 0.68524, val loss: 0.68478, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.67769, val loss: 0.67678, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.67040, val loss: 0.66904, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.66334, val loss: 0.66154, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.65651, val loss: 0.65428, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.64990, val loss: 0.64724, in 0.000s 1 tree, 6 leaves, max depth = 3, train loss: 0.64349, val loss: 0.64042, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.63729, val loss: 0.63381, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.63129, val loss: 0.62740, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.62547, val loss: 0.62119, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.61983, val loss: 0.61516, in 0.000s Fit 11 trees in 0.346 s, (76 total leaves) Time spent computing histograms: 0.046s Time spent finding best splits: 0.005s Time spent applying splits: 0.002s Time spent predicting: 0.000s Trial 81, Fold 4: Log loss = 0.6188015647415763, Average precision = 0.8267691012009457, ROC-AUC = 0.8702258900405588, Elapsed Time = 0.3649242999999842 seconds Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 81, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.141 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 7 leaves, max depth = 5, train loss: 0.68522, val loss: 0.68468, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.67770, val loss: 0.67662, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.67042, val loss: 0.66881, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.66338, val loss: 0.66124, in 0.000s 1 tree, 7 leaves, max depth = 5, train loss: 0.65656, val loss: 0.65391, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.64997, val loss: 0.64681, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.64358, val loss: 0.63992, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.63730, val loss: 0.63319, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.63121, val loss: 0.62666, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.62532, val loss: 0.62034, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.61969, val loss: 0.61424, in 0.000s Fit 11 trees in 0.344 s, (78 total leaves) Time spent computing histograms: 0.042s Time spent finding best splits: 0.004s Time spent applying splits: 0.002s Time spent predicting: 0.000s Trial 81, Fold 5: Log loss = 0.6219450285121826, Average precision = 0.8066400069708548, ROC-AUC = 0.8550896085316687, Elapsed Time = 0.359162999999171 seconds
Optimization Progress: 82%|########2 | 82/100 [16:52<03:48, 12.69s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 82, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 73 leaves, max depth = 10, train loss: 0.68500, val loss: 0.68496, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.67689, val loss: 0.67690, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.66899, val loss: 0.66908, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.66132, val loss: 0.66148, in 0.031s 1 tree, 84 leaves, max depth = 14, train loss: 0.65406, val loss: 0.65418, in 0.016s 1 tree, 129 leaves, max depth = 12, train loss: 0.64690, val loss: 0.64701, in 0.016s 1 tree, 76 leaves, max depth = 10, train loss: 0.64000, val loss: 0.64006, in 0.031s 1 tree, 120 leaves, max depth = 13, train loss: 0.63308, val loss: 0.63320, in 0.016s 1 tree, 81 leaves, max depth = 10, train loss: 0.62654, val loss: 0.62661, in 0.016s 1 tree, 92 leaves, max depth = 10, train loss: 0.62017, val loss: 0.62015, in 0.016s 1 tree, 116 leaves, max depth = 15, train loss: 0.61377, val loss: 0.61380, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.60753, val loss: 0.60760, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.60162, val loss: 0.60173, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.59583, val loss: 0.59589, in 0.016s 1 tree, 145 leaves, max depth = 15, train loss: 0.59011, val loss: 0.59012, in 0.016s 1 tree, 120 leaves, max depth = 15, train loss: 0.58446, val loss: 0.58451, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.57896, val loss: 0.57904, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.57379, val loss: 0.57389, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.56851, val loss: 0.56860, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.56338, val loss: 0.56351, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.55834, val loss: 0.55848, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.55340, val loss: 0.55346, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.54880, val loss: 0.54886, in 0.031s 1 tree, 145 leaves, max depth = 14, train loss: 0.54428, val loss: 0.54429, in 0.031s 1 tree, 148 leaves, max depth = 15, train loss: 0.53965, val loss: 0.53959, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.53517, val loss: 0.53516, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.53079, val loss: 0.53081, in 0.016s 1 tree, 148 leaves, max depth = 15, train loss: 0.52646, val loss: 0.52642, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.52226, val loss: 0.52226, in 0.031s 1 tree, 122 leaves, max depth = 14, train loss: 0.51833, val loss: 0.51834, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.51432, val loss: 0.51438, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.51050, val loss: 0.51056, in 0.031s 1 tree, 120 leaves, max depth = 15, train loss: 0.50665, val loss: 0.50673, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.50289, val loss: 0.50300, in 0.016s 1 tree, 128 leaves, max depth = 13, train loss: 0.49912, val loss: 0.49922, in 0.031s 1 tree, 144 leaves, max depth = 14, train loss: 0.49557, val loss: 0.49567, in 0.031s 1 tree, 121 leaves, max depth = 14, train loss: 0.49204, val loss: 0.49219, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.48858, val loss: 0.48876, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.48533, val loss: 0.48554, in 0.031s 1 tree, 122 leaves, max depth = 15, train loss: 0.48203, val loss: 0.48228, in 0.031s 1 tree, 129 leaves, max depth = 15, train loss: 0.47868, val loss: 0.47893, in 0.016s 1 tree, 126 leaves, max depth = 13, train loss: 0.47542, val loss: 0.47567, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.47243, val loss: 0.47272, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.46934, val loss: 0.46958, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.46643, val loss: 0.46667, in 0.016s 1 tree, 134 leaves, max depth = 15, train loss: 0.46234, val loss: 0.46267, in 0.031s 1 tree, 127 leaves, max depth = 13, train loss: 0.45936, val loss: 0.45968, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.45537, val loss: 0.45581, in 0.016s 1 tree, 126 leaves, max depth = 13, train loss: 0.45267, val loss: 0.45310, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.44995, val loss: 0.45043, in 0.031s 1 tree, 119 leaves, max depth = 16, train loss: 0.44613, val loss: 0.44675, in 0.031s 1 tree, 145 leaves, max depth = 15, train loss: 0.44346, val loss: 0.44405, in 0.016s 1 tree, 122 leaves, max depth = 16, train loss: 0.43977, val loss: 0.44052, in 0.031s 1 tree, 128 leaves, max depth = 15, train loss: 0.43715, val loss: 0.43790, in 0.016s 1 tree, 117 leaves, max depth = 15, train loss: 0.43360, val loss: 0.43447, in 0.031s 1 tree, 94 leaves, max depth = 13, train loss: 0.43119, val loss: 0.43201, in 0.016s 1 tree, 147 leaves, max depth = 14, train loss: 0.42876, val loss: 0.42955, in 0.031s 1 tree, 129 leaves, max depth = 13, train loss: 0.42633, val loss: 0.42714, in 0.031s 1 tree, 127 leaves, max depth = 13, train loss: 0.42395, val loss: 0.42476, in 0.016s 1 tree, 127 leaves, max depth = 13, train loss: 0.42162, val loss: 0.42243, in 0.031s 1 tree, 128 leaves, max depth = 13, train loss: 0.41934, val loss: 0.42015, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.41608, val loss: 0.41702, in 0.016s 1 tree, 131 leaves, max depth = 13, train loss: 0.41387, val loss: 0.41483, in 0.031s 1 tree, 117 leaves, max depth = 15, train loss: 0.41072, val loss: 0.41182, in 0.016s 1 tree, 130 leaves, max depth = 13, train loss: 0.40859, val loss: 0.40971, in 0.031s 1 tree, 94 leaves, max depth = 12, train loss: 0.40662, val loss: 0.40769, in 0.031s 1 tree, 129 leaves, max depth = 12, train loss: 0.40456, val loss: 0.40566, in 0.016s 1 tree, 129 leaves, max depth = 12, train loss: 0.40255, val loss: 0.40368, in 0.031s 1 tree, 128 leaves, max depth = 12, train loss: 0.40058, val loss: 0.40173, in 0.016s 1 tree, 130 leaves, max depth = 14, train loss: 0.39864, val loss: 0.39981, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.39684, val loss: 0.39802, in 0.031s 1 tree, 134 leaves, max depth = 15, train loss: 0.39400, val loss: 0.39529, in 0.031s 1 tree, 118 leaves, max depth = 15, train loss: 0.39119, val loss: 0.39262, in 0.016s 1 tree, 145 leaves, max depth = 14, train loss: 0.38942, val loss: 0.39084, in 0.031s 1 tree, 100 leaves, max depth = 15, train loss: 0.38784, val loss: 0.38921, in 0.016s 1 tree, 118 leaves, max depth = 15, train loss: 0.38514, val loss: 0.38664, in 0.031s 1 tree, 125 leaves, max depth = 13, train loss: 0.38341, val loss: 0.38494, in 0.031s 1 tree, 114 leaves, max depth = 18, train loss: 0.38081, val loss: 0.38251, in 0.016s 1 tree, 92 leaves, max depth = 15, train loss: 0.37836, val loss: 0.38012, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.37671, val loss: 0.37848, in 0.031s 1 tree, 118 leaves, max depth = 15, train loss: 0.37423, val loss: 0.37614, in 0.016s 1 tree, 143 leaves, max depth = 14, train loss: 0.37266, val loss: 0.37458, in 0.031s 1 tree, 119 leaves, max depth = 15, train loss: 0.37026, val loss: 0.37232, in 0.031s 1 tree, 123 leaves, max depth = 13, train loss: 0.36880, val loss: 0.37089, in 0.016s 1 tree, 135 leaves, max depth = 15, train loss: 0.36651, val loss: 0.36871, in 0.031s 1 tree, 127 leaves, max depth = 13, train loss: 0.36499, val loss: 0.36723, in 0.031s 1 tree, 117 leaves, max depth = 15, train loss: 0.36273, val loss: 0.36510, in 0.016s 1 tree, 119 leaves, max depth = 17, train loss: 0.36052, val loss: 0.36305, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.35907, val loss: 0.36162, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.35764, val loss: 0.36022, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.35565, val loss: 0.35824, in 0.016s 1 tree, 61 leaves, max depth = 15, train loss: 0.35371, val loss: 0.35633, in 0.031s 1 tree, 67 leaves, max depth = 14, train loss: 0.35181, val loss: 0.35445, in 0.016s 1 tree, 62 leaves, max depth = 16, train loss: 0.34996, val loss: 0.35266, in 0.016s 1 tree, 66 leaves, max depth = 14, train loss: 0.34815, val loss: 0.35087, in 0.031s 1 tree, 68 leaves, max depth = 14, train loss: 0.34638, val loss: 0.34912, in 0.016s 1 tree, 120 leaves, max depth = 17, train loss: 0.34456, val loss: 0.34750, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.34306, val loss: 0.34607, in 0.031s 1 tree, 126 leaves, max depth = 17, train loss: 0.34158, val loss: 0.34465, in 0.016s 1 tree, 67 leaves, max depth = 14, train loss: 0.33991, val loss: 0.34300, in 0.016s Fit 100 trees in 2.768 s, (11849 total leaves) Time spent computing histograms: 0.865s Time spent finding best splits: 0.237s Time spent applying splits: 0.224s Time spent predicting: 0.000s Trial 82, Fold 1: Log loss = 0.34719319393428133, Average precision = 0.955178807528165, ROC-AUC = 0.9496816793564762, Elapsed Time = 2.782788900000014 seconds Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 82, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 68 leaves, max depth = 12, train loss: 0.68493, val loss: 0.68479, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.67671, val loss: 0.67650, in 0.016s 1 tree, 108 leaves, max depth = 16, train loss: 0.66879, val loss: 0.66848, in 0.031s 1 tree, 107 leaves, max depth = 16, train loss: 0.66110, val loss: 0.66068, in 0.031s 1 tree, 91 leaves, max depth = 12, train loss: 0.65366, val loss: 0.65311, in 0.016s 1 tree, 141 leaves, max depth = 12, train loss: 0.64641, val loss: 0.64582, in 0.031s 1 tree, 93 leaves, max depth = 12, train loss: 0.63943, val loss: 0.63873, in 0.031s 1 tree, 120 leaves, max depth = 14, train loss: 0.63240, val loss: 0.63162, in 0.016s [9/100] 1 tree, 120 leaves, max depth = 14, train loss: 0.62556, val loss: 0.62469, in 0.031s 1 tree, 123 leaves, max depth = 17, train loss: 0.61931, val loss: 0.61836, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.61280, val loss: 0.61177, in 0.031s 1 tree, 124 leaves, max depth = 25, train loss: 0.60688, val loss: 0.60580, in 0.016s 1 tree, 100 leaves, max depth = 14, train loss: 0.60083, val loss: 0.59964, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.59485, val loss: 0.59362, in 0.016s 1 tree, 127 leaves, max depth = 25, train loss: 0.58933, val loss: 0.58807, in 0.031s 1 tree, 98 leaves, max depth = 17, train loss: 0.58409, val loss: 0.58274, in 0.031s 1 tree, 128 leaves, max depth = 15, train loss: 0.57850, val loss: 0.57711, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.57300, val loss: 0.57153, in 0.031s 1 tree, 128 leaves, max depth = 25, train loss: 0.56799, val loss: 0.56650, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.56300, val loss: 0.56150, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.55791, val loss: 0.55638, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.55294, val loss: 0.55139, in 0.016s 1 tree, 122 leaves, max depth = 14, train loss: 0.54804, val loss: 0.54644, in 0.031s 1 tree, 101 leaves, max depth = 17, train loss: 0.54370, val loss: 0.54205, in 0.016s 1 tree, 125 leaves, max depth = 17, train loss: 0.53934, val loss: 0.53766, in 0.031s 1 tree, 127 leaves, max depth = 15, train loss: 0.53476, val loss: 0.53305, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.53027, val loss: 0.52852, in 0.031s 1 tree, 128 leaves, max depth = 15, train loss: 0.52589, val loss: 0.52412, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.52161, val loss: 0.51981, in 0.016s 1 tree, 124 leaves, max depth = 18, train loss: 0.51772, val loss: 0.51589, in 0.016s 1 tree, 147 leaves, max depth = 14, train loss: 0.51365, val loss: 0.51183, in 0.031s 1 tree, 151 leaves, max depth = 15, train loss: 0.50969, val loss: 0.50787, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.50575, val loss: 0.50391, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.50189, val loss: 0.50004, in 0.031s 1 tree, 151 leaves, max depth = 14, train loss: 0.49819, val loss: 0.49634, in 0.031s 1 tree, 147 leaves, max depth = 14, train loss: 0.49455, val loss: 0.49271, in 0.031s 1 tree, 146 leaves, max depth = 14, train loss: 0.49099, val loss: 0.48914, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.48754, val loss: 0.48571, in 0.031s 1 tree, 150 leaves, max depth = 14, train loss: 0.48412, val loss: 0.48230, in 0.031s 1 tree, 129 leaves, max depth = 14, train loss: 0.48074, val loss: 0.47889, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.47748, val loss: 0.47561, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.47422, val loss: 0.47233, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.47104, val loss: 0.46912, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.46792, val loss: 0.46599, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.46487, val loss: 0.46291, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.46189, val loss: 0.45991, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.45897, val loss: 0.45697, in 0.031s 1 tree, 119 leaves, max depth = 18, train loss: 0.45499, val loss: 0.45306, in 0.031s 1 tree, 142 leaves, max depth = 18, train loss: 0.45115, val loss: 0.44930, in 0.031s 1 tree, 151 leaves, max depth = 14, train loss: 0.44843, val loss: 0.44659, in 0.031s 1 tree, 119 leaves, max depth = 22, train loss: 0.44467, val loss: 0.44293, in 0.031s 1 tree, 120 leaves, max depth = 15, train loss: 0.44098, val loss: 0.43928, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.43834, val loss: 0.43664, in 0.031s 1 tree, 150 leaves, max depth = 14, train loss: 0.43580, val loss: 0.43412, in 0.016s 1 tree, 121 leaves, max depth = 18, train loss: 0.43230, val loss: 0.43070, in 0.031s 1 tree, 103 leaves, max depth = 13, train loss: 0.42994, val loss: 0.42830, in 0.031s 1 tree, 154 leaves, max depth = 13, train loss: 0.42754, val loss: 0.42593, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.42513, val loss: 0.42353, in 0.016s 1 tree, 132 leaves, max depth = 14, train loss: 0.42277, val loss: 0.42119, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.42046, val loss: 0.41889, in 0.016s 1 tree, 131 leaves, max depth = 14, train loss: 0.41821, val loss: 0.41664, in 0.047s 1 tree, 120 leaves, max depth = 16, train loss: 0.41500, val loss: 0.41352, in 0.031s 1 tree, 131 leaves, max depth = 13, train loss: 0.41285, val loss: 0.41134, in 0.031s 1 tree, 121 leaves, max depth = 17, train loss: 0.40975, val loss: 0.40832, in 0.047s 1 tree, 130 leaves, max depth = 13, train loss: 0.40764, val loss: 0.40622, in 0.016s 1 tree, 130 leaves, max depth = 13, train loss: 0.40557, val loss: 0.40417, in 0.031s 1 tree, 126 leaves, max depth = 13, train loss: 0.40354, val loss: 0.40215, in 0.031s 1 tree, 128 leaves, max depth = 13, train loss: 0.40155, val loss: 0.40015, in 0.031s 1 tree, 128 leaves, max depth = 13, train loss: 0.39960, val loss: 0.39820, in 0.031s 1 tree, 119 leaves, max depth = 17, train loss: 0.39672, val loss: 0.39540, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.39398, val loss: 0.39271, in 0.031s 1 tree, 147 leaves, max depth = 13, train loss: 0.39216, val loss: 0.39093, in 0.031s 1 tree, 115 leaves, max depth = 23, train loss: 0.38943, val loss: 0.38828, in 0.031s 1 tree, 147 leaves, max depth = 14, train loss: 0.38769, val loss: 0.38657, in 0.031s 1 tree, 104 leaves, max depth = 16, train loss: 0.38613, val loss: 0.38502, in 0.016s 1 tree, 143 leaves, max depth = 17, train loss: 0.38355, val loss: 0.38255, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.38184, val loss: 0.38085, in 0.031s 1 tree, 152 leaves, max depth = 18, train loss: 0.38025, val loss: 0.37933, in 0.031s 1 tree, 143 leaves, max depth = 17, train loss: 0.37778, val loss: 0.37696, in 0.031s 1 tree, 121 leaves, max depth = 15, train loss: 0.37530, val loss: 0.37455, in 0.031s 1 tree, 152 leaves, max depth = 15, train loss: 0.37373, val loss: 0.37302, in 0.031s 1 tree, 114 leaves, max depth = 23, train loss: 0.37134, val loss: 0.37073, in 0.031s 1 tree, 130 leaves, max depth = 15, train loss: 0.36977, val loss: 0.36918, in 0.031s 1 tree, 114 leaves, max depth = 23, train loss: 0.36746, val loss: 0.36696, in 0.031s 1 tree, 95 leaves, max depth = 15, train loss: 0.36527, val loss: 0.36483, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.36377, val loss: 0.36334, in 0.032s 1 tree, 119 leaves, max depth = 17, train loss: 0.36158, val loss: 0.36122, in 0.031s 1 tree, 129 leaves, max depth = 14, train loss: 0.36013, val loss: 0.35979, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.35870, val loss: 0.35838, in 0.031s 1 tree, 68 leaves, max depth = 16, train loss: 0.35672, val loss: 0.35645, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.35533, val loss: 0.35507, in 0.031s 1 tree, 143 leaves, max depth = 17, train loss: 0.35333, val loss: 0.35317, in 0.031s 1 tree, 68 leaves, max depth = 16, train loss: 0.35146, val loss: 0.35134, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.35013, val loss: 0.35005, in 0.031s 1 tree, 68 leaves, max depth = 18, train loss: 0.34831, val loss: 0.34829, in 0.016s 1 tree, 124 leaves, max depth = 17, train loss: 0.34647, val loss: 0.34659, in 0.031s 1 tree, 62 leaves, max depth = 19, train loss: 0.34472, val loss: 0.34489, in 0.016s 1 tree, 122 leaves, max depth = 19, train loss: 0.34288, val loss: 0.34312, in 0.031s 1 tree, 123 leaves, max depth = 20, train loss: 0.34141, val loss: 0.34173, in 0.031s 1 tree, 123 leaves, max depth = 20, train loss: 0.33997, val loss: 0.34036, in 0.031s Fit 100 trees in 3.159 s, (12361 total leaves) Time spent computing histograms: 0.969s Time spent finding best splits: 0.275s Time spent applying splits: 0.270s Time spent predicting: 0.016s Trial 82, Fold 2: Log loss = 0.344785585703399, Average precision = 0.9539294557132714, ROC-AUC = 0.9512520861526808, Elapsed Time = 3.155847199999698 seconds Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 82, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 69 leaves, max depth = 11, train loss: 0.68499, val loss: 0.68497, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.67725, val loss: 0.67731, in 0.031s 1 tree, 114 leaves, max depth = 15, train loss: 0.66935, val loss: 0.66944, in 0.031s 1 tree, 121 leaves, max depth = 13, train loss: 0.66160, val loss: 0.66178, in 0.016s 1 tree, 116 leaves, max depth = 15, train loss: 0.65413, val loss: 0.65433, in 0.031s 1 tree, 121 leaves, max depth = 14, train loss: 0.64731, val loss: 0.64754, in 0.016s 1 tree, 117 leaves, max depth = 16, train loss: 0.64022, val loss: 0.64049, in 0.031s 1 tree, 94 leaves, max depth = 16, train loss: 0.63348, val loss: 0.63369, in 0.016s 1 tree, 146 leaves, max depth = 15, train loss: 0.62712, val loss: 0.62741, in 0.031s 1 tree, 124 leaves, max depth = 18, train loss: 0.62084, val loss: 0.62124, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.61443, val loss: 0.61484, in 0.031s 1 tree, 125 leaves, max depth = 16, train loss: 0.60818, val loss: 0.60861, in 0.031s 1 tree, 128 leaves, max depth = 17, train loss: 0.60236, val loss: 0.60273, in 0.016s 1 tree, 125 leaves, max depth = 18, train loss: 0.59668, val loss: 0.59715, in 0.031s 1 tree, 87 leaves, max depth = 16, train loss: 0.59094, val loss: 0.59143, in 0.016s 1 tree, 121 leaves, max depth = 16, train loss: 0.58553, val loss: 0.58608, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.57998, val loss: 0.58055, in 0.016s 1 tree, 146 leaves, max depth = 15, train loss: 0.57455, val loss: 0.57522, in 0.031s 1 tree, 124 leaves, max depth = 18, train loss: 0.56952, val loss: 0.57025, in 0.031s 1 tree, 127 leaves, max depth = 13, train loss: 0.56430, val loss: 0.56503, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.55917, val loss: 0.55994, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.55417, val loss: 0.55498, in 0.031s 1 tree, 142 leaves, max depth = 16, train loss: 0.54963, val loss: 0.55050, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.54519, val loss: 0.54608, in 0.031s 1 tree, 122 leaves, max depth = 15, train loss: 0.54052, val loss: 0.54145, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.53595, val loss: 0.53693, in 0.016s 1 tree, 123 leaves, max depth = 16, train loss: 0.53175, val loss: 0.53277, in 0.031s 1 tree, 127 leaves, max depth = 18, train loss: 0.52754, val loss: 0.52866, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.52361, val loss: 0.52476, in 0.031s 1 tree, 124 leaves, max depth = 16, train loss: 0.51941, val loss: 0.52061, in 0.031s 1 tree, 143 leaves, max depth = 16, train loss: 0.51535, val loss: 0.51662, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.51134, val loss: 0.51262, in 0.031s 1 tree, 120 leaves, max depth = 15, train loss: 0.50741, val loss: 0.50875, in 0.031s 1 tree, 141 leaves, max depth = 14, train loss: 0.50391, val loss: 0.50535, in 0.016s 1 tree, 144 leaves, max depth = 16, train loss: 0.50019, val loss: 0.50171, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.49662, val loss: 0.49812, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.49302, val loss: 0.49453, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.48949, val loss: 0.49105, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.48603, val loss: 0.48764, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.48265, val loss: 0.48429, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.47933, val loss: 0.48100, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.47609, val loss: 0.47778, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.47293, val loss: 0.47463, in 0.031s 1 tree, 116 leaves, max depth = 15, train loss: 0.46870, val loss: 0.47070, in 0.031s 1 tree, 145 leaves, max depth = 15, train loss: 0.46568, val loss: 0.46777, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.46267, val loss: 0.46481, in 0.016s 1 tree, 145 leaves, max depth = 15, train loss: 0.45978, val loss: 0.46200, in 0.031s 1 tree, 112 leaves, max depth = 17, train loss: 0.45583, val loss: 0.45831, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.45300, val loss: 0.45554, in 0.047s 1 tree, 125 leaves, max depth = 17, train loss: 0.45030, val loss: 0.45293, in 0.016s 1 tree, 143 leaves, max depth = 15, train loss: 0.44762, val loss: 0.45033, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.44496, val loss: 0.44769, in 0.031s 1 tree, 136 leaves, max depth = 16, train loss: 0.44128, val loss: 0.44435, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.43871, val loss: 0.44183, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.43618, val loss: 0.43934, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.43371, val loss: 0.43692, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.43129, val loss: 0.43453, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.42892, val loss: 0.43218, in 0.031s 1 tree, 89 leaves, max depth = 14, train loss: 0.42670, val loss: 0.42998, in 0.031s 1 tree, 116 leaves, max depth = 16, train loss: 0.42329, val loss: 0.42686, in 0.031s 1 tree, 113 leaves, max depth = 17, train loss: 0.41999, val loss: 0.42382, in 0.016s 1 tree, 87 leaves, max depth = 14, train loss: 0.41787, val loss: 0.42172, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.41570, val loss: 0.41960, in 0.016s 1 tree, 88 leaves, max depth = 15, train loss: 0.41366, val loss: 0.41757, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.41157, val loss: 0.41551, in 0.031s 1 tree, 125 leaves, max depth = 16, train loss: 0.40952, val loss: 0.41351, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.40753, val loss: 0.41154, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.40556, val loss: 0.40961, in 0.016s 1 tree, 117 leaves, max depth = 16, train loss: 0.40252, val loss: 0.40684, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.39957, val loss: 0.40415, in 0.031s 1 tree, 135 leaves, max depth = 19, train loss: 0.39672, val loss: 0.40161, in 0.031s 1 tree, 115 leaves, max depth = 19, train loss: 0.39390, val loss: 0.39904, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.39207, val loss: 0.39724, in 0.031s 1 tree, 125 leaves, max depth = 16, train loss: 0.39027, val loss: 0.39549, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.38861, val loss: 0.39389, in 0.016s 1 tree, 116 leaves, max depth = 17, train loss: 0.38591, val loss: 0.39146, in 0.031s 1 tree, 116 leaves, max depth = 18, train loss: 0.38327, val loss: 0.38909, in 0.031s 1 tree, 118 leaves, max depth = 16, train loss: 0.38070, val loss: 0.38677, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.37914, val loss: 0.38526, in 0.031s 1 tree, 116 leaves, max depth = 16, train loss: 0.37665, val loss: 0.38302, in 0.031s 1 tree, 145 leaves, max depth = 14, train loss: 0.37506, val loss: 0.38152, in 0.031s 1 tree, 137 leaves, max depth = 15, train loss: 0.37267, val loss: 0.37944, in 0.031s 1 tree, 127 leaves, max depth = 15, train loss: 0.37110, val loss: 0.37794, in 0.031s 1 tree, 112 leaves, max depth = 14, train loss: 0.36878, val loss: 0.37584, in 0.016s 1 tree, 99 leaves, max depth = 13, train loss: 0.36735, val loss: 0.37443, in 0.031s 1 tree, 117 leaves, max depth = 16, train loss: 0.36508, val loss: 0.37239, in 0.016s 1 tree, 113 leaves, max depth = 14, train loss: 0.36287, val loss: 0.37041, in 0.031s 1 tree, 64 leaves, max depth = 13, train loss: 0.36081, val loss: 0.36855, in 0.031s 1 tree, 145 leaves, max depth = 13, train loss: 0.35940, val loss: 0.36723, in 0.031s 1 tree, 63 leaves, max depth = 14, train loss: 0.35741, val loss: 0.36543, in 0.016s 1 tree, 123 leaves, max depth = 19, train loss: 0.35581, val loss: 0.36379, in 0.016s 1 tree, 122 leaves, max depth = 19, train loss: 0.35425, val loss: 0.36219, in 0.031s 1 tree, 123 leaves, max depth = 19, train loss: 0.35271, val loss: 0.36061, in 0.016s 1 tree, 123 leaves, max depth = 19, train loss: 0.35120, val loss: 0.35906, in 0.031s 1 tree, 122 leaves, max depth = 19, train loss: 0.34973, val loss: 0.35755, in 0.016s 1 tree, 64 leaves, max depth = 14, train loss: 0.34786, val loss: 0.35587, in 0.016s 1 tree, 64 leaves, max depth = 14, train loss: 0.34604, val loss: 0.35424, in 0.031s 1 tree, 116 leaves, max depth = 15, train loss: 0.34413, val loss: 0.35257, in 0.016s 1 tree, 123 leaves, max depth = 18, train loss: 0.34271, val loss: 0.35112, in 0.031s 1 tree, 122 leaves, max depth = 18, train loss: 0.34132, val loss: 0.34969, in 0.016s Fit 100 trees in 2.939 s, (11986 total leaves) Time spent computing histograms: 0.927s Time spent finding best splits: 0.250s Time spent applying splits: 0.238s Time spent predicting: 0.000s Trial 82, Fold 3: Log loss = 0.342112439021262, Average precision = 0.9573984711296334, ROC-AUC = 0.9542221103283219, Elapsed Time = 2.9520584999991115 seconds Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 82, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 79 leaves, max depth = 12, train loss: 0.68500, val loss: 0.68470, in 0.031s 1 tree, 113 leaves, max depth = 14, train loss: 0.67691, val loss: 0.67637, in 0.016s 1 tree, 136 leaves, max depth = 12, train loss: 0.66914, val loss: 0.66829, in 0.016s 1 tree, 135 leaves, max depth = 12, train loss: 0.66159, val loss: 0.66041, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.65443, val loss: 0.65297, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.64753, val loss: 0.64593, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.64045, val loss: 0.63862, in 0.031s 1 tree, 99 leaves, max depth = 14, train loss: 0.63372, val loss: 0.63159, in 0.031s 1 tree, 145 leaves, max depth = 14, train loss: 0.62746, val loss: 0.62502, in 0.031s 1 tree, 126 leaves, max depth = 19, train loss: 0.62112, val loss: 0.61850, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.61471, val loss: 0.61189, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.60846, val loss: 0.60543, in 0.031s 1 tree, 126 leaves, max depth = 17, train loss: 0.60265, val loss: 0.59934, in 0.016s 1 tree, 122 leaves, max depth = 14, train loss: 0.59705, val loss: 0.59353, in 0.016s 1 tree, 148 leaves, max depth = 14, train loss: 0.59132, val loss: 0.58753, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.58589, val loss: 0.58187, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.58034, val loss: 0.57612, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.57493, val loss: 0.57050, in 0.031s 1 tree, 91 leaves, max depth = 14, train loss: 0.56979, val loss: 0.56511, in 0.016s 1 tree, 145 leaves, max depth = 14, train loss: 0.56497, val loss: 0.56014, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.55989, val loss: 0.55486, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.55495, val loss: 0.54973, in 0.031s 1 tree, 146 leaves, max depth = 14, train loss: 0.55021, val loss: 0.54473, in 0.031s 1 tree, 146 leaves, max depth = 13, train loss: 0.54587, val loss: 0.54014, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.54125, val loss: 0.53540, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.53704, val loss: 0.53101, in 0.031s 1 tree, 125 leaves, max depth = 17, train loss: 0.53262, val loss: 0.52647, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.52847, val loss: 0.52215, in 0.031s 1 tree, 147 leaves, max depth = 13, train loss: 0.52431, val loss: 0.51781, in 0.031s 1 tree, 146 leaves, max depth = 13, train loss: 0.52025, val loss: 0.51351, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.51622, val loss: 0.50930, in 0.031s 1 tree, 144 leaves, max depth = 13, train loss: 0.51243, val loss: 0.50532, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.50863, val loss: 0.50136, in 0.016s 1 tree, 124 leaves, max depth = 20, train loss: 0.50496, val loss: 0.49757, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.50125, val loss: 0.49371, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.49761, val loss: 0.48995, in 0.016s 1 tree, 149 leaves, max depth = 14, train loss: 0.49412, val loss: 0.48630, in 0.047s 1 tree, 127 leaves, max depth = 17, train loss: 0.49064, val loss: 0.48267, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.48723, val loss: 0.47911, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.48388, val loss: 0.47557, in 0.031s 1 tree, 128 leaves, max depth = 17, train loss: 0.48061, val loss: 0.47217, in 0.031s 1 tree, 150 leaves, max depth = 14, train loss: 0.47749, val loss: 0.46888, in 0.031s 1 tree, 120 leaves, max depth = 14, train loss: 0.47323, val loss: 0.46456, in 0.031s 1 tree, 130 leaves, max depth = 14, train loss: 0.47005, val loss: 0.46126, in 0.016s 1 tree, 124 leaves, max depth = 14, train loss: 0.46698, val loss: 0.45804, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.46403, val loss: 0.45496, in 0.016s 1 tree, 149 leaves, max depth = 14, train loss: 0.46120, val loss: 0.45197, in 0.031s 1 tree, 131 leaves, max depth = 15, train loss: 0.45826, val loss: 0.44891, in 0.031s 1 tree, 92 leaves, max depth = 14, train loss: 0.45559, val loss: 0.44607, in 0.016s 1 tree, 141 leaves, max depth = 14, train loss: 0.45179, val loss: 0.44217, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.44905, val loss: 0.43932, in 0.016s 1 tree, 125 leaves, max depth = 17, train loss: 0.44643, val loss: 0.43658, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.44379, val loss: 0.43385, in 0.031s 1 tree, 131 leaves, max depth = 15, train loss: 0.44117, val loss: 0.43112, in 0.031s 1 tree, 130 leaves, max depth = 15, train loss: 0.43860, val loss: 0.42844, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.43609, val loss: 0.42581, in 0.062s 1 tree, 114 leaves, max depth = 15, train loss: 0.43257, val loss: 0.42228, in 0.063s 1 tree, 129 leaves, max depth = 15, train loss: 0.43015, val loss: 0.41975, in 0.031s 1 tree, 131 leaves, max depth = 15, train loss: 0.42778, val loss: 0.41727, in 0.047s 1 tree, 112 leaves, max depth = 14, train loss: 0.42442, val loss: 0.41389, in 0.031s 1 tree, 127 leaves, max depth = 15, train loss: 0.42213, val loss: 0.41152, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.42006, val loss: 0.40943, in 0.031s 1 tree, 146 leaves, max depth = 12, train loss: 0.41794, val loss: 0.40719, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.41578, val loss: 0.40494, in 0.047s 1 tree, 117 leaves, max depth = 17, train loss: 0.41261, val loss: 0.40174, in 0.031s 1 tree, 149 leaves, max depth = 14, train loss: 0.41058, val loss: 0.39959, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.40867, val loss: 0.39762, in 0.031s 1 tree, 138 leaves, max depth = 17, train loss: 0.40570, val loss: 0.39459, in 0.031s 1 tree, 148 leaves, max depth = 13, train loss: 0.40377, val loss: 0.39255, in 0.031s 1 tree, 117 leaves, max depth = 17, train loss: 0.40083, val loss: 0.38959, in 0.031s 1 tree, 147 leaves, max depth = 13, train loss: 0.39897, val loss: 0.38763, in 0.032s 1 tree, 139 leaves, max depth = 17, train loss: 0.39619, val loss: 0.38480, in 0.031s 1 tree, 119 leaves, max depth = 17, train loss: 0.39341, val loss: 0.38200, in 0.031s 1 tree, 119 leaves, max depth = 17, train loss: 0.39069, val loss: 0.37927, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.38893, val loss: 0.37747, in 0.031s 1 tree, 145 leaves, max depth = 15, train loss: 0.38736, val loss: 0.37581, in 0.031s 1 tree, 150 leaves, max depth = 13, train loss: 0.38567, val loss: 0.37402, in 0.031s 1 tree, 118 leaves, max depth = 17, train loss: 0.38308, val loss: 0.37142, in 0.031s 1 tree, 129 leaves, max depth = 16, train loss: 0.38139, val loss: 0.36964, in 0.016s 1 tree, 119 leaves, max depth = 17, train loss: 0.37888, val loss: 0.36712, in 0.031s 1 tree, 149 leaves, max depth = 14, train loss: 0.37730, val loss: 0.36545, in 0.031s 1 tree, 117 leaves, max depth = 17, train loss: 0.37487, val loss: 0.36301, in 0.031s 1 tree, 112 leaves, max depth = 14, train loss: 0.37256, val loss: 0.36067, in 0.031s 1 tree, 129 leaves, max depth = 16, train loss: 0.37099, val loss: 0.35904, in 0.031s 1 tree, 128 leaves, max depth = 16, train loss: 0.36945, val loss: 0.35743, in 0.031s 1 tree, 113 leaves, max depth = 17, train loss: 0.36723, val loss: 0.35518, in 0.031s 1 tree, 117 leaves, max depth = 17, train loss: 0.36500, val loss: 0.35294, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.36353, val loss: 0.35142, in 0.016s 1 tree, 67 leaves, max depth = 16, train loss: 0.36149, val loss: 0.34928, in 0.031s 1 tree, 62 leaves, max depth = 13, train loss: 0.35950, val loss: 0.34723, in 0.031s 1 tree, 124 leaves, max depth = 18, train loss: 0.35787, val loss: 0.34559, in 0.047s 1 tree, 67 leaves, max depth = 16, train loss: 0.35594, val loss: 0.34359, in 0.016s 1 tree, 68 leaves, max depth = 13, train loss: 0.35405, val loss: 0.34163, in 0.016s 1 tree, 123 leaves, max depth = 17, train loss: 0.35249, val loss: 0.34008, in 0.031s 1 tree, 124 leaves, max depth = 18, train loss: 0.35097, val loss: 0.33855, in 0.031s 1 tree, 123 leaves, max depth = 17, train loss: 0.34947, val loss: 0.33707, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.34760, val loss: 0.33534, in 0.031s 1 tree, 61 leaves, max depth = 13, train loss: 0.34584, val loss: 0.33352, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.34404, val loss: 0.33186, in 0.031s 1 tree, 66 leaves, max depth = 16, train loss: 0.34235, val loss: 0.33010, in 0.016s Fit 100 trees in 3.252 s, (12459 total leaves) Time spent computing histograms: 1.017s Time spent finding best splits: 0.295s Time spent applying splits: 0.285s Time spent predicting: 0.000s Trial 82, Fold 4: Log loss = 0.34486513459364354, Average precision = 0.9576524629502526, ROC-AUC = 0.952854257721315, Elapsed Time = 3.2568659000007756 seconds Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 82, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 70 leaves, max depth = 12, train loss: 0.68497, val loss: 0.68470, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.67712, val loss: 0.67656, in 0.016s 1 tree, 122 leaves, max depth = 15, train loss: 0.66919, val loss: 0.66845, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.66149, val loss: 0.66050, in 0.031s 1 tree, 123 leaves, max depth = 15, train loss: 0.65400, val loss: 0.65283, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.64693, val loss: 0.64545, in 0.031s 1 tree, 120 leaves, max depth = 15, train loss: 0.63982, val loss: 0.63818, in 0.031s 1 tree, 98 leaves, max depth = 14, train loss: 0.63294, val loss: 0.63099, in 0.016s 1 tree, 143 leaves, max depth = 16, train loss: 0.62649, val loss: 0.62426, in 0.031s 1 tree, 123 leaves, max depth = 14, train loss: 0.62020, val loss: 0.61778, in 0.031s 1 tree, 129 leaves, max depth = 14, train loss: 0.61365, val loss: 0.61100, in 0.031s 1 tree, 129 leaves, max depth = 14, train loss: 0.60727, val loss: 0.60441, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.60143, val loss: 0.59835, in 0.031s 1 tree, 122 leaves, max depth = 14, train loss: 0.59575, val loss: 0.59248, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.58981, val loss: 0.58635, in 0.016s 1 tree, 132 leaves, max depth = 14, train loss: 0.58401, val loss: 0.58038, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.57839, val loss: 0.57458, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.57289, val loss: 0.56894, in 0.031s 1 tree, 148 leaves, max depth = 13, train loss: 0.56758, val loss: 0.56345, in 0.031s 1 tree, 88 leaves, max depth = 14, train loss: 0.56282, val loss: 0.55855, in 0.031s 1 tree, 149 leaves, max depth = 13, train loss: 0.55775, val loss: 0.55331, in 0.016s 1 tree, 131 leaves, max depth = 14, train loss: 0.55273, val loss: 0.54814, in 0.031s 1 tree, 129 leaves, max depth = 14, train loss: 0.54782, val loss: 0.54307, in 0.031s 1 tree, 130 leaves, max depth = 14, train loss: 0.54302, val loss: 0.53814, in 0.016s 1 tree, 129 leaves, max depth = 14, train loss: 0.53834, val loss: 0.53333, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.53375, val loss: 0.52859, in 0.031s 1 tree, 130 leaves, max depth = 14, train loss: 0.52928, val loss: 0.52400, in 0.016s 1 tree, 149 leaves, max depth = 13, train loss: 0.52498, val loss: 0.51955, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.52072, val loss: 0.51518, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.51655, val loss: 0.51089, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.51246, val loss: 0.50669, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.50847, val loss: 0.50258, in 0.031s 1 tree, 147 leaves, max depth = 13, train loss: 0.50462, val loss: 0.49862, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.50080, val loss: 0.49468, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.49706, val loss: 0.49085, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.49361, val loss: 0.48730, in 0.031s 1 tree, 149 leaves, max depth = 13, train loss: 0.49007, val loss: 0.48363, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.48654, val loss: 0.47999, in 0.016s 1 tree, 128 leaves, max depth = 14, train loss: 0.48309, val loss: 0.47642, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.47977, val loss: 0.47298, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.47646, val loss: 0.46958, in 0.016s 1 tree, 149 leaves, max depth = 13, train loss: 0.47328, val loss: 0.46630, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.47029, val loss: 0.46322, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.46718, val loss: 0.46000, in 0.031s 1 tree, 148 leaves, max depth = 14, train loss: 0.46419, val loss: 0.45690, in 0.032s 1 tree, 125 leaves, max depth = 17, train loss: 0.46120, val loss: 0.45384, in 0.016s 1 tree, 124 leaves, max depth = 17, train loss: 0.45828, val loss: 0.45085, in 0.031s 1 tree, 108 leaves, max depth = 19, train loss: 0.45428, val loss: 0.44688, in 0.031s 1 tree, 130 leaves, max depth = 14, train loss: 0.45145, val loss: 0.44398, in 0.016s 1 tree, 95 leaves, max depth = 13, train loss: 0.44882, val loss: 0.44122, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.44612, val loss: 0.43845, in 0.031s 1 tree, 125 leaves, max depth = 16, train loss: 0.44361, val loss: 0.43588, in 0.031s 1 tree, 116 leaves, max depth = 14, train loss: 0.43986, val loss: 0.43217, in 0.016s 1 tree, 145 leaves, max depth = 15, train loss: 0.43736, val loss: 0.42960, in 0.047s 1 tree, 132 leaves, max depth = 14, train loss: 0.43484, val loss: 0.42702, in 0.016s 1 tree, 123 leaves, max depth = 15, train loss: 0.43238, val loss: 0.42450, in 0.031s 1 tree, 144 leaves, max depth = 13, train loss: 0.43002, val loss: 0.42207, in 0.031s 1 tree, 137 leaves, max depth = 13, train loss: 0.42657, val loss: 0.41865, in 0.016s 1 tree, 125 leaves, max depth = 14, train loss: 0.42423, val loss: 0.41626, in 0.031s 1 tree, 145 leaves, max depth = 13, train loss: 0.42200, val loss: 0.41396, in 0.031s 1 tree, 91 leaves, max depth = 13, train loss: 0.41876, val loss: 0.41069, in 0.031s 1 tree, 127 leaves, max depth = 14, train loss: 0.41652, val loss: 0.40840, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.41435, val loss: 0.40618, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.41113, val loss: 0.40301, in 0.031s 1 tree, 125 leaves, max depth = 16, train loss: 0.40904, val loss: 0.40087, in 0.016s 1 tree, 132 leaves, max depth = 19, train loss: 0.40600, val loss: 0.39787, in 0.038s 1 tree, 145 leaves, max depth = 12, train loss: 0.40402, val loss: 0.39585, in 0.025s 1 tree, 115 leaves, max depth = 16, train loss: 0.40101, val loss: 0.39289, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.39905, val loss: 0.39091, in 0.016s 1 tree, 115 leaves, max depth = 16, train loss: 0.39613, val loss: 0.38804, in 0.031s 1 tree, 124 leaves, max depth = 15, train loss: 0.39424, val loss: 0.38615, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.39238, val loss: 0.38426, in 0.016s 1 tree, 116 leaves, max depth = 14, train loss: 0.38958, val loss: 0.38153, in 0.031s 1 tree, 136 leaves, max depth = 14, train loss: 0.38690, val loss: 0.37890, in 0.031s 1 tree, 144 leaves, max depth = 12, train loss: 0.38517, val loss: 0.37715, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.38342, val loss: 0.37536, in 0.016s 1 tree, 113 leaves, max depth = 20, train loss: 0.38081, val loss: 0.37281, in 0.047s 1 tree, 115 leaves, max depth = 14, train loss: 0.37825, val loss: 0.37030, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.37657, val loss: 0.36861, in 0.031s 1 tree, 117 leaves, max depth = 16, train loss: 0.37407, val loss: 0.36619, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.37258, val loss: 0.36472, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.37100, val loss: 0.36312, in 0.016s 1 tree, 116 leaves, max depth = 14, train loss: 0.36861, val loss: 0.36079, in 0.031s 1 tree, 138 leaves, max depth = 14, train loss: 0.36632, val loss: 0.35857, in 0.031s 1 tree, 124 leaves, max depth = 14, train loss: 0.36480, val loss: 0.35705, in 0.031s 1 tree, 126 leaves, max depth = 14, train loss: 0.36331, val loss: 0.35554, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.36107, val loss: 0.35336, in 0.031s 1 tree, 123 leaves, max depth = 16, train loss: 0.35962, val loss: 0.35193, in 0.031s 1 tree, 115 leaves, max depth = 13, train loss: 0.35744, val loss: 0.34982, in 0.031s 1 tree, 137 leaves, max depth = 13, train loss: 0.35536, val loss: 0.34780, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.35340, val loss: 0.34582, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.35203, val loss: 0.34447, in 0.031s 1 tree, 115 leaves, max depth = 14, train loss: 0.35001, val loss: 0.34252, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.34868, val loss: 0.34122, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.34683, val loss: 0.33936, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.34504, val loss: 0.33754, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.34353, val loss: 0.33609, in 0.031s 1 tree, 129 leaves, max depth = 16, train loss: 0.34204, val loss: 0.33464, in 0.016s 1 tree, 66 leaves, max depth = 12, train loss: 0.34031, val loss: 0.33290, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.33862, val loss: 0.33120, in 0.016s Fit 100 trees in 3.049 s, (12317 total leaves) Time spent computing histograms: 0.946s Time spent finding best splits: 0.266s Time spent applying splits: 0.255s Time spent predicting: 0.031s Trial 82, Fold 5: Log loss = 0.34798127203454254, Average precision = 0.9529458646450839, ROC-AUC = 0.9496271854040095, Elapsed Time = 3.070813899999848 seconds
Optimization Progress: 83%|########2 | 83/100 [17:14<04:21, 15.41s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 83, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 67 leaves, max depth = 15, train loss: 0.66953, val loss: 0.66899, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.64669, val loss: 0.64604, in 0.016s 1 tree, 88 leaves, max depth = 11, train loss: 0.62569, val loss: 0.62495, in 0.016s 1 tree, 97 leaves, max depth = 11, train loss: 0.60662, val loss: 0.60563, in 0.016s 1 tree, 92 leaves, max depth = 13, train loss: 0.58877, val loss: 0.58770, in 0.016s 1 tree, 91 leaves, max depth = 11, train loss: 0.57351, val loss: 0.57232, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.55812, val loss: 0.55687, in 0.016s 1 tree, 99 leaves, max depth = 11, train loss: 0.54378, val loss: 0.54230, in 0.016s 1 tree, 102 leaves, max depth = 11, train loss: 0.53044, val loss: 0.52875, in 0.016s 1 tree, 74 leaves, max depth = 15, train loss: 0.51852, val loss: 0.51656, in 0.016s 1 tree, 91 leaves, max depth = 13, train loss: 0.50665, val loss: 0.50462, in 0.016s 1 tree, 80 leaves, max depth = 11, train loss: 0.49627, val loss: 0.49408, in 0.016s 1 tree, 94 leaves, max depth = 15, train loss: 0.48614, val loss: 0.48388, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.47327, val loss: 0.47132, in 0.016s 1 tree, 94 leaves, max depth = 12, train loss: 0.46460, val loss: 0.46264, in 0.031s 1 tree, 95 leaves, max depth = 14, train loss: 0.45643, val loss: 0.45438, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.44546, val loss: 0.44355, in 0.016s 1 tree, 101 leaves, max depth = 12, train loss: 0.43764, val loss: 0.43559, in 0.016s 1 tree, 74 leaves, max depth = 15, train loss: 0.43075, val loss: 0.42853, in 0.016s 1 tree, 73 leaves, max depth = 11, train loss: 0.42398, val loss: 0.42160, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.41792, val loss: 0.41539, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.41193, val loss: 0.40928, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.40602, val loss: 0.40342, in 0.016s 1 tree, 89 leaves, max depth = 14, train loss: 0.40033, val loss: 0.39780, in 0.016s 1 tree, 93 leaves, max depth = 11, train loss: 0.39516, val loss: 0.39269, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.39044, val loss: 0.38795, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.38594, val loss: 0.38356, in 0.016s 1 tree, 94 leaves, max depth = 15, train loss: 0.38129, val loss: 0.37895, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.37407, val loss: 0.37185, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.36739, val loss: 0.36528, in 0.016s 1 tree, 91 leaves, max depth = 15, train loss: 0.36271, val loss: 0.36067, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.35666, val loss: 0.35475, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.35103, val loss: 0.34971, in 0.016s 1 tree, 66 leaves, max depth = 10, train loss: 0.34529, val loss: 0.34423, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.34132, val loss: 0.34040, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.33655, val loss: 0.33617, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.33277, val loss: 0.33246, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.32823, val loss: 0.32800, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.32383, val loss: 0.32363, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.31979, val loss: 0.32009, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.31603, val loss: 0.31679, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.31238, val loss: 0.31319, in 0.000s 1 tree, 93 leaves, max depth = 14, train loss: 0.30968, val loss: 0.31061, in 0.031s 1 tree, 88 leaves, max depth = 15, train loss: 0.30662, val loss: 0.30763, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.30377, val loss: 0.30486, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.30067, val loss: 0.30218, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.29766, val loss: 0.29923, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.29481, val loss: 0.29642, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.29210, val loss: 0.29412, in 0.016s 1 tree, 122 leaves, max depth = 14, train loss: 0.28942, val loss: 0.29150, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.28693, val loss: 0.28900, in 0.016s 1 tree, 90 leaves, max depth = 19, train loss: 0.28461, val loss: 0.28683, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.28241, val loss: 0.28470, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.28078, val loss: 0.28280, in 0.016s 1 tree, 64 leaves, max depth = 14, train loss: 0.27828, val loss: 0.28043, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.27626, val loss: 0.27845, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.27416, val loss: 0.27672, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.27218, val loss: 0.27501, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.27033, val loss: 0.27344, in 0.016s 1 tree, 90 leaves, max depth = 14, train loss: 0.26847, val loss: 0.27170, in 0.016s 1 tree, 119 leaves, max depth = 14, train loss: 0.26651, val loss: 0.26980, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.26486, val loss: 0.26840, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.26316, val loss: 0.26678, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.26154, val loss: 0.26527, in 0.016s 1 tree, 118 leaves, max depth = 14, train loss: 0.25987, val loss: 0.26364, in 0.031s 1 tree, 24 leaves, max depth = 12, train loss: 0.25842, val loss: 0.26221, in 0.016s 1 tree, 119 leaves, max depth = 14, train loss: 0.25685, val loss: 0.26071, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.25545, val loss: 0.25921, in 0.016s 1 tree, 43 leaves, max depth = 13, train loss: 0.25413, val loss: 0.25800, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.25285, val loss: 0.25675, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.25164, val loss: 0.25573, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.25050, val loss: 0.25478, in 0.016s 1 tree, 110 leaves, max depth = 19, train loss: 0.24893, val loss: 0.25306, in 0.016s 1 tree, 110 leaves, max depth = 16, train loss: 0.24739, val loss: 0.25139, in 0.016s 1 tree, 27 leaves, max depth = 7, train loss: 0.24625, val loss: 0.25038, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.24524, val loss: 0.24937, in 0.016s 1 tree, 119 leaves, max depth = 15, train loss: 0.24399, val loss: 0.24818, in 0.016s 1 tree, 108 leaves, max depth = 14, train loss: 0.24265, val loss: 0.24674, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.24167, val loss: 0.24581, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.24062, val loss: 0.24497, in 0.016s 1 tree, 108 leaves, max depth = 14, train loss: 0.23969, val loss: 0.24422, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.23801, val loss: 0.24243, in 0.016s 1 tree, 69 leaves, max depth = 13, train loss: 0.23731, val loss: 0.24169, in 0.016s 1 tree, 73 leaves, max depth = 16, train loss: 0.23577, val loss: 0.24005, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.23489, val loss: 0.23935, in 0.016s Fit 85 trees in 1.658 s, (5881 total leaves) Time spent computing histograms: 0.504s Time spent finding best splits: 0.102s Time spent applying splits: 0.095s Time spent predicting: 0.000s Trial 83, Fold 1: Log loss = 0.2419060173779556, Average precision = 0.9677093062637296, ROC-AUC = 0.962729584582001, Elapsed Time = 1.6626142999994045 seconds Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 83, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 67 leaves, max depth = 13, train loss: 0.66954, val loss: 0.66906, in 0.016s 1 tree, 88 leaves, max depth = 12, train loss: 0.64664, val loss: 0.64581, in 0.016s 1 tree, 88 leaves, max depth = 12, train loss: 0.62558, val loss: 0.62443, in 0.016s 1 tree, 91 leaves, max depth = 15, train loss: 0.60627, val loss: 0.60484, in 0.016s 1 tree, 97 leaves, max depth = 15, train loss: 0.58862, val loss: 0.58700, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.57306, val loss: 0.57102, in 0.031s 1 tree, 92 leaves, max depth = 14, train loss: 0.55744, val loss: 0.55518, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.54311, val loss: 0.54068, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.53001, val loss: 0.52725, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.51748, val loss: 0.51455, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.50580, val loss: 0.50276, in 0.031s 1 tree, 93 leaves, max depth = 13, train loss: 0.49474, val loss: 0.49152, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.48135, val loss: 0.47814, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.47214, val loss: 0.46897, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.46324, val loss: 0.46015, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.45481, val loss: 0.45149, in 0.031s 1 tree, 73 leaves, max depth = 13, train loss: 0.44737, val loss: 0.44396, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.43687, val loss: 0.43347, in 0.016s 1 tree, 71 leaves, max depth = 11, train loss: 0.43045, val loss: 0.42681, in 0.016s 1 tree, 99 leaves, max depth = 13, train loss: 0.42356, val loss: 0.41989, in 0.031s 1 tree, 91 leaves, max depth = 12, train loss: 0.41702, val loss: 0.41336, in 0.016s 1 tree, 96 leaves, max depth = 13, train loss: 0.41078, val loss: 0.40706, in 0.016s 1 tree, 97 leaves, max depth = 13, train loss: 0.40489, val loss: 0.40115, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.39966, val loss: 0.39595, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.39465, val loss: 0.39090, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.38652, val loss: 0.38298, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.38184, val loss: 0.37834, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.37492, val loss: 0.37156, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.36803, val loss: 0.36484, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.36207, val loss: 0.35898, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35648, val loss: 0.35350, in 0.000s 1 tree, 92 leaves, max depth = 14, train loss: 0.35192, val loss: 0.34907, in 0.031s 1 tree, 92 leaves, max depth = 17, train loss: 0.34837, val loss: 0.34567, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.34343, val loss: 0.34083, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.33879, val loss: 0.33629, in 0.000s 1 tree, 94 leaves, max depth = 14, train loss: 0.33554, val loss: 0.33308, in 0.031s 1 tree, 92 leaves, max depth = 14, train loss: 0.33173, val loss: 0.32939, in 0.018s 1 tree, 42 leaves, max depth = 11, train loss: 0.32736, val loss: 0.32536, in 0.014s 1 tree, 93 leaves, max depth = 15, train loss: 0.32446, val loss: 0.32265, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.32047, val loss: 0.31899, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.31713, val loss: 0.31577, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.31350, val loss: 0.31242, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.31011, val loss: 0.30930, in 0.031s 1 tree, 28 leaves, max depth = 10, train loss: 0.30681, val loss: 0.30609, in 0.000s 1 tree, 93 leaves, max depth = 14, train loss: 0.30387, val loss: 0.30324, in 0.031s 1 tree, 28 leaves, max depth = 9, train loss: 0.30086, val loss: 0.30031, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.29801, val loss: 0.29771, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.29538, val loss: 0.29528, in 0.016s 1 tree, 118 leaves, max depth = 14, train loss: 0.29265, val loss: 0.29278, in 0.031s 1 tree, 28 leaves, max depth = 9, train loss: 0.29014, val loss: 0.29030, in 0.016s 1 tree, 95 leaves, max depth = 12, train loss: 0.28825, val loss: 0.28861, in 0.016s 1 tree, 85 leaves, max depth = 19, train loss: 0.28653, val loss: 0.28706, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.28429, val loss: 0.28487, in 0.016s 1 tree, 89 leaves, max depth = 13, train loss: 0.28204, val loss: 0.28276, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.27991, val loss: 0.28083, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.27770, val loss: 0.27879, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.27504, val loss: 0.27611, in 0.016s 1 tree, 121 leaves, max depth = 13, train loss: 0.27296, val loss: 0.27428, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.27114, val loss: 0.27264, in 0.016s 1 tree, 94 leaves, max depth = 13, train loss: 0.26928, val loss: 0.27085, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.26760, val loss: 0.26936, in 0.016s 1 tree, 94 leaves, max depth = 14, train loss: 0.26547, val loss: 0.26709, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.26391, val loss: 0.26557, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.26236, val loss: 0.26402, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.26116, val loss: 0.26296, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.25979, val loss: 0.26175, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.25843, val loss: 0.26054, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.25658, val loss: 0.25860, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.25530, val loss: 0.25738, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.25407, val loss: 0.25614, in 0.016s 1 tree, 111 leaves, max depth = 17, train loss: 0.25247, val loss: 0.25452, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.25108, val loss: 0.25318, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.24992, val loss: 0.25221, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.24885, val loss: 0.25127, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.24782, val loss: 0.25030, in 0.016s 1 tree, 108 leaves, max depth = 15, train loss: 0.24634, val loss: 0.24876, in 0.031s 1 tree, 123 leaves, max depth = 12, train loss: 0.24503, val loss: 0.24768, in 0.016s 1 tree, 72 leaves, max depth = 12, train loss: 0.24422, val loss: 0.24707, in 0.016s 1 tree, 63 leaves, max depth = 10, train loss: 0.24318, val loss: 0.24608, in 0.016s 1 tree, 111 leaves, max depth = 17, train loss: 0.24194, val loss: 0.24480, in 0.016s 1 tree, 26 leaves, max depth = 7, train loss: 0.24093, val loss: 0.24388, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.23988, val loss: 0.24310, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.23902, val loss: 0.24240, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.23809, val loss: 0.24171, in 0.016s 1 tree, 42 leaves, max depth = 9, train loss: 0.23726, val loss: 0.24101, in 0.016s Fit 85 trees in 1.799 s, (5781 total leaves) Time spent computing histograms: 0.540s Time spent finding best splits: 0.114s Time spent applying splits: 0.105s Time spent predicting: 0.016s Trial 83, Fold 2: Log loss = 0.24140072050409492, Average precision = 0.9669439635515518, ROC-AUC = 0.9637237055231034, Elapsed Time = 1.8088112000004912 seconds Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 83, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 70 leaves, max depth = 15, train loss: 0.66953, val loss: 0.66918, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.64822, val loss: 0.64789, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.62710, val loss: 0.62678, in 0.016s 1 tree, 92 leaves, max depth = 12, train loss: 0.60768, val loss: 0.60728, in 0.016s 1 tree, 99 leaves, max depth = 12, train loss: 0.58997, val loss: 0.58949, in 0.016s 1 tree, 101 leaves, max depth = 11, train loss: 0.57456, val loss: 0.57402, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.55938, val loss: 0.55869, in 0.016s 1 tree, 102 leaves, max depth = 12, train loss: 0.54516, val loss: 0.54443, in 0.031s 1 tree, 100 leaves, max depth = 13, train loss: 0.53182, val loss: 0.53116, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.52012, val loss: 0.51944, in 0.016s 1 tree, 95 leaves, max depth = 12, train loss: 0.50826, val loss: 0.50759, in 0.031s 1 tree, 65 leaves, max depth = 10, train loss: 0.49435, val loss: 0.49448, in 0.016s 1 tree, 73 leaves, max depth = 13, train loss: 0.48409, val loss: 0.48427, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.47427, val loss: 0.47447, in 0.016s 1 tree, 95 leaves, max depth = 13, train loss: 0.46493, val loss: 0.46527, in 0.016s 1 tree, 92 leaves, max depth = 11, train loss: 0.45636, val loss: 0.45691, in 0.031s 1 tree, 97 leaves, max depth = 13, train loss: 0.44809, val loss: 0.44875, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.44114, val loss: 0.44188, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.43063, val loss: 0.43208, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.42401, val loss: 0.42541, in 0.031s 1 tree, 65 leaves, max depth = 9, train loss: 0.41479, val loss: 0.41690, in 0.016s 1 tree, 97 leaves, max depth = 11, train loss: 0.40856, val loss: 0.41070, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.39995, val loss: 0.40284, in 0.016s 1 tree, 101 leaves, max depth = 12, train loss: 0.39478, val loss: 0.39781, in 0.016s 1 tree, 99 leaves, max depth = 11, train loss: 0.38939, val loss: 0.39245, in 0.031s 1 tree, 96 leaves, max depth = 12, train loss: 0.38459, val loss: 0.38776, in 0.016s 1 tree, 94 leaves, max depth = 11, train loss: 0.38009, val loss: 0.38333, in 0.016s 1 tree, 97 leaves, max depth = 11, train loss: 0.37550, val loss: 0.37881, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.37088, val loss: 0.37439, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.36411, val loss: 0.36829, in 0.016s 1 tree, 94 leaves, max depth = 16, train loss: 0.35950, val loss: 0.36357, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.35344, val loss: 0.35812, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.34810, val loss: 0.35364, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.34439, val loss: 0.35010, in 0.031s 1 tree, 27 leaves, max depth = 10, train loss: 0.33931, val loss: 0.34552, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.33459, val loss: 0.34125, in 0.000s 1 tree, 43 leaves, max depth = 12, train loss: 0.33012, val loss: 0.33752, in 0.016s 1 tree, 91 leaves, max depth = 13, train loss: 0.32723, val loss: 0.33476, in 0.031s 1 tree, 43 leaves, max depth = 11, train loss: 0.32312, val loss: 0.33139, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.31919, val loss: 0.32787, in 0.000s 1 tree, 80 leaves, max depth = 11, train loss: 0.31641, val loss: 0.32521, in 0.031s 1 tree, 59 leaves, max depth = 11, train loss: 0.31248, val loss: 0.32181, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.30931, val loss: 0.31854, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.30602, val loss: 0.31560, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.30298, val loss: 0.31289, in 0.016s 1 tree, 90 leaves, max depth = 12, train loss: 0.30066, val loss: 0.31072, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.29784, val loss: 0.30782, in 0.016s 1 tree, 94 leaves, max depth = 15, train loss: 0.29519, val loss: 0.30510, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.29217, val loss: 0.30245, in 0.016s 1 tree, 116 leaves, max depth = 13, train loss: 0.28965, val loss: 0.29973, in 0.031s 1 tree, 43 leaves, max depth = 11, train loss: 0.28698, val loss: 0.29760, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.28470, val loss: 0.29524, in 0.016s 1 tree, 118 leaves, max depth = 16, train loss: 0.28248, val loss: 0.29283, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.28013, val loss: 0.29102, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.27797, val loss: 0.28913, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.27582, val loss: 0.28745, in 0.016s 1 tree, 94 leaves, max depth = 16, train loss: 0.27384, val loss: 0.28541, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.27190, val loss: 0.28375, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.27007, val loss: 0.28240, in 0.016s 1 tree, 116 leaves, max depth = 14, train loss: 0.26821, val loss: 0.28037, in 0.031s 1 tree, 95 leaves, max depth = 13, train loss: 0.26647, val loss: 0.27857, in 0.016s 1 tree, 95 leaves, max depth = 13, train loss: 0.26484, val loss: 0.27692, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.26316, val loss: 0.27567, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.26156, val loss: 0.27449, in 0.016s 1 tree, 32 leaves, max depth = 14, train loss: 0.25998, val loss: 0.27300, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.25852, val loss: 0.27190, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.25715, val loss: 0.27084, in 0.016s 1 tree, 114 leaves, max depth = 15, train loss: 0.25519, val loss: 0.26866, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.25389, val loss: 0.26758, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.25242, val loss: 0.26606, in 0.031s 1 tree, 44 leaves, max depth = 12, train loss: 0.25123, val loss: 0.26515, in 0.000s 1 tree, 26 leaves, max depth = 9, train loss: 0.25009, val loss: 0.26415, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.24875, val loss: 0.26281, in 0.016s 1 tree, 92 leaves, max depth = 13, train loss: 0.24750, val loss: 0.26156, in 0.016s 1 tree, 100 leaves, max depth = 18, train loss: 0.24606, val loss: 0.25990, in 0.031s 1 tree, 47 leaves, max depth = 11, train loss: 0.24501, val loss: 0.25916, in 0.016s 1 tree, 49 leaves, max depth = 9, train loss: 0.24434, val loss: 0.25852, in 0.016s 1 tree, 30 leaves, max depth = 13, train loss: 0.24337, val loss: 0.25754, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.24238, val loss: 0.25690, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.24145, val loss: 0.25630, in 0.016s 1 tree, 101 leaves, max depth = 18, train loss: 0.24015, val loss: 0.25478, in 0.016s 1 tree, 47 leaves, max depth = 10, train loss: 0.23929, val loss: 0.25421, in 0.016s 1 tree, 109 leaves, max depth = 16, train loss: 0.23820, val loss: 0.25287, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.23744, val loss: 0.25207, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.23640, val loss: 0.25103, in 0.016s Fit 85 trees in 1.846 s, (6094 total leaves) Time spent computing histograms: 0.573s Time spent finding best splits: 0.119s Time spent applying splits: 0.111s Time spent predicting: 0.000s Trial 83, Fold 3: Log loss = 0.23801422688332105, Average precision = 0.9672258396658524, ROC-AUC = 0.9640686397848426, Elapsed Time = 1.8602890000001935 seconds Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 83, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 70 leaves, max depth = 12, train loss: 0.66948, val loss: 0.66858, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.64844, val loss: 0.64635, in 0.016s 1 tree, 98 leaves, max depth = 13, train loss: 0.62726, val loss: 0.62432, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.60783, val loss: 0.60410, in 0.031s 1 tree, 77 leaves, max depth = 13, train loss: 0.59014, val loss: 0.58560, in 0.016s 1 tree, 102 leaves, max depth = 13, train loss: 0.57445, val loss: 0.56915, in 0.016s 1 tree, 98 leaves, max depth = 14, train loss: 0.55878, val loss: 0.55279, in 0.031s 1 tree, 103 leaves, max depth = 13, train loss: 0.54445, val loss: 0.53768, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.53130, val loss: 0.52388, in 0.016s 1 tree, 99 leaves, max depth = 14, train loss: 0.51951, val loss: 0.51148, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.50757, val loss: 0.49899, in 0.031s 1 tree, 98 leaves, max depth = 14, train loss: 0.49643, val loss: 0.48730, in 0.016s 1 tree, 58 leaves, max depth = 10, train loss: 0.48306, val loss: 0.47364, in 0.016s 1 tree, 94 leaves, max depth = 13, train loss: 0.47371, val loss: 0.46392, in 0.016s 1 tree, 96 leaves, max depth = 12, train loss: 0.46438, val loss: 0.45414, in 0.031s 1 tree, 96 leaves, max depth = 14, train loss: 0.45563, val loss: 0.44496, in 0.016s 1 tree, 93 leaves, max depth = 12, train loss: 0.44748, val loss: 0.43636, in 0.016s 1 tree, 75 leaves, max depth = 14, train loss: 0.44011, val loss: 0.42852, in 0.016s 1 tree, 100 leaves, max depth = 13, train loss: 0.43309, val loss: 0.42099, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.42308, val loss: 0.41082, in 0.016s 1 tree, 94 leaves, max depth = 11, train loss: 0.41699, val loss: 0.40454, in 0.031s 1 tree, 58 leaves, max depth = 12, train loss: 0.40800, val loss: 0.39543, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.39959, val loss: 0.38689, in 0.016s 1 tree, 100 leaves, max depth = 12, train loss: 0.39413, val loss: 0.38102, in 0.016s 1 tree, 71 leaves, max depth = 13, train loss: 0.38914, val loss: 0.37555, in 0.016s 1 tree, 102 leaves, max depth = 13, train loss: 0.38424, val loss: 0.37027, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.37720, val loss: 0.36312, in 0.016s 1 tree, 92 leaves, max depth = 13, train loss: 0.37282, val loss: 0.35858, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.36867, val loss: 0.35407, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.36476, val loss: 0.34987, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.35886, val loss: 0.34369, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.35442, val loss: 0.33917, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.34914, val loss: 0.33403, in 0.016s 1 tree, 59 leaves, max depth = 11, train loss: 0.34368, val loss: 0.32853, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.33977, val loss: 0.32465, in 0.031s 1 tree, 28 leaves, max depth = 11, train loss: 0.33520, val loss: 0.31988, in 0.000s 1 tree, 42 leaves, max depth = 11, train loss: 0.33080, val loss: 0.31566, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.32666, val loss: 0.31136, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.32279, val loss: 0.30762, in 0.016s 1 tree, 92 leaves, max depth = 15, train loss: 0.31933, val loss: 0.30413, in 0.016s 1 tree, 123 leaves, max depth = 14, train loss: 0.31597, val loss: 0.30064, in 0.031s 1 tree, 59 leaves, max depth = 9, train loss: 0.31207, val loss: 0.29668, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.30874, val loss: 0.29353, in 0.016s 1 tree, 90 leaves, max depth = 15, train loss: 0.30645, val loss: 0.29108, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.30332, val loss: 0.28784, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.30047, val loss: 0.28482, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.29778, val loss: 0.28197, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.29507, val loss: 0.27928, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.29246, val loss: 0.27678, in 0.016s 1 tree, 92 leaves, max depth = 15, train loss: 0.28990, val loss: 0.27422, in 0.031s 1 tree, 27 leaves, max depth = 10, train loss: 0.28755, val loss: 0.27179, in 0.016s 1 tree, 125 leaves, max depth = 12, train loss: 0.28507, val loss: 0.26924, in 0.016s 1 tree, 124 leaves, max depth = 16, train loss: 0.28277, val loss: 0.26689, in 0.031s 1 tree, 100 leaves, max depth = 12, train loss: 0.28109, val loss: 0.26529, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.27897, val loss: 0.26336, in 0.016s 1 tree, 85 leaves, max depth = 12, train loss: 0.27750, val loss: 0.26185, in 0.016s 1 tree, 121 leaves, max depth = 13, train loss: 0.27548, val loss: 0.25974, in 0.016s 1 tree, 94 leaves, max depth = 13, train loss: 0.27361, val loss: 0.25789, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.27165, val loss: 0.25610, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.26988, val loss: 0.25422, in 0.016s 1 tree, 123 leaves, max depth = 13, train loss: 0.26807, val loss: 0.25234, in 0.031s 1 tree, 49 leaves, max depth = 12, train loss: 0.26637, val loss: 0.25079, in 0.016s 1 tree, 119 leaves, max depth = 14, train loss: 0.26469, val loss: 0.24903, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.26285, val loss: 0.24730, in 0.016s 1 tree, 42 leaves, max depth = 9, train loss: 0.26131, val loss: 0.24592, in 0.016s 1 tree, 88 leaves, max depth = 14, train loss: 0.25979, val loss: 0.24443, in 0.031s 1 tree, 45 leaves, max depth = 14, train loss: 0.25832, val loss: 0.24306, in 0.016s 1 tree, 122 leaves, max depth = 14, train loss: 0.25685, val loss: 0.24155, in 0.016s 1 tree, 89 leaves, max depth = 17, train loss: 0.25526, val loss: 0.24007, in 0.031s 1 tree, 46 leaves, max depth = 13, train loss: 0.25392, val loss: 0.23880, in 0.000s 1 tree, 91 leaves, max depth = 14, train loss: 0.25264, val loss: 0.23759, in 0.031s 1 tree, 74 leaves, max depth = 12, train loss: 0.25171, val loss: 0.23681, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.25036, val loss: 0.23553, in 0.016s 1 tree, 111 leaves, max depth = 17, train loss: 0.24895, val loss: 0.23416, in 0.016s 1 tree, 89 leaves, max depth = 14, train loss: 0.24761, val loss: 0.23294, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.24633, val loss: 0.23188, in 0.016s 1 tree, 45 leaves, max depth = 12, train loss: 0.24519, val loss: 0.23085, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.24401, val loss: 0.22981, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.24324, val loss: 0.22919, in 0.016s 1 tree, 107 leaves, max depth = 13, train loss: 0.24195, val loss: 0.22797, in 0.016s 1 tree, 27 leaves, max depth = 7, train loss: 0.24091, val loss: 0.22711, in 0.000s 1 tree, 110 leaves, max depth = 15, train loss: 0.23975, val loss: 0.22598, in 0.031s 1 tree, 37 leaves, max depth = 8, train loss: 0.23894, val loss: 0.22530, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.23801, val loss: 0.22451, in 0.016s 1 tree, 72 leaves, max depth = 15, train loss: 0.23640, val loss: 0.22303, in 0.016s Fit 85 trees in 1.908 s, (6280 total leaves) Time spent computing histograms: 0.574s Time spent finding best splits: 0.121s Time spent applying splits: 0.114s Time spent predicting: 0.000s Trial 83, Fold 4: Log loss = 0.2407239049784999, Average precision = 0.9676442621459418, ROC-AUC = 0.9632780776173069, Elapsed Time = 1.9141239000000496 seconds Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 83, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 69 leaves, max depth = 12, train loss: 0.66934, val loss: 0.66800, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.64809, val loss: 0.64580, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.62700, val loss: 0.62397, in 0.016s 1 tree, 97 leaves, max depth = 14, train loss: 0.60752, val loss: 0.60378, in 0.016s 1 tree, 93 leaves, max depth = 13, train loss: 0.59071, val loss: 0.58659, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.57489, val loss: 0.57017, in 0.031s 1 tree, 97 leaves, max depth = 12, train loss: 0.55910, val loss: 0.55387, in 0.016s 1 tree, 103 leaves, max depth = 12, train loss: 0.54464, val loss: 0.53891, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.53119, val loss: 0.52498, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.51919, val loss: 0.51243, in 0.031s 1 tree, 76 leaves, max depth = 14, train loss: 0.50758, val loss: 0.50027, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.49681, val loss: 0.48898, in 0.016s 1 tree, 96 leaves, max depth = 14, train loss: 0.48697, val loss: 0.47872, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.47409, val loss: 0.46573, in 0.016s 1 tree, 94 leaves, max depth = 12, train loss: 0.46530, val loss: 0.45680, in 0.016s 1 tree, 95 leaves, max depth = 15, train loss: 0.45686, val loss: 0.44800, in 0.031s 1 tree, 98 leaves, max depth = 13, train loss: 0.44842, val loss: 0.43934, in 0.016s 1 tree, 59 leaves, max depth = 10, train loss: 0.43771, val loss: 0.42861, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.43051, val loss: 0.42100, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.42368, val loss: 0.41387, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.41785, val loss: 0.40760, in 0.031s 1 tree, 76 leaves, max depth = 15, train loss: 0.41184, val loss: 0.40126, in 0.016s 1 tree, 98 leaves, max depth = 14, train loss: 0.40583, val loss: 0.39514, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.40045, val loss: 0.38950, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.39525, val loss: 0.38418, in 0.016s 1 tree, 94 leaves, max depth = 12, train loss: 0.39060, val loss: 0.37938, in 0.031s 1 tree, 92 leaves, max depth = 12, train loss: 0.38614, val loss: 0.37491, in 0.016s 1 tree, 95 leaves, max depth = 13, train loss: 0.38154, val loss: 0.37025, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.37429, val loss: 0.36297, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.36758, val loss: 0.35622, in 0.016s 1 tree, 90 leaves, max depth = 15, train loss: 0.36284, val loss: 0.35153, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.35673, val loss: 0.34538, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.35067, val loss: 0.33988, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.34533, val loss: 0.33439, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.34021, val loss: 0.32932, in 0.031s 1 tree, 43 leaves, max depth = 11, train loss: 0.33544, val loss: 0.32503, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.33105, val loss: 0.32114, in 0.047s 1 tree, 28 leaves, max depth = 10, train loss: 0.32682, val loss: 0.31684, in 0.016s 1 tree, 90 leaves, max depth = 13, train loss: 0.32313, val loss: 0.31321, in 0.031s 1 tree, 94 leaves, max depth = 13, train loss: 0.31961, val loss: 0.30974, in 0.016s 1 tree, 91 leaves, max depth = 14, train loss: 0.31625, val loss: 0.30648, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.31252, val loss: 0.30326, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.30899, val loss: 0.29968, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.30573, val loss: 0.29641, in 0.016s 1 tree, 94 leaves, max depth = 14, train loss: 0.30279, val loss: 0.29358, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.29971, val loss: 0.29091, in 0.031s 1 tree, 42 leaves, max depth = 11, train loss: 0.29677, val loss: 0.28843, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.29400, val loss: 0.28553, in 0.016s 1 tree, 95 leaves, max depth = 13, train loss: 0.29137, val loss: 0.28297, in 0.031s 1 tree, 91 leaves, max depth = 14, train loss: 0.28885, val loss: 0.28053, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.28638, val loss: 0.27807, in 0.016s 1 tree, 27 leaves, max depth = 13, train loss: 0.28409, val loss: 0.27568, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.28178, val loss: 0.27327, in 0.016s 1 tree, 120 leaves, max depth = 14, train loss: 0.27931, val loss: 0.27094, in 0.031s 1 tree, 28 leaves, max depth = 11, train loss: 0.27733, val loss: 0.26890, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.27526, val loss: 0.26697, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.27321, val loss: 0.26533, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.27130, val loss: 0.26380, in 0.016s 1 tree, 117 leaves, max depth = 14, train loss: 0.26913, val loss: 0.26175, in 0.016s 1 tree, 89 leaves, max depth = 11, train loss: 0.26721, val loss: 0.25992, in 0.031s 1 tree, 26 leaves, max depth = 12, train loss: 0.26564, val loss: 0.25829, in 0.016s 1 tree, 121 leaves, max depth = 15, train loss: 0.26370, val loss: 0.25648, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.26172, val loss: 0.25436, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.26014, val loss: 0.25306, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.25863, val loss: 0.25184, in 0.016s 1 tree, 69 leaves, max depth = 11, train loss: 0.25683, val loss: 0.25015, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.25527, val loss: 0.24874, in 0.016s 1 tree, 90 leaves, max depth = 15, train loss: 0.25385, val loss: 0.24742, in 0.031s 1 tree, 118 leaves, max depth = 15, train loss: 0.25236, val loss: 0.24604, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.25107, val loss: 0.24494, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.24967, val loss: 0.24367, in 0.016s 1 tree, 40 leaves, max depth = 13, train loss: 0.24824, val loss: 0.24236, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.24715, val loss: 0.24150, in 0.000s 1 tree, 80 leaves, max depth = 13, train loss: 0.24605, val loss: 0.24056, in 0.031s 1 tree, 94 leaves, max depth = 18, train loss: 0.24438, val loss: 0.23895, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.24296, val loss: 0.23746, in 0.016s 1 tree, 121 leaves, max depth = 14, train loss: 0.24180, val loss: 0.23640, in 0.031s 1 tree, 115 leaves, max depth = 15, train loss: 0.24016, val loss: 0.23497, in 0.016s 1 tree, 88 leaves, max depth = 13, train loss: 0.23917, val loss: 0.23412, in 0.031s 1 tree, 107 leaves, max depth = 14, train loss: 0.23802, val loss: 0.23292, in 0.016s 1 tree, 107 leaves, max depth = 15, train loss: 0.23689, val loss: 0.23174, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.23580, val loss: 0.23077, in 0.016s 1 tree, 47 leaves, max depth = 16, train loss: 0.23482, val loss: 0.22985, in 0.016s 1 tree, 41 leaves, max depth = 14, train loss: 0.23373, val loss: 0.22892, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.23277, val loss: 0.22806, in 0.016s Fit 85 trees in 2.018 s, (6083 total leaves) Time spent computing histograms: 0.630s Time spent finding best splits: 0.141s Time spent applying splits: 0.131s Time spent predicting: 0.000s Trial 83, Fold 5: Log loss = 0.2437184623531028, Average precision = 0.9649102280878095, ROC-AUC = 0.9611272504319716, Elapsed Time = 2.023743600000671 seconds
Optimization Progress: 84%|########4 | 84/100 [17:30<04:08, 15.54s/it]
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 84, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.190 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 76 leaves, max depth = 19, train loss: 0.68533, val loss: 0.68510, in 0.016s 1 tree, 76 leaves, max depth = 19, train loss: 0.67777, val loss: 0.67731, in 0.016s 1 tree, 76 leaves, max depth = 19, train loss: 0.67046, val loss: 0.66978, in 0.000s 1 tree, 80 leaves, max depth = 15, train loss: 0.66345, val loss: 0.66261, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.65659, val loss: 0.65554, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.64996, val loss: 0.64869, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.64353, val loss: 0.64205, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.63730, val loss: 0.63562, in 0.000s 1 tree, 78 leaves, max depth = 19, train loss: 0.63126, val loss: 0.62938, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.62541, val loss: 0.62333, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.61974, val loss: 0.61746, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.61424, val loss: 0.61177, in 0.000s 1 tree, 81 leaves, max depth = 17, train loss: 0.60900, val loss: 0.60636, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.60382, val loss: 0.60099, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.59880, val loss: 0.59579, in 0.000s 1 tree, 82 leaves, max depth = 21, train loss: 0.59397, val loss: 0.59080, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.58924, val loss: 0.58589, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.58465, val loss: 0.58112, in 0.000s 1 tree, 81 leaves, max depth = 17, train loss: 0.58027, val loss: 0.57659, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.57594, val loss: 0.57209, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.57173, val loss: 0.56772, in 0.000s 1 tree, 77 leaves, max depth = 19, train loss: 0.56765, val loss: 0.56347, in 0.016s 1 tree, 77 leaves, max depth = 19, train loss: 0.56369, val loss: 0.55934, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.55983, val loss: 0.55533, in 0.000s 1 tree, 125 leaves, max depth = 14, train loss: 0.55587, val loss: 0.55165, in 0.016s 1 tree, 78 leaves, max depth = 19, train loss: 0.55218, val loss: 0.54780, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.54837, val loss: 0.54428, in 0.000s 1 tree, 79 leaves, max depth = 18, train loss: 0.54489, val loss: 0.54069, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.54123, val loss: 0.53731, in 0.016s 1 tree, 82 leaves, max depth = 19, train loss: 0.53783, val loss: 0.53376, in 0.016s 1 tree, 82 leaves, max depth = 19, train loss: 0.53453, val loss: 0.53030, in 0.000s 1 tree, 124 leaves, max depth = 15, train loss: 0.53105, val loss: 0.52708, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.52766, val loss: 0.52396, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.52437, val loss: 0.52093, in 0.016s 1 tree, 82 leaves, max depth = 19, train loss: 0.52128, val loss: 0.51769, in 0.000s 1 tree, 125 leaves, max depth = 15, train loss: 0.51811, val loss: 0.51477, in 0.016s 1 tree, 82 leaves, max depth = 19, train loss: 0.51515, val loss: 0.51165, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.51210, val loss: 0.50885, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.50929, val loss: 0.50592, in 0.000s 1 tree, 126 leaves, max depth = 15, train loss: 0.50635, val loss: 0.50321, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.50361, val loss: 0.50032, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.50077, val loss: 0.49772, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.49818, val loss: 0.49501, in 0.000s 1 tree, 125 leaves, max depth = 14, train loss: 0.49544, val loss: 0.49250, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.49277, val loss: 0.49006, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.49026, val loss: 0.48741, in 0.000s 1 tree, 126 leaves, max depth = 14, train loss: 0.48769, val loss: 0.48506, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.48528, val loss: 0.48250, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.48279, val loss: 0.48024, in 0.016s 1 tree, 80 leaves, max depth = 19, train loss: 0.48050, val loss: 0.47784, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.47810, val loss: 0.47566, in 0.000s 1 tree, 126 leaves, max depth = 14, train loss: 0.47577, val loss: 0.47353, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.47355, val loss: 0.47118, in 0.016s 1 tree, 126 leaves, max depth = 13, train loss: 0.47129, val loss: 0.46912, in 0.000s 1 tree, 79 leaves, max depth = 19, train loss: 0.46918, val loss: 0.46687, in 0.000s 1 tree, 126 leaves, max depth = 13, train loss: 0.46700, val loss: 0.46489, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.46493, val loss: 0.46269, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.46282, val loss: 0.46078, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.46076, val loss: 0.45892, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.45879, val loss: 0.45681, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.45688, val loss: 0.45476, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.45490, val loss: 0.45298, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.45297, val loss: 0.45124, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.45115, val loss: 0.44929, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.44928, val loss: 0.44761, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.44748, val loss: 0.44573, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.44567, val loss: 0.44410, in 0.016s 1 tree, 80 leaves, max depth = 19, train loss: 0.44399, val loss: 0.44233, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.44223, val loss: 0.44075, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.44052, val loss: 0.43922, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.43890, val loss: 0.43747, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.43724, val loss: 0.43599, in 0.000s 1 tree, 82 leaves, max depth = 16, train loss: 0.43564, val loss: 0.43430, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.43403, val loss: 0.43286, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.43252, val loss: 0.43122, in 0.016s Fit 75 trees in 1.190 s, (7250 total leaves) Time spent computing histograms: 0.366s Time spent finding best splits: 0.101s Time spent applying splits: 0.110s Time spent predicting: 0.000s Trial 84, Fold 1: Log loss = 0.43436533814762274, Average precision = 0.9183205254227605, ROC-AUC = 0.9288089890929774, Elapsed Time = 1.2009637000010116 seconds Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 84, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 80 leaves, max depth = 17, train loss: 0.68537, val loss: 0.68499, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.67785, val loss: 0.67710, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.67058, val loss: 0.66946, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.66359, val loss: 0.66214, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.65677, val loss: 0.65498, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.65016, val loss: 0.64803, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.64377, val loss: 0.64130, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.63757, val loss: 0.63478, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.63157, val loss: 0.62846, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.62575, val loss: 0.62233, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.62011, val loss: 0.61638, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.61464, val loss: 0.61061, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.60937, val loss: 0.60510, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.60422, val loss: 0.59966, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.59923, val loss: 0.59439, in 0.016s 1 tree, 81 leaves, max depth = 18, train loss: 0.59441, val loss: 0.58935, in 0.000s 1 tree, 81 leaves, max depth = 18, train loss: 0.58974, val loss: 0.58446, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.58517, val loss: 0.57962, in 0.016s 1 tree, 81 leaves, max depth = 19, train loss: 0.58077, val loss: 0.57501, in 0.000s 1 tree, 80 leaves, max depth = 16, train loss: 0.57646, val loss: 0.57044, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.57230, val loss: 0.56609, in 0.016s 1 tree, 81 leaves, max depth = 18, train loss: 0.56827, val loss: 0.56186, in 0.000s 1 tree, 81 leaves, max depth = 17, train loss: 0.56432, val loss: 0.55767, in 0.016s 1 tree, 82 leaves, max depth = 17, train loss: 0.56051, val loss: 0.55367, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.55682, val loss: 0.54979, in 0.016s 1 tree, 129 leaves, max depth = 18, train loss: 0.55292, val loss: 0.54600, in 0.016s 1 tree, 129 leaves, max depth = 18, train loss: 0.54913, val loss: 0.54231, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.54564, val loss: 0.53864, in 0.000s 1 tree, 129 leaves, max depth = 18, train loss: 0.54200, val loss: 0.53511, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.53865, val loss: 0.53158, in 0.016s 1 tree, 83 leaves, max depth = 16, train loss: 0.53536, val loss: 0.52807, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.53189, val loss: 0.52470, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.52852, val loss: 0.52144, in 0.000s 1 tree, 82 leaves, max depth = 18, train loss: 0.52543, val loss: 0.51818, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.52219, val loss: 0.51504, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.51904, val loss: 0.51199, in 0.016s 1 tree, 81 leaves, max depth = 20, train loss: 0.51611, val loss: 0.50890, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.51307, val loss: 0.50596, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.51026, val loss: 0.50299, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.50733, val loss: 0.50016, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.50463, val loss: 0.49730, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.50181, val loss: 0.49458, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.49920, val loss: 0.49182, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.49648, val loss: 0.48920, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.49384, val loss: 0.48664, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.49136, val loss: 0.48402, in 0.016s 1 tree, 127 leaves, max depth = 18, train loss: 0.48880, val loss: 0.48156, in 0.016s 1 tree, 81 leaves, max depth = 20, train loss: 0.48641, val loss: 0.47903, in 0.000s 1 tree, 127 leaves, max depth = 18, train loss: 0.48395, val loss: 0.47666, in 0.016s 1 tree, 81 leaves, max depth = 20, train loss: 0.48165, val loss: 0.47422, in 0.016s 1 tree, 127 leaves, max depth = 18, train loss: 0.47927, val loss: 0.47193, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.47705, val loss: 0.46958, in 0.016s 1 tree, 128 leaves, max depth = 18, train loss: 0.47475, val loss: 0.46737, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.47262, val loss: 0.46511, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.47039, val loss: 0.46297, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.46822, val loss: 0.46090, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.46619, val loss: 0.45873, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.46409, val loss: 0.45673, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.46213, val loss: 0.45464, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.46022, val loss: 0.45261, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.45835, val loss: 0.45063, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.45635, val loss: 0.44871, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.45440, val loss: 0.44685, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.45250, val loss: 0.44504, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.45066, val loss: 0.44327, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.44890, val loss: 0.44141, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.44711, val loss: 0.43970, in 0.016s 1 tree, 83 leaves, max depth = 18, train loss: 0.44542, val loss: 0.43790, in 0.000s 1 tree, 127 leaves, max depth = 17, train loss: 0.44369, val loss: 0.43625, in 0.016s 1 tree, 83 leaves, max depth = 18, train loss: 0.44206, val loss: 0.43451, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.44038, val loss: 0.43291, in 0.016s 1 tree, 82 leaves, max depth = 18, train loss: 0.43880, val loss: 0.43123, in 0.000s 1 tree, 128 leaves, max depth = 16, train loss: 0.43717, val loss: 0.42969, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.43559, val loss: 0.42818, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.43407, val loss: 0.42656, in 0.016s Fit 75 trees in 1.221 s, (7371 total leaves) Time spent computing histograms: 0.386s Time spent finding best splits: 0.108s Time spent applying splits: 0.120s Time spent predicting: 0.000s Trial 84, Fold 2: Log loss = 0.43684396533371944, Average precision = 0.9113542035048336, ROC-AUC = 0.9271392871678127, Elapsed Time = 1.2313483999987511 seconds Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 84, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 12, train loss: 0.68547, val loss: 0.68519, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.67798, val loss: 0.67748, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.67073, val loss: 0.67002, in 0.000s 1 tree, 82 leaves, max depth = 12, train loss: 0.66377, val loss: 0.66280, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.65698, val loss: 0.65580, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.65039, val loss: 0.64903, in 0.000s 1 tree, 82 leaves, max depth = 12, train loss: 0.64408, val loss: 0.64246, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.63790, val loss: 0.63610, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.63198, val loss: 0.62993, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.62617, val loss: 0.62394, in 0.000s 1 tree, 80 leaves, max depth = 14, train loss: 0.62055, val loss: 0.61814, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.61515, val loss: 0.61251, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.60991, val loss: 0.60711, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.60478, val loss: 0.60181, in 0.000s 1 tree, 80 leaves, max depth = 14, train loss: 0.59980, val loss: 0.59667, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.59502, val loss: 0.59167, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.59033, val loss: 0.58682, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.58577, val loss: 0.58212, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.58140, val loss: 0.57754, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.57711, val loss: 0.57310, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.57294, val loss: 0.56879, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.56890, val loss: 0.56460, in 0.000s 1 tree, 83 leaves, max depth = 13, train loss: 0.56501, val loss: 0.56053, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.56120, val loss: 0.55657, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.55722, val loss: 0.55290, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.55360, val loss: 0.54911, in 0.000s 1 tree, 128 leaves, max depth = 14, train loss: 0.54978, val loss: 0.54558, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.54630, val loss: 0.54198, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.54263, val loss: 0.53860, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.53906, val loss: 0.53532, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.53579, val loss: 0.53187, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.53235, val loss: 0.52872, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.52919, val loss: 0.52543, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.52589, val loss: 0.52240, in 0.000s 1 tree, 81 leaves, max depth = 16, train loss: 0.52285, val loss: 0.51925, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.51967, val loss: 0.51634, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.51678, val loss: 0.51326, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.51371, val loss: 0.51046, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.51091, val loss: 0.50754, in 0.000s 1 tree, 127 leaves, max depth = 16, train loss: 0.50796, val loss: 0.50485, in 0.000s 1 tree, 80 leaves, max depth = 15, train loss: 0.50526, val loss: 0.50204, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.50242, val loss: 0.49944, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.49982, val loss: 0.49674, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.49708, val loss: 0.49423, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.49441, val loss: 0.49180, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.49194, val loss: 0.48923, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.48936, val loss: 0.48688, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.48685, val loss: 0.48461, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.48450, val loss: 0.48215, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.48208, val loss: 0.47995, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.47982, val loss: 0.47758, in 0.000s 1 tree, 129 leaves, max depth = 15, train loss: 0.47748, val loss: 0.47546, in 0.031s 1 tree, 75 leaves, max depth = 18, train loss: 0.47531, val loss: 0.47315, in 0.000s 1 tree, 129 leaves, max depth = 16, train loss: 0.47305, val loss: 0.47111, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.47095, val loss: 0.46890, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.46876, val loss: 0.46693, in 0.016s 1 tree, 129 leaves, max depth = 16, train loss: 0.46663, val loss: 0.46502, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.46462, val loss: 0.46290, in 0.016s 1 tree, 129 leaves, max depth = 16, train loss: 0.46256, val loss: 0.46106, in 0.016s 1 tree, 75 leaves, max depth = 18, train loss: 0.46064, val loss: 0.45900, in 0.000s 1 tree, 81 leaves, max depth = 14, train loss: 0.45876, val loss: 0.45701, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.45678, val loss: 0.45524, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.45485, val loss: 0.45352, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.45304, val loss: 0.45162, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.45117, val loss: 0.44996, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.44943, val loss: 0.44812, in 0.000s 1 tree, 127 leaves, max depth = 15, train loss: 0.44762, val loss: 0.44651, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.44586, val loss: 0.44495, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.44419, val loss: 0.44319, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.44248, val loss: 0.44167, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.44087, val loss: 0.43997, in 0.000s 1 tree, 127 leaves, max depth = 14, train loss: 0.43921, val loss: 0.43851, in 0.031s 1 tree, 128 leaves, max depth = 14, train loss: 0.43760, val loss: 0.43708, in 0.000s 1 tree, 78 leaves, max depth = 15, train loss: 0.43605, val loss: 0.43544, in 0.016s 1 tree, 127 leaves, max depth = 14, train loss: 0.43449, val loss: 0.43407, in 0.016s Fit 75 trees in 1.267 s, (7409 total leaves) Time spent computing histograms: 0.387s Time spent finding best splits: 0.107s Time spent applying splits: 0.123s Time spent predicting: 0.016s Trial 84, Fold 3: Log loss = 0.43330142446967024, Average precision = 0.9190328847460254, ROC-AUC = 0.9310932549046047, Elapsed Time = 1.269082899998466 seconds Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 84, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 74 leaves, max depth = 16, train loss: 0.68541, val loss: 0.68496, in 0.016s 1 tree, 74 leaves, max depth = 16, train loss: 0.67792, val loss: 0.67703, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.67068, val loss: 0.66935, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.66375, val loss: 0.66200, in 0.000s 1 tree, 76 leaves, max depth = 16, train loss: 0.65696, val loss: 0.65479, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.65038, val loss: 0.64780, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.64402, val loss: 0.64103, in 0.000s 1 tree, 76 leaves, max depth = 16, train loss: 0.63785, val loss: 0.63446, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.63187, val loss: 0.62808, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.62608, val loss: 0.62190, in 0.016s 1 tree, 76 leaves, max depth = 16, train loss: 0.62046, val loss: 0.61590, in 0.000s 1 tree, 77 leaves, max depth = 16, train loss: 0.61502, val loss: 0.61008, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.60976, val loss: 0.60448, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.60463, val loss: 0.59899, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.59966, val loss: 0.59366, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.59486, val loss: 0.58854, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.59018, val loss: 0.58350, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.58564, val loss: 0.57861, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.58126, val loss: 0.57392, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.57698, val loss: 0.56930, in 0.000s 1 tree, 75 leaves, max depth = 16, train loss: 0.57282, val loss: 0.56480, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.56879, val loss: 0.56044, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.56487, val loss: 0.55619, in 0.016s 1 tree, 75 leaves, max depth = 16, train loss: 0.56106, val loss: 0.55206, in 0.000s 1 tree, 125 leaves, max depth = 14, train loss: 0.55704, val loss: 0.54818, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.55340, val loss: 0.54422, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.54953, val loss: 0.54050, in 0.016s 1 tree, 79 leaves, max depth = 16, train loss: 0.54606, val loss: 0.53674, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.54235, val loss: 0.53316, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.53875, val loss: 0.52969, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.53544, val loss: 0.52608, in 0.000s 1 tree, 126 leaves, max depth = 15, train loss: 0.53198, val loss: 0.52275, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.52883, val loss: 0.51932, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.52549, val loss: 0.51611, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.52246, val loss: 0.51282, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.51925, val loss: 0.50973, in 0.016s 1 tree, 78 leaves, max depth = 16, train loss: 0.51633, val loss: 0.50652, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.51323, val loss: 0.50355, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.51044, val loss: 0.50050, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.50746, val loss: 0.49764, in 0.016s 1 tree, 81 leaves, max depth = 16, train loss: 0.50478, val loss: 0.49470, in 0.000s 1 tree, 128 leaves, max depth = 15, train loss: 0.50190, val loss: 0.49194, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.49932, val loss: 0.48912, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.49655, val loss: 0.48646, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.49384, val loss: 0.48387, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.49122, val loss: 0.48136, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.48879, val loss: 0.47870, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.48625, val loss: 0.47628, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.48392, val loss: 0.47371, in 0.016s 1 tree, 130 leaves, max depth = 18, train loss: 0.48147, val loss: 0.47137, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.47920, val loss: 0.46885, in 0.016s 1 tree, 130 leaves, max depth = 18, train loss: 0.47683, val loss: 0.46659, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.47465, val loss: 0.46416, in 0.016s 1 tree, 130 leaves, max depth = 18, train loss: 0.47236, val loss: 0.46198, in 0.016s 1 tree, 130 leaves, max depth = 18, train loss: 0.47013, val loss: 0.45986, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.46806, val loss: 0.45758, in 0.016s 1 tree, 129 leaves, max depth = 18, train loss: 0.46591, val loss: 0.45553, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.46392, val loss: 0.45332, in 0.016s 1 tree, 129 leaves, max depth = 18, train loss: 0.46183, val loss: 0.45133, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.45985, val loss: 0.44915, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.45798, val loss: 0.44707, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.45597, val loss: 0.44516, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.45402, val loss: 0.44331, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.45212, val loss: 0.44150, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.45029, val loss: 0.43948, in 0.000s 1 tree, 80 leaves, max depth = 15, train loss: 0.44856, val loss: 0.43755, in 0.016s 1 tree, 130 leaves, max depth = 17, train loss: 0.44673, val loss: 0.43581, in 0.031s 1 tree, 130 leaves, max depth = 17, train loss: 0.44495, val loss: 0.43412, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.44329, val loss: 0.43227, in 0.016s 1 tree, 130 leaves, max depth = 15, train loss: 0.44156, val loss: 0.43063, in 0.031s 1 tree, 130 leaves, max depth = 17, train loss: 0.43988, val loss: 0.42904, in 0.016s 1 tree, 77 leaves, max depth = 16, train loss: 0.43829, val loss: 0.42727, in 0.016s 1 tree, 130 leaves, max depth = 13, train loss: 0.43666, val loss: 0.42573, in 0.016s 1 tree, 79 leaves, max depth = 12, train loss: 0.43508, val loss: 0.42397, in 0.016s 1 tree, 130 leaves, max depth = 13, train loss: 0.43350, val loss: 0.42247, in 0.016s Fit 75 trees in 1.361 s, (7308 total leaves) Time spent computing histograms: 0.426s Time spent finding best splits: 0.123s Time spent applying splits: 0.136s Time spent predicting: 0.031s Trial 84, Fold 4: Log loss = 0.4347714117107563, Average precision = 0.9173394608907867, ROC-AUC = 0.9293842887663302, Elapsed Time = 1.3692844999986846 seconds Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 84, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 79 leaves, max depth = 17, train loss: 0.68540, val loss: 0.68489, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.67791, val loss: 0.67690, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.67059, val loss: 0.66909, in 0.000s 1 tree, 78 leaves, max depth = 14, train loss: 0.66357, val loss: 0.66160, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.65671, val loss: 0.65426, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.65005, val loss: 0.64715, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.64361, val loss: 0.64025, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.63737, val loss: 0.63356, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.63132, val loss: 0.62708, in 0.000s 1 tree, 79 leaves, max depth = 17, train loss: 0.62552, val loss: 0.62084, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.61983, val loss: 0.61473, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.61437, val loss: 0.60885, in 0.016s 1 tree, 76 leaves, max depth = 14, train loss: 0.60908, val loss: 0.60315, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.60389, val loss: 0.59755, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.59885, val loss: 0.59212, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.59402, val loss: 0.58689, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.58927, val loss: 0.58176, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.58466, val loss: 0.57677, in 0.016s 1 tree, 78 leaves, max depth = 17, train loss: 0.58024, val loss: 0.57197, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.57590, val loss: 0.56726, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.57168, val loss: 0.56268, in 0.000s 1 tree, 80 leaves, max depth = 17, train loss: 0.56759, val loss: 0.55823, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.56361, val loss: 0.55390, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.55975, val loss: 0.54969, in 0.000s 1 tree, 78 leaves, max depth = 15, train loss: 0.55603, val loss: 0.54563, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.55218, val loss: 0.54195, in 0.016s 1 tree, 78 leaves, max depth = 15, train loss: 0.54861, val loss: 0.53806, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.54491, val loss: 0.53453, in 0.016s 1 tree, 80 leaves, max depth = 19, train loss: 0.54145, val loss: 0.53075, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.53790, val loss: 0.52736, in 0.000s 1 tree, 79 leaves, max depth = 19, train loss: 0.53458, val loss: 0.52373, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.53116, val loss: 0.52048, in 0.016s 1 tree, 79 leaves, max depth = 17, train loss: 0.52801, val loss: 0.51705, in 0.016s 1 tree, 124 leaves, max depth = 15, train loss: 0.52471, val loss: 0.51392, in 0.016s 1 tree, 80 leaves, max depth = 17, train loss: 0.52168, val loss: 0.51062, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.51851, val loss: 0.50762, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.51543, val loss: 0.50470, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.51255, val loss: 0.50156, in 0.016s 1 tree, 81 leaves, max depth = 17, train loss: 0.50976, val loss: 0.49850, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.50681, val loss: 0.49572, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.50412, val loss: 0.49278, in 0.000s 1 tree, 126 leaves, max depth = 16, train loss: 0.50128, val loss: 0.49010, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.49870, val loss: 0.48726, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.49596, val loss: 0.48468, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.49329, val loss: 0.48218, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.49070, val loss: 0.47974, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.48826, val loss: 0.47707, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.48576, val loss: 0.47473, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.48341, val loss: 0.47214, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.48099, val loss: 0.46988, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.47873, val loss: 0.46739, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.47640, val loss: 0.46520, in 0.016s 1 tree, 80 leaves, max depth = 18, train loss: 0.47422, val loss: 0.46281, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.47196, val loss: 0.46070, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.46977, val loss: 0.45866, in 0.031s 1 tree, 80 leaves, max depth = 18, train loss: 0.46769, val loss: 0.45636, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.46557, val loss: 0.45439, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.46356, val loss: 0.45216, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.46150, val loss: 0.45025, in 0.016s 1 tree, 80 leaves, max depth = 19, train loss: 0.45957, val loss: 0.44811, in 0.000s 1 tree, 82 leaves, max depth = 15, train loss: 0.45769, val loss: 0.44602, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.45571, val loss: 0.44419, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.45378, val loss: 0.44241, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.45191, val loss: 0.44068, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.45012, val loss: 0.43868, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.44838, val loss: 0.43674, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.44657, val loss: 0.43508, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.44481, val loss: 0.43346, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.44314, val loss: 0.43159, in 0.016s 1 tree, 126 leaves, max depth = 13, train loss: 0.44144, val loss: 0.43003, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.43983, val loss: 0.42822, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.43817, val loss: 0.42671, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.43656, val loss: 0.42524, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.43502, val loss: 0.42350, in 0.016s 1 tree, 125 leaves, max depth = 16, train loss: 0.43346, val loss: 0.42207, in 0.016s Fit 75 trees in 1.345 s, (7286 total leaves) Time spent computing histograms: 0.416s Time spent finding best splits: 0.120s Time spent applying splits: 0.134s Time spent predicting: 0.016s Trial 84, Fold 5: Log loss = 0.43985658498218044, Average precision = 0.9133008795843532, ROC-AUC = 0.9265036136967467, Elapsed Time = 1.3525437000007514 seconds
Optimization Progress: 85%|########5 | 85/100 [17:43<03:42, 14.82s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 85, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 71 leaves, max depth = 12, train loss: 0.67320, val loss: 0.67335, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.65476, val loss: 0.65504, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.63751, val loss: 0.63791, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.62134, val loss: 0.62183, in 0.016s 1 tree, 70 leaves, max depth = 12, train loss: 0.60616, val loss: 0.60674, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.59190, val loss: 0.59259, in 0.031s 1 tree, 73 leaves, max depth = 12, train loss: 0.57848, val loss: 0.57927, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.56584, val loss: 0.56672, in 0.016s 1 tree, 71 leaves, max depth = 12, train loss: 0.55391, val loss: 0.55489, in 0.031s 1 tree, 76 leaves, max depth = 12, train loss: 0.54265, val loss: 0.54371, in 0.016s 1 tree, 88 leaves, max depth = 14, train loss: 0.53222, val loss: 0.53317, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.52236, val loss: 0.52327, in 0.016s 1 tree, 80 leaves, max depth = 11, train loss: 0.51249, val loss: 0.51332, in 0.031s 1 tree, 78 leaves, max depth = 11, train loss: 0.50315, val loss: 0.50390, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.49429, val loss: 0.49494, in 0.016s 1 tree, 80 leaves, max depth = 11, train loss: 0.48589, val loss: 0.48649, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.47828, val loss: 0.47880, in 0.031s 1 tree, 80 leaves, max depth = 12, train loss: 0.47103, val loss: 0.47147, in 0.016s 1 tree, 78 leaves, max depth = 11, train loss: 0.46375, val loss: 0.46416, in 0.015s 1 tree, 78 leaves, max depth = 11, train loss: 0.45684, val loss: 0.45723, in 0.031s 1 tree, 80 leaves, max depth = 11, train loss: 0.45026, val loss: 0.45062, in 0.016s 1 tree, 78 leaves, max depth = 11, train loss: 0.44401, val loss: 0.44436, in 0.016s 1 tree, 78 leaves, max depth = 11, train loss: 0.43807, val loss: 0.43836, in 0.016s 1 tree, 79 leaves, max depth = 15, train loss: 0.42934, val loss: 0.42995, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.42114, val loss: 0.42201, in 0.016s 1 tree, 83 leaves, max depth = 11, train loss: 0.41588, val loss: 0.41673, in 0.016s 1 tree, 81 leaves, max depth = 11, train loss: 0.41086, val loss: 0.41171, in 0.031s 1 tree, 78 leaves, max depth = 16, train loss: 0.40346, val loss: 0.40460, in 0.016s 1 tree, 83 leaves, max depth = 11, train loss: 0.39884, val loss: 0.39999, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.39201, val loss: 0.39344, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.38773, val loss: 0.38923, in 0.016s 1 tree, 80 leaves, max depth = 16, train loss: 0.38141, val loss: 0.38321, in 0.031s 1 tree, 83 leaves, max depth = 11, train loss: 0.37748, val loss: 0.37932, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.37163, val loss: 0.37376, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.36807, val loss: 0.37031, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.36264, val loss: 0.36518, in 0.016s Fit 36 trees in 1.017 s, (2826 total leaves) Time spent computing histograms: 0.262s Time spent finding best splits: 0.065s Time spent applying splits: 0.052s Time spent predicting: 0.016s Trial 85, Fold 1: Log loss = 0.3676550507126004, Average precision = 0.952459670240728, ROC-AUC = 0.9474979298404095, Elapsed Time = 1.0264220000008208 seconds Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 85, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 73 leaves, max depth = 12, train loss: 0.67321, val loss: 0.67284, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.65466, val loss: 0.65394, in 0.031s 1 tree, 74 leaves, max depth = 11, train loss: 0.63740, val loss: 0.63633, in 0.016s 1 tree, 74 leaves, max depth = 11, train loss: 0.62122, val loss: 0.61983, in 0.031s 1 tree, 73 leaves, max depth = 12, train loss: 0.60595, val loss: 0.60430, in 0.016s 1 tree, 78 leaves, max depth = 11, train loss: 0.59142, val loss: 0.58955, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.57773, val loss: 0.57560, in 0.031s 1 tree, 76 leaves, max depth = 11, train loss: 0.56483, val loss: 0.56249, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.55268, val loss: 0.55015, in 0.016s 1 tree, 74 leaves, max depth = 11, train loss: 0.54118, val loss: 0.53843, in 0.031s 1 tree, 76 leaves, max depth = 11, train loss: 0.53033, val loss: 0.52741, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.52002, val loss: 0.51689, in 0.031s 1 tree, 74 leaves, max depth = 11, train loss: 0.51026, val loss: 0.50694, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.50102, val loss: 0.49756, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.49230, val loss: 0.48869, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.48398, val loss: 0.48019, in 0.031s 1 tree, 82 leaves, max depth = 12, train loss: 0.47645, val loss: 0.47261, in 0.016s 1 tree, 85 leaves, max depth = 14, train loss: 0.46930, val loss: 0.46542, in 0.016s 1 tree, 77 leaves, max depth = 11, train loss: 0.46211, val loss: 0.45811, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.45528, val loss: 0.45118, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.44878, val loss: 0.44454, in 0.031s 1 tree, 76 leaves, max depth = 14, train loss: 0.44260, val loss: 0.43824, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.43379, val loss: 0.42960, in 0.031s 1 tree, 79 leaves, max depth = 15, train loss: 0.42813, val loss: 0.42387, in 0.016s 1 tree, 80 leaves, max depth = 14, train loss: 0.42003, val loss: 0.41595, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.41484, val loss: 0.41071, in 0.031s 1 tree, 75 leaves, max depth = 12, train loss: 0.40989, val loss: 0.40566, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.40260, val loss: 0.39856, in 0.031s 1 tree, 75 leaves, max depth = 12, train loss: 0.39804, val loss: 0.39395, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.39131, val loss: 0.38741, in 0.031s 1 tree, 77 leaves, max depth = 14, train loss: 0.38711, val loss: 0.38320, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.38090, val loss: 0.37717, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.37702, val loss: 0.37326, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.37330, val loss: 0.36950, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.36762, val loss: 0.36399, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.36229, val loss: 0.35882, in 0.016s Fit 36 trees in 1.111 s, (2776 total leaves) Time spent computing histograms: 0.299s Time spent finding best splits: 0.073s Time spent applying splits: 0.056s Time spent predicting: 0.000s Trial 85, Fold 2: Log loss = 0.36578762426480615, Average precision = 0.9499341777478105, ROC-AUC = 0.9485677437016323, Elapsed Time = 1.1277160000008735 seconds Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 85, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 73 leaves, max depth = 12, train loss: 0.67328, val loss: 0.67336, in 0.016s 1 tree, 73 leaves, max depth = 12, train loss: 0.65488, val loss: 0.65500, in 0.031s 1 tree, 72 leaves, max depth = 13, train loss: 0.63766, val loss: 0.63781, in 0.016s 1 tree, 75 leaves, max depth = 12, train loss: 0.62149, val loss: 0.62161, in 0.016s 1 tree, 73 leaves, max depth = 11, train loss: 0.60634, val loss: 0.60650, in 0.031s 1 tree, 72 leaves, max depth = 12, train loss: 0.59211, val loss: 0.59226, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.57849, val loss: 0.57872, in 0.016s 1 tree, 78 leaves, max depth = 14, train loss: 0.56565, val loss: 0.56589, in 0.031s 1 tree, 83 leaves, max depth = 15, train loss: 0.55406, val loss: 0.55440, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.54257, val loss: 0.54293, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.53172, val loss: 0.53217, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.52145, val loss: 0.52193, in 0.016s 1 tree, 82 leaves, max depth = 14, train loss: 0.51174, val loss: 0.51229, in 0.031s 1 tree, 79 leaves, max depth = 14, train loss: 0.50253, val loss: 0.50314, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.49380, val loss: 0.49445, in 0.031s 1 tree, 83 leaves, max depth = 13, train loss: 0.48552, val loss: 0.48619, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.47767, val loss: 0.47836, in 0.016s 1 tree, 82 leaves, max depth = 12, train loss: 0.47020, val loss: 0.47093, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.46311, val loss: 0.46385, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.45637, val loss: 0.45718, in 0.016s 1 tree, 83 leaves, max depth = 13, train loss: 0.44995, val loss: 0.45080, in 0.016s 1 tree, 84 leaves, max depth = 15, train loss: 0.44076, val loss: 0.44230, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.43485, val loss: 0.43643, in 0.031s 1 tree, 85 leaves, max depth = 15, train loss: 0.42641, val loss: 0.42864, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.41844, val loss: 0.42130, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.41323, val loss: 0.41612, in 0.031s 1 tree, 80 leaves, max depth = 13, train loss: 0.40823, val loss: 0.41118, in 0.016s 1 tree, 82 leaves, max depth = 15, train loss: 0.40107, val loss: 0.40461, in 0.031s 1 tree, 81 leaves, max depth = 17, train loss: 0.39650, val loss: 0.40011, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.38988, val loss: 0.39407, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.38566, val loss: 0.38990, in 0.031s 1 tree, 81 leaves, max depth = 15, train loss: 0.37954, val loss: 0.38433, in 0.016s 1 tree, 84 leaves, max depth = 16, train loss: 0.37562, val loss: 0.38048, in 0.016s 1 tree, 81 leaves, max depth = 15, train loss: 0.36995, val loss: 0.37533, in 0.031s 1 tree, 85 leaves, max depth = 12, train loss: 0.36635, val loss: 0.37180, in 0.016s 1 tree, 83 leaves, max depth = 15, train loss: 0.36110, val loss: 0.36705, in 0.016s Fit 36 trees in 1.095 s, (2887 total leaves) Time spent computing histograms: 0.287s Time spent finding best splits: 0.072s Time spent applying splits: 0.058s Time spent predicting: 0.000s Trial 85, Fold 3: Log loss = 0.36100000465176585, Average precision = 0.9513218767088868, ROC-AUC = 0.9504091986015737, Elapsed Time = 1.107314300001235 seconds Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 85, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 12, train loss: 0.67342, val loss: 0.67281, in 0.016s 1 tree, 76 leaves, max depth = 12, train loss: 0.65512, val loss: 0.65384, in 0.031s 1 tree, 74 leaves, max depth = 13, train loss: 0.63800, val loss: 0.63604, in 0.016s 1 tree, 96 leaves, max depth = 13, train loss: 0.62209, val loss: 0.61939, in 0.031s 1 tree, 73 leaves, max depth = 12, train loss: 0.60693, val loss: 0.60369, in 0.031s 1 tree, 74 leaves, max depth = 12, train loss: 0.59274, val loss: 0.58898, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.57934, val loss: 0.57507, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.56676, val loss: 0.56200, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.55458, val loss: 0.54933, in 0.016s 1 tree, 74 leaves, max depth = 12, train loss: 0.54313, val loss: 0.53739, in 0.031s 1 tree, 77 leaves, max depth = 12, train loss: 0.53231, val loss: 0.52607, in 0.016s 1 tree, 77 leaves, max depth = 12, train loss: 0.52202, val loss: 0.51534, in 0.031s 1 tree, 77 leaves, max depth = 11, train loss: 0.51229, val loss: 0.50518, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.50307, val loss: 0.49554, in 0.031s 1 tree, 77 leaves, max depth = 11, train loss: 0.49431, val loss: 0.48642, in 0.016s 1 tree, 79 leaves, max depth = 11, train loss: 0.48601, val loss: 0.47774, in 0.031s 1 tree, 76 leaves, max depth = 11, train loss: 0.47812, val loss: 0.46950, in 0.016s 1 tree, 75 leaves, max depth = 11, train loss: 0.47065, val loss: 0.46166, in 0.031s 1 tree, 76 leaves, max depth = 11, train loss: 0.46354, val loss: 0.45420, in 0.016s 1 tree, 78 leaves, max depth = 12, train loss: 0.45678, val loss: 0.44710, in 0.031s 1 tree, 76 leaves, max depth = 11, train loss: 0.45036, val loss: 0.44035, in 0.016s 1 tree, 76 leaves, max depth = 11, train loss: 0.44424, val loss: 0.43391, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.43539, val loss: 0.42494, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.42980, val loss: 0.41902, in 0.016s 1 tree, 78 leaves, max depth = 13, train loss: 0.42166, val loss: 0.41078, in 0.016s 1 tree, 78 leaves, max depth = 11, train loss: 0.41652, val loss: 0.40536, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.40906, val loss: 0.39784, in 0.016s 1 tree, 81 leaves, max depth = 11, train loss: 0.40434, val loss: 0.39287, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.39743, val loss: 0.38587, in 0.016s 1 tree, 81 leaves, max depth = 12, train loss: 0.39319, val loss: 0.38139, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.38680, val loss: 0.37493, in 0.031s 1 tree, 81 leaves, max depth = 12, train loss: 0.38289, val loss: 0.37081, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.37697, val loss: 0.36484, in 0.016s 1 tree, 82 leaves, max depth = 11, train loss: 0.37326, val loss: 0.36091, in 0.031s 1 tree, 76 leaves, max depth = 13, train loss: 0.36778, val loss: 0.35538, in 0.016s 1 tree, 83 leaves, max depth = 12, train loss: 0.36445, val loss: 0.35186, in 0.016s Fit 36 trees in 1.158 s, (2799 total leaves) Time spent computing histograms: 0.300s Time spent finding best splits: 0.076s Time spent applying splits: 0.059s Time spent predicting: 0.000s Trial 85, Fold 4: Log loss = 0.3651511463110123, Average precision = 0.9530434339767936, ROC-AUC = 0.9493210840242161, Elapsed Time = 1.1653210000004037 seconds Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 85, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 14, train loss: 0.67324, val loss: 0.67264, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.65453, val loss: 0.65331, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.63702, val loss: 0.63521, in 0.031s 1 tree, 73 leaves, max depth = 13, train loss: 0.62062, val loss: 0.61825, in 0.016s 1 tree, 90 leaves, max depth = 12, train loss: 0.60566, val loss: 0.60276, in 0.016s 1 tree, 75 leaves, max depth = 13, train loss: 0.59115, val loss: 0.58775, in 0.031s 1 tree, 75 leaves, max depth = 13, train loss: 0.57751, val loss: 0.57363, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.56465, val loss: 0.56031, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.55243, val loss: 0.54765, in 0.031s 1 tree, 79 leaves, max depth = 13, train loss: 0.54089, val loss: 0.53570, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.52998, val loss: 0.52441, in 0.031s 1 tree, 82 leaves, max depth = 13, train loss: 0.52017, val loss: 0.51424, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.51036, val loss: 0.50404, in 0.016s 1 tree, 80 leaves, max depth = 13, train loss: 0.50107, val loss: 0.49442, in 0.016s 1 tree, 74 leaves, max depth = 13, train loss: 0.49232, val loss: 0.48537, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.48396, val loss: 0.47668, in 0.016s 1 tree, 83 leaves, max depth = 14, train loss: 0.47641, val loss: 0.46882, in 0.031s 1 tree, 84 leaves, max depth = 14, train loss: 0.46925, val loss: 0.46137, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.46201, val loss: 0.45386, in 0.031s 1 tree, 80 leaves, max depth = 13, train loss: 0.45513, val loss: 0.44667, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.44859, val loss: 0.43989, in 0.016s 1 tree, 79 leaves, max depth = 13, train loss: 0.44236, val loss: 0.43336, in 0.031s 1 tree, 78 leaves, max depth = 13, train loss: 0.43644, val loss: 0.42719, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.42778, val loss: 0.41859, in 0.031s 1 tree, 96 leaves, max depth = 14, train loss: 0.41971, val loss: 0.41056, in 0.016s 1 tree, 84 leaves, max depth = 14, train loss: 0.41446, val loss: 0.40509, in 0.031s 1 tree, 82 leaves, max depth = 14, train loss: 0.40946, val loss: 0.39992, in 0.016s 1 tree, 80 leaves, max depth = 15, train loss: 0.40211, val loss: 0.39262, in 0.016s 1 tree, 77 leaves, max depth = 13, train loss: 0.39754, val loss: 0.38792, in 0.031s 1 tree, 79 leaves, max depth = 15, train loss: 0.39075, val loss: 0.38118, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.38655, val loss: 0.37685, in 0.016s 1 tree, 77 leaves, max depth = 14, train loss: 0.38027, val loss: 0.37067, in 0.016s 1 tree, 82 leaves, max depth = 13, train loss: 0.37641, val loss: 0.36679, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.37264, val loss: 0.36296, in 0.016s 1 tree, 79 leaves, max depth = 14, train loss: 0.36694, val loss: 0.35736, in 0.016s 1 tree, 81 leaves, max depth = 13, train loss: 0.36152, val loss: 0.35200, in 0.031s Fit 36 trees in 1.142 s, (2874 total leaves) Time spent computing histograms: 0.293s Time spent finding best splits: 0.072s Time spent applying splits: 0.058s Time spent predicting: 0.000s Trial 85, Fold 5: Log loss = 0.36914487007096985, Average precision = 0.9498696078413703, ROC-AUC = 0.9474313211823941, Elapsed Time = 1.1547817999999097 seconds
Optimization Progress: 86%|########6 | 86/100 [17:55<03:16, 14.02s/it]
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 86, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 12, train loss: 0.67757, val loss: 0.67715, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.66302, val loss: 0.66218, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.64939, val loss: 0.64816, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.63662, val loss: 0.63501, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.62465, val loss: 0.62267, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.61341, val loss: 0.61107, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.60286, val loss: 0.60017, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.59293, val loss: 0.58991, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.58360, val loss: 0.58024, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.57481, val loss: 0.57113, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.56654, val loss: 0.56255, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.55874, val loss: 0.55446, in 0.000s 1 tree, 31 leaves, max depth = 14, train loss: 0.55145, val loss: 0.54680, in 0.016s Fit 13 trees in 0.361 s, (377 total leaves) Time spent computing histograms: 0.051s Time spent finding best splits: 0.007s Time spent applying splits: 0.006s Time spent predicting: 0.000s Trial 86, Fold 1: Log loss = 0.5523366704044703, Average precision = 0.8140478710455946, ROC-AUC = 0.8619023571739245, Elapsed Time = 0.3603768000011769 seconds Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 86, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 9, train loss: 0.67775, val loss: 0.67702, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.66336, val loss: 0.66191, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.64990, val loss: 0.64776, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.63728, val loss: 0.63449, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.62545, val loss: 0.62202, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.61434, val loss: 0.61036, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.60390, val loss: 0.59934, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.59408, val loss: 0.58902, in 0.000s 1 tree, 29 leaves, max depth = 9, train loss: 0.58485, val loss: 0.57923, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.57615, val loss: 0.57007, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.56796, val loss: 0.56144, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.56010, val loss: 0.55310, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.55226, val loss: 0.54561, in 0.016s Fit 13 trees in 0.376 s, (407 total leaves) Time spent computing histograms: 0.046s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 86, Fold 2: Log loss = 0.5521814942210168, Average precision = 0.8878644760545134, ROC-AUC = 0.9063182770192243, Elapsed Time = 0.37138969999978144 seconds Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 86, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0.158 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 11, train loss: 0.67789, val loss: 0.67739, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.66362, val loss: 0.66265, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.65013, val loss: 0.64872, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.63762, val loss: 0.63576, in 0.000s 1 tree, 21 leaves, max depth = 8, train loss: 0.62575, val loss: 0.62348, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.61473, val loss: 0.61205, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.60425, val loss: 0.60119, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.59452, val loss: 0.59107, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.58525, val loss: 0.58145, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.57663, val loss: 0.57247, in 0.000s 1 tree, 22 leaves, max depth = 7, train loss: 0.56840, val loss: 0.56391, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.56075, val loss: 0.55593, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.55293, val loss: 0.54866, in 0.016s Fit 13 trees in 0.377 s, (357 total leaves) Time spent computing histograms: 0.057s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 86, Fold 3: Log loss = 0.5495966806223601, Average precision = 0.8902275823415812, ROC-AUC = 0.9079231666120907, Elapsed Time = 0.38836579999951937 seconds Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 86, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 12, train loss: 0.67773, val loss: 0.67684, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.66332, val loss: 0.66157, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.64984, val loss: 0.64725, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.63721, val loss: 0.63381, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.62536, val loss: 0.62117, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.61424, val loss: 0.60929, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.60379, val loss: 0.59810, in 0.000s 1 tree, 32 leaves, max depth = 13, train loss: 0.59403, val loss: 0.58762, in 0.016s 1 tree, 29 leaves, max depth = 12, train loss: 0.58478, val loss: 0.57768, in 0.000s 1 tree, 32 leaves, max depth = 13, train loss: 0.57613, val loss: 0.56836, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.56792, val loss: 0.55950, in 0.000s 1 tree, 33 leaves, max depth = 13, train loss: 0.56024, val loss: 0.55118, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.55295, val loss: 0.54326, in 0.000s Fit 13 trees in 0.376 s, (378 total leaves) Time spent computing histograms: 0.050s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 86, Fold 4: Log loss = 0.5517180277152086, Average precision = 0.8204335615774272, ROC-AUC = 0.8677278482370867, Elapsed Time = 0.3881531000006362 seconds Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 86, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 27 leaves, max depth = 10, train loss: 0.67760, val loss: 0.67656, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.66306, val loss: 0.66102, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.64952, val loss: 0.64655, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.63675, val loss: 0.63285, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.62485, val loss: 0.62008, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.61360, val loss: 0.60796, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.60309, val loss: 0.59664, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.59314, val loss: 0.58587, in 0.016s 1 tree, 27 leaves, max depth = 12, train loss: 0.58385, val loss: 0.57582, in 0.000s 1 tree, 26 leaves, max depth = 12, train loss: 0.57510, val loss: 0.56633, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.56678, val loss: 0.55726, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.55901, val loss: 0.54880, in 0.016s 1 tree, 51 leaves, max depth = 11, train loss: 0.55133, val loss: 0.54145, in 0.000s Fit 13 trees in 0.376 s, (378 total leaves) Time spent computing histograms: 0.059s Time spent finding best splits: 0.007s Time spent applying splits: 0.007s Time spent predicting: 0.000s Trial 86, Fold 5: Log loss = 0.5551145966827552, Average precision = 0.8855353230294833, ROC-AUC = 0.9000196941827843, Elapsed Time = 0.39433289999942644 seconds
Optimization Progress: 87%|########7 | 87/100 [18:04<02:39, 12.30s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 87, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 47 leaves, max depth = 13, train loss: 0.68085, val loss: 0.68060, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.66875, val loss: 0.66851, in 0.016s 1 tree, 44 leaves, max depth = 18, train loss: 0.65796, val loss: 0.65750, in 0.016s 1 tree, 42 leaves, max depth = 13, train loss: 0.64705, val loss: 0.64650, in 0.016s 1 tree, 46 leaves, max depth = 15, train loss: 0.63693, val loss: 0.63619, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.62728, val loss: 0.62638, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.61796, val loss: 0.61691, in 0.000s 1 tree, 59 leaves, max depth = 14, train loss: 0.60916, val loss: 0.60806, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.60046, val loss: 0.59943, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.59218, val loss: 0.59101, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.58417, val loss: 0.58311, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.57650, val loss: 0.57522, in 0.000s 1 tree, 46 leaves, max depth = 13, train loss: 0.56870, val loss: 0.56728, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.56093, val loss: 0.55950, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.55389, val loss: 0.55236, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.54732, val loss: 0.54558, in 0.016s 1 tree, 50 leaves, max depth = 10, train loss: 0.54136, val loss: 0.53949, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.53522, val loss: 0.53330, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.52941, val loss: 0.52746, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.52363, val loss: 0.52160, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.51746, val loss: 0.51543, in 0.016s 1 tree, 45 leaves, max depth = 16, train loss: 0.51185, val loss: 0.50975, in 0.016s 1 tree, 46 leaves, max depth = 15, train loss: 0.50677, val loss: 0.50463, in 0.016s 1 tree, 44 leaves, max depth = 15, train loss: 0.50151, val loss: 0.49930, in 0.016s 1 tree, 53 leaves, max depth = 14, train loss: 0.49589, val loss: 0.49368, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.49078, val loss: 0.48840, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.48596, val loss: 0.48355, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.48137, val loss: 0.47883, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.47672, val loss: 0.47407, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.47080, val loss: 0.46825, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.46649, val loss: 0.46387, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.46211, val loss: 0.45940, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.45856, val loss: 0.45580, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.45455, val loss: 0.45174, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.45113, val loss: 0.44829, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.44707, val loss: 0.44424, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.44362, val loss: 0.44063, in 0.000s 1 tree, 47 leaves, max depth = 14, train loss: 0.44017, val loss: 0.43710, in 0.000s 1 tree, 45 leaves, max depth = 11, train loss: 0.43653, val loss: 0.43350, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.43339, val loss: 0.43011, in 0.031s 1 tree, 45 leaves, max depth = 10, train loss: 0.43047, val loss: 0.42717, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.42730, val loss: 0.42393, in 0.016s 1 tree, 51 leaves, max depth = 14, train loss: 0.42447, val loss: 0.42104, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.42156, val loss: 0.41800, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.41842, val loss: 0.41493, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.41563, val loss: 0.41195, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.41309, val loss: 0.40942, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.41054, val loss: 0.40681, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.40787, val loss: 0.40416, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.40530, val loss: 0.40162, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.40316, val loss: 0.39945, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.40012, val loss: 0.39643, in 0.016s Fit 52 trees in 1.096 s, (2178 total leaves) Time spent computing histograms: 0.341s Time spent finding best splits: 0.043s Time spent applying splits: 0.038s Time spent predicting: 0.000s Trial 87, Fold 1: Log loss = 0.4021835494331139, Average precision = 0.9526312436607483, ROC-AUC = 0.9463635988873117, Elapsed Time = 1.0909823999991204 seconds Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 87, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 43 leaves, max depth = 11, train loss: 0.68094, val loss: 0.68068, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.66868, val loss: 0.66811, in 0.016s 1 tree, 44 leaves, max depth = 15, train loss: 0.65779, val loss: 0.65699, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.64673, val loss: 0.64556, in 0.016s 1 tree, 45 leaves, max depth = 15, train loss: 0.63662, val loss: 0.63542, in 0.016s 1 tree, 44 leaves, max depth = 14, train loss: 0.62704, val loss: 0.62569, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.61777, val loss: 0.61636, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.60887, val loss: 0.60719, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.60014, val loss: 0.59835, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.59196, val loss: 0.58985, in 0.000s 1 tree, 50 leaves, max depth = 12, train loss: 0.58329, val loss: 0.58101, in 0.031s 1 tree, 51 leaves, max depth = 14, train loss: 0.57580, val loss: 0.57322, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.56815, val loss: 0.56548, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.56051, val loss: 0.55761, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.55365, val loss: 0.55065, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.54660, val loss: 0.54348, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.53960, val loss: 0.53647, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.53283, val loss: 0.52962, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.52661, val loss: 0.52332, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.52058, val loss: 0.51723, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.51451, val loss: 0.51103, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.50870, val loss: 0.50504, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.50355, val loss: 0.49971, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.49837, val loss: 0.49448, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.49291, val loss: 0.48892, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.48777, val loss: 0.48370, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.48311, val loss: 0.47904, in 0.016s 1 tree, 50 leaves, max depth = 17, train loss: 0.47851, val loss: 0.47439, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.47371, val loss: 0.46947, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.46978, val loss: 0.46537, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.46522, val loss: 0.46076, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.46116, val loss: 0.45663, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.45678, val loss: 0.45219, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.45313, val loss: 0.44850, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.44935, val loss: 0.44475, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.44536, val loss: 0.44068, in 0.016s 1 tree, 49 leaves, max depth = 15, train loss: 0.44183, val loss: 0.43712, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.43661, val loss: 0.43191, in 0.016s 1 tree, 49 leaves, max depth = 9, train loss: 0.43331, val loss: 0.42855, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.43019, val loss: 0.42534, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42720, val loss: 0.42222, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.42411, val loss: 0.41907, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.42076, val loss: 0.41569, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.41762, val loss: 0.41257, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.41465, val loss: 0.40949, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.41218, val loss: 0.40697, in 0.016s 1 tree, 48 leaves, max depth = 17, train loss: 0.40924, val loss: 0.40395, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.40672, val loss: 0.40142, in 0.016s 1 tree, 52 leaves, max depth = 15, train loss: 0.40415, val loss: 0.39886, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39974, val loss: 0.39456, in 0.016s 1 tree, 45 leaves, max depth = 14, train loss: 0.39752, val loss: 0.39236, in 0.016s 1 tree, 68 leaves, max depth = 11, train loss: 0.39453, val loss: 0.38944, in 0.016s Fit 52 trees in 1.141 s, (2254 total leaves) Time spent computing histograms: 0.341s Time spent finding best splits: 0.047s Time spent applying splits: 0.042s Time spent predicting: 0.000s Trial 87, Fold 2: Log loss = 0.396659992982574, Average precision = 0.9505285581424341, ROC-AUC = 0.9472481449591964, Elapsed Time = 1.1540588999996544 seconds Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 87, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 40 leaves, max depth = 14, train loss: 0.68086, val loss: 0.68065, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.66881, val loss: 0.66850, in 0.016s 1 tree, 50 leaves, max depth = 17, train loss: 0.65794, val loss: 0.65761, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.64763, val loss: 0.64714, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.63774, val loss: 0.63711, in 0.016s 1 tree, 43 leaves, max depth = 16, train loss: 0.62808, val loss: 0.62742, in 0.016s 1 tree, 46 leaves, max depth = 16, train loss: 0.61876, val loss: 0.61813, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.60983, val loss: 0.60907, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.60121, val loss: 0.60045, in 0.016s 1 tree, 51 leaves, max depth = 17, train loss: 0.59284, val loss: 0.59201, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.58488, val loss: 0.58397, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.57719, val loss: 0.57616, in 0.016s 1 tree, 49 leaves, max depth = 14, train loss: 0.56912, val loss: 0.56806, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.56137, val loss: 0.56023, in 0.016s 1 tree, 48 leaves, max depth = 15, train loss: 0.55389, val loss: 0.55275, in 0.031s 1 tree, 40 leaves, max depth = 12, train loss: 0.54729, val loss: 0.54609, in 0.016s 1 tree, 56 leaves, max depth = 17, train loss: 0.54032, val loss: 0.53909, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.53361, val loss: 0.53231, in 0.016s 1 tree, 45 leaves, max depth = 14, train loss: 0.52746, val loss: 0.52604, in 0.016s 1 tree, 57 leaves, max depth = 15, train loss: 0.52131, val loss: 0.51999, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.51507, val loss: 0.51373, in 0.016s 1 tree, 45 leaves, max depth = 16, train loss: 0.50955, val loss: 0.50824, in 0.031s 1 tree, 49 leaves, max depth = 12, train loss: 0.50454, val loss: 0.50309, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.49933, val loss: 0.49791, in 0.016s 1 tree, 58 leaves, max depth = 12, train loss: 0.49388, val loss: 0.49248, in 0.016s 1 tree, 50 leaves, max depth = 14, train loss: 0.48873, val loss: 0.48736, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.48395, val loss: 0.48264, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.47942, val loss: 0.47818, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.47460, val loss: 0.47334, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47064, val loss: 0.46941, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.46611, val loss: 0.46487, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.46202, val loss: 0.46074, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.45784, val loss: 0.45659, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.45377, val loss: 0.45254, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.45039, val loss: 0.44907, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.44637, val loss: 0.44512, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.44302, val loss: 0.44179, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.43945, val loss: 0.43821, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.43627, val loss: 0.43503, in 0.000s 1 tree, 45 leaves, max depth = 12, train loss: 0.43295, val loss: 0.43169, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.43006, val loss: 0.42887, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.42701, val loss: 0.42587, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.42373, val loss: 0.42258, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.41879, val loss: 0.41807, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.41570, val loss: 0.41501, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.41289, val loss: 0.41230, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.41043, val loss: 0.40982, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.40782, val loss: 0.40734, in 0.016s 1 tree, 65 leaves, max depth = 15, train loss: 0.40476, val loss: 0.40409, in 0.016s 1 tree, 11 leaves, max depth = 5, train loss: 0.40041, val loss: 0.40011, in 0.016s 1 tree, 49 leaves, max depth = 15, train loss: 0.39797, val loss: 0.39772, in 0.016s 1 tree, 46 leaves, max depth = 16, train loss: 0.39577, val loss: 0.39551, in 0.016s Fit 52 trees in 1.204 s, (2312 total leaves) Time spent computing histograms: 0.354s Time spent finding best splits: 0.049s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 87, Fold 3: Log loss = 0.39246991409418885, Average precision = 0.9549524192568721, ROC-AUC = 0.9508018890071367, Elapsed Time = 1.2186887999996543 seconds Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 87, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 43 leaves, max depth = 12, train loss: 0.68110, val loss: 0.68057, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.66909, val loss: 0.66809, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.65825, val loss: 0.65675, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.64793, val loss: 0.64594, in 0.016s 1 tree, 46 leaves, max depth = 16, train loss: 0.63791, val loss: 0.63538, in 0.016s 1 tree, 45 leaves, max depth = 15, train loss: 0.62835, val loss: 0.62525, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.61912, val loss: 0.61549, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.61031, val loss: 0.60631, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.60174, val loss: 0.59731, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.59361, val loss: 0.58888, in 0.031s 1 tree, 57 leaves, max depth = 12, train loss: 0.58505, val loss: 0.57983, in 0.016s 1 tree, 48 leaves, max depth = 11, train loss: 0.57758, val loss: 0.57202, in 0.016s 1 tree, 45 leaves, max depth = 15, train loss: 0.57013, val loss: 0.56425, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.56239, val loss: 0.55616, in 0.016s 1 tree, 49 leaves, max depth = 15, train loss: 0.55474, val loss: 0.54810, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.54831, val loss: 0.54125, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.54135, val loss: 0.53385, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.53467, val loss: 0.52680, in 0.016s 1 tree, 47 leaves, max depth = 15, train loss: 0.52844, val loss: 0.52018, in 0.016s 1 tree, 47 leaves, max depth = 16, train loss: 0.52254, val loss: 0.51395, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.51707, val loss: 0.50825, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.51131, val loss: 0.50210, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.50615, val loss: 0.49671, in 0.016s 1 tree, 46 leaves, max depth = 15, train loss: 0.50101, val loss: 0.49126, in 0.016s 1 tree, 54 leaves, max depth = 14, train loss: 0.49542, val loss: 0.48541, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.49032, val loss: 0.48001, in 0.016s 1 tree, 58 leaves, max depth = 15, train loss: 0.48569, val loss: 0.47511, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.48112, val loss: 0.47025, in 0.031s 1 tree, 45 leaves, max depth = 17, train loss: 0.47638, val loss: 0.46522, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.47243, val loss: 0.46100, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.46792, val loss: 0.45622, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.46382, val loss: 0.45186, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.45961, val loss: 0.44741, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.45582, val loss: 0.44340, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.45193, val loss: 0.43928, in 0.016s 1 tree, 47 leaves, max depth = 15, train loss: 0.44799, val loss: 0.43508, in 0.031s 1 tree, 54 leaves, max depth = 14, train loss: 0.44445, val loss: 0.43136, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.44097, val loss: 0.42765, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.43724, val loss: 0.42374, in 0.016s 1 tree, 15 leaves, max depth = 11, train loss: 0.43239, val loss: 0.41882, in 0.016s 1 tree, 48 leaves, max depth = 14, train loss: 0.42914, val loss: 0.41529, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.42637, val loss: 0.41232, in 0.000s 1 tree, 45 leaves, max depth = 13, train loss: 0.42349, val loss: 0.40922, in 0.016s 1 tree, 20 leaves, max depth = 9, train loss: 0.41867, val loss: 0.40435, in 0.031s 1 tree, 45 leaves, max depth = 11, train loss: 0.41559, val loss: 0.40110, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.41286, val loss: 0.39815, in 0.016s 1 tree, 48 leaves, max depth = 13, train loss: 0.41035, val loss: 0.39550, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.40789, val loss: 0.39282, in 0.016s 1 tree, 60 leaves, max depth = 10, train loss: 0.40522, val loss: 0.39005, in 0.016s 1 tree, 53 leaves, max depth = 16, train loss: 0.40210, val loss: 0.38692, in 0.016s 1 tree, 46 leaves, max depth = 15, train loss: 0.39983, val loss: 0.38445, in 0.016s 1 tree, 70 leaves, max depth = 14, train loss: 0.39681, val loss: 0.38137, in 0.016s Fit 52 trees in 1.220 s, (2272 total leaves) Time spent computing histograms: 0.367s Time spent finding best splits: 0.050s Time spent applying splits: 0.045s Time spent predicting: 0.000s Trial 87, Fold 4: Log loss = 0.39664251896804825, Average precision = 0.9532641220458598, ROC-AUC = 0.9476337415442868, Elapsed Time = 1.2226099999988946 seconds Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 87, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 43 leaves, max depth = 14, train loss: 0.68085, val loss: 0.68005, in 0.016s 1 tree, 42 leaves, max depth = 14, train loss: 0.66863, val loss: 0.66730, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.65792, val loss: 0.65601, in 0.016s 1 tree, 41 leaves, max depth = 13, train loss: 0.64696, val loss: 0.64452, in 0.016s 1 tree, 49 leaves, max depth = 13, train loss: 0.63702, val loss: 0.63400, in 0.016s 1 tree, 45 leaves, max depth = 14, train loss: 0.62734, val loss: 0.62376, in 0.016s 1 tree, 46 leaves, max depth = 14, train loss: 0.61812, val loss: 0.61402, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.60930, val loss: 0.60495, in 0.062s 1 tree, 51 leaves, max depth = 11, train loss: 0.60047, val loss: 0.59568, in 0.063s 1 tree, 48 leaves, max depth = 11, train loss: 0.59230, val loss: 0.58717, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.58360, val loss: 0.57819, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.57601, val loss: 0.57028, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.56872, val loss: 0.56273, in 0.031s 1 tree, 43 leaves, max depth = 12, train loss: 0.56121, val loss: 0.55491, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.55453, val loss: 0.54798, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.54792, val loss: 0.54123, in 0.031s 1 tree, 28 leaves, max depth = 10, train loss: 0.54157, val loss: 0.53454, in 0.016s 1 tree, 48 leaves, max depth = 18, train loss: 0.53511, val loss: 0.52781, in 0.016s 1 tree, 45 leaves, max depth = 13, train loss: 0.52863, val loss: 0.52099, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.52278, val loss: 0.51476, in 0.016s 1 tree, 52 leaves, max depth = 16, train loss: 0.51724, val loss: 0.50888, in 0.016s 1 tree, 42 leaves, max depth = 13, train loss: 0.51177, val loss: 0.50306, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.50635, val loss: 0.49725, in 0.031s 1 tree, 50 leaves, max depth = 13, train loss: 0.50067, val loss: 0.49137, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.49526, val loss: 0.48564, in 0.000s 1 tree, 62 leaves, max depth = 11, train loss: 0.48992, val loss: 0.48014, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.48495, val loss: 0.47490, in 0.016s 1 tree, 43 leaves, max depth = 14, train loss: 0.48085, val loss: 0.47052, in 0.016s 1 tree, 46 leaves, max depth = 13, train loss: 0.47635, val loss: 0.46581, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.47210, val loss: 0.46146, in 0.031s 1 tree, 50 leaves, max depth = 14, train loss: 0.46747, val loss: 0.45668, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.46359, val loss: 0.45258, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.45778, val loss: 0.44671, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.45388, val loss: 0.44271, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.44866, val loss: 0.43739, in 0.031s 1 tree, 50 leaves, max depth = 14, train loss: 0.44460, val loss: 0.43318, in 0.016s 1 tree, 51 leaves, max depth = 13, train loss: 0.44115, val loss: 0.42952, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.43767, val loss: 0.42573, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.43416, val loss: 0.42201, in 0.016s 1 tree, 45 leaves, max depth = 11, train loss: 0.43068, val loss: 0.41831, in 0.016s 1 tree, 44 leaves, max depth = 13, train loss: 0.42745, val loss: 0.41488, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.42453, val loss: 0.41193, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.42143, val loss: 0.40860, in 0.031s 1 tree, 51 leaves, max depth = 11, train loss: 0.41812, val loss: 0.40519, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.41515, val loss: 0.40205, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.41265, val loss: 0.39948, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.40966, val loss: 0.39636, in 0.031s 1 tree, 46 leaves, max depth = 12, train loss: 0.40714, val loss: 0.39377, in 0.016s 1 tree, 52 leaves, max depth = 16, train loss: 0.40455, val loss: 0.39110, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40208, val loss: 0.38846, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.39999, val loss: 0.38622, in 0.016s 1 tree, 47 leaves, max depth = 14, train loss: 0.39760, val loss: 0.38375, in 0.031s Fit 52 trees in 1.377 s, (2143 total leaves) Time spent computing histograms: 0.435s Time spent finding best splits: 0.070s Time spent applying splits: 0.060s Time spent predicting: 0.016s Trial 87, Fold 5: Log loss = 0.4032658384987923, Average precision = 0.9493148596627419, ROC-AUC = 0.9450400014863534, Elapsed Time = 1.3754774999997608 seconds
Optimization Progress: 88%|########8 | 88/100 [18:16<02:29, 12.42s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 88, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.127 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 12, train loss: 0.66967, val loss: 0.66915, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.64700, val loss: 0.64635, in 0.016s 1 tree, 71 leaves, max depth = 10, train loss: 0.62636, val loss: 0.62541, in 0.016s 1 tree, 68 leaves, max depth = 10, train loss: 0.60731, val loss: 0.60607, in 0.016s 1 tree, 65 leaves, max depth = 11, train loss: 0.58948, val loss: 0.58812, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.57294, val loss: 0.57148, in 0.016s 1 tree, 45 leaves, max depth = 8, train loss: 0.55918, val loss: 0.55744, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.54569, val loss: 0.54363, in 0.016s 1 tree, 66 leaves, max depth = 11, train loss: 0.53226, val loss: 0.53015, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.52018, val loss: 0.51795, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.50840, val loss: 0.50610, in 0.016s 1 tree, 72 leaves, max depth = 11, train loss: 0.49820, val loss: 0.49571, in 0.016s Fit 12 trees in 0.486 s, (740 total leaves) Time spent computing histograms: 0.079s Time spent finding best splits: 0.013s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 88, Fold 1: Log loss = 0.49899187329362027, Average precision = 0.9206425128050846, ROC-AUC = 0.9312720458553791, Elapsed Time = 0.4972512000003917 seconds Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 88, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 45 leaves, max depth = 11, train loss: 0.66964, val loss: 0.66913, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.64686, val loss: 0.64596, in 0.016s 1 tree, 69 leaves, max depth = 12, train loss: 0.62617, val loss: 0.62496, in 0.031s 1 tree, 68 leaves, max depth = 11, train loss: 0.60709, val loss: 0.60558, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.58917, val loss: 0.58735, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.57257, val loss: 0.57046, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.55850, val loss: 0.55589, in 0.032s 1 tree, 47 leaves, max depth = 10, train loss: 0.54504, val loss: 0.54229, in 0.016s 1 tree, 64 leaves, max depth = 11, train loss: 0.53151, val loss: 0.52855, in 0.016s 1 tree, 63 leaves, max depth = 12, train loss: 0.51949, val loss: 0.51648, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.50784, val loss: 0.50457, in 0.031s 1 tree, 72 leaves, max depth = 10, train loss: 0.49764, val loss: 0.49416, in 0.016s Fit 12 trees in 0.596 s, (724 total leaves) Time spent computing histograms: 0.091s Time spent finding best splits: 0.016s Time spent applying splits: 0.016s Time spent predicting: 0.000s Trial 88, Fold 2: Log loss = 0.4988965466589309, Average precision = 0.914735125078572, ROC-AUC = 0.9307612791852722, Elapsed Time = 0.5944717000002129 seconds Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 88, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 12, train loss: 0.66970, val loss: 0.66945, in 0.031s 1 tree, 61 leaves, max depth = 13, train loss: 0.64701, val loss: 0.64678, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.62636, val loss: 0.62616, in 0.016s 1 tree, 67 leaves, max depth = 12, train loss: 0.60731, val loss: 0.60711, in 0.016s 1 tree, 62 leaves, max depth = 12, train loss: 0.58943, val loss: 0.58927, in 0.031s 1 tree, 64 leaves, max depth = 12, train loss: 0.57287, val loss: 0.57272, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.55883, val loss: 0.55854, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.54520, val loss: 0.54491, in 0.016s 1 tree, 64 leaves, max depth = 10, train loss: 0.53216, val loss: 0.53198, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.52017, val loss: 0.52003, in 0.016s 1 tree, 65 leaves, max depth = 10, train loss: 0.50836, val loss: 0.50821, in 0.016s 1 tree, 72 leaves, max depth = 12, train loss: 0.49817, val loss: 0.49802, in 0.031s Fit 12 trees in 0.596 s, (728 total leaves) Time spent computing histograms: 0.093s Time spent finding best splits: 0.016s Time spent applying splits: 0.017s Time spent predicting: 0.000s Trial 88, Fold 3: Log loss = 0.49575776300203145, Average precision = 0.9247529398996359, ROC-AUC = 0.9354105920919573, Elapsed Time = 0.59648659999948 seconds Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 88, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.190 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 48 leaves, max depth = 11, train loss: 0.66968, val loss: 0.66863, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.64698, val loss: 0.64491, in 0.016s 1 tree, 72 leaves, max depth = 12, train loss: 0.62635, val loss: 0.62322, in 0.031s 1 tree, 72 leaves, max depth = 12, train loss: 0.60732, val loss: 0.60316, in 0.016s 1 tree, 65 leaves, max depth = 12, train loss: 0.58945, val loss: 0.58443, in 0.031s 1 tree, 66 leaves, max depth = 12, train loss: 0.57289, val loss: 0.56707, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.55877, val loss: 0.55225, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.54537, val loss: 0.53821, in 0.031s 1 tree, 50 leaves, max depth = 13, train loss: 0.53306, val loss: 0.52515, in 0.016s 1 tree, 61 leaves, max depth = 15, train loss: 0.52117, val loss: 0.51254, in 0.016s 1 tree, 64 leaves, max depth = 13, train loss: 0.50999, val loss: 0.50074, in 0.016s 1 tree, 64 leaves, max depth = 10, train loss: 0.49954, val loss: 0.48980, in 0.032s Fit 12 trees in 0.659 s, (723 total leaves) Time spent computing histograms: 0.108s Time spent finding best splits: 0.017s Time spent applying splits: 0.017s Time spent predicting: 0.000s Trial 88, Fold 4: Log loss = 0.4991357854614416, Average precision = 0.9226061890411278, ROC-AUC = 0.9325586087922726, Elapsed Time = 0.669846900000266 seconds Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 88, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.143 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 46 leaves, max depth = 10, train loss: 0.66951, val loss: 0.66803, in 0.016s 1 tree, 62 leaves, max depth = 11, train loss: 0.64671, val loss: 0.64448, in 0.016s 1 tree, 70 leaves, max depth = 11, train loss: 0.62602, val loss: 0.62305, in 0.016s 1 tree, 70 leaves, max depth = 11, train loss: 0.60694, val loss: 0.60327, in 0.016s 1 tree, 63 leaves, max depth = 11, train loss: 0.58901, val loss: 0.58468, in 0.031s 1 tree, 64 leaves, max depth = 12, train loss: 0.57239, val loss: 0.56749, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.55828, val loss: 0.55268, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.54483, val loss: 0.53841, in 0.016s 1 tree, 48 leaves, max depth = 12, train loss: 0.53237, val loss: 0.52522, in 0.016s 1 tree, 64 leaves, max depth = 13, train loss: 0.52030, val loss: 0.51262, in 0.031s 1 tree, 60 leaves, max depth = 10, train loss: 0.50915, val loss: 0.50088, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.49859, val loss: 0.48986, in 0.016s Fit 12 trees in 0.580 s, (702 total leaves) Time spent computing histograms: 0.093s Time spent finding best splits: 0.013s Time spent applying splits: 0.014s Time spent predicting: 0.000s Trial 88, Fold 5: Log loss = 0.5017683253521471, Average precision = 0.9171203694323937, ROC-AUC = 0.9293585270237631, Elapsed Time = 0.5883169000007911 seconds
Optimization Progress: 89%|########9 | 89/100 [18:26<02:07, 11.58s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 89, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.158 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 76 leaves, max depth = 15, train loss: 0.64936, val loss: 0.64820, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.61314, val loss: 0.61094, in 0.016s 1 tree, 87 leaves, max depth = 17, train loss: 0.58324, val loss: 0.58025, in 0.016s 1 tree, 79 leaves, max depth = 18, train loss: 0.55869, val loss: 0.55509, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.53618, val loss: 0.53443, in 0.016s 1 tree, 89 leaves, max depth = 16, train loss: 0.51692, val loss: 0.51438, in 0.016s 1 tree, 128 leaves, max depth = 17, train loss: 0.49916, val loss: 0.49824, in 0.016s 1 tree, 78 leaves, max depth = 18, train loss: 0.48429, val loss: 0.48275, in 0.016s 1 tree, 89 leaves, max depth = 16, train loss: 0.47150, val loss: 0.46932, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.45773, val loss: 0.45700, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.44606, val loss: 0.44664, in 0.031s 1 tree, 78 leaves, max depth = 17, train loss: 0.43656, val loss: 0.43657, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.42812, val loss: 0.42761, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42134, val loss: 0.42011, in 0.000s 1 tree, 89 leaves, max depth = 18, train loss: 0.41459, val loss: 0.41301, in 0.016s 1 tree, 128 leaves, max depth = 14, train loss: 0.40536, val loss: 0.40511, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.39984, val loss: 0.39897, in 0.000s 1 tree, 85 leaves, max depth = 12, train loss: 0.39429, val loss: 0.39318, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38968, val loss: 0.38801, in 0.016s 1 tree, 91 leaves, max depth = 12, train loss: 0.38485, val loss: 0.38291, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38100, val loss: 0.37855, in 0.016s 1 tree, 193 leaves, max depth = 14, train loss: 0.37530, val loss: 0.37433, in 0.031s 1 tree, 128 leaves, max depth = 15, train loss: 0.36812, val loss: 0.36851, in 0.016s 1 tree, 91 leaves, max depth = 16, train loss: 0.36431, val loss: 0.36448, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.36100, val loss: 0.36070, in 0.000s 1 tree, 127 leaves, max depth = 16, train loss: 0.35480, val loss: 0.35582, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.34954, val loss: 0.35175, in 0.031s 1 tree, 111 leaves, max depth = 15, train loss: 0.34641, val loss: 0.34804, in 0.016s 1 tree, 128 leaves, max depth = 15, train loss: 0.34188, val loss: 0.34459, in 0.016s 1 tree, 129 leaves, max depth = 15, train loss: 0.33804, val loss: 0.34174, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.33528, val loss: 0.33889, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33224, val loss: 0.33537, in 0.016s 1 tree, 113 leaves, max depth = 15, train loss: 0.32977, val loss: 0.33269, in 0.016s 1 tree, 129 leaves, max depth = 16, train loss: 0.32632, val loss: 0.33020, in 0.016s 1 tree, 91 leaves, max depth = 13, train loss: 0.32410, val loss: 0.32786, in 0.016s 1 tree, 111 leaves, max depth = 14, train loss: 0.32207, val loss: 0.32539, in 0.016s 1 tree, 162 leaves, max depth = 17, train loss: 0.31949, val loss: 0.32364, in 0.016s 1 tree, 128 leaves, max depth = 16, train loss: 0.31673, val loss: 0.32173, in 0.016s 1 tree, 110 leaves, max depth = 15, train loss: 0.31492, val loss: 0.31973, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.31288, val loss: 0.31842, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.31007, val loss: 0.31513, in 0.000s 1 tree, 111 leaves, max depth = 17, train loss: 0.30837, val loss: 0.31305, in 0.016s 1 tree, 146 leaves, max depth = 23, train loss: 0.30621, val loss: 0.31151, in 0.031s 1 tree, 128 leaves, max depth = 17, train loss: 0.30408, val loss: 0.31009, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30157, val loss: 0.30714, in 0.000s 1 tree, 111 leaves, max depth = 15, train loss: 0.30005, val loss: 0.30528, in 0.031s 1 tree, 128 leaves, max depth = 17, train loss: 0.29813, val loss: 0.30408, in 0.016s 1 tree, 50 leaves, max depth = 15, train loss: 0.29674, val loss: 0.30265, in 0.016s 1 tree, 129 leaves, max depth = 16, train loss: 0.29514, val loss: 0.30164, in 0.016s 1 tree, 50 leaves, max depth = 12, train loss: 0.29394, val loss: 0.30040, in 0.016s 1 tree, 112 leaves, max depth = 17, train loss: 0.29267, val loss: 0.29906, in 0.016s 1 tree, 129 leaves, max depth = 17, train loss: 0.29124, val loss: 0.29821, in 0.016s 1 tree, 156 leaves, max depth = 20, train loss: 0.28954, val loss: 0.29709, in 0.016s 1 tree, 85 leaves, max depth = 13, train loss: 0.28831, val loss: 0.29573, in 0.000s 1 tree, 129 leaves, max depth = 16, train loss: 0.28708, val loss: 0.29500, in 0.031s Fit 55 trees in 1.220 s, (5283 total leaves) Time spent computing histograms: 0.345s Time spent finding best splits: 0.101s Time spent applying splits: 0.116s Time spent predicting: 0.016s Trial 89, Fold 1: Log loss = 0.2991288879401835, Average precision = 0.9549775656537438, ROC-AUC = 0.9511204850805122, Elapsed Time = 1.2206474000013259 seconds Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 89, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.190 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 73 leaves, max depth = 18, train loss: 0.64921, val loss: 0.64738, in 0.016s 1 tree, 88 leaves, max depth = 14, train loss: 0.61322, val loss: 0.60940, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.58345, val loss: 0.57803, in 0.016s 1 tree, 74 leaves, max depth = 17, train loss: 0.55882, val loss: 0.55233, in 0.016s 1 tree, 127 leaves, max depth = 15, train loss: 0.53656, val loss: 0.53099, in 0.031s 1 tree, 81 leaves, max depth = 14, train loss: 0.51743, val loss: 0.51072, in 0.016s 1 tree, 126 leaves, max depth = 15, train loss: 0.49984, val loss: 0.49397, in 0.031s 1 tree, 83 leaves, max depth = 23, train loss: 0.48489, val loss: 0.47802, in 0.016s 1 tree, 88 leaves, max depth = 14, train loss: 0.47215, val loss: 0.46429, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.45852, val loss: 0.45145, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.44697, val loss: 0.44064, in 0.016s 1 tree, 83 leaves, max depth = 17, train loss: 0.43745, val loss: 0.43035, in 0.031s 1 tree, 79 leaves, max depth = 22, train loss: 0.42935, val loss: 0.42165, in 0.016s 1 tree, 87 leaves, max depth = 16, train loss: 0.42203, val loss: 0.41366, in 0.016s 1 tree, 88 leaves, max depth = 13, train loss: 0.41603, val loss: 0.40721, in 0.016s 1 tree, 126 leaves, max depth = 14, train loss: 0.40693, val loss: 0.39888, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40077, val loss: 0.39249, in 0.000s 1 tree, 84 leaves, max depth = 19, train loss: 0.39617, val loss: 0.38743, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.39109, val loss: 0.38213, in 0.000s 1 tree, 88 leaves, max depth = 17, train loss: 0.38700, val loss: 0.37764, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38276, val loss: 0.37320, in 0.016s 1 tree, 196 leaves, max depth = 15, train loss: 0.37709, val loss: 0.36868, in 0.031s 1 tree, 126 leaves, max depth = 17, train loss: 0.36998, val loss: 0.36235, in 0.016s 1 tree, 88 leaves, max depth = 16, train loss: 0.36677, val loss: 0.35883, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36316, val loss: 0.35503, in 0.000s 1 tree, 126 leaves, max depth = 17, train loss: 0.35700, val loss: 0.34962, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.35176, val loss: 0.34507, in 0.031s 1 tree, 107 leaves, max depth = 15, train loss: 0.34889, val loss: 0.34207, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.34439, val loss: 0.33821, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.34058, val loss: 0.33497, in 0.031s 1 tree, 85 leaves, max depth = 14, train loss: 0.33807, val loss: 0.33250, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33478, val loss: 0.32903, in 0.016s 1 tree, 145 leaves, max depth = 18, train loss: 0.33218, val loss: 0.32760, in 0.031s 1 tree, 127 leaves, max depth = 18, train loss: 0.32907, val loss: 0.32504, in 0.016s 1 tree, 113 leaves, max depth = 19, train loss: 0.32678, val loss: 0.32247, in 0.031s 1 tree, 107 leaves, max depth = 14, train loss: 0.32474, val loss: 0.32033, in 0.016s 1 tree, 163 leaves, max depth = 15, train loss: 0.32216, val loss: 0.31875, in 0.031s 1 tree, 126 leaves, max depth = 19, train loss: 0.31966, val loss: 0.31670, in 0.016s 1 tree, 7 leaves, max depth = 3, train loss: 0.31789, val loss: 0.31470, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.31617, val loss: 0.31283, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.31313, val loss: 0.30963, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.31178, val loss: 0.30830, in 0.016s 1 tree, 144 leaves, max depth = 19, train loss: 0.30982, val loss: 0.30740, in 0.031s 1 tree, 126 leaves, max depth = 21, train loss: 0.30769, val loss: 0.30572, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30499, val loss: 0.30285, in 0.016s 1 tree, 127 leaves, max depth = 21, train loss: 0.30312, val loss: 0.30139, in 0.031s 1 tree, 113 leaves, max depth = 16, train loss: 0.30161, val loss: 0.29972, in 0.016s 1 tree, 108 leaves, max depth = 15, train loss: 0.29955, val loss: 0.29825, in 0.016s 1 tree, 109 leaves, max depth = 16, train loss: 0.29819, val loss: 0.29685, in 0.031s 1 tree, 127 leaves, max depth = 22, train loss: 0.29667, val loss: 0.29569, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.29561, val loss: 0.29465, in 0.016s 1 tree, 159 leaves, max depth = 23, train loss: 0.29394, val loss: 0.29403, in 0.031s 1 tree, 87 leaves, max depth = 15, train loss: 0.29276, val loss: 0.29300, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.29191, val loss: 0.29231, in 0.016s 1 tree, 127 leaves, max depth = 23, train loss: 0.29058, val loss: 0.29128, in 0.016s Fit 55 trees in 1.487 s, (5085 total leaves) Time spent computing histograms: 0.429s Time spent finding best splits: 0.119s Time spent applying splits: 0.145s Time spent predicting: 0.000s Trial 89, Fold 2: Log loss = 0.3012319816631841, Average precision = 0.9527567543609047, ROC-AUC = 0.950794187210029, Elapsed Time = 1.4833385000001726 seconds Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 89, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.206 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 77 leaves, max depth = 17, train loss: 0.64955, val loss: 0.64800, in 0.016s 1 tree, 89 leaves, max depth = 13, train loss: 0.61373, val loss: 0.61076, in 0.016s 1 tree, 86 leaves, max depth = 13, train loss: 0.58412, val loss: 0.57993, in 0.016s 1 tree, 77 leaves, max depth = 17, train loss: 0.55973, val loss: 0.55458, in 0.016s 1 tree, 126 leaves, max depth = 16, train loss: 0.53704, val loss: 0.53367, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.51807, val loss: 0.51363, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.50016, val loss: 0.49723, in 0.016s 1 tree, 76 leaves, max depth = 18, train loss: 0.48540, val loss: 0.48162, in 0.016s 1 tree, 88 leaves, max depth = 15, train loss: 0.47286, val loss: 0.46819, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.45899, val loss: 0.45558, in 0.078s 1 tree, 125 leaves, max depth = 16, train loss: 0.44723, val loss: 0.44497, in 0.047s 1 tree, 76 leaves, max depth = 19, train loss: 0.43785, val loss: 0.43488, in 0.016s 1 tree, 86 leaves, max depth = 14, train loss: 0.42952, val loss: 0.42596, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.42267, val loss: 0.41961, in 0.000s 1 tree, 89 leaves, max depth = 15, train loss: 0.41608, val loss: 0.41252, in 0.031s 1 tree, 125 leaves, max depth = 14, train loss: 0.40679, val loss: 0.40423, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40121, val loss: 0.39906, in 0.000s 1 tree, 80 leaves, max depth = 15, train loss: 0.39577, val loss: 0.39338, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.39115, val loss: 0.38909, in 0.000s 1 tree, 88 leaves, max depth = 14, train loss: 0.38642, val loss: 0.38416, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38254, val loss: 0.38055, in 0.000s 1 tree, 196 leaves, max depth = 13, train loss: 0.37681, val loss: 0.37618, in 0.031s 1 tree, 126 leaves, max depth = 16, train loss: 0.36953, val loss: 0.36980, in 0.031s 1 tree, 88 leaves, max depth = 14, train loss: 0.36581, val loss: 0.36587, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.36248, val loss: 0.36277, in 0.000s 1 tree, 126 leaves, max depth = 15, train loss: 0.35619, val loss: 0.35730, in 0.031s 1 tree, 125 leaves, max depth = 15, train loss: 0.35083, val loss: 0.35272, in 0.031s 1 tree, 77 leaves, max depth = 13, train loss: 0.34795, val loss: 0.34963, in 0.031s 1 tree, 126 leaves, max depth = 15, train loss: 0.34338, val loss: 0.34577, in 0.016s 1 tree, 125 leaves, max depth = 15, train loss: 0.33949, val loss: 0.34254, in 0.031s 1 tree, 112 leaves, max depth = 15, train loss: 0.33676, val loss: 0.33920, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.33369, val loss: 0.33632, in 0.000s 1 tree, 88 leaves, max depth = 16, train loss: 0.33131, val loss: 0.33380, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.32781, val loss: 0.33092, in 0.031s 1 tree, 112 leaves, max depth = 15, train loss: 0.32565, val loss: 0.32827, in 0.031s 1 tree, 145 leaves, max depth = 20, train loss: 0.32301, val loss: 0.32680, in 0.031s 1 tree, 52 leaves, max depth = 13, train loss: 0.32126, val loss: 0.32503, in 0.016s 1 tree, 126 leaves, max depth = 17, train loss: 0.31847, val loss: 0.32274, in 0.031s 1 tree, 109 leaves, max depth = 25, train loss: 0.31665, val loss: 0.32035, in 0.031s 1 tree, 164 leaves, max depth = 16, train loss: 0.31439, val loss: 0.31881, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.31154, val loss: 0.31613, in 0.016s 1 tree, 108 leaves, max depth = 16, train loss: 0.30971, val loss: 0.31396, in 0.031s 1 tree, 146 leaves, max depth = 23, train loss: 0.30761, val loss: 0.31301, in 0.031s 1 tree, 124 leaves, max depth = 20, train loss: 0.30546, val loss: 0.31129, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.30291, val loss: 0.30890, in 0.016s 1 tree, 108 leaves, max depth = 16, train loss: 0.30128, val loss: 0.30695, in 0.031s 1 tree, 126 leaves, max depth = 18, train loss: 0.29934, val loss: 0.30552, in 0.031s 1 tree, 89 leaves, max depth = 16, train loss: 0.29786, val loss: 0.30388, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.29616, val loss: 0.30259, in 0.031s 1 tree, 52 leaves, max depth = 12, train loss: 0.29505, val loss: 0.30145, in 0.016s 1 tree, 109 leaves, max depth = 23, train loss: 0.29383, val loss: 0.29982, in 0.031s 1 tree, 124 leaves, max depth = 20, train loss: 0.29234, val loss: 0.29869, in 0.031s 1 tree, 150 leaves, max depth = 18, train loss: 0.29072, val loss: 0.29763, in 0.031s 1 tree, 89 leaves, max depth = 13, train loss: 0.28960, val loss: 0.29650, in 0.016s 1 tree, 126 leaves, max depth = 18, train loss: 0.28829, val loss: 0.29551, in 0.031s Fit 55 trees in 1.706 s, (5168 total leaves) Time spent computing histograms: 0.495s Time spent finding best splits: 0.153s Time spent applying splits: 0.181s Time spent predicting: 0.000s Trial 89, Fold 3: Log loss = 0.2924300948581894, Average precision = 0.9579560866011733, ROC-AUC = 0.9552850605792104, Elapsed Time = 1.7165589000014734 seconds Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 89, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0.205 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 81 leaves, max depth = 15, train loss: 0.64942, val loss: 0.64717, in 0.016s 1 tree, 89 leaves, max depth = 16, train loss: 0.61341, val loss: 0.60887, in 0.016s 1 tree, 91 leaves, max depth = 16, train loss: 0.58364, val loss: 0.57720, in 0.031s 1 tree, 80 leaves, max depth = 15, train loss: 0.55910, val loss: 0.55094, in 0.016s 1 tree, 131 leaves, max depth = 16, train loss: 0.53671, val loss: 0.52931, in 0.016s 1 tree, 89 leaves, max depth = 16, train loss: 0.51759, val loss: 0.50852, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.49994, val loss: 0.49155, in 0.016s 1 tree, 85 leaves, max depth = 16, train loss: 0.48508, val loss: 0.47527, in 0.016s 1 tree, 89 leaves, max depth = 14, train loss: 0.47242, val loss: 0.46123, in 0.031s 1 tree, 131 leaves, max depth = 17, train loss: 0.45877, val loss: 0.44821, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.44719, val loss: 0.43720, in 0.031s 1 tree, 85 leaves, max depth = 18, train loss: 0.43773, val loss: 0.42657, in 0.016s 1 tree, 89 leaves, max depth = 15, train loss: 0.42934, val loss: 0.41727, in 0.016s 1 tree, 88 leaves, max depth = 15, train loss: 0.42212, val loss: 0.40904, in 0.016s 1 tree, 87 leaves, max depth = 17, train loss: 0.41626, val loss: 0.40227, in 0.016s 1 tree, 131 leaves, max depth = 15, train loss: 0.40722, val loss: 0.39380, in 0.031s 1 tree, 4 leaves, max depth = 3, train loss: 0.40108, val loss: 0.38718, in 0.000s 1 tree, 91 leaves, max depth = 15, train loss: 0.39647, val loss: 0.38197, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.39140, val loss: 0.37650, in 0.016s 1 tree, 88 leaves, max depth = 14, train loss: 0.38740, val loss: 0.37186, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.38318, val loss: 0.36730, in 0.016s 1 tree, 195 leaves, max depth = 15, train loss: 0.37761, val loss: 0.36273, in 0.016s 1 tree, 130 leaves, max depth = 16, train loss: 0.37053, val loss: 0.35630, in 0.031s 1 tree, 88 leaves, max depth = 13, train loss: 0.36741, val loss: 0.35267, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.36381, val loss: 0.34877, in 0.000s 1 tree, 132 leaves, max depth = 14, train loss: 0.35766, val loss: 0.34327, in 0.031s 1 tree, 131 leaves, max depth = 14, train loss: 0.35244, val loss: 0.33861, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.34997, val loss: 0.33613, in 0.000s 1 tree, 132 leaves, max depth = 16, train loss: 0.34555, val loss: 0.33222, in 0.031s 1 tree, 131 leaves, max depth = 16, train loss: 0.34180, val loss: 0.32892, in 0.016s 1 tree, 114 leaves, max depth = 16, train loss: 0.33919, val loss: 0.32622, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.33592, val loss: 0.32265, in 0.016s 1 tree, 114 leaves, max depth = 16, train loss: 0.33375, val loss: 0.32044, in 0.016s 1 tree, 130 leaves, max depth = 16, train loss: 0.33033, val loss: 0.31749, in 0.031s 1 tree, 83 leaves, max depth = 13, train loss: 0.32829, val loss: 0.31527, in 0.016s 1 tree, 145 leaves, max depth = 22, train loss: 0.32581, val loss: 0.31335, in 0.016s 1 tree, 164 leaves, max depth = 23, train loss: 0.32333, val loss: 0.31161, in 0.032s 1 tree, 132 leaves, max depth = 17, train loss: 0.32085, val loss: 0.30953, in 0.031s 1 tree, 113 leaves, max depth = 25, train loss: 0.31889, val loss: 0.30756, in 0.031s 1 tree, 105 leaves, max depth = 15, train loss: 0.31708, val loss: 0.30577, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.31406, val loss: 0.30247, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.31269, val loss: 0.30116, in 0.016s 1 tree, 145 leaves, max depth = 23, train loss: 0.31071, val loss: 0.29969, in 0.031s 1 tree, 130 leaves, max depth = 16, train loss: 0.30859, val loss: 0.29794, in 0.016s 1 tree, 4 leaves, max depth = 3, train loss: 0.30590, val loss: 0.29499, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.30436, val loss: 0.29356, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.30244, val loss: 0.29199, in 0.031s 1 tree, 107 leaves, max depth = 17, train loss: 0.30108, val loss: 0.29093, in 0.016s 1 tree, 132 leaves, max depth = 17, train loss: 0.29938, val loss: 0.28958, in 0.016s 1 tree, 152 leaves, max depth = 20, train loss: 0.29757, val loss: 0.28869, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.29650, val loss: 0.28769, in 0.016s 1 tree, 131 leaves, max depth = 17, train loss: 0.29506, val loss: 0.28656, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.29390, val loss: 0.28527, in 0.031s 1 tree, 110 leaves, max depth = 16, train loss: 0.29196, val loss: 0.28370, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.29079, val loss: 0.28261, in 0.031s Fit 55 trees in 1.440 s, (5348 total leaves) Time spent computing histograms: 0.405s Time spent finding best splits: 0.114s Time spent applying splits: 0.135s Time spent predicting: 0.000s Trial 89, Fold 4: Log loss = 0.2984179934391109, Average precision = 0.9564092380626489, ROC-AUC = 0.9529589482130042, Elapsed Time = 1.4476890999994794 seconds Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 89, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.189 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 79 leaves, max depth = 15, train loss: 0.64887, val loss: 0.64617, in 0.016s 1 tree, 77 leaves, max depth = 15, train loss: 0.61270, val loss: 0.60729, in 0.016s 1 tree, 86 leaves, max depth = 16, train loss: 0.58265, val loss: 0.57491, in 0.016s 1 tree, 74 leaves, max depth = 14, train loss: 0.55783, val loss: 0.54778, in 0.016s 1 tree, 127 leaves, max depth = 20, train loss: 0.53545, val loss: 0.52651, in 0.031s 1 tree, 77 leaves, max depth = 15, train loss: 0.51620, val loss: 0.50536, in 0.031s 1 tree, 127 leaves, max depth = 18, train loss: 0.49852, val loss: 0.48872, in 0.016s 1 tree, 76 leaves, max depth = 15, train loss: 0.48349, val loss: 0.47216, in 0.016s 1 tree, 85 leaves, max depth = 15, train loss: 0.47065, val loss: 0.45794, in 0.016s 1 tree, 127 leaves, max depth = 16, train loss: 0.45695, val loss: 0.44527, in 0.016s 1 tree, 127 leaves, max depth = 17, train loss: 0.44533, val loss: 0.43459, in 0.031s 1 tree, 76 leaves, max depth = 15, train loss: 0.43576, val loss: 0.42380, in 0.016s 1 tree, 73 leaves, max depth = 16, train loss: 0.42766, val loss: 0.41459, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.42034, val loss: 0.40623, in 0.016s 1 tree, 88 leaves, max depth = 17, train loss: 0.41437, val loss: 0.39923, in 0.016s 1 tree, 126 leaves, max depth = 19, train loss: 0.40521, val loss: 0.39111, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.39901, val loss: 0.38516, in 0.000s 1 tree, 86 leaves, max depth = 15, train loss: 0.39435, val loss: 0.37976, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38923, val loss: 0.37488, in 0.016s 1 tree, 87 leaves, max depth = 15, train loss: 0.38517, val loss: 0.37011, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38090, val loss: 0.36607, in 0.000s 1 tree, 186 leaves, max depth = 13, train loss: 0.37521, val loss: 0.36211, in 0.031s 1 tree, 126 leaves, max depth = 19, train loss: 0.36803, val loss: 0.35594, in 0.016s 1 tree, 87 leaves, max depth = 14, train loss: 0.36484, val loss: 0.35216, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.36121, val loss: 0.34873, in 0.000s 1 tree, 126 leaves, max depth = 17, train loss: 0.35498, val loss: 0.34345, in 0.031s 1 tree, 127 leaves, max depth = 19, train loss: 0.34968, val loss: 0.33902, in 0.016s 1 tree, 107 leaves, max depth = 16, train loss: 0.34672, val loss: 0.33593, in 0.016s 1 tree, 126 leaves, max depth = 18, train loss: 0.34217, val loss: 0.33217, in 0.031s 1 tree, 127 leaves, max depth = 17, train loss: 0.33831, val loss: 0.32899, in 0.016s 1 tree, 114 leaves, max depth = 16, train loss: 0.33576, val loss: 0.32640, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.33244, val loss: 0.32323, in 0.000s 1 tree, 114 leaves, max depth = 14, train loss: 0.33032, val loss: 0.32110, in 0.016s 1 tree, 126 leaves, max depth = 20, train loss: 0.32682, val loss: 0.31826, in 0.031s 1 tree, 81 leaves, max depth = 13, train loss: 0.32475, val loss: 0.31589, in 0.016s 1 tree, 137 leaves, max depth = 26, train loss: 0.32233, val loss: 0.31414, in 0.031s 1 tree, 166 leaves, max depth = 16, train loss: 0.31982, val loss: 0.31227, in 0.016s 1 tree, 126 leaves, max depth = 22, train loss: 0.31729, val loss: 0.31027, in 0.016s 1 tree, 111 leaves, max depth = 16, train loss: 0.31540, val loss: 0.30833, in 0.016s 1 tree, 107 leaves, max depth = 17, train loss: 0.31365, val loss: 0.30654, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.31058, val loss: 0.30358, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.30922, val loss: 0.30198, in 0.016s 1 tree, 107 leaves, max depth = 14, train loss: 0.30673, val loss: 0.30020, in 0.031s 1 tree, 126 leaves, max depth = 23, train loss: 0.30460, val loss: 0.29857, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.30188, val loss: 0.29593, in 0.000s 1 tree, 107 leaves, max depth = 16, train loss: 0.30033, val loss: 0.29440, in 0.031s 1 tree, 126 leaves, max depth = 24, train loss: 0.29840, val loss: 0.29294, in 0.016s 1 tree, 81 leaves, max depth = 14, train loss: 0.29704, val loss: 0.29145, in 0.031s 1 tree, 127 leaves, max depth = 24, train loss: 0.29536, val loss: 0.29022, in 0.016s 1 tree, 159 leaves, max depth = 19, train loss: 0.29353, val loss: 0.28964, in 0.031s 1 tree, 111 leaves, max depth = 17, train loss: 0.29233, val loss: 0.28836, in 0.016s 1 tree, 126 leaves, max depth = 24, train loss: 0.29088, val loss: 0.28729, in 0.031s 1 tree, 89 leaves, max depth = 15, train loss: 0.28972, val loss: 0.28587, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.28803, val loss: 0.28481, in 0.016s 1 tree, 107 leaves, max depth = 18, train loss: 0.28686, val loss: 0.28366, in 0.016s Fit 55 trees in 1.424 s, (5237 total leaves) Time spent computing histograms: 0.414s Time spent finding best splits: 0.117s Time spent applying splits: 0.142s Time spent predicting: 0.016s Trial 89, Fold 5: Log loss = 0.3030563569382536, Average precision = 0.9536670025897026, ROC-AUC = 0.950168441001059, Elapsed Time = 1.432476100000713 seconds
Optimization Progress: 90%|######### | 90/100 [18:41<02:07, 12.76s/it]
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 90, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.157 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 7, train loss: 0.67072, val loss: 0.67079, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.65012, val loss: 0.65020, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63101, val loss: 0.63108, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61323, val loss: 0.61326, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.59667, val loss: 0.59655, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.58126, val loss: 0.58117, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.56683, val loss: 0.56673, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55333, val loss: 0.55314, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54069, val loss: 0.54052, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.52882, val loss: 0.52857, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51769, val loss: 0.51747, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50720, val loss: 0.50691, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49734, val loss: 0.49705, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48805, val loss: 0.48774, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47929, val loss: 0.47902, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47075, val loss: 0.47045, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.46295, val loss: 0.46265, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45533, val loss: 0.45497, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44813, val loss: 0.44774, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.43793, val loss: 0.43786, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43142, val loss: 0.43134, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42527, val loss: 0.42520, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.41625, val loss: 0.41651, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40776, val loss: 0.40834, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40240, val loss: 0.40297, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.39469, val loss: 0.39558, in 0.000s 1 tree, 17 leaves, max depth = 9, train loss: 0.38993, val loss: 0.39080, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.38531, val loss: 0.38614, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.37842, val loss: 0.37958, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37420, val loss: 0.37536, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36788, val loss: 0.36936, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36401, val loss: 0.36551, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35822, val loss: 0.36007, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.35280, val loss: 0.35484, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.34793, val loss: 0.35000, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.34337, val loss: 0.34545, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.33947, val loss: 0.34158, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.33578, val loss: 0.33799, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.33168, val loss: 0.33392, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.32763, val loss: 0.33025, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32391, val loss: 0.32656, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32061, val loss: 0.32339, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.31701, val loss: 0.32015, in 0.016s 1 tree, 17 leaves, max depth = 10, train loss: 0.31374, val loss: 0.31691, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.31071, val loss: 0.31395, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.30750, val loss: 0.31108, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.30461, val loss: 0.30820, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.30185, val loss: 0.30555, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.29903, val loss: 0.30307, in 0.000s Fit 49 trees in 1.126 s, (833 total leaves) Time spent computing histograms: 0.340s Time spent finding best splits: 0.056s Time spent applying splits: 0.022s Time spent predicting: 0.000s Trial 90, Fold 1: Log loss = 0.30327338909954293, Average precision = 0.9586633313997285, ROC-AUC = 0.9539470146685594, Elapsed Time = 1.1228356000010535 seconds Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 90, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67046, val loss: 0.67015, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.64968, val loss: 0.64902, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63025, val loss: 0.62928, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61220, val loss: 0.61085, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.59555, val loss: 0.59390, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.58002, val loss: 0.57808, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56539, val loss: 0.56326, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.55181, val loss: 0.54936, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.53892, val loss: 0.53619, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.52680, val loss: 0.52386, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51544, val loss: 0.51227, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50475, val loss: 0.50133, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49469, val loss: 0.49108, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48523, val loss: 0.48137, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47630, val loss: 0.47227, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.46789, val loss: 0.46371, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45995, val loss: 0.45555, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45255, val loss: 0.44796, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.44547, val loss: 0.44066, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43543, val loss: 0.43076, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42902, val loss: 0.42422, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.41990, val loss: 0.41522, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41412, val loss: 0.40940, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40582, val loss: 0.40124, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40053, val loss: 0.39589, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.39295, val loss: 0.38841, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38817, val loss: 0.38366, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.38122, val loss: 0.37687, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.37686, val loss: 0.37253, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.37051, val loss: 0.36631, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36646, val loss: 0.36223, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36063, val loss: 0.35652, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.35692, val loss: 0.35273, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.35183, val loss: 0.34779, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.34705, val loss: 0.34312, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.34258, val loss: 0.33873, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.33875, val loss: 0.33501, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.33496, val loss: 0.33132, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.33082, val loss: 0.32753, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.32696, val loss: 0.32381, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.32317, val loss: 0.32033, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.31969, val loss: 0.31697, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.31644, val loss: 0.31386, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.31306, val loss: 0.31077, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.30999, val loss: 0.30780, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.30703, val loss: 0.30491, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.30402, val loss: 0.30215, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.30125, val loss: 0.29950, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.29858, val loss: 0.29693, in 0.016s Fit 49 trees in 1.127 s, (833 total leaves) Time spent computing histograms: 0.338s Time spent finding best splits: 0.055s Time spent applying splits: 0.021s Time spent predicting: 0.016s Trial 90, Fold 2: Log loss = 0.30164434202133084, Average precision = 0.9563519071978452, ROC-AUC = 0.9546788546441348, Elapsed Time = 1.1341358000008768 seconds Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 90, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67083, val loss: 0.67081, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65031, val loss: 0.65023, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.63125, val loss: 0.63107, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.61357, val loss: 0.61331, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.59683, val loss: 0.59653, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.58121, val loss: 0.58088, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.56663, val loss: 0.56625, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.55297, val loss: 0.55258, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.54020, val loss: 0.53972, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.52827, val loss: 0.52765, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.51699, val loss: 0.51639, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50638, val loss: 0.50574, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49641, val loss: 0.49570, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48700, val loss: 0.48624, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.47814, val loss: 0.47737, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46978, val loss: 0.46896, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46187, val loss: 0.46108, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.45437, val loss: 0.45355, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44387, val loss: 0.44379, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43405, val loss: 0.43462, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42757, val loss: 0.42822, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42146, val loss: 0.42214, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.41565, val loss: 0.41632, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40726, val loss: 0.40857, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40199, val loss: 0.40335, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39427, val loss: 0.39622, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38943, val loss: 0.39133, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38236, val loss: 0.38490, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37797, val loss: 0.38057, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37150, val loss: 0.37466, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36744, val loss: 0.37059, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36151, val loss: 0.36518, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35595, val loss: 0.36009, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.35241, val loss: 0.35666, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.34752, val loss: 0.35220, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.34293, val loss: 0.34803, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.33862, val loss: 0.34414, in 0.000s 1 tree, 17 leaves, max depth = 6, train loss: 0.33492, val loss: 0.34036, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.33072, val loss: 0.33680, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32693, val loss: 0.33338, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32358, val loss: 0.32995, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.31985, val loss: 0.32683, in 0.031s 1 tree, 17 leaves, max depth = 8, train loss: 0.31634, val loss: 0.32394, in 0.032s 1 tree, 17 leaves, max depth = 6, train loss: 0.31313, val loss: 0.32105, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.31012, val loss: 0.31791, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.30699, val loss: 0.31528, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.30415, val loss: 0.31270, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.30140, val loss: 0.30982, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.29861, val loss: 0.30743, in 0.016s Fit 49 trees in 1.205 s, (833 total leaves) Time spent computing histograms: 0.365s Time spent finding best splits: 0.064s Time spent applying splits: 0.038s Time spent predicting: 0.016s Trial 90, Fold 3: Log loss = 0.2972712315038747, Average precision = 0.9594648412397699, ROC-AUC = 0.9567885554410532, Elapsed Time = 1.2156603000003088 seconds Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 90, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 6, train loss: 0.67088, val loss: 0.67003, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.65037, val loss: 0.64870, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.63135, val loss: 0.62890, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61368, val loss: 0.61051, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.59713, val loss: 0.59316, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.58175, val loss: 0.57714, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.56740, val loss: 0.56212, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.55397, val loss: 0.54806, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.54137, val loss: 0.53483, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.52957, val loss: 0.52241, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.51849, val loss: 0.51073, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.50803, val loss: 0.49970, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.49821, val loss: 0.48935, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48888, val loss: 0.47961, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47995, val loss: 0.47018, in 0.016s 1 tree, 17 leaves, max depth = 9, train loss: 0.47153, val loss: 0.46130, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46352, val loss: 0.45285, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45595, val loss: 0.44488, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44881, val loss: 0.43730, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43875, val loss: 0.42708, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43233, val loss: 0.42028, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.42319, val loss: 0.41099, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41733, val loss: 0.40477, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40905, val loss: 0.39637, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.40383, val loss: 0.39080, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39629, val loss: 0.38315, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.39156, val loss: 0.37817, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.38460, val loss: 0.37110, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38029, val loss: 0.36653, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.37391, val loss: 0.36008, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36993, val loss: 0.35577, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.36413, val loss: 0.34992, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35865, val loss: 0.34435, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.35526, val loss: 0.34067, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.35041, val loss: 0.33561, in 0.000s 1 tree, 17 leaves, max depth = 8, train loss: 0.34587, val loss: 0.33088, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.34160, val loss: 0.32640, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.33741, val loss: 0.32246, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.33369, val loss: 0.31862, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.32994, val loss: 0.31470, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.32649, val loss: 0.31116, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32285, val loss: 0.30775, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.31965, val loss: 0.30446, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.31642, val loss: 0.30110, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.31319, val loss: 0.29810, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.31028, val loss: 0.29505, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.30741, val loss: 0.29209, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.30453, val loss: 0.28942, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.30178, val loss: 0.28687, in 0.016s Fit 49 trees in 1.127 s, (833 total leaves) Time spent computing histograms: 0.335s Time spent finding best splits: 0.056s Time spent applying splits: 0.022s Time spent predicting: 0.000s Trial 90, Fold 4: Log loss = 0.30193565745199424, Average precision = 0.9581189580828368, ROC-AUC = 0.9541854289910847, Elapsed Time = 1.1225206999988586 seconds Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 90, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 17 leaves, max depth = 7, train loss: 0.67063, val loss: 0.66979, in 0.000s 1 tree, 17 leaves, max depth = 7, train loss: 0.64993, val loss: 0.64834, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.63076, val loss: 0.62843, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.61268, val loss: 0.60966, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.59584, val loss: 0.59217, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.58014, val loss: 0.57586, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.56547, val loss: 0.56059, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.55175, val loss: 0.54632, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.53889, val loss: 0.53292, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.52688, val loss: 0.52042, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.51555, val loss: 0.50867, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.50490, val loss: 0.49754, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.49488, val loss: 0.48707, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.48545, val loss: 0.47724, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.47652, val loss: 0.46794, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46810, val loss: 0.45917, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.46016, val loss: 0.45091, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.45266, val loss: 0.44310, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.44557, val loss: 0.43574, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.43546, val loss: 0.42563, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.42905, val loss: 0.41897, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.41986, val loss: 0.40980, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.41403, val loss: 0.40378, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.40567, val loss: 0.39548, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.40039, val loss: 0.38998, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.39274, val loss: 0.38237, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38792, val loss: 0.37741, in 0.031s 1 tree, 17 leaves, max depth = 6, train loss: 0.38093, val loss: 0.37045, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.37655, val loss: 0.36591, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.37237, val loss: 0.36162, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36611, val loss: 0.35542, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.36023, val loss: 0.34959, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.35654, val loss: 0.34584, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.35124, val loss: 0.34050, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.34640, val loss: 0.33558, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.34187, val loss: 0.33096, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.33797, val loss: 0.32712, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.33378, val loss: 0.32286, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.33015, val loss: 0.31931, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.32616, val loss: 0.31571, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.32248, val loss: 0.31199, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.31919, val loss: 0.30877, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.31593, val loss: 0.30546, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.31245, val loss: 0.30234, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.30929, val loss: 0.29914, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.30617, val loss: 0.29640, in 0.016s 1 tree, 17 leaves, max depth = 6, train loss: 0.30331, val loss: 0.29342, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.30043, val loss: 0.29080, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.29783, val loss: 0.28815, in 0.016s Fit 49 trees in 1.143 s, (833 total leaves) Time spent computing histograms: 0.346s Time spent finding best splits: 0.060s Time spent applying splits: 0.022s Time spent predicting: 0.000s Trial 90, Fold 5: Log loss = 0.3066667178325203, Average precision = 0.9558979910376308, ROC-AUC = 0.9514793303977854, Elapsed Time = 1.152712999999494 seconds
Optimization Progress: 91%|#########1| 91/100 [18:55<01:55, 12.88s/it]
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 91, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.143 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.66806, val loss: 0.66726, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.65533, val loss: 0.65349, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.63376, val loss: 0.63130, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61459, val loss: 0.61135, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.59735, val loss: 0.59338, in 0.000s 1 tree, 24 leaves, max depth = 11, train loss: 0.58161, val loss: 0.57708, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.56757, val loss: 0.56237, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.55498, val loss: 0.54928, in 0.000s 1 tree, 47 leaves, max depth = 8, train loss: 0.54589, val loss: 0.54077, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.54088, val loss: 0.53491, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.53026, val loss: 0.52387, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.52507, val loss: 0.51845, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.51565, val loss: 0.50865, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.50706, val loss: 0.49964, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.49931, val loss: 0.49152, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.49226, val loss: 0.48410, in 0.000s 1 tree, 47 leaves, max depth = 14, train loss: 0.48762, val loss: 0.47935, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48125, val loss: 0.47261, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.47544, val loss: 0.46647, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.46981, val loss: 0.46050, in 0.000s 1 tree, 24 leaves, max depth = 11, train loss: 0.46469, val loss: 0.45506, in 0.016s 1 tree, 24 leaves, max depth = 11, train loss: 0.46003, val loss: 0.45009, in 0.000s 1 tree, 24 leaves, max depth = 7, train loss: 0.45609, val loss: 0.44590, in 0.016s 1 tree, 45 leaves, max depth = 9, train loss: 0.45153, val loss: 0.44177, in 0.000s 1 tree, 24 leaves, max depth = 10, train loss: 0.44798, val loss: 0.43795, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44464, val loss: 0.43440, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.44129, val loss: 0.43103, in 0.000s 1 tree, 24 leaves, max depth = 10, train loss: 0.43845, val loss: 0.42800, in 0.016s 1 tree, 47 leaves, max depth = 8, train loss: 0.43352, val loss: 0.42369, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.42609, val loss: 0.41700, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.42372, val loss: 0.41446, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.42142, val loss: 0.41189, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.41786, val loss: 0.40850, in 0.016s 1 tree, 47 leaves, max depth = 12, train loss: 0.41479, val loss: 0.40553, in 0.000s 1 tree, 44 leaves, max depth = 9, train loss: 0.41173, val loss: 0.40287, in 0.016s 1 tree, 43 leaves, max depth = 9, train loss: 0.40895, val loss: 0.40048, in 0.000s Fit 36 trees in 0.502 s, (957 total leaves) Time spent computing histograms: 0.146s Time spent finding best splits: 0.013s Time spent applying splits: 0.017s Time spent predicting: 0.000s Trial 91, Fold 1: Log loss = 0.40882904263038683, Average precision = 0.9161066816081005, ROC-AUC = 0.9231154527704889, Elapsed Time = 0.5137892999991891 seconds Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 91, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.143 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 9, train loss: 0.66805, val loss: 0.66675, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.65515, val loss: 0.65251, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.63362, val loss: 0.62991, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.61439, val loss: 0.60970, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.59711, val loss: 0.59149, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.58147, val loss: 0.57490, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.56740, val loss: 0.56001, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.55476, val loss: 0.54661, in 0.000s 1 tree, 47 leaves, max depth = 8, train loss: 0.54576, val loss: 0.53806, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.54083, val loss: 0.53256, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.53016, val loss: 0.52122, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.52585, val loss: 0.51649, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.51655, val loss: 0.50662, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.50812, val loss: 0.49754, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.50044, val loss: 0.48931, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.49346, val loss: 0.48179, in 0.000s 1 tree, 47 leaves, max depth = 12, train loss: 0.48860, val loss: 0.47745, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.48233, val loss: 0.47075, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.47665, val loss: 0.46456, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.47120, val loss: 0.45863, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.46638, val loss: 0.45339, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.46186, val loss: 0.44845, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.45799, val loss: 0.44424, in 0.000s 1 tree, 45 leaves, max depth = 10, train loss: 0.45333, val loss: 0.43982, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44985, val loss: 0.43604, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.44660, val loss: 0.43247, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.44318, val loss: 0.42914, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.44039, val loss: 0.42605, in 0.016s 1 tree, 47 leaves, max depth = 8, train loss: 0.43535, val loss: 0.42149, in 0.000s 1 tree, 43 leaves, max depth = 11, train loss: 0.42777, val loss: 0.41435, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.42544, val loss: 0.41174, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.42320, val loss: 0.40923, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.41916, val loss: 0.40576, in 0.000s 1 tree, 47 leaves, max depth = 11, train loss: 0.41594, val loss: 0.40311, in 0.016s 1 tree, 44 leaves, max depth = 9, train loss: 0.41283, val loss: 0.40025, in 0.000s 1 tree, 44 leaves, max depth = 9, train loss: 0.41001, val loss: 0.39767, in 0.016s Fit 36 trees in 0.534 s, (975 total leaves) Time spent computing histograms: 0.145s Time spent finding best splits: 0.013s Time spent applying splits: 0.018s Time spent predicting: 0.000s Trial 91, Fold 2: Log loss = 0.41045980898028744, Average precision = 0.9138552951984288, ROC-AUC = 0.9266183370202987, Elapsed Time = 0.5324492999989161 seconds Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 91, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.66816, val loss: 0.66729, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.65521, val loss: 0.65401, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.63385, val loss: 0.63179, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61475, val loss: 0.61195, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.59758, val loss: 0.59409, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.58213, val loss: 0.57801, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.56816, val loss: 0.56344, in 0.000s 1 tree, 24 leaves, max depth = 10, train loss: 0.55562, val loss: 0.55025, in 0.000s 1 tree, 47 leaves, max depth = 8, train loss: 0.54651, val loss: 0.54188, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.54166, val loss: 0.53596, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.53112, val loss: 0.52482, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.52597, val loss: 0.51997, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.51662, val loss: 0.51008, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50814, val loss: 0.50113, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.50044, val loss: 0.49295, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.49345, val loss: 0.48550, in 0.000s 1 tree, 47 leaves, max depth = 14, train loss: 0.48872, val loss: 0.48115, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48240, val loss: 0.47451, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.47669, val loss: 0.46843, in 0.000s 1 tree, 23 leaves, max depth = 8, train loss: 0.47118, val loss: 0.46268, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.46617, val loss: 0.45744, in 0.000s 1 tree, 23 leaves, max depth = 8, train loss: 0.46162, val loss: 0.45266, in 0.000s 1 tree, 24 leaves, max depth = 8, train loss: 0.45776, val loss: 0.44851, in 0.016s 1 tree, 44 leaves, max depth = 12, train loss: 0.45323, val loss: 0.44430, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.44971, val loss: 0.44057, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44645, val loss: 0.43703, in 0.000s 1 tree, 18 leaves, max depth = 6, train loss: 0.44310, val loss: 0.43407, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.44030, val loss: 0.43101, in 0.000s 1 tree, 47 leaves, max depth = 9, train loss: 0.43538, val loss: 0.42666, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.42809, val loss: 0.42002, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.42575, val loss: 0.41744, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.42348, val loss: 0.41501, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.41981, val loss: 0.41179, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.41665, val loss: 0.40908, in 0.000s 1 tree, 43 leaves, max depth = 11, train loss: 0.41361, val loss: 0.40630, in 0.016s 1 tree, 43 leaves, max depth = 11, train loss: 0.41085, val loss: 0.40378, in 0.000s Fit 36 trees in 0.549 s, (953 total leaves) Time spent computing histograms: 0.146s Time spent finding best splits: 0.014s Time spent applying splits: 0.019s Time spent predicting: 0.000s Trial 91, Fold 3: Log loss = 0.40375749891006, Average precision = 0.9206012984117338, ROC-AUC = 0.9313995669645867, Elapsed Time = 0.5566916999996465 seconds Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 91, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.66813, val loss: 0.66664, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.65509, val loss: 0.65268, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.63373, val loss: 0.62998, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61464, val loss: 0.60970, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.59747, val loss: 0.59140, in 0.000s 1 tree, 24 leaves, max depth = 12, train loss: 0.58201, val loss: 0.57474, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.56804, val loss: 0.55974, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.55542, val loss: 0.54612, in 0.000s 1 tree, 47 leaves, max depth = 8, train loss: 0.54645, val loss: 0.53727, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.54147, val loss: 0.53184, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.53087, val loss: 0.52032, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.52578, val loss: 0.51536, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.51638, val loss: 0.50510, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.50786, val loss: 0.49569, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.50009, val loss: 0.48711, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.49305, val loss: 0.47929, in 0.000s 1 tree, 47 leaves, max depth = 17, train loss: 0.48832, val loss: 0.47437, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.48200, val loss: 0.46727, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.47627, val loss: 0.46079, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.47080, val loss: 0.45457, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.46583, val loss: 0.44889, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46130, val loss: 0.44368, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.45736, val loss: 0.43915, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.45287, val loss: 0.43441, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.44936, val loss: 0.43030, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44609, val loss: 0.42647, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.44319, val loss: 0.42302, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44057, val loss: 0.41991, in 0.000s 1 tree, 47 leaves, max depth = 9, train loss: 0.43554, val loss: 0.41522, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.42810, val loss: 0.40813, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.42593, val loss: 0.40552, in 0.016s 1 tree, 24 leaves, max depth = 7, train loss: 0.42373, val loss: 0.40292, in 0.000s 1 tree, 30 leaves, max depth = 8, train loss: 0.41993, val loss: 0.39918, in 0.016s 1 tree, 47 leaves, max depth = 17, train loss: 0.41668, val loss: 0.39579, in 0.000s 1 tree, 44 leaves, max depth = 8, train loss: 0.41367, val loss: 0.39259, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.41092, val loss: 0.38966, in 0.000s Fit 36 trees in 0.564 s, (987 total leaves) Time spent computing histograms: 0.153s Time spent finding best splits: 0.014s Time spent applying splits: 0.019s Time spent predicting: 0.000s Trial 91, Fold 4: Log loss = 0.4106653964918873, Average precision = 0.9136860523512662, ROC-AUC = 0.9226894622202193, Elapsed Time = 0.5814295000000129 seconds Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 91, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 24 leaves, max depth = 11, train loss: 0.66782, val loss: 0.66597, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.65508, val loss: 0.65183, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.63345, val loss: 0.62867, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.61406, val loss: 0.60786, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.59664, val loss: 0.58909, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.58098, val loss: 0.57199, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.56680, val loss: 0.55659, in 0.000s 1 tree, 24 leaves, max depth = 11, train loss: 0.55406, val loss: 0.54254, in 0.000s 1 tree, 47 leaves, max depth = 7, train loss: 0.54519, val loss: 0.53448, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.54015, val loss: 0.52876, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.52940, val loss: 0.51684, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.52498, val loss: 0.51201, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.51559, val loss: 0.50152, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.50710, val loss: 0.49193, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.49937, val loss: 0.48315, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.49234, val loss: 0.47512, in 0.000s 1 tree, 47 leaves, max depth = 10, train loss: 0.48754, val loss: 0.47022, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.48123, val loss: 0.46299, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.47551, val loss: 0.45637, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.47006, val loss: 0.45013, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.46511, val loss: 0.44441, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.46060, val loss: 0.43917, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.45668, val loss: 0.43465, in 0.000s 1 tree, 45 leaves, max depth = 10, train loss: 0.45215, val loss: 0.42999, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44864, val loss: 0.42579, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.44538, val loss: 0.42198, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.44200, val loss: 0.41920, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.43919, val loss: 0.41582, in 0.016s 1 tree, 47 leaves, max depth = 8, train loss: 0.43422, val loss: 0.41173, in 0.000s 1 tree, 43 leaves, max depth = 13, train loss: 0.42671, val loss: 0.40494, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.42437, val loss: 0.40208, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.42212, val loss: 0.39933, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.41837, val loss: 0.39585, in 0.000s 1 tree, 47 leaves, max depth = 17, train loss: 0.41520, val loss: 0.39277, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.41216, val loss: 0.38975, in 0.000s 1 tree, 43 leaves, max depth = 10, train loss: 0.40940, val loss: 0.38700, in 0.016s Fit 36 trees in 0.596 s, (973 total leaves) Time spent computing histograms: 0.161s Time spent finding best splits: 0.015s Time spent applying splits: 0.020s Time spent predicting: 0.000s Trial 91, Fold 5: Log loss = 0.4167221246317307, Average precision = 0.9144840675045608, ROC-AUC = 0.9236757520019322, Elapsed Time = 0.5988780999996379 seconds
Optimization Progress: 92%|#########2| 92/100 [19:04<01:34, 11.76s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 92, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.174 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 7 leaves, max depth = 4, train loss: 0.67285, val loss: 0.67218, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.65443, val loss: 0.65314, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.63752, val loss: 0.63563, in 0.016s 1 tree, 6 leaves, max depth = 5, train loss: 0.62230, val loss: 0.61982, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.60796, val loss: 0.60494, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.59474, val loss: 0.59119, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.58254, val loss: 0.57848, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.57127, val loss: 0.56672, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.56084, val loss: 0.55583, in 0.000s 1 tree, 7 leaves, max depth = 4, train loss: 0.55119, val loss: 0.54572, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.54164, val loss: 0.53685, in 0.000s 1 tree, 27 leaves, max depth = 9, train loss: 0.53282, val loss: 0.52867, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.52461, val loss: 0.52005, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.51683, val loss: 0.51185, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.50961, val loss: 0.50422, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.50197, val loss: 0.49720, in 0.016s 1 tree, 15 leaves, max depth = 8, train loss: 0.49561, val loss: 0.49047, in 0.000s 1 tree, 30 leaves, max depth = 9, train loss: 0.48865, val loss: 0.48410, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.48291, val loss: 0.47800, in 0.000s 1 tree, 28 leaves, max depth = 9, train loss: 0.47656, val loss: 0.47222, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.47066, val loss: 0.46686, in 0.016s Fit 21 trees in 0.470 s, (304 total leaves) Time spent computing histograms: 0.094s Time spent finding best splits: 0.009s Time spent applying splits: 0.008s Time spent predicting: 0.000s Trial 92, Fold 1: Log loss = 0.47138854252755547, Average precision = 0.9163772901012393, ROC-AUC = 0.9268395742819863, Elapsed Time = 0.4720883000009053 seconds Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 92, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 8 leaves, max depth = 6, train loss: 0.67330, val loss: 0.67232, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.65518, val loss: 0.65328, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.63855, val loss: 0.63578, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.62327, val loss: 0.61966, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.60920, val loss: 0.60480, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.59624, val loss: 0.59108, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.58400, val loss: 0.57813, in 0.000s 1 tree, 8 leaves, max depth = 6, train loss: 0.57295, val loss: 0.56640, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.56248, val loss: 0.55529, in 0.000s 1 tree, 11 leaves, max depth = 9, train loss: 0.55303, val loss: 0.54521, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.54349, val loss: 0.53600, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.53468, val loss: 0.52750, in 0.000s 1 tree, 10 leaves, max depth = 6, train loss: 0.52646, val loss: 0.51872, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.51863, val loss: 0.51035, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.51137, val loss: 0.50256, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.50374, val loss: 0.49524, in 0.000s 1 tree, 13 leaves, max depth = 8, train loss: 0.49738, val loss: 0.48841, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.49044, val loss: 0.48175, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.48470, val loss: 0.47557, in 0.000s 1 tree, 26 leaves, max depth = 10, train loss: 0.47837, val loss: 0.46952, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.47249, val loss: 0.46391, in 0.000s Fit 21 trees in 0.486 s, (295 total leaves) Time spent computing histograms: 0.096s Time spent finding best splits: 0.008s Time spent applying splits: 0.008s Time spent predicting: 0.000s Trial 92, Fold 2: Log loss = 0.4728401946769687, Average precision = 0.9123714330399207, ROC-AUC = 0.927864641582353, Elapsed Time = 0.5001008999988699 seconds Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 92, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 6 leaves, max depth = 4, train loss: 0.67322, val loss: 0.67253, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.65521, val loss: 0.65394, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.63855, val loss: 0.63665, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.62334, val loss: 0.62091, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.60922, val loss: 0.60622, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.59629, val loss: 0.59281, in 0.000s 1 tree, 5 leaves, max depth = 4, train loss: 0.58440, val loss: 0.58035, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.57325, val loss: 0.56871, in 0.000s 1 tree, 7 leaves, max depth = 6, train loss: 0.56309, val loss: 0.55801, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.55352, val loss: 0.54801, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.54401, val loss: 0.53919, in 0.000s 1 tree, 10 leaves, max depth = 5, train loss: 0.53553, val loss: 0.53034, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.52695, val loss: 0.52241, in 0.000s 1 tree, 14 leaves, max depth = 8, train loss: 0.51933, val loss: 0.51444, in 0.016s 1 tree, 7 leaves, max depth = 6, train loss: 0.51232, val loss: 0.50697, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.50470, val loss: 0.49996, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.49834, val loss: 0.49328, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.49139, val loss: 0.48691, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.48565, val loss: 0.48086, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.47931, val loss: 0.47507, in 0.000s 1 tree, 29 leaves, max depth = 10, train loss: 0.47342, val loss: 0.46972, in 0.000s Fit 21 trees in 0.486 s, (296 total leaves) Time spent computing histograms: 0.102s Time spent finding best splits: 0.009s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 92, Fold 3: Log loss = 0.4695607971578864, Average precision = 0.9199735082968026, ROC-AUC = 0.9317574201105643, Elapsed Time = 0.4958059999989928 seconds Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 92, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.188 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 8 leaves, max depth = 5, train loss: 0.67312, val loss: 0.67192, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.65487, val loss: 0.65253, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.63813, val loss: 0.63469, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.62294, val loss: 0.61850, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.60875, val loss: 0.60330, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.59566, val loss: 0.58925, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.58359, val loss: 0.57623, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.57244, val loss: 0.56418, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.56213, val loss: 0.55299, in 0.000s 1 tree, 26 leaves, max depth = 10, train loss: 0.55239, val loss: 0.54336, in 0.016s 1 tree, 20 leaves, max depth = 13, train loss: 0.54327, val loss: 0.53348, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.53450, val loss: 0.52482, in 0.000s 1 tree, 19 leaves, max depth = 13, train loss: 0.52631, val loss: 0.51592, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.51861, val loss: 0.50746, in 0.000s 1 tree, 19 leaves, max depth = 10, train loss: 0.51154, val loss: 0.49972, in 0.016s 1 tree, 23 leaves, max depth = 10, train loss: 0.50396, val loss: 0.49226, in 0.000s 1 tree, 8 leaves, max depth = 4, train loss: 0.49751, val loss: 0.48512, in 0.016s 1 tree, 23 leaves, max depth = 10, train loss: 0.49062, val loss: 0.47835, in 0.000s 1 tree, 12 leaves, max depth = 6, train loss: 0.48485, val loss: 0.47195, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.47857, val loss: 0.46581, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.47275, val loss: 0.46011, in 0.016s Fit 21 trees in 0.516 s, (320 total leaves) Time spent computing histograms: 0.105s Time spent finding best splits: 0.009s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 92, Fold 4: Log loss = 0.47093547119013984, Average precision = 0.9171902347863797, ROC-AUC = 0.928863444256076, Elapsed Time = 0.5190452000006189 seconds Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 92, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 11 leaves, max depth = 7, train loss: 0.67310, val loss: 0.67178, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.65487, val loss: 0.65228, in 0.000s 1 tree, 11 leaves, max depth = 7, train loss: 0.63814, val loss: 0.63435, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.62276, val loss: 0.61782, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.60860, val loss: 0.60257, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.59543, val loss: 0.58829, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.58337, val loss: 0.57523, in 0.016s 1 tree, 14 leaves, max depth = 7, train loss: 0.57222, val loss: 0.56310, in 0.000s 1 tree, 8 leaves, max depth = 5, train loss: 0.56178, val loss: 0.55169, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.55223, val loss: 0.54123, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.54281, val loss: 0.53219, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.53415, val loss: 0.52263, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.52567, val loss: 0.51453, in 0.000s 1 tree, 14 leaves, max depth = 7, train loss: 0.51795, val loss: 0.50603, in 0.016s 1 tree, 15 leaves, max depth = 8, train loss: 0.51079, val loss: 0.49814, in 0.000s 1 tree, 29 leaves, max depth = 11, train loss: 0.50327, val loss: 0.49101, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.49682, val loss: 0.48384, in 0.000s 1 tree, 30 leaves, max depth = 11, train loss: 0.48997, val loss: 0.47738, in 0.016s 1 tree, 16 leaves, max depth = 8, train loss: 0.48414, val loss: 0.47087, in 0.000s 1 tree, 27 leaves, max depth = 12, train loss: 0.47789, val loss: 0.46502, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.47210, val loss: 0.45959, in 0.000s Fit 21 trees in 0.454 s, (357 total leaves) Time spent computing histograms: 0.095s Time spent finding best splits: 0.008s Time spent applying splits: 0.009s Time spent predicting: 0.000s Trial 92, Fold 5: Log loss = 0.47663555439002386, Average precision = 0.9146373133272797, ROC-AUC = 0.9261888783605522, Elapsed Time = 0.46562550000089686 seconds
Optimization Progress: 93%|#########3| 93/100 [19:13<01:17, 11.05s/it]
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 93, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.126 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 128 leaves, max depth = 12, train loss: 0.67794, val loss: 0.67791, in 0.016s 1 tree, 207 leaves, max depth = 16, train loss: 0.66330, val loss: 0.66346, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.65023, val loss: 0.65032, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.63690, val loss: 0.63718, in 0.031s 1 tree, 248 leaves, max depth = 14, train loss: 0.62439, val loss: 0.62466, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.61230, val loss: 0.61272, in 0.031s 1 tree, 248 leaves, max depth = 24, train loss: 0.60136, val loss: 0.60184, in 0.031s 1 tree, 248 leaves, max depth = 24, train loss: 0.59107, val loss: 0.59145, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.58047, val loss: 0.58095, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.57033, val loss: 0.57087, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.56077, val loss: 0.56131, in 0.031s 1 tree, 248 leaves, max depth = 23, train loss: 0.55199, val loss: 0.55260, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.54321, val loss: 0.54378, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.53463, val loss: 0.53532, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.52627, val loss: 0.52701, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.51876, val loss: 0.51962, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.51117, val loss: 0.51211, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.50388, val loss: 0.50495, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.49732, val loss: 0.49858, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.49055, val loss: 0.49176, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.48405, val loss: 0.48539, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.47809, val loss: 0.47955, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.47194, val loss: 0.47349, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.46625, val loss: 0.46779, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.46088, val loss: 0.46239, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.45535, val loss: 0.45691, in 0.031s 1 tree, 215 leaves, max depth = 15, train loss: 0.45023, val loss: 0.45168, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.44510, val loss: 0.44662, in 0.047s 1 tree, 248 leaves, max depth = 15, train loss: 0.44026, val loss: 0.44176, in 0.031s 1 tree, 193 leaves, max depth = 15, train loss: 0.43340, val loss: 0.43525, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.42884, val loss: 0.43081, in 0.047s 1 tree, 248 leaves, max depth = 17, train loss: 0.42437, val loss: 0.42642, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.42013, val loss: 0.42221, in 0.031s 1 tree, 193 leaves, max depth = 18, train loss: 0.41388, val loss: 0.41627, in 0.031s 1 tree, 236 leaves, max depth = 16, train loss: 0.40801, val loss: 0.41059, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.40415, val loss: 0.40676, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.40043, val loss: 0.40310, in 0.031s 1 tree, 192 leaves, max depth = 16, train loss: 0.39489, val loss: 0.39786, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.39127, val loss: 0.39437, in 0.031s 1 tree, 193 leaves, max depth = 16, train loss: 0.38605, val loss: 0.38944, in 0.016s 1 tree, 195 leaves, max depth = 21, train loss: 0.38101, val loss: 0.38470, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.37775, val loss: 0.38163, in 0.031s 1 tree, 240 leaves, max depth = 21, train loss: 0.37309, val loss: 0.37719, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.36990, val loss: 0.37412, in 0.031s 1 tree, 192 leaves, max depth = 19, train loss: 0.36546, val loss: 0.37002, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.36244, val loss: 0.36711, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.35983, val loss: 0.36472, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.35594, val loss: 0.36089, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.35223, val loss: 0.35728, in 0.016s 1 tree, 95 leaves, max depth = 18, train loss: 0.34871, val loss: 0.35386, in 0.016s 1 tree, 94 leaves, max depth = 15, train loss: 0.34531, val loss: 0.35049, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.34239, val loss: 0.34774, in 0.016s 1 tree, 248 leaves, max depth = 20, train loss: 0.33956, val loss: 0.34511, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.33683, val loss: 0.34249, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.33419, val loss: 0.33995, in 0.031s 1 tree, 141 leaves, max depth = 18, train loss: 0.33093, val loss: 0.33704, in 0.016s 1 tree, 94 leaves, max depth = 21, train loss: 0.32796, val loss: 0.33416, in 0.016s 1 tree, 141 leaves, max depth = 18, train loss: 0.32494, val loss: 0.33148, in 0.016s 1 tree, 94 leaves, max depth = 21, train loss: 0.32218, val loss: 0.32879, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.31977, val loss: 0.32648, in 0.031s 1 tree, 195 leaves, max depth = 18, train loss: 0.31682, val loss: 0.32381, in 0.031s 1 tree, 141 leaves, max depth = 19, train loss: 0.31409, val loss: 0.32140, in 0.016s 1 tree, 141 leaves, max depth = 19, train loss: 0.31147, val loss: 0.31910, in 0.031s 1 tree, 248 leaves, max depth = 25, train loss: 0.30926, val loss: 0.31699, in 0.016s 1 tree, 94 leaves, max depth = 17, train loss: 0.30690, val loss: 0.31466, in 0.031s 1 tree, 142 leaves, max depth = 19, train loss: 0.30447, val loss: 0.31253, in 0.016s 1 tree, 248 leaves, max depth = 22, train loss: 0.30238, val loss: 0.31055, in 0.031s 1 tree, 95 leaves, max depth = 17, train loss: 0.30021, val loss: 0.30842, in 0.016s 1 tree, 141 leaves, max depth = 18, train loss: 0.29797, val loss: 0.30646, in 0.016s 1 tree, 94 leaves, max depth = 19, train loss: 0.29593, val loss: 0.30449, in 0.016s 1 tree, 248 leaves, max depth = 24, train loss: 0.29387, val loss: 0.30251, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.29186, val loss: 0.30061, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.29031, val loss: 0.29925, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.28880, val loss: 0.29783, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.28693, val loss: 0.29605, in 0.031s 1 tree, 143 leaves, max depth = 18, train loss: 0.28494, val loss: 0.29433, in 0.016s 1 tree, 248 leaves, max depth = 21, train loss: 0.28315, val loss: 0.29261, in 0.031s 1 tree, 141 leaves, max depth = 18, train loss: 0.28127, val loss: 0.29099, in 0.016s 1 tree, 143 leaves, max depth = 18, train loss: 0.27944, val loss: 0.28941, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.27777, val loss: 0.28776, in 0.016s 1 tree, 146 leaves, max depth = 18, train loss: 0.27603, val loss: 0.28624, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.27446, val loss: 0.28469, in 0.016s 1 tree, 143 leaves, max depth = 18, train loss: 0.27285, val loss: 0.28328, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.27138, val loss: 0.28182, in 0.016s 1 tree, 248 leaves, max depth = 22, train loss: 0.26979, val loss: 0.28031, in 0.031s Fit 85 trees in 2.595 s, (17472 total leaves) Time spent computing histograms: 0.715s Time spent finding best splits: 0.314s Time spent applying splits: 0.272s Time spent predicting: 0.000s Trial 93, Fold 1: Log loss = 0.28539871775659487, Average precision = 0.9620041346120233, ROC-AUC = 0.9569296277177939, Elapsed Time = 2.5979539999989356 seconds Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 93, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 131 leaves, max depth = 14, train loss: 0.67803, val loss: 0.67785, in 0.016s 1 tree, 221 leaves, max depth = 15, train loss: 0.66342, val loss: 0.66318, in 0.031s 1 tree, 146 leaves, max depth = 15, train loss: 0.64986, val loss: 0.64952, in 0.031s 1 tree, 241 leaves, max depth = 17, train loss: 0.63663, val loss: 0.63622, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.62447, val loss: 0.62408, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.61300, val loss: 0.61259, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.60148, val loss: 0.60097, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.59032, val loss: 0.58978, in 0.031s 1 tree, 248 leaves, max depth = 14, train loss: 0.57991, val loss: 0.57940, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.56985, val loss: 0.56930, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.56008, val loss: 0.55952, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.55086, val loss: 0.55029, in 0.031s 1 tree, 248 leaves, max depth = 14, train loss: 0.54213, val loss: 0.54163, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.53404, val loss: 0.53351, in 0.016s 1 tree, 248 leaves, max depth = 20, train loss: 0.52579, val loss: 0.52525, in 0.047s 1 tree, 248 leaves, max depth = 20, train loss: 0.51785, val loss: 0.51732, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.51023, val loss: 0.50974, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.50293, val loss: 0.50246, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.49591, val loss: 0.49548, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.48916, val loss: 0.48877, in 0.031s 1 tree, 213 leaves, max depth = 16, train loss: 0.48323, val loss: 0.48288, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.47697, val loss: 0.47665, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.47130, val loss: 0.47105, in 0.031s 1 tree, 248 leaves, max depth = 14, train loss: 0.46554, val loss: 0.46541, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.46046, val loss: 0.46027, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.45505, val loss: 0.45491, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.44983, val loss: 0.44973, in 0.031s 1 tree, 236 leaves, max depth = 19, train loss: 0.44285, val loss: 0.44300, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.43797, val loss: 0.43817, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.43324, val loss: 0.43356, in 0.031s 1 tree, 215 leaves, max depth = 17, train loss: 0.42888, val loss: 0.42919, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.42450, val loss: 0.42486, in 0.031s 1 tree, 235 leaves, max depth = 17, train loss: 0.41829, val loss: 0.41895, in 0.031s 1 tree, 196 leaves, max depth = 21, train loss: 0.41231, val loss: 0.41316, in 0.031s 1 tree, 234 leaves, max depth = 18, train loss: 0.40664, val loss: 0.40775, in 0.031s 1 tree, 206 leaves, max depth = 21, train loss: 0.40299, val loss: 0.40424, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.39921, val loss: 0.40050, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.39587, val loss: 0.39721, in 0.031s 1 tree, 146 leaves, max depth = 17, train loss: 0.39083, val loss: 0.39235, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.38739, val loss: 0.38896, in 0.031s 1 tree, 214 leaves, max depth = 16, train loss: 0.38422, val loss: 0.38585, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.38123, val loss: 0.38307, in 0.047s 1 tree, 248 leaves, max depth = 21, train loss: 0.37811, val loss: 0.38005, in 0.031s 1 tree, 196 leaves, max depth = 23, train loss: 0.37344, val loss: 0.37556, in 0.031s 1 tree, 236 leaves, max depth = 17, train loss: 0.36900, val loss: 0.37142, in 0.032s 1 tree, 248 leaves, max depth = 20, train loss: 0.36629, val loss: 0.36886, in 0.031s 1 tree, 95 leaves, max depth = 18, train loss: 0.36231, val loss: 0.36501, in 0.031s 1 tree, 95 leaves, max depth = 16, train loss: 0.35851, val loss: 0.36133, in 0.016s 1 tree, 248 leaves, max depth = 23, train loss: 0.35540, val loss: 0.35840, in 0.031s 1 tree, 95 leaves, max depth = 16, train loss: 0.35180, val loss: 0.35492, in 0.016s 1 tree, 248 leaves, max depth = 23, train loss: 0.34886, val loss: 0.35215, in 0.031s 1 tree, 248 leaves, max depth = 23, train loss: 0.34601, val loss: 0.34942, in 0.031s 1 tree, 248 leaves, max depth = 23, train loss: 0.34324, val loss: 0.34676, in 0.047s 1 tree, 248 leaves, max depth = 22, train loss: 0.34046, val loss: 0.34419, in 0.031s 1 tree, 140 leaves, max depth = 17, train loss: 0.33710, val loss: 0.34109, in 0.031s 1 tree, 248 leaves, max depth = 23, train loss: 0.33459, val loss: 0.33875, in 0.031s 1 tree, 161 leaves, max depth = 18, train loss: 0.33129, val loss: 0.33570, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.32827, val loss: 0.33282, in 0.031s 1 tree, 93 leaves, max depth = 19, train loss: 0.32540, val loss: 0.33007, in 0.016s 1 tree, 236 leaves, max depth = 20, train loss: 0.32242, val loss: 0.32734, in 0.047s 1 tree, 142 leaves, max depth = 17, train loss: 0.31961, val loss: 0.32477, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.31764, val loss: 0.32299, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.31542, val loss: 0.32094, in 0.031s 1 tree, 143 leaves, max depth = 17, train loss: 0.31280, val loss: 0.31852, in 0.031s 1 tree, 94 leaves, max depth = 16, train loss: 0.31035, val loss: 0.31619, in 0.016s 1 tree, 234 leaves, max depth = 17, train loss: 0.30781, val loss: 0.31391, in 0.031s 1 tree, 143 leaves, max depth = 17, train loss: 0.30541, val loss: 0.31171, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.30326, val loss: 0.30976, in 0.031s 1 tree, 248 leaves, max depth = 24, train loss: 0.30118, val loss: 0.30788, in 0.031s 1 tree, 248 leaves, max depth = 24, train loss: 0.29916, val loss: 0.30607, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.29724, val loss: 0.30433, in 0.047s 1 tree, 248 leaves, max depth = 18, train loss: 0.29576, val loss: 0.30300, in 0.031s 1 tree, 143 leaves, max depth = 18, train loss: 0.29361, val loss: 0.30103, in 0.031s 1 tree, 232 leaves, max depth = 19, train loss: 0.29215, val loss: 0.29972, in 0.031s 1 tree, 142 leaves, max depth = 18, train loss: 0.29010, val loss: 0.29784, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.28817, val loss: 0.29603, in 0.016s 1 tree, 183 leaves, max depth = 22, train loss: 0.28618, val loss: 0.29418, in 0.031s 1 tree, 95 leaves, max depth = 16, train loss: 0.28440, val loss: 0.29252, in 0.016s 1 tree, 194 leaves, max depth = 20, train loss: 0.28246, val loss: 0.29070, in 0.031s 1 tree, 94 leaves, max depth = 17, train loss: 0.28082, val loss: 0.28917, in 0.016s 1 tree, 248 leaves, max depth = 23, train loss: 0.27925, val loss: 0.28773, in 0.047s 1 tree, 144 leaves, max depth = 19, train loss: 0.27751, val loss: 0.28615, in 0.031s 1 tree, 248 leaves, max depth = 25, train loss: 0.27590, val loss: 0.28477, in 0.031s 1 tree, 94 leaves, max depth = 16, train loss: 0.27442, val loss: 0.28336, in 0.016s 1 tree, 247 leaves, max depth = 16, train loss: 0.27320, val loss: 0.28237, in 0.031s Fit 85 trees in 2.955 s, (17901 total leaves) Time spent computing histograms: 0.797s Time spent finding best splits: 0.366s Time spent applying splits: 0.320s Time spent predicting: 0.000s Trial 93, Fold 2: Log loss = 0.2836545940853594, Average precision = 0.9613487091262867, ROC-AUC = 0.9583427290503834, Elapsed Time = 2.9765826999991987 seconds Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 93, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 124 leaves, max depth = 15, train loss: 0.67811, val loss: 0.67812, in 0.016s 1 tree, 203 leaves, max depth = 17, train loss: 0.66365, val loss: 0.66372, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.65048, val loss: 0.65067, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.63734, val loss: 0.63760, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.62499, val loss: 0.62538, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.61305, val loss: 0.61351, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.60199, val loss: 0.60253, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.59159, val loss: 0.59225, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.58107, val loss: 0.58184, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.57087, val loss: 0.57173, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.56145, val loss: 0.56240, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.55264, val loss: 0.55374, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.54383, val loss: 0.54496, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.53555, val loss: 0.53686, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.52726, val loss: 0.52863, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.51936, val loss: 0.52083, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.51221, val loss: 0.51382, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.50532, val loss: 0.50720, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.49823, val loss: 0.50018, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.49141, val loss: 0.49345, in 0.031s 1 tree, 190 leaves, max depth = 16, train loss: 0.48310, val loss: 0.48566, in 0.031s 1 tree, 189 leaves, max depth = 17, train loss: 0.47509, val loss: 0.47816, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.46905, val loss: 0.47229, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.46355, val loss: 0.46695, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.45808, val loss: 0.46169, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.45254, val loss: 0.45624, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.44719, val loss: 0.45099, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.44213, val loss: 0.44610, in 0.031s 1 tree, 191 leaves, max depth = 16, train loss: 0.43547, val loss: 0.43993, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.43065, val loss: 0.43519, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.42600, val loss: 0.43067, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.42160, val loss: 0.42650, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.41727, val loss: 0.42228, in 0.031s 1 tree, 189 leaves, max depth = 17, train loss: 0.41133, val loss: 0.41684, in 0.031s 1 tree, 236 leaves, max depth = 17, train loss: 0.40572, val loss: 0.41179, in 0.047s 1 tree, 248 leaves, max depth = 17, train loss: 0.40184, val loss: 0.40810, in 0.016s 1 tree, 248 leaves, max depth = 18, train loss: 0.39801, val loss: 0.40438, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.39432, val loss: 0.40083, in 0.047s 1 tree, 209 leaves, max depth = 19, train loss: 0.39089, val loss: 0.39746, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.38744, val loss: 0.39416, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.38410, val loss: 0.39098, in 0.031s 1 tree, 191 leaves, max depth = 16, train loss: 0.37921, val loss: 0.38657, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.37616, val loss: 0.38373, in 0.031s 1 tree, 190 leaves, max depth = 17, train loss: 0.37151, val loss: 0.37956, in 0.031s 1 tree, 237 leaves, max depth = 17, train loss: 0.36713, val loss: 0.37571, in 0.031s 1 tree, 238 leaves, max depth = 17, train loss: 0.36293, val loss: 0.37202, in 0.047s 1 tree, 248 leaves, max depth = 19, train loss: 0.36007, val loss: 0.36934, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.35626, val loss: 0.36589, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.35262, val loss: 0.36259, in 0.016s 1 tree, 94 leaves, max depth = 18, train loss: 0.34914, val loss: 0.35945, in 0.016s 1 tree, 93 leaves, max depth = 14, train loss: 0.34581, val loss: 0.35642, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.34289, val loss: 0.35343, in 0.031s 1 tree, 138 leaves, max depth = 16, train loss: 0.33956, val loss: 0.35063, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.33723, val loss: 0.34851, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.33455, val loss: 0.34574, in 0.031s 1 tree, 94 leaves, max depth = 14, train loss: 0.33157, val loss: 0.34309, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.32900, val loss: 0.34050, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.32650, val loss: 0.33795, in 0.031s 1 tree, 94 leaves, max depth = 19, train loss: 0.32374, val loss: 0.33549, in 0.026s 1 tree, 136 leaves, max depth = 17, train loss: 0.32087, val loss: 0.33309, in 0.022s 1 tree, 136 leaves, max depth = 17, train loss: 0.31812, val loss: 0.33078, in 0.016s 1 tree, 238 leaves, max depth = 17, train loss: 0.31543, val loss: 0.32851, in 0.047s 1 tree, 248 leaves, max depth = 19, train loss: 0.31346, val loss: 0.32673, in 0.031s 1 tree, 93 leaves, max depth = 13, train loss: 0.31106, val loss: 0.32459, in 0.016s 1 tree, 248 leaves, max depth = 18, train loss: 0.30920, val loss: 0.32286, in 0.047s 1 tree, 92 leaves, max depth = 14, train loss: 0.30695, val loss: 0.32086, in 0.016s 1 tree, 248 leaves, max depth = 20, train loss: 0.30484, val loss: 0.31869, in 0.031s 1 tree, 93 leaves, max depth = 14, train loss: 0.30271, val loss: 0.31683, in 0.016s 1 tree, 248 leaves, max depth = 18, train loss: 0.30110, val loss: 0.31549, in 0.047s 1 tree, 139 leaves, max depth = 15, train loss: 0.29883, val loss: 0.31362, in 0.016s 1 tree, 248 leaves, max depth = 20, train loss: 0.29688, val loss: 0.31162, in 0.031s 1 tree, 92 leaves, max depth = 14, train loss: 0.29494, val loss: 0.30993, in 0.031s 1 tree, 139 leaves, max depth = 15, train loss: 0.29283, val loss: 0.30820, in 0.016s 1 tree, 93 leaves, max depth = 19, train loss: 0.29103, val loss: 0.30663, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.28920, val loss: 0.30481, in 0.016s 1 tree, 248 leaves, max depth = 21, train loss: 0.28744, val loss: 0.30305, in 0.047s 1 tree, 248 leaves, max depth = 19, train loss: 0.28573, val loss: 0.30127, in 0.031s 1 tree, 92 leaves, max depth = 14, train loss: 0.28405, val loss: 0.29983, in 0.016s 1 tree, 138 leaves, max depth = 15, train loss: 0.28218, val loss: 0.29827, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.28062, val loss: 0.29692, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.27936, val loss: 0.29585, in 0.032s 1 tree, 92 leaves, max depth = 23, train loss: 0.27788, val loss: 0.29458, in 0.016s 1 tree, 138 leaves, max depth = 15, train loss: 0.27618, val loss: 0.29317, in 0.031s 1 tree, 138 leaves, max depth = 15, train loss: 0.27454, val loss: 0.29180, in 0.016s 1 tree, 92 leaves, max depth = 14, train loss: 0.27318, val loss: 0.29064, in 0.031s Fit 85 trees in 2.800 s, (17271 total leaves) Time spent computing histograms: 0.760s Time spent finding best splits: 0.339s Time spent applying splits: 0.295s Time spent predicting: 0.000s Trial 93, Fold 3: Log loss = 0.28081531736409293, Average precision = 0.9617123144966295, ROC-AUC = 0.9583356909304542, Elapsed Time = 2.7998050000005605 seconds Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 93, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 132 leaves, max depth = 14, train loss: 0.67813, val loss: 0.67759, in 0.016s 1 tree, 220 leaves, max depth = 18, train loss: 0.66367, val loss: 0.66276, in 0.031s 1 tree, 222 leaves, max depth = 17, train loss: 0.64993, val loss: 0.64865, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.63756, val loss: 0.63597, in 0.016s 1 tree, 248 leaves, max depth = 18, train loss: 0.62585, val loss: 0.62386, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.61386, val loss: 0.61152, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.60227, val loss: 0.59964, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.59126, val loss: 0.58832, in 0.047s 1 tree, 248 leaves, max depth = 17, train loss: 0.58094, val loss: 0.57756, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.57097, val loss: 0.56727, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.56155, val loss: 0.55747, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.55239, val loss: 0.54796, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.54347, val loss: 0.53879, in 0.031s 1 tree, 186 leaves, max depth = 16, train loss: 0.53517, val loss: 0.53014, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.52699, val loss: 0.52175, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.51913, val loss: 0.51366, in 0.016s 1 tree, 211 leaves, max depth = 16, train loss: 0.51180, val loss: 0.50603, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.50456, val loss: 0.49856, in 0.031s 1 tree, 182 leaves, max depth = 16, train loss: 0.49783, val loss: 0.49156, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.49113, val loss: 0.48471, in 0.031s 1 tree, 212 leaves, max depth = 21, train loss: 0.48527, val loss: 0.47863, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.47913, val loss: 0.47226, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.47315, val loss: 0.46614, in 0.047s 1 tree, 248 leaves, max depth = 16, train loss: 0.46741, val loss: 0.46022, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.46186, val loss: 0.45456, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.45653, val loss: 0.44911, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.45140, val loss: 0.44386, in 0.016s 1 tree, 239 leaves, max depth = 19, train loss: 0.44439, val loss: 0.43676, in 0.047s 1 tree, 194 leaves, max depth = 16, train loss: 0.43765, val loss: 0.42998, in 0.016s 1 tree, 248 leaves, max depth = 17, train loss: 0.43296, val loss: 0.42520, in 0.031s 1 tree, 213 leaves, max depth = 17, train loss: 0.42865, val loss: 0.42073, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.42429, val loss: 0.41627, in 0.031s 1 tree, 248 leaves, max depth = 24, train loss: 0.42031, val loss: 0.41228, in 0.047s 1 tree, 248 leaves, max depth = 18, train loss: 0.41621, val loss: 0.40807, in 0.031s 1 tree, 238 leaves, max depth = 20, train loss: 0.41038, val loss: 0.40220, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.40667, val loss: 0.39838, in 0.032s 1 tree, 237 leaves, max depth = 20, train loss: 0.40118, val loss: 0.39286, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.39766, val loss: 0.38924, in 0.031s 1 tree, 195 leaves, max depth = 17, train loss: 0.39241, val loss: 0.38405, in 0.031s 1 tree, 248 leaves, max depth = 25, train loss: 0.38921, val loss: 0.38088, in 0.031s 1 tree, 195 leaves, max depth = 17, train loss: 0.38428, val loss: 0.37597, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.38111, val loss: 0.37273, in 0.031s 1 tree, 194 leaves, max depth = 20, train loss: 0.37644, val loss: 0.36811, in 0.031s 1 tree, 194 leaves, max depth = 17, train loss: 0.37196, val loss: 0.36366, in 0.047s 1 tree, 193 leaves, max depth = 17, train loss: 0.36765, val loss: 0.35939, in 0.016s 1 tree, 195 leaves, max depth = 17, train loss: 0.36348, val loss: 0.35522, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.36070, val loss: 0.35235, in 0.031s 1 tree, 195 leaves, max depth = 17, train loss: 0.35676, val loss: 0.34841, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.35421, val loss: 0.34578, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.35163, val loss: 0.34319, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.34923, val loss: 0.34072, in 0.031s 1 tree, 248 leaves, max depth = 24, train loss: 0.34640, val loss: 0.33795, in 0.031s 1 tree, 96 leaves, max depth = 19, train loss: 0.34310, val loss: 0.33455, in 0.016s 1 tree, 96 leaves, max depth = 15, train loss: 0.33994, val loss: 0.33133, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.33683, val loss: 0.32844, in 0.031s 1 tree, 94 leaves, max depth = 14, train loss: 0.33393, val loss: 0.32541, in 0.016s 1 tree, 248 leaves, max depth = 21, train loss: 0.33138, val loss: 0.32291, in 0.047s 1 tree, 195 leaves, max depth = 17, train loss: 0.32837, val loss: 0.31995, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.32592, val loss: 0.31755, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.32346, val loss: 0.31509, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.32116, val loss: 0.31286, in 0.031s 1 tree, 248 leaves, max depth = 21, train loss: 0.31893, val loss: 0.31071, in 0.031s 1 tree, 96 leaves, max depth = 14, train loss: 0.31642, val loss: 0.30812, in 0.016s 1 tree, 248 leaves, max depth = 20, train loss: 0.31429, val loss: 0.30607, in 0.031s 1 tree, 139 leaves, max depth = 15, train loss: 0.31179, val loss: 0.30374, in 0.016s 1 tree, 140 leaves, max depth = 15, train loss: 0.30937, val loss: 0.30152, in 0.031s 1 tree, 94 leaves, max depth = 17, train loss: 0.30711, val loss: 0.29917, in 0.031s 1 tree, 140 leaves, max depth = 15, train loss: 0.30485, val loss: 0.29710, in 0.016s 1 tree, 139 leaves, max depth = 15, train loss: 0.30269, val loss: 0.29510, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.30068, val loss: 0.29308, in 0.047s 1 tree, 94 leaves, max depth = 17, train loss: 0.29864, val loss: 0.29099, in 0.016s 1 tree, 139 leaves, max depth = 15, train loss: 0.29663, val loss: 0.28916, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.29473, val loss: 0.28720, in 0.016s 1 tree, 141 leaves, max depth = 15, train loss: 0.29282, val loss: 0.28544, in 0.031s 1 tree, 94 leaves, max depth = 14, train loss: 0.29105, val loss: 0.28361, in 0.016s 1 tree, 248 leaves, max depth = 25, train loss: 0.28930, val loss: 0.28193, in 0.031s 1 tree, 141 leaves, max depth = 16, train loss: 0.28753, val loss: 0.28030, in 0.031s 1 tree, 94 leaves, max depth = 15, train loss: 0.28588, val loss: 0.27858, in 0.016s 1 tree, 95 leaves, max depth = 16, train loss: 0.28429, val loss: 0.27688, in 0.016s 1 tree, 141 leaves, max depth = 16, train loss: 0.28266, val loss: 0.27539, in 0.031s 1 tree, 141 leaves, max depth = 16, train loss: 0.28110, val loss: 0.27397, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.27941, val loss: 0.27228, in 0.047s 1 tree, 142 leaves, max depth = 16, train loss: 0.27793, val loss: 0.27091, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.27632, val loss: 0.26930, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.27492, val loss: 0.26785, in 0.031s Fit 85 trees in 2.799 s, (17289 total leaves) Time spent computing histograms: 0.763s Time spent finding best splits: 0.335s Time spent applying splits: 0.290s Time spent predicting: 0.000s Trial 93, Fold 4: Log loss = 0.2826723555976151, Average precision = 0.9625027075561599, ROC-AUC = 0.9576732243896507, Elapsed Time = 2.8056574999991426 seconds Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 93, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.141 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 126 leaves, max depth = 15, train loss: 0.67788, val loss: 0.67727, in 0.016s 1 tree, 233 leaves, max depth = 17, train loss: 0.66319, val loss: 0.66213, in 0.031s 1 tree, 235 leaves, max depth = 16, train loss: 0.64925, val loss: 0.64774, in 0.031s 1 tree, 236 leaves, max depth = 16, train loss: 0.63600, val loss: 0.63405, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.62332, val loss: 0.62094, in 0.016s 1 tree, 246 leaves, max depth = 16, train loss: 0.61124, val loss: 0.60844, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.59977, val loss: 0.59659, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.58879, val loss: 0.58526, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.57827, val loss: 0.57436, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.56822, val loss: 0.56394, in 0.031s 1 tree, 248 leaves, max depth = 20, train loss: 0.55889, val loss: 0.55428, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.54967, val loss: 0.54478, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.54091, val loss: 0.53575, in 0.031s 1 tree, 245 leaves, max depth = 20, train loss: 0.53279, val loss: 0.52751, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.52465, val loss: 0.51912, in 0.031s 1 tree, 183 leaves, max depth = 17, train loss: 0.51705, val loss: 0.51118, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.50960, val loss: 0.50352, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.50270, val loss: 0.49645, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.49572, val loss: 0.48924, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.48940, val loss: 0.48293, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.48294, val loss: 0.47626, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.47675, val loss: 0.47000, in 0.031s 1 tree, 248 leaves, max depth = 17, train loss: 0.47107, val loss: 0.46417, in 0.016s 1 tree, 248 leaves, max depth = 16, train loss: 0.46538, val loss: 0.45835, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.45983, val loss: 0.45265, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.45448, val loss: 0.44714, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.44934, val loss: 0.44190, in 0.031s 1 tree, 235 leaves, max depth = 18, train loss: 0.44230, val loss: 0.43494, in 0.031s 1 tree, 192 leaves, max depth = 19, train loss: 0.43545, val loss: 0.42814, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.43076, val loss: 0.42333, in 0.016s 1 tree, 181 leaves, max depth = 21, train loss: 0.42647, val loss: 0.41895, in 0.031s 1 tree, 182 leaves, max depth = 18, train loss: 0.42232, val loss: 0.41469, in 0.031s 1 tree, 236 leaves, max depth = 18, train loss: 0.41627, val loss: 0.40874, in 0.031s 1 tree, 248 leaves, max depth = 22, train loss: 0.41222, val loss: 0.40464, in 0.031s 1 tree, 183 leaves, max depth = 17, train loss: 0.40866, val loss: 0.40098, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.40482, val loss: 0.39707, in 0.016s 1 tree, 248 leaves, max depth = 21, train loss: 0.40109, val loss: 0.39330, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.39749, val loss: 0.38971, in 0.031s 1 tree, 192 leaves, max depth = 18, train loss: 0.39206, val loss: 0.38440, in 0.031s 1 tree, 213 leaves, max depth = 15, train loss: 0.38885, val loss: 0.38100, in 0.031s 1 tree, 191 leaves, max depth = 18, train loss: 0.38378, val loss: 0.37604, in 0.031s 1 tree, 191 leaves, max depth = 18, train loss: 0.37891, val loss: 0.37127, in 0.016s 1 tree, 191 leaves, max depth = 19, train loss: 0.37424, val loss: 0.36669, in 0.016s 1 tree, 142 leaves, max depth = 16, train loss: 0.36990, val loss: 0.36233, in 0.031s 1 tree, 248 leaves, max depth = 14, train loss: 0.36699, val loss: 0.35946, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.36410, val loss: 0.35657, in 0.031s 1 tree, 248 leaves, max depth = 16, train loss: 0.36136, val loss: 0.35380, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.35875, val loss: 0.35130, in 0.031s 1 tree, 91 leaves, max depth = 16, train loss: 0.35495, val loss: 0.34742, in 0.031s 1 tree, 94 leaves, max depth = 16, train loss: 0.35129, val loss: 0.34374, in 0.016s 1 tree, 94 leaves, max depth = 16, train loss: 0.34779, val loss: 0.34022, in 0.016s 1 tree, 93 leaves, max depth = 16, train loss: 0.34444, val loss: 0.33684, in 0.016s 1 tree, 139 leaves, max depth = 18, train loss: 0.34117, val loss: 0.33387, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.33834, val loss: 0.33115, in 0.031s 1 tree, 93 leaves, max depth = 17, train loss: 0.33528, val loss: 0.32806, in 0.016s 1 tree, 248 leaves, max depth = 19, train loss: 0.33257, val loss: 0.32547, in 0.031s 1 tree, 93 leaves, max depth = 16, train loss: 0.32967, val loss: 0.32254, in 0.022s 1 tree, 248 leaves, max depth = 18, train loss: 0.32714, val loss: 0.32006, in 0.025s 1 tree, 191 leaves, max depth = 18, train loss: 0.32413, val loss: 0.31721, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.32169, val loss: 0.31485, in 0.031s 1 tree, 137 leaves, max depth = 17, train loss: 0.31899, val loss: 0.31239, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.31666, val loss: 0.31015, in 0.031s 1 tree, 137 leaves, max depth = 17, train loss: 0.31410, val loss: 0.30786, in 0.016s 1 tree, 138 leaves, max depth = 17, train loss: 0.31163, val loss: 0.30566, in 0.031s 1 tree, 248 leaves, max depth = 15, train loss: 0.30984, val loss: 0.30400, in 0.031s 1 tree, 94 leaves, max depth = 16, train loss: 0.30751, val loss: 0.30166, in 0.016s 1 tree, 139 leaves, max depth = 17, train loss: 0.30520, val loss: 0.29959, in 0.031s 1 tree, 94 leaves, max depth = 22, train loss: 0.30303, val loss: 0.29741, in 0.016s 1 tree, 138 leaves, max depth = 17, train loss: 0.30088, val loss: 0.29549, in 0.016s 1 tree, 92 leaves, max depth = 16, train loss: 0.29884, val loss: 0.29340, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.29686, val loss: 0.29151, in 0.031s 1 tree, 137 leaves, max depth = 17, train loss: 0.29488, val loss: 0.28971, in 0.031s 1 tree, 93 leaves, max depth = 15, train loss: 0.29299, val loss: 0.28781, in 0.016s 1 tree, 138 leaves, max depth = 17, train loss: 0.29112, val loss: 0.28616, in 0.078s 1 tree, 248 leaves, max depth = 19, train loss: 0.28924, val loss: 0.28439, in 0.062s 1 tree, 92 leaves, max depth = 15, train loss: 0.28752, val loss: 0.28264, in 0.016s 1 tree, 93 leaves, max depth = 15, train loss: 0.28584, val loss: 0.28096, in 0.016s 1 tree, 241 leaves, max depth = 18, train loss: 0.28432, val loss: 0.27972, in 0.047s 1 tree, 92 leaves, max depth = 15, train loss: 0.28274, val loss: 0.27810, in 0.031s 1 tree, 248 leaves, max depth = 18, train loss: 0.28104, val loss: 0.27651, in 0.031s 1 tree, 137 leaves, max depth = 17, train loss: 0.27942, val loss: 0.27507, in 0.031s 1 tree, 137 leaves, max depth = 17, train loss: 0.27787, val loss: 0.27368, in 0.031s 1 tree, 248 leaves, max depth = 19, train loss: 0.27616, val loss: 0.27204, in 0.047s 1 tree, 134 leaves, max depth = 17, train loss: 0.27470, val loss: 0.27080, in 0.031s 1 tree, 92 leaves, max depth = 15, train loss: 0.27332, val loss: 0.26939, in 0.016s Fit 85 trees in 2.813 s, (16748 total leaves) Time spent computing histograms: 0.778s Time spent finding best splits: 0.348s Time spent applying splits: 0.309s Time spent predicting: 0.000s Trial 93, Fold 5: Log loss = 0.2892328390065862, Average precision = 0.9588432364280189, ROC-AUC = 0.9540768073128588, Elapsed Time = 2.826665499998853 seconds
Optimization Progress: 94%|#########3| 94/100 [19:34<01:24, 14.08s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 94, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.188 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 8, train loss: 0.68468, val loss: 0.68456, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.67578, val loss: 0.67558, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.66743, val loss: 0.66703, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.65936, val loss: 0.65894, in 0.031s 1 tree, 25 leaves, max depth = 11, train loss: 0.65200, val loss: 0.65144, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.64487, val loss: 0.64418, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.63743, val loss: 0.63670, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.63053, val loss: 0.62967, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.62364, val loss: 0.62264, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.61613, val loss: 0.61515, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.60934, val loss: 0.60819, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.60293, val loss: 0.60174, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.59716, val loss: 0.59581, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.59064, val loss: 0.58923, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.58448, val loss: 0.58300, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.57855, val loss: 0.57696, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.57325, val loss: 0.57155, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.56725, val loss: 0.56545, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.56246, val loss: 0.56036, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55687, val loss: 0.55468, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.55190, val loss: 0.54954, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.54656, val loss: 0.54413, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.54135, val loss: 0.53885, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.53644, val loss: 0.53391, in 0.016s 1 tree, 9 leaves, max depth = 6, train loss: 0.53202, val loss: 0.52934, in 0.016s 1 tree, 20 leaves, max depth = 10, train loss: 0.52786, val loss: 0.52512, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.52338, val loss: 0.52060, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.51921, val loss: 0.51631, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51509, val loss: 0.51205, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51094, val loss: 0.50785, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.50705, val loss: 0.50391, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.50260, val loss: 0.49947, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49855, val loss: 0.49536, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.49475, val loss: 0.49154, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.49139, val loss: 0.48799, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48805, val loss: 0.48460, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48484, val loss: 0.48143, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.48134, val loss: 0.47786, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.47804, val loss: 0.47448, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.47457, val loss: 0.47095, in 0.016s 1 tree, 22 leaves, max depth = 9, train loss: 0.47139, val loss: 0.46764, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.46821, val loss: 0.46439, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.46513, val loss: 0.46133, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.46249, val loss: 0.45861, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45958, val loss: 0.45562, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45694, val loss: 0.45288, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.45454, val loss: 0.45042, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.45156, val loss: 0.44738, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.44869, val loss: 0.44444, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44557, val loss: 0.44137, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44324, val loss: 0.43905, in 0.016s Fit 51 trees in 1.063 s, (1186 total leaves) Time spent computing histograms: 0.315s Time spent finding best splits: 0.028s Time spent applying splits: 0.024s Time spent predicting: 0.000s Trial 94, Fold 1: Log loss = 0.44411788190088125, Average precision = 0.9523659075828259, ROC-AUC = 0.9449224780497363, Elapsed Time = 1.0696496999989904 seconds Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 94, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.141 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 9, train loss: 0.68484, val loss: 0.68446, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.67619, val loss: 0.67571, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.66761, val loss: 0.66698, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.65949, val loss: 0.65852, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.65215, val loss: 0.65083, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.64512, val loss: 0.64348, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.63755, val loss: 0.63570, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.62996, val loss: 0.62795, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.62318, val loss: 0.62092, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.61637, val loss: 0.61390, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.60940, val loss: 0.60668, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.60298, val loss: 0.60015, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.59723, val loss: 0.59411, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59049, val loss: 0.58729, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.58412, val loss: 0.58078, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.57831, val loss: 0.57494, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.57297, val loss: 0.56941, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.56723, val loss: 0.56362, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.56161, val loss: 0.55790, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.55603, val loss: 0.55222, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.55103, val loss: 0.54714, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.54546, val loss: 0.54147, in 0.016s 1 tree, 25 leaves, max depth = 6, train loss: 0.54017, val loss: 0.53608, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.53566, val loss: 0.53147, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53139, val loss: 0.52700, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.52703, val loss: 0.52248, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.52254, val loss: 0.51798, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51861, val loss: 0.51396, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.51455, val loss: 0.50971, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51075, val loss: 0.50574, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50645, val loss: 0.50136, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50265, val loss: 0.49742, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.49838, val loss: 0.49308, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.49468, val loss: 0.48934, in 0.015s 1 tree, 25 leaves, max depth = 8, train loss: 0.49102, val loss: 0.48564, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.48764, val loss: 0.48216, in 0.016s 1 tree, 25 leaves, max depth = 6, train loss: 0.48413, val loss: 0.47865, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48061, val loss: 0.47505, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.47729, val loss: 0.47165, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.47375, val loss: 0.46806, in 0.000s 1 tree, 18 leaves, max depth = 9, train loss: 0.47058, val loss: 0.46488, in 0.031s 1 tree, 25 leaves, max depth = 8, train loss: 0.46746, val loss: 0.46169, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.46443, val loss: 0.45858, in 0.016s 1 tree, 11 leaves, max depth = 7, train loss: 0.46176, val loss: 0.45584, in 0.063s 1 tree, 25 leaves, max depth = 10, train loss: 0.45887, val loss: 0.45294, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.45631, val loss: 0.45028, in 0.031s 1 tree, 25 leaves, max depth = 15, train loss: 0.45394, val loss: 0.44777, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.45093, val loss: 0.44473, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44806, val loss: 0.44176, in 0.031s 1 tree, 25 leaves, max depth = 10, train loss: 0.44561, val loss: 0.43918, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44334, val loss: 0.43682, in 0.016s Fit 51 trees in 1.079 s, (1220 total leaves) Time spent computing histograms: 0.348s Time spent finding best splits: 0.043s Time spent applying splits: 0.033s Time spent predicting: 0.000s Trial 94, Fold 2: Log loss = 0.4446945884712925, Average precision = 0.9498656301917219, ROC-AUC = 0.9459203518363455, Elapsed Time = 1.083164300000135 seconds Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 94, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 8, train loss: 0.68478, val loss: 0.68467, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.67572, val loss: 0.67551, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.66732, val loss: 0.66711, in 0.031s 1 tree, 25 leaves, max depth = 7, train loss: 0.65930, val loss: 0.65893, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.65199, val loss: 0.65142, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.64489, val loss: 0.64418, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.63753, val loss: 0.63672, in 0.078s 1 tree, 17 leaves, max depth = 8, train loss: 0.63067, val loss: 0.62969, in 0.047s 1 tree, 25 leaves, max depth = 8, train loss: 0.62376, val loss: 0.62266, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.61627, val loss: 0.61513, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.60948, val loss: 0.60833, in 0.016s 1 tree, 25 leaves, max depth = 6, train loss: 0.60310, val loss: 0.60185, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.59735, val loss: 0.59599, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59066, val loss: 0.58928, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.58451, val loss: 0.58306, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.57861, val loss: 0.57716, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.57330, val loss: 0.57169, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.56758, val loss: 0.56598, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.56204, val loss: 0.56032, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55660, val loss: 0.55471, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55159, val loss: 0.54960, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.54613, val loss: 0.54411, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.54099, val loss: 0.53896, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53649, val loss: 0.53441, in 0.017s 1 tree, 25 leaves, max depth = 12, train loss: 0.53170, val loss: 0.52963, in 0.015s 1 tree, 25 leaves, max depth = 8, train loss: 0.52737, val loss: 0.52521, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.52288, val loss: 0.52075, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51878, val loss: 0.51665, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51472, val loss: 0.51256, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51085, val loss: 0.50865, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.50668, val loss: 0.50443, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.50289, val loss: 0.50062, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49911, val loss: 0.49674, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.49541, val loss: 0.49306, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49140, val loss: 0.48906, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48797, val loss: 0.48562, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.48462, val loss: 0.48225, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.48114, val loss: 0.47876, in 0.016s 1 tree, 21 leaves, max depth = 9, train loss: 0.47652, val loss: 0.47446, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.47320, val loss: 0.47115, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.46995, val loss: 0.46796, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.46683, val loss: 0.46481, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.46249, val loss: 0.46073, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.45990, val loss: 0.45800, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.45698, val loss: 0.45511, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.45443, val loss: 0.45254, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.45207, val loss: 0.45012, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44910, val loss: 0.44718, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44620, val loss: 0.44427, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44367, val loss: 0.44168, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.44139, val loss: 0.43935, in 0.000s Fit 51 trees in 1.221 s, (1213 total leaves) Time spent computing histograms: 0.394s Time spent finding best splits: 0.060s Time spent applying splits: 0.036s Time spent predicting: 0.016s Trial 94, Fold 3: Log loss = 0.43768270158311073, Average precision = 0.9558801258586747, ROC-AUC = 0.9505323705107338, Elapsed Time = 1.2125231999998505 seconds Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 94, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 11, train loss: 0.68484, val loss: 0.68458, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.67602, val loss: 0.67530, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.66758, val loss: 0.66643, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.65952, val loss: 0.65806, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.65219, val loss: 0.65047, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.64518, val loss: 0.64319, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.63776, val loss: 0.63553, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.63090, val loss: 0.62832, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.62390, val loss: 0.62100, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.61642, val loss: 0.61322, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.60971, val loss: 0.60610, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.60332, val loss: 0.59945, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.59756, val loss: 0.59346, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.59106, val loss: 0.58661, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.58487, val loss: 0.58014, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.57906, val loss: 0.57398, in 0.031s 1 tree, 12 leaves, max depth = 6, train loss: 0.57376, val loss: 0.56837, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.56814, val loss: 0.56241, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.56234, val loss: 0.55630, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55690, val loss: 0.55054, in 0.016s 1 tree, 24 leaves, max depth = 9, train loss: 0.55194, val loss: 0.54525, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.54663, val loss: 0.53963, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.54173, val loss: 0.53445, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53701, val loss: 0.52943, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53289, val loss: 0.52509, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.52851, val loss: 0.52053, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.52410, val loss: 0.51590, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.52019, val loss: 0.51176, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51613, val loss: 0.50751, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51228, val loss: 0.50349, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.50806, val loss: 0.49898, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.50423, val loss: 0.49497, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.50044, val loss: 0.49087, in 0.031s 1 tree, 25 leaves, max depth = 9, train loss: 0.49681, val loss: 0.48698, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.49290, val loss: 0.48280, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48949, val loss: 0.47922, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.48608, val loss: 0.47562, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.48258, val loss: 0.47195, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.47935, val loss: 0.46847, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.47622, val loss: 0.46507, in 0.000s 1 tree, 23 leaves, max depth = 9, train loss: 0.47298, val loss: 0.46163, in 0.031s 1 tree, 25 leaves, max depth = 11, train loss: 0.46988, val loss: 0.45825, in 0.000s 1 tree, 9 leaves, max depth = 5, train loss: 0.46554, val loss: 0.45384, in 0.016s 1 tree, 10 leaves, max depth = 7, train loss: 0.46293, val loss: 0.45101, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46008, val loss: 0.44798, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.45753, val loss: 0.44518, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.45515, val loss: 0.44270, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.45217, val loss: 0.43953, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44895, val loss: 0.43628, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44640, val loss: 0.43350, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44411, val loss: 0.43116, in 0.016s Fit 51 trees in 1.127 s, (1223 total leaves) Time spent computing histograms: 0.337s Time spent finding best splits: 0.031s Time spent applying splits: 0.027s Time spent predicting: 0.016s Trial 94, Fold 4: Log loss = 0.4435109531285845, Average precision = 0.9534755101482932, ROC-AUC = 0.9469630517822271, Elapsed Time = 1.126024299999699 seconds Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 94, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 11, train loss: 0.68469, val loss: 0.68429, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.67607, val loss: 0.67519, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.66717, val loss: 0.66585, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.65875, val loss: 0.65700, in 0.016s 1 tree, 22 leaves, max depth = 10, train loss: 0.65068, val loss: 0.64847, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.64251, val loss: 0.63993, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.63533, val loss: 0.63232, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.62808, val loss: 0.62482, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.62136, val loss: 0.61776, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.61415, val loss: 0.61020, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.60761, val loss: 0.60346, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.60112, val loss: 0.59654, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.59518, val loss: 0.59025, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.58862, val loss: 0.58336, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.58291, val loss: 0.57728, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.57712, val loss: 0.57113, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.57153, val loss: 0.56516, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.56652, val loss: 0.55989, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.56160, val loss: 0.55468, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.55640, val loss: 0.54931, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.55177, val loss: 0.54440, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.54643, val loss: 0.53871, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.54063, val loss: 0.53273, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53542, val loss: 0.52722, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53095, val loss: 0.52242, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.52643, val loss: 0.51761, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.52247, val loss: 0.51340, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51872, val loss: 0.50936, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51412, val loss: 0.50448, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.51011, val loss: 0.50021, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.50623, val loss: 0.49609, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50249, val loss: 0.49213, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49911, val loss: 0.48846, in 0.016s 1 tree, 20 leaves, max depth = 7, train loss: 0.49508, val loss: 0.48421, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.49163, val loss: 0.48063, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.48703, val loss: 0.47589, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.48356, val loss: 0.47218, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48027, val loss: 0.46860, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.47701, val loss: 0.46509, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.47325, val loss: 0.46117, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.47026, val loss: 0.45807, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.46715, val loss: 0.45470, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.46450, val loss: 0.45178, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.46127, val loss: 0.44837, in 0.016s 1 tree, 25 leaves, max depth = 6, train loss: 0.45842, val loss: 0.44541, in 0.031s 1 tree, 6 leaves, max depth = 4, train loss: 0.45573, val loss: 0.44255, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.45276, val loss: 0.43939, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45020, val loss: 0.43673, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.44763, val loss: 0.43397, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44448, val loss: 0.43077, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.44229, val loss: 0.42841, in 0.031s Fit 51 trees in 1.173 s, (1191 total leaves) Time spent computing histograms: 0.362s Time spent finding best splits: 0.033s Time spent applying splits: 0.028s Time spent predicting: 0.000s Trial 94, Fold 5: Log loss = 0.4475411045755514, Average precision = 0.9518987120846267, ROC-AUC = 0.9456943685784887, Elapsed Time = 1.1737415000006877 seconds
Optimization Progress: 95%|#########5| 95/100 [19:47<01:07, 13.55s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 95, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.141 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 12, train loss: 0.67525, val loss: 0.67497, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.65881, val loss: 0.65818, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.64364, val loss: 0.64269, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.62939, val loss: 0.62833, in 0.000s 1 tree, 28 leaves, max depth = 10, train loss: 0.61602, val loss: 0.61486, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.60305, val loss: 0.60166, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.59104, val loss: 0.58948, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.57859, val loss: 0.57686, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.56705, val loss: 0.56515, in 0.016s 1 tree, 32 leaves, max depth = 13, train loss: 0.55563, val loss: 0.55387, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.54514, val loss: 0.54323, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.53559, val loss: 0.53375, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.52677, val loss: 0.52488, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.51777, val loss: 0.51574, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.50944, val loss: 0.50729, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.50165, val loss: 0.49932, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.49438, val loss: 0.49205, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.48681, val loss: 0.48429, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.48074, val loss: 0.47777, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.47386, val loss: 0.47087, in 0.016s 1 tree, 18 leaves, max depth = 9, train loss: 0.46731, val loss: 0.46422, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.46107, val loss: 0.45780, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.45527, val loss: 0.45194, in 0.016s 1 tree, 23 leaves, max depth = 8, train loss: 0.45025, val loss: 0.44686, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.44510, val loss: 0.44146, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.44043, val loss: 0.43670, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.43552, val loss: 0.43171, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.43107, val loss: 0.42719, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.42703, val loss: 0.42313, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.41983, val loss: 0.41618, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.41536, val loss: 0.41170, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.41054, val loss: 0.40699, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.40713, val loss: 0.40354, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.40360, val loss: 0.39988, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.39991, val loss: 0.39623, in 0.000s 1 tree, 28 leaves, max depth = 11, train loss: 0.39565, val loss: 0.39201, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.39155, val loss: 0.38800, in 0.016s 1 tree, 10 leaves, max depth = 6, train loss: 0.38581, val loss: 0.38236, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.38020, val loss: 0.37700, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.37714, val loss: 0.37388, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.37202, val loss: 0.36888, in 0.016s 1 tree, 30 leaves, max depth = 15, train loss: 0.36887, val loss: 0.36581, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.36634, val loss: 0.36312, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.36364, val loss: 0.36050, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.36037, val loss: 0.35727, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.35774, val loss: 0.35475, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.35319, val loss: 0.35053, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.34939, val loss: 0.34664, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34535, val loss: 0.34274, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.34106, val loss: 0.33882, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.33830, val loss: 0.33621, in 0.016s 1 tree, 34 leaves, max depth = 13, train loss: 0.33651, val loss: 0.33457, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33337, val loss: 0.33134, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.33048, val loss: 0.32836, in 0.016s 1 tree, 29 leaves, max depth = 15, train loss: 0.32861, val loss: 0.32654, in 0.016s 1 tree, 26 leaves, max depth = 8, train loss: 0.32609, val loss: 0.32415, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.32383, val loss: 0.32183, in 0.000s 1 tree, 16 leaves, max depth = 8, train loss: 0.32076, val loss: 0.31884, in 0.016s Fit 58 trees in 1.157 s, (1435 total leaves) Time spent computing histograms: 0.371s Time spent finding best splits: 0.038s Time spent applying splits: 0.031s Time spent predicting: 0.000s Trial 95, Fold 1: Log loss = 0.3234746455634853, Average precision = 0.9569561453670864, ROC-AUC = 0.950871124948022, Elapsed Time = 1.1658042000017304 seconds Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 95, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.126 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 9, train loss: 0.67519, val loss: 0.67479, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.65885, val loss: 0.65813, in 0.000s 1 tree, 23 leaves, max depth = 11, train loss: 0.64347, val loss: 0.64249, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.62764, val loss: 0.62627, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.61422, val loss: 0.61238, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.60100, val loss: 0.59891, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.58904, val loss: 0.58647, in 0.031s 1 tree, 33 leaves, max depth = 8, train loss: 0.57722, val loss: 0.57436, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.56625, val loss: 0.56315, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.55608, val loss: 0.55262, in 0.000s 1 tree, 38 leaves, max depth = 12, train loss: 0.54560, val loss: 0.54188, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.53641, val loss: 0.53246, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.52767, val loss: 0.52340, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.51860, val loss: 0.51409, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.51010, val loss: 0.50539, in 0.031s 1 tree, 15 leaves, max depth = 10, train loss: 0.50225, val loss: 0.49753, in 0.000s 1 tree, 28 leaves, max depth = 15, train loss: 0.49430, val loss: 0.48952, in 0.031s 1 tree, 33 leaves, max depth = 10, train loss: 0.48678, val loss: 0.48191, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.48008, val loss: 0.47498, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.47370, val loss: 0.46857, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.46717, val loss: 0.46192, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.46144, val loss: 0.45622, in 0.016s 1 tree, 7 leaves, max depth = 5, train loss: 0.45338, val loss: 0.44808, in 0.016s 1 tree, 27 leaves, max depth = 13, train loss: 0.44770, val loss: 0.44234, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.44282, val loss: 0.43737, in 0.016s 1 tree, 37 leaves, max depth = 13, train loss: 0.43741, val loss: 0.43192, in 0.016s 1 tree, 29 leaves, max depth = 15, train loss: 0.43277, val loss: 0.42725, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.42858, val loss: 0.42289, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.42383, val loss: 0.41809, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.41935, val loss: 0.41347, in 0.016s 1 tree, 22 leaves, max depth = 8, train loss: 0.41552, val loss: 0.40947, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.41068, val loss: 0.40467, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.40717, val loss: 0.40103, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.40331, val loss: 0.39717, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.39758, val loss: 0.39144, in 0.016s 1 tree, 44 leaves, max depth = 11, train loss: 0.39340, val loss: 0.38734, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.38823, val loss: 0.38214, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.38497, val loss: 0.37878, in 0.016s 1 tree, 17 leaves, max depth = 8, train loss: 0.38183, val loss: 0.37562, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.37666, val loss: 0.37056, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.37406, val loss: 0.36800, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.36861, val loss: 0.36288, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.36399, val loss: 0.35850, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.36056, val loss: 0.35514, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.35825, val loss: 0.35289, in 0.031s 1 tree, 31 leaves, max depth = 10, train loss: 0.35510, val loss: 0.34978, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35199, val loss: 0.34675, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.34913, val loss: 0.34393, in 0.016s 1 tree, 12 leaves, max depth = 6, train loss: 0.34631, val loss: 0.34117, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34227, val loss: 0.33725, in 0.000s 1 tree, 43 leaves, max depth = 10, train loss: 0.33951, val loss: 0.33459, in 0.031s 1 tree, 36 leaves, max depth = 11, train loss: 0.33751, val loss: 0.33263, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33381, val loss: 0.32908, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.33200, val loss: 0.32725, in 0.031s 1 tree, 10 leaves, max depth = 6, train loss: 0.32818, val loss: 0.32372, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.32661, val loss: 0.32214, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.32372, val loss: 0.31925, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.32035, val loss: 0.31615, in 0.016s Fit 58 trees in 1.220 s, (1488 total leaves) Time spent computing histograms: 0.403s Time spent finding best splits: 0.043s Time spent applying splits: 0.035s Time spent predicting: 0.000s Trial 95, Fold 2: Log loss = 0.3229884704946013, Average precision = 0.9535500219361897, ROC-AUC = 0.9504192943060286, Elapsed Time = 1.2325305000013032 seconds Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 95, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 10, train loss: 0.67539, val loss: 0.67508, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.65870, val loss: 0.65806, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.64326, val loss: 0.64260, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.62912, val loss: 0.62821, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.61589, val loss: 0.61469, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.60287, val loss: 0.60152, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.59092, val loss: 0.58938, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.57934, val loss: 0.57761, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.56861, val loss: 0.56658, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.55849, val loss: 0.55645, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.54812, val loss: 0.54600, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.53895, val loss: 0.53674, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.53013, val loss: 0.52787, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.52111, val loss: 0.51879, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.51271, val loss: 0.51017, in 0.016s 1 tree, 26 leaves, max depth = 8, train loss: 0.50465, val loss: 0.50226, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.49728, val loss: 0.49482, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.48959, val loss: 0.48714, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.48346, val loss: 0.48101, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.47707, val loss: 0.47464, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.47086, val loss: 0.46849, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.46413, val loss: 0.46185, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.45792, val loss: 0.45564, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.45270, val loss: 0.45047, in 0.031s 1 tree, 28 leaves, max depth = 11, train loss: 0.44763, val loss: 0.44536, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.44294, val loss: 0.44051, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.43797, val loss: 0.43560, in 0.016s 1 tree, 26 leaves, max depth = 10, train loss: 0.43342, val loss: 0.43107, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.42934, val loss: 0.42691, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.42534, val loss: 0.42304, in 0.016s 1 tree, 17 leaves, max depth = 11, train loss: 0.42092, val loss: 0.41860, in 0.016s 1 tree, 29 leaves, max depth = 8, train loss: 0.41734, val loss: 0.41502, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.41389, val loss: 0.41158, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.41025, val loss: 0.40806, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.40570, val loss: 0.40335, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.40131, val loss: 0.39881, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.39795, val loss: 0.39548, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.39463, val loss: 0.39215, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.38880, val loss: 0.38702, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.38382, val loss: 0.38224, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.38001, val loss: 0.37834, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.37477, val loss: 0.37356, in 0.000s 1 tree, 9 leaves, max depth = 4, train loss: 0.36975, val loss: 0.36906, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.36705, val loss: 0.36639, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.36468, val loss: 0.36412, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.36017, val loss: 0.36014, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.35836, val loss: 0.35834, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35520, val loss: 0.35506, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.35145, val loss: 0.35151, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.34854, val loss: 0.34843, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.34631, val loss: 0.34639, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.34294, val loss: 0.34318, in 0.016s 1 tree, 21 leaves, max depth = 8, train loss: 0.34128, val loss: 0.34161, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33737, val loss: 0.33825, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.33540, val loss: 0.33641, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.33282, val loss: 0.33374, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.33112, val loss: 0.33204, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.32960, val loss: 0.33055, in 0.016s Fit 58 trees in 1.251 s, (1446 total leaves) Time spent computing histograms: 0.400s Time spent finding best splits: 0.043s Time spent applying splits: 0.034s Time spent predicting: 0.000s Trial 95, Fold 3: Log loss = 0.3254918967385569, Average precision = 0.958430490667888, ROC-AUC = 0.9538103083375871, Elapsed Time = 1.2599082999986422 seconds Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 95, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.157 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 30 leaves, max depth = 9, train loss: 0.67529, val loss: 0.67443, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.65848, val loss: 0.65684, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.64325, val loss: 0.64077, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.62807, val loss: 0.62484, in 0.016s 1 tree, 30 leaves, max depth = 10, train loss: 0.61434, val loss: 0.61040, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.60054, val loss: 0.59589, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.58826, val loss: 0.58314, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.57694, val loss: 0.57130, in 0.016s 1 tree, 27 leaves, max depth = 14, train loss: 0.56579, val loss: 0.55964, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.55463, val loss: 0.54804, in 0.016s 1 tree, 25 leaves, max depth = 17, train loss: 0.54476, val loss: 0.53755, in 0.031s 1 tree, 29 leaves, max depth = 12, train loss: 0.53524, val loss: 0.52751, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.52566, val loss: 0.51755, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.51650, val loss: 0.50790, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.50845, val loss: 0.49952, in 0.016s 1 tree, 28 leaves, max depth = 13, train loss: 0.50116, val loss: 0.49187, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.49377, val loss: 0.48408, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.48697, val loss: 0.47689, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.48004, val loss: 0.46942, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.47391, val loss: 0.46300, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.46846, val loss: 0.45733, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.46251, val loss: 0.45101, in 0.000s 1 tree, 28 leaves, max depth = 12, train loss: 0.45703, val loss: 0.44508, in 0.031s 1 tree, 28 leaves, max depth = 11, train loss: 0.45132, val loss: 0.43893, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.44646, val loss: 0.43376, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.44105, val loss: 0.42792, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.43646, val loss: 0.42295, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.43218, val loss: 0.41844, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.42717, val loss: 0.41318, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.42287, val loss: 0.40854, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.41913, val loss: 0.40445, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.41511, val loss: 0.40015, in 0.016s 1 tree, 8 leaves, max depth = 4, train loss: 0.40854, val loss: 0.39342, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.40513, val loss: 0.38971, in 0.016s 1 tree, 14 leaves, max depth = 6, train loss: 0.40149, val loss: 0.38578, in 0.016s 1 tree, 26 leaves, max depth = 11, train loss: 0.39725, val loss: 0.38150, in 0.016s 1 tree, 10 leaves, max depth = 5, train loss: 0.39397, val loss: 0.37792, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.39126, val loss: 0.37509, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.38727, val loss: 0.37104, in 0.016s 1 tree, 27 leaves, max depth = 8, train loss: 0.38439, val loss: 0.36787, in 0.000s 1 tree, 27 leaves, max depth = 11, train loss: 0.38176, val loss: 0.36509, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.37895, val loss: 0.36224, in 0.031s 1 tree, 11 leaves, max depth = 6, train loss: 0.37671, val loss: 0.35986, in 0.000s 1 tree, 27 leaves, max depth = 10, train loss: 0.37438, val loss: 0.35732, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.36930, val loss: 0.35212, in 0.000s 1 tree, 9 leaves, max depth = 6, train loss: 0.36513, val loss: 0.34756, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.36074, val loss: 0.34314, in 0.016s 1 tree, 24 leaves, max depth = 10, train loss: 0.35847, val loss: 0.34073, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.35537, val loss: 0.33763, in 0.016s 1 tree, 9 leaves, max depth = 7, train loss: 0.35111, val loss: 0.33350, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.34894, val loss: 0.33113, in 0.016s 1 tree, 7 leaves, max depth = 4, train loss: 0.34479, val loss: 0.32717, in 0.016s 1 tree, 6 leaves, max depth = 4, train loss: 0.34268, val loss: 0.32485, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.34088, val loss: 0.32293, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33916, val loss: 0.32107, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.33700, val loss: 0.31885, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.33523, val loss: 0.31695, in 0.016s 1 tree, 29 leaves, max depth = 11, train loss: 0.33322, val loss: 0.31487, in 0.016s Fit 58 trees in 1.282 s, (1459 total leaves) Time spent computing histograms: 0.406s Time spent finding best splits: 0.044s Time spent applying splits: 0.036s Time spent predicting: 0.000s Trial 95, Fold 4: Log loss = 0.33272664317341477, Average precision = 0.955768917238855, ROC-AUC = 0.9497438696824682, Elapsed Time = 1.2968270999990636 seconds Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 95, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 10, train loss: 0.67523, val loss: 0.67407, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.65890, val loss: 0.65665, in 0.016s 1 tree, 28 leaves, max depth = 10, train loss: 0.64378, val loss: 0.64069, in 0.016s 1 tree, 27 leaves, max depth = 10, train loss: 0.62942, val loss: 0.62585, in 0.016s 1 tree, 30 leaves, max depth = 11, train loss: 0.61599, val loss: 0.61195, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.60332, val loss: 0.59837, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.59130, val loss: 0.58595, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.57877, val loss: 0.57279, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.56698, val loss: 0.56042, in 0.031s 1 tree, 30 leaves, max depth = 12, train loss: 0.55565, val loss: 0.54863, in 0.016s 1 tree, 28 leaves, max depth = 12, train loss: 0.54515, val loss: 0.53751, in 0.016s 1 tree, 27 leaves, max depth = 11, train loss: 0.53571, val loss: 0.52774, in 0.000s 1 tree, 24 leaves, max depth = 9, train loss: 0.52696, val loss: 0.51869, in 0.031s 1 tree, 29 leaves, max depth = 10, train loss: 0.51792, val loss: 0.50922, in 0.000s 1 tree, 29 leaves, max depth = 12, train loss: 0.50960, val loss: 0.50033, in 0.031s 1 tree, 16 leaves, max depth = 6, train loss: 0.50177, val loss: 0.49204, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.49398, val loss: 0.48391, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.48662, val loss: 0.47612, in 0.016s 1 tree, 4 leaves, max depth = 2, train loss: 0.48051, val loss: 0.46962, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.47364, val loss: 0.46229, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.46704, val loss: 0.45536, in 0.016s 1 tree, 20 leaves, max depth = 8, train loss: 0.46089, val loss: 0.44883, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.45502, val loss: 0.44255, in 0.016s 1 tree, 28 leaves, max depth = 7, train loss: 0.44991, val loss: 0.43733, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.44468, val loss: 0.43174, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.43985, val loss: 0.42659, in 0.016s 1 tree, 33 leaves, max depth = 14, train loss: 0.43490, val loss: 0.42139, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.43052, val loss: 0.41686, in 0.016s 1 tree, 29 leaves, max depth = 10, train loss: 0.42647, val loss: 0.41265, in 0.031s 1 tree, 13 leaves, max depth = 5, train loss: 0.41927, val loss: 0.40547, in 0.000s 1 tree, 30 leaves, max depth = 12, train loss: 0.41504, val loss: 0.40092, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.41016, val loss: 0.39594, in 0.031s 1 tree, 15 leaves, max depth = 6, train loss: 0.40343, val loss: 0.38932, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.39988, val loss: 0.38561, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.39546, val loss: 0.38109, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.39123, val loss: 0.37682, in 0.016s 1 tree, 8 leaves, max depth = 5, train loss: 0.38795, val loss: 0.37348, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.38404, val loss: 0.36961, in 0.000s 1 tree, 6 leaves, max depth = 4, train loss: 0.37901, val loss: 0.36439, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.37428, val loss: 0.35939, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37067, val loss: 0.35579, in 0.016s 1 tree, 6 leaves, max depth = 3, train loss: 0.36582, val loss: 0.35085, in 0.016s 1 tree, 9 leaves, max depth = 5, train loss: 0.36116, val loss: 0.34613, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.35856, val loss: 0.34337, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.35631, val loss: 0.34113, in 0.016s 1 tree, 18 leaves, max depth = 6, train loss: 0.35422, val loss: 0.33906, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.35208, val loss: 0.33690, in 0.031s 1 tree, 4 leaves, max depth = 2, train loss: 0.34805, val loss: 0.33322, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.34412, val loss: 0.32917, in 0.000s 1 tree, 7 leaves, max depth = 3, train loss: 0.34001, val loss: 0.32521, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33656, val loss: 0.32173, in 0.016s 1 tree, 29 leaves, max depth = 13, train loss: 0.33465, val loss: 0.31966, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.33143, val loss: 0.31642, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.32865, val loss: 0.31374, in 0.016s 1 tree, 34 leaves, max depth = 7, train loss: 0.32696, val loss: 0.31193, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.32434, val loss: 0.30938, in 0.016s 1 tree, 33 leaves, max depth = 13, train loss: 0.32242, val loss: 0.30746, in 0.031s 1 tree, 34 leaves, max depth = 10, train loss: 0.32029, val loss: 0.30527, in 0.016s Fit 58 trees in 1.298 s, (1422 total leaves) Time spent computing histograms: 0.411s Time spent finding best splits: 0.044s Time spent applying splits: 0.035s Time spent predicting: 0.000s Trial 95, Fold 5: Log loss = 0.3282869013454593, Average precision = 0.9549812129532772, ROC-AUC = 0.9502090927670328, Elapsed Time = 1.3063646999999037 seconds
Optimization Progress: 96%|#########6| 96/100 [20:00<00:53, 13.40s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 96, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 101 leaves, max depth = 15, train loss: 0.68483, val loss: 0.68463, in 0.016s 1 tree, 103 leaves, max depth = 17, train loss: 0.67663, val loss: 0.67620, in 0.016s 1 tree, 103 leaves, max depth = 17, train loss: 0.66871, val loss: 0.66805, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.66116, val loss: 0.66033, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.65386, val loss: 0.65283, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.64681, val loss: 0.64558, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.64007, val loss: 0.63868, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.63356, val loss: 0.63201, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.62719, val loss: 0.62545, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.62095, val loss: 0.61901, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.61505, val loss: 0.61289, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.60928, val loss: 0.60695, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.60371, val loss: 0.60120, in 0.016s 1 tree, 104 leaves, max depth = 18, train loss: 0.59824, val loss: 0.59554, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.59302, val loss: 0.59015, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.58797, val loss: 0.58494, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.58300, val loss: 0.57979, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.57827, val loss: 0.57493, in 0.000s 1 tree, 103 leaves, max depth = 20, train loss: 0.57372, val loss: 0.57026, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.56920, val loss: 0.56557, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.56483, val loss: 0.56102, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.56059, val loss: 0.55661, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.55648, val loss: 0.55233, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.55250, val loss: 0.54818, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.54870, val loss: 0.54425, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.54448, val loss: 0.54029, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.54039, val loss: 0.53647, in 0.031s 1 tree, 106 leaves, max depth = 17, train loss: 0.53684, val loss: 0.53276, in 0.000s 1 tree, 163 leaves, max depth = 16, train loss: 0.53292, val loss: 0.52909, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.52913, val loss: 0.52555, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.52578, val loss: 0.52210, in 0.016s 1 tree, 106 leaves, max depth = 16, train loss: 0.52253, val loss: 0.51869, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.51931, val loss: 0.51530, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.51573, val loss: 0.51197, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.51227, val loss: 0.50875, in 0.016s 1 tree, 104 leaves, max depth = 19, train loss: 0.50928, val loss: 0.50562, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.50632, val loss: 0.50251, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.50302, val loss: 0.49945, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.50025, val loss: 0.49654, in 0.000s 1 tree, 106 leaves, max depth = 17, train loss: 0.49757, val loss: 0.49371, in 0.000s 1 tree, 106 leaves, max depth = 21, train loss: 0.49495, val loss: 0.49101, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.49183, val loss: 0.48812, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.48933, val loss: 0.48550, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.48632, val loss: 0.48271, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.48340, val loss: 0.48001, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.48057, val loss: 0.47739, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.47782, val loss: 0.47485, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.47546, val loss: 0.47234, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.47281, val loss: 0.46989, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.47024, val loss: 0.46752, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.46805, val loss: 0.46519, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.46557, val loss: 0.46291, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.46316, val loss: 0.46069, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.46102, val loss: 0.45841, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.45870, val loss: 0.45628, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.45644, val loss: 0.45421, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.45424, val loss: 0.45220, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.45228, val loss: 0.45009, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.45016, val loss: 0.44816, in 0.031s 1 tree, 104 leaves, max depth = 18, train loss: 0.44821, val loss: 0.44611, in 0.000s 1 tree, 106 leaves, max depth = 14, train loss: 0.44638, val loss: 0.44414, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.44434, val loss: 0.44229, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.44236, val loss: 0.44049, in 0.016s 1 tree, 104 leaves, max depth = 20, train loss: 0.44062, val loss: 0.43867, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.43871, val loss: 0.43693, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.43697, val loss: 0.43508, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.43533, val loss: 0.43331, in 0.018s 1 tree, 104 leaves, max depth = 17, train loss: 0.43369, val loss: 0.43158, in 0.014s 1 tree, 163 leaves, max depth = 16, train loss: 0.43186, val loss: 0.42992, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.43008, val loss: 0.42832, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.42835, val loss: 0.42675, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.42667, val loss: 0.42523, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.42517, val loss: 0.42360, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.42372, val loss: 0.42209, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.42230, val loss: 0.42057, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.42093, val loss: 0.41907, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41955, val loss: 0.41757, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.41821, val loss: 0.41615, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.41690, val loss: 0.41475, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41565, val loss: 0.41337, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41439, val loss: 0.41200, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.41280, val loss: 0.41058, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41159, val loss: 0.40925, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.41005, val loss: 0.40788, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.40856, val loss: 0.40655, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40737, val loss: 0.40524, in 0.000s 1 tree, 163 leaves, max depth = 16, train loss: 0.40592, val loss: 0.40395, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40476, val loss: 0.40268, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40362, val loss: 0.40143, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.40222, val loss: 0.40018, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40112, val loss: 0.39897, in 0.016s Fit 91 trees in 1.799 s, (11094 total leaves) Time spent computing histograms: 0.554s Time spent finding best splits: 0.170s Time spent applying splits: 0.207s Time spent predicting: 0.000s Trial 96, Fold 1: Log loss = 0.40533120527321725, Average precision = 0.9457526290398302, ROC-AUC = 0.9421818317058832, Elapsed Time = 1.8046297000000777 seconds Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 96, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 105 leaves, max depth = 18, train loss: 0.68479, val loss: 0.68447, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.67669, val loss: 0.67601, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.66888, val loss: 0.66784, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.66134, val loss: 0.65995, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.65408, val loss: 0.65235, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.64707, val loss: 0.64501, in 0.016s 1 tree, 105 leaves, max depth = 20, train loss: 0.64030, val loss: 0.63796, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.63376, val loss: 0.63114, in 0.016s 1 tree, 8 leaves, max depth = 6, train loss: 0.62743, val loss: 0.62449, in 0.000s 1 tree, 105 leaves, max depth = 14, train loss: 0.62123, val loss: 0.61798, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.61531, val loss: 0.61177, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.60956, val loss: 0.60573, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.60402, val loss: 0.59991, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.59858, val loss: 0.59419, in 0.016s 1 tree, 104 leaves, max depth = 19, train loss: 0.59340, val loss: 0.58877, in 0.016s 1 tree, 106 leaves, max depth = 17, train loss: 0.58837, val loss: 0.58353, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.58342, val loss: 0.57832, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.57869, val loss: 0.57334, in 0.016s 1 tree, 104 leaves, max depth = 23, train loss: 0.57413, val loss: 0.56857, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.56963, val loss: 0.56383, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.56528, val loss: 0.55923, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.56106, val loss: 0.55478, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.55697, val loss: 0.55046, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.55300, val loss: 0.54628, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.54921, val loss: 0.54227, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.54499, val loss: 0.53820, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.54089, val loss: 0.53427, in 0.031s 1 tree, 104 leaves, max depth = 17, train loss: 0.53734, val loss: 0.53051, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.53342, val loss: 0.52674, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.52961, val loss: 0.52309, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.52626, val loss: 0.51957, in 0.016s 1 tree, 104 leaves, max depth = 17, train loss: 0.52301, val loss: 0.51613, in 0.031s 1 tree, 106 leaves, max depth = 14, train loss: 0.51980, val loss: 0.51272, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.51622, val loss: 0.50930, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.51275, val loss: 0.50598, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.50977, val loss: 0.50281, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.50683, val loss: 0.49968, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.50352, val loss: 0.49652, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.50075, val loss: 0.49358, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.49807, val loss: 0.49073, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.49547, val loss: 0.48795, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48496, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.48984, val loss: 0.48230, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.48683, val loss: 0.47943, in 0.031s 1 tree, 161 leaves, max depth = 17, train loss: 0.48391, val loss: 0.47665, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.48107, val loss: 0.47394, in 0.031s 1 tree, 161 leaves, max depth = 17, train loss: 0.47832, val loss: 0.47132, in 0.016s 1 tree, 106 leaves, max depth = 15, train loss: 0.47597, val loss: 0.46882, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.47331, val loss: 0.46629, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.47074, val loss: 0.46384, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.46855, val loss: 0.46151, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.46606, val loss: 0.45915, in 0.031s 1 tree, 161 leaves, max depth = 16, train loss: 0.46365, val loss: 0.45686, in 0.016s 1 tree, 106 leaves, max depth = 14, train loss: 0.46153, val loss: 0.45459, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.45920, val loss: 0.45238, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.45694, val loss: 0.45024, in 0.031s 1 tree, 161 leaves, max depth = 16, train loss: 0.45474, val loss: 0.44816, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.45278, val loss: 0.44607, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.45066, val loss: 0.44406, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.44877, val loss: 0.44204, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.44694, val loss: 0.44008, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.44490, val loss: 0.43816, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.44292, val loss: 0.43629, in 0.031s 1 tree, 105 leaves, max depth = 20, train loss: 0.44117, val loss: 0.43444, in 0.016s 1 tree, 161 leaves, max depth = 15, train loss: 0.43925, val loss: 0.43264, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.43757, val loss: 0.43084, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.43594, val loss: 0.42909, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.43436, val loss: 0.42742, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.43252, val loss: 0.42569, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.43073, val loss: 0.42402, in 0.031s 1 tree, 161 leaves, max depth = 16, train loss: 0.42899, val loss: 0.42239, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.42730, val loss: 0.42081, in 0.031s 1 tree, 105 leaves, max depth = 15, train loss: 0.42581, val loss: 0.41921, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.42436, val loss: 0.41768, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.42295, val loss: 0.41617, in 0.000s 1 tree, 105 leaves, max depth = 15, train loss: 0.42159, val loss: 0.41471, in 0.031s 1 tree, 106 leaves, max depth = 17, train loss: 0.42023, val loss: 0.41324, in 0.016s 1 tree, 106 leaves, max depth = 13, train loss: 0.41894, val loss: 0.41185, in 0.000s 1 tree, 106 leaves, max depth = 13, train loss: 0.41770, val loss: 0.41051, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41648, val loss: 0.40924, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.41524, val loss: 0.40790, in 0.016s 1 tree, 161 leaves, max depth = 15, train loss: 0.41364, val loss: 0.40642, in 0.016s 1 tree, 106 leaves, max depth = 18, train loss: 0.41245, val loss: 0.40512, in 0.016s 1 tree, 161 leaves, max depth = 15, train loss: 0.41089, val loss: 0.40368, in 0.016s 1 tree, 161 leaves, max depth = 15, train loss: 0.40939, val loss: 0.40228, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40822, val loss: 0.40108, in 0.016s 1 tree, 160 leaves, max depth = 15, train loss: 0.40676, val loss: 0.39972, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40563, val loss: 0.39855, in 0.016s 1 tree, 105 leaves, max depth = 13, train loss: 0.40452, val loss: 0.39736, in 0.016s 1 tree, 160 leaves, max depth = 15, train loss: 0.40310, val loss: 0.39604, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.40198, val loss: 0.39488, in 0.031s Fit 91 trees in 1.877 s, (10965 total leaves) Time spent computing histograms: 0.573s Time spent finding best splits: 0.171s Time spent applying splits: 0.208s Time spent predicting: 0.000s Trial 96, Fold 2: Log loss = 0.4061720059908957, Average precision = 0.9421895019687312, ROC-AUC = 0.9427703713055864, Elapsed Time = 1.8791829000001599 seconds Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 96, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.172 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 99 leaves, max depth = 15, train loss: 0.68486, val loss: 0.68458, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.67682, val loss: 0.67626, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.66905, val loss: 0.66824, in 0.016s 1 tree, 101 leaves, max depth = 13, train loss: 0.66155, val loss: 0.66049, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.65433, val loss: 0.65307, in 0.016s 1 tree, 102 leaves, max depth = 16, train loss: 0.64736, val loss: 0.64591, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.64065, val loss: 0.63895, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.63417, val loss: 0.63224, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.62786, val loss: 0.62569, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.62168, val loss: 0.61932, in 0.016s 1 tree, 102 leaves, max depth = 20, train loss: 0.61582, val loss: 0.61326, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.61011, val loss: 0.60733, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.60460, val loss: 0.60167, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.59917, val loss: 0.59608, in 0.016s 1 tree, 102 leaves, max depth = 20, train loss: 0.59404, val loss: 0.59076, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.58903, val loss: 0.58553, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.58411, val loss: 0.58045, in 0.016s 1 tree, 103 leaves, max depth = 14, train loss: 0.57941, val loss: 0.57557, in 0.000s 1 tree, 103 leaves, max depth = 20, train loss: 0.57489, val loss: 0.57087, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.57041, val loss: 0.56624, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.56607, val loss: 0.56176, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.56187, val loss: 0.55742, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.55739, val loss: 0.55329, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.55338, val loss: 0.54914, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.54955, val loss: 0.54515, in 0.016s 1 tree, 162 leaves, max depth = 18, train loss: 0.54531, val loss: 0.54125, in 0.031s 1 tree, 162 leaves, max depth = 18, train loss: 0.54121, val loss: 0.53748, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.53761, val loss: 0.53376, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.53368, val loss: 0.53015, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.52987, val loss: 0.52665, in 0.032s 1 tree, 100 leaves, max depth = 15, train loss: 0.52649, val loss: 0.52312, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.52321, val loss: 0.51972, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.51996, val loss: 0.51634, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.51637, val loss: 0.51305, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.51289, val loss: 0.50987, in 0.031s 1 tree, 104 leaves, max depth = 15, train loss: 0.50989, val loss: 0.50671, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.50692, val loss: 0.50361, in 0.016s 1 tree, 162 leaves, max depth = 18, train loss: 0.50360, val loss: 0.50058, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.50081, val loss: 0.49764, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.49810, val loss: 0.49482, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.49548, val loss: 0.49204, in 0.016s 1 tree, 163 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48918, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.48983, val loss: 0.48652, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.48681, val loss: 0.48378, in 0.031s 1 tree, 163 leaves, max depth = 18, train loss: 0.48389, val loss: 0.48111, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.48104, val loss: 0.47853, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.47828, val loss: 0.47602, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.47591, val loss: 0.47353, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.47325, val loss: 0.47112, in 0.031s 1 tree, 163 leaves, max depth = 18, train loss: 0.47067, val loss: 0.46878, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.46846, val loss: 0.46647, in 0.016s 1 tree, 162 leaves, max depth = 18, train loss: 0.46596, val loss: 0.46421, in 0.031s 1 tree, 162 leaves, max depth = 18, train loss: 0.46354, val loss: 0.46203, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.46141, val loss: 0.45977, in 0.016s 1 tree, 162 leaves, max depth = 15, train loss: 0.45907, val loss: 0.45766, in 0.031s 1 tree, 163 leaves, max depth = 15, train loss: 0.45680, val loss: 0.45562, in 0.016s 1 tree, 163 leaves, max depth = 18, train loss: 0.45459, val loss: 0.45364, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.45261, val loss: 0.45156, in 0.016s 1 tree, 163 leaves, max depth = 17, train loss: 0.45048, val loss: 0.44965, in 0.031s 1 tree, 105 leaves, max depth = 15, train loss: 0.44859, val loss: 0.44761, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.44674, val loss: 0.44567, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.44469, val loss: 0.44383, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.44270, val loss: 0.44205, in 0.031s 1 tree, 105 leaves, max depth = 15, train loss: 0.44095, val loss: 0.44016, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.43903, val loss: 0.43845, in 0.031s 1 tree, 105 leaves, max depth = 15, train loss: 0.43735, val loss: 0.43663, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.43571, val loss: 0.43486, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.43407, val loss: 0.43310, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.43222, val loss: 0.43146, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.43043, val loss: 0.42986, in 0.031s 1 tree, 162 leaves, max depth = 16, train loss: 0.42869, val loss: 0.42832, in 0.031s 1 tree, 163 leaves, max depth = 16, train loss: 0.42700, val loss: 0.42682, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.42551, val loss: 0.42520, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.42406, val loss: 0.42364, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.42266, val loss: 0.42211, in 0.031s 1 tree, 102 leaves, max depth = 13, train loss: 0.42128, val loss: 0.42062, in 0.000s 1 tree, 5 leaves, max depth = 3, train loss: 0.42000, val loss: 0.41943, in 0.000s 1 tree, 104 leaves, max depth = 13, train loss: 0.41863, val loss: 0.41795, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.41735, val loss: 0.41656, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.41612, val loss: 0.41541, in 0.000s 1 tree, 104 leaves, max depth = 13, train loss: 0.41483, val loss: 0.41403, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.41323, val loss: 0.41262, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.41200, val loss: 0.41129, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.41045, val loss: 0.40993, in 0.016s 1 tree, 162 leaves, max depth = 16, train loss: 0.40895, val loss: 0.40861, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40777, val loss: 0.40751, in 0.000s 1 tree, 163 leaves, max depth = 16, train loss: 0.40631, val loss: 0.40623, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40517, val loss: 0.40517, in 0.000s 1 tree, 104 leaves, max depth = 13, train loss: 0.40404, val loss: 0.40394, in 0.016s 1 tree, 163 leaves, max depth = 16, train loss: 0.40262, val loss: 0.40271, in 0.031s 1 tree, 103 leaves, max depth = 13, train loss: 0.40150, val loss: 0.40149, in 0.016s Fit 91 trees in 1.986 s, (10977 total leaves) Time spent computing histograms: 0.635s Time spent finding best splits: 0.181s Time spent applying splits: 0.218s Time spent predicting: 0.016s Trial 96, Fold 3: Log loss = 0.4005417862920448, Average precision = 0.9481568863042917, ROC-AUC = 0.9469454118952915, Elapsed Time = 1.9903591000002052 seconds Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 96, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.142 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 96 leaves, max depth = 16, train loss: 0.68486, val loss: 0.68446, in 0.016s 1 tree, 99 leaves, max depth = 19, train loss: 0.67674, val loss: 0.67586, in 0.000s 1 tree, 99 leaves, max depth = 19, train loss: 0.66890, val loss: 0.66754, in 0.031s 1 tree, 103 leaves, max depth = 19, train loss: 0.66138, val loss: 0.65964, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.65413, val loss: 0.65198, in 0.016s 1 tree, 102 leaves, max depth = 19, train loss: 0.64716, val loss: 0.64458, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.64044, val loss: 0.63746, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.63394, val loss: 0.63057, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.62762, val loss: 0.62386, in 0.016s 1 tree, 104 leaves, max depth = 19, train loss: 0.62144, val loss: 0.61727, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.61556, val loss: 0.61098, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.60981, val loss: 0.60492, in 0.016s 1 tree, 104 leaves, max depth = 13, train loss: 0.60428, val loss: 0.59902, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.59887, val loss: 0.59321, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.59368, val loss: 0.58768, in 0.016s 1 tree, 103 leaves, max depth = 13, train loss: 0.58867, val loss: 0.58231, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.58376, val loss: 0.57702, in 0.016s 1 tree, 103 leaves, max depth = 18, train loss: 0.57903, val loss: 0.57201, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.57450, val loss: 0.56716, in 0.016s 1 tree, 104 leaves, max depth = 20, train loss: 0.57003, val loss: 0.56233, in 0.016s 1 tree, 104 leaves, max depth = 20, train loss: 0.56571, val loss: 0.55765, in 0.016s 1 tree, 104 leaves, max depth = 20, train loss: 0.56152, val loss: 0.55310, in 0.000s 1 tree, 104 leaves, max depth = 20, train loss: 0.55746, val loss: 0.54870, in 0.031s 1 tree, 105 leaves, max depth = 20, train loss: 0.55352, val loss: 0.54443, in 0.016s 1 tree, 103 leaves, max depth = 21, train loss: 0.54974, val loss: 0.54038, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.54552, val loss: 0.53628, in 0.016s 1 tree, 161 leaves, max depth = 18, train loss: 0.54143, val loss: 0.53232, in 0.016s 1 tree, 103 leaves, max depth = 21, train loss: 0.53789, val loss: 0.52848, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.53397, val loss: 0.52468, in 0.031s 1 tree, 161 leaves, max depth = 16, train loss: 0.53018, val loss: 0.52100, in 0.016s 1 tree, 102 leaves, max depth = 15, train loss: 0.52685, val loss: 0.51742, in 0.016s 1 tree, 103 leaves, max depth = 20, train loss: 0.52361, val loss: 0.51390, in 0.016s 1 tree, 104 leaves, max depth = 20, train loss: 0.52042, val loss: 0.51041, in 0.031s 1 tree, 161 leaves, max depth = 16, train loss: 0.51685, val loss: 0.50696, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.51339, val loss: 0.50361, in 0.031s 1 tree, 104 leaves, max depth = 21, train loss: 0.51041, val loss: 0.50040, in 0.016s 1 tree, 105 leaves, max depth = 18, train loss: 0.50750, val loss: 0.49720, in 0.016s 1 tree, 160 leaves, max depth = 18, train loss: 0.50420, val loss: 0.49401, in 0.016s 1 tree, 103 leaves, max depth = 21, train loss: 0.50144, val loss: 0.49103, in 0.016s 1 tree, 103 leaves, max depth = 22, train loss: 0.49877, val loss: 0.48810, in 0.016s 1 tree, 103 leaves, max depth = 21, train loss: 0.49616, val loss: 0.48529, in 0.016s 1 tree, 160 leaves, max depth = 18, train loss: 0.49304, val loss: 0.48228, in 0.016s 1 tree, 104 leaves, max depth = 21, train loss: 0.49055, val loss: 0.47959, in 0.031s 1 tree, 161 leaves, max depth = 18, train loss: 0.48755, val loss: 0.47670, in 0.016s 1 tree, 161 leaves, max depth = 18, train loss: 0.48464, val loss: 0.47389, in 0.016s 1 tree, 160 leaves, max depth = 18, train loss: 0.48181, val loss: 0.47117, in 0.031s 1 tree, 161 leaves, max depth = 18, train loss: 0.47907, val loss: 0.46853, in 0.016s 1 tree, 105 leaves, max depth = 15, train loss: 0.47674, val loss: 0.46594, in 0.016s 1 tree, 160 leaves, max depth = 19, train loss: 0.47410, val loss: 0.46340, in 0.031s 1 tree, 161 leaves, max depth = 19, train loss: 0.47153, val loss: 0.46093, in 0.016s 1 tree, 103 leaves, max depth = 20, train loss: 0.46935, val loss: 0.45852, in 0.016s 1 tree, 160 leaves, max depth = 19, train loss: 0.46688, val loss: 0.45614, in 0.031s 1 tree, 161 leaves, max depth = 19, train loss: 0.46447, val loss: 0.45383, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.46237, val loss: 0.45149, in 0.016s 1 tree, 160 leaves, max depth = 18, train loss: 0.46006, val loss: 0.44927, in 0.031s 1 tree, 161 leaves, max depth = 18, train loss: 0.45780, val loss: 0.44710, in 0.016s 1 tree, 161 leaves, max depth = 21, train loss: 0.45561, val loss: 0.44501, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.45360, val loss: 0.44281, in 0.016s 1 tree, 160 leaves, max depth = 18, train loss: 0.45149, val loss: 0.44079, in 0.016s 1 tree, 104 leaves, max depth = 14, train loss: 0.44961, val loss: 0.43871, in 0.016s 1 tree, 103 leaves, max depth = 19, train loss: 0.44778, val loss: 0.43668, in 0.031s 1 tree, 161 leaves, max depth = 16, train loss: 0.44576, val loss: 0.43474, in 0.016s 1 tree, 161 leaves, max depth = 16, train loss: 0.44379, val loss: 0.43286, in 0.031s 1 tree, 104 leaves, max depth = 14, train loss: 0.44199, val loss: 0.43089, in 0.016s 1 tree, 161 leaves, max depth = 18, train loss: 0.44009, val loss: 0.42908, in 0.016s 1 tree, 102 leaves, max depth = 17, train loss: 0.43841, val loss: 0.42720, in 0.016s 1 tree, 103 leaves, max depth = 15, train loss: 0.43672, val loss: 0.42536, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.43515, val loss: 0.42360, in 0.016s 1 tree, 161 leaves, max depth = 18, train loss: 0.43333, val loss: 0.42186, in 0.031s 1 tree, 161 leaves, max depth = 18, train loss: 0.43156, val loss: 0.42018, in 0.031s 1 tree, 161 leaves, max depth = 18, train loss: 0.42985, val loss: 0.41855, in 0.016s 1 tree, 161 leaves, max depth = 18, train loss: 0.42818, val loss: 0.41696, in 0.031s 1 tree, 103 leaves, max depth = 17, train loss: 0.42668, val loss: 0.41528, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.42523, val loss: 0.41367, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.42382, val loss: 0.41209, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.42246, val loss: 0.41057, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.42111, val loss: 0.40902, in 0.047s 1 tree, 104 leaves, max depth = 14, train loss: 0.41977, val loss: 0.40755, in 0.094s 1 tree, 103 leaves, max depth = 16, train loss: 0.41852, val loss: 0.40613, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41730, val loss: 0.40482, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.41607, val loss: 0.40341, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.41449, val loss: 0.40192, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.41331, val loss: 0.40056, in 0.031s 1 tree, 161 leaves, max depth = 17, train loss: 0.41178, val loss: 0.39911, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.41029, val loss: 0.39771, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40913, val loss: 0.39646, in 0.016s 1 tree, 161 leaves, max depth = 17, train loss: 0.40769, val loss: 0.39510, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40656, val loss: 0.39389, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.40545, val loss: 0.39261, in 0.016s 1 tree, 161 leaves, max depth = 20, train loss: 0.40405, val loss: 0.39129, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.40298, val loss: 0.39005, in 0.016s Fit 91 trees in 2.126 s, (10942 total leaves) Time spent computing histograms: 0.681s Time spent finding best splits: 0.209s Time spent applying splits: 0.268s Time spent predicting: 0.031s Trial 96, Fold 4: Log loss = 0.40511379728362673, Average precision = 0.9467197122543686, ROC-AUC = 0.9438916339406427, Elapsed Time = 2.1346135000003414 seconds Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 96, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 102 leaves, max depth = 13, train loss: 0.68473, val loss: 0.68423, in 0.016s 1 tree, 99 leaves, max depth = 16, train loss: 0.67653, val loss: 0.67551, in 0.016s 1 tree, 99 leaves, max depth = 16, train loss: 0.66861, val loss: 0.66709, in 0.016s 1 tree, 101 leaves, max depth = 14, train loss: 0.66105, val loss: 0.65905, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.65374, val loss: 0.65127, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.64667, val loss: 0.64375, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.63986, val loss: 0.63650, in 0.016s 1 tree, 103 leaves, max depth = 18, train loss: 0.63328, val loss: 0.62949, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.62689, val loss: 0.62263, in 0.000s 1 tree, 104 leaves, max depth = 16, train loss: 0.62065, val loss: 0.61595, in 0.031s 1 tree, 105 leaves, max depth = 17, train loss: 0.61469, val loss: 0.60952, in 0.016s 1 tree, 104 leaves, max depth = 15, train loss: 0.60891, val loss: 0.60330, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.60333, val loss: 0.59727, in 0.016s 1 tree, 104 leaves, max depth = 16, train loss: 0.59785, val loss: 0.59138, in 0.016s 1 tree, 99 leaves, max depth = 20, train loss: 0.59263, val loss: 0.58579, in 0.000s 1 tree, 104 leaves, max depth = 15, train loss: 0.58756, val loss: 0.58030, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.58258, val loss: 0.57494, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.57783, val loss: 0.56982, in 0.016s 1 tree, 103 leaves, max depth = 18, train loss: 0.57324, val loss: 0.56487, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.56871, val loss: 0.55997, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.56433, val loss: 0.55523, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.56008, val loss: 0.55062, in 0.000s 1 tree, 105 leaves, max depth = 16, train loss: 0.55597, val loss: 0.54615, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.55198, val loss: 0.54182, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.54816, val loss: 0.53768, in 0.016s 1 tree, 158 leaves, max depth = 17, train loss: 0.54406, val loss: 0.53379, in 0.031s 1 tree, 158 leaves, max depth = 17, train loss: 0.54008, val loss: 0.53004, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.53649, val loss: 0.52613, in 0.016s 1 tree, 158 leaves, max depth = 17, train loss: 0.53267, val loss: 0.52254, in 0.031s 1 tree, 158 leaves, max depth = 17, train loss: 0.52898, val loss: 0.51906, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.52559, val loss: 0.51536, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.52231, val loss: 0.51174, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.51907, val loss: 0.50820, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.51560, val loss: 0.50494, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.51223, val loss: 0.50179, in 0.031s 1 tree, 103 leaves, max depth = 18, train loss: 0.50922, val loss: 0.49850, in 0.016s 1 tree, 105 leaves, max depth = 14, train loss: 0.50625, val loss: 0.49523, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.50304, val loss: 0.49224, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.50024, val loss: 0.48916, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.49752, val loss: 0.48617, in 0.031s 1 tree, 102 leaves, max depth = 15, train loss: 0.49489, val loss: 0.48326, in 0.016s 1 tree, 159 leaves, max depth = 18, train loss: 0.49185, val loss: 0.48044, in 0.016s 1 tree, 105 leaves, max depth = 17, train loss: 0.48933, val loss: 0.47765, in 0.016s 1 tree, 158 leaves, max depth = 17, train loss: 0.48640, val loss: 0.47495, in 0.016s 1 tree, 158 leaves, max depth = 17, train loss: 0.48357, val loss: 0.47232, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.48082, val loss: 0.46978, in 0.031s 1 tree, 159 leaves, max depth = 18, train loss: 0.47815, val loss: 0.46732, in 0.031s 1 tree, 105 leaves, max depth = 14, train loss: 0.47576, val loss: 0.46468, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.47319, val loss: 0.46231, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.47069, val loss: 0.46001, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.46847, val loss: 0.45754, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.46606, val loss: 0.45534, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.46372, val loss: 0.45320, in 0.031s 1 tree, 105 leaves, max depth = 13, train loss: 0.46157, val loss: 0.45080, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45931, val loss: 0.44874, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45712, val loss: 0.44674, in 0.031s 1 tree, 159 leaves, max depth = 16, train loss: 0.45500, val loss: 0.44481, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.45300, val loss: 0.44258, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.45095, val loss: 0.44071, in 0.016s 1 tree, 103 leaves, max depth = 16, train loss: 0.44903, val loss: 0.43857, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.44716, val loss: 0.43648, in 0.016s 1 tree, 158 leaves, max depth = 15, train loss: 0.44519, val loss: 0.43470, in 0.031s 1 tree, 158 leaves, max depth = 15, train loss: 0.44327, val loss: 0.43297, in 0.016s 1 tree, 99 leaves, max depth = 15, train loss: 0.44149, val loss: 0.43099, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.43964, val loss: 0.42932, in 0.031s 1 tree, 105 leaves, max depth = 16, train loss: 0.43792, val loss: 0.42739, in 0.000s 1 tree, 105 leaves, max depth = 16, train loss: 0.43625, val loss: 0.42551, in 0.016s 1 tree, 103 leaves, max depth = 17, train loss: 0.43464, val loss: 0.42369, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43287, val loss: 0.42211, in 0.016s 1 tree, 159 leaves, max depth = 16, train loss: 0.43114, val loss: 0.42057, in 0.031s 1 tree, 159 leaves, max depth = 15, train loss: 0.42947, val loss: 0.41907, in 0.016s 1 tree, 159 leaves, max depth = 15, train loss: 0.42784, val loss: 0.41763, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42631, val loss: 0.41589, in 0.016s 1 tree, 100 leaves, max depth = 15, train loss: 0.42483, val loss: 0.41423, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42339, val loss: 0.41259, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.42199, val loss: 0.41100, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.42059, val loss: 0.40942, in 0.016s 1 tree, 101 leaves, max depth = 15, train loss: 0.41928, val loss: 0.40792, in 0.016s 1 tree, 105 leaves, max depth = 16, train loss: 0.41800, val loss: 0.40645, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.41678, val loss: 0.40527, in 0.000s 1 tree, 105 leaves, max depth = 19, train loss: 0.41550, val loss: 0.40382, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.41397, val loss: 0.40247, in 0.031s 1 tree, 105 leaves, max depth = 19, train loss: 0.41274, val loss: 0.40107, in 0.016s 1 tree, 159 leaves, max depth = 19, train loss: 0.41124, val loss: 0.39977, in 0.016s 1 tree, 159 leaves, max depth = 17, train loss: 0.40980, val loss: 0.39850, in 0.031s 1 tree, 5 leaves, max depth = 3, train loss: 0.40863, val loss: 0.39738, in 0.000s 1 tree, 159 leaves, max depth = 17, train loss: 0.40723, val loss: 0.39616, in 0.016s 1 tree, 5 leaves, max depth = 3, train loss: 0.40610, val loss: 0.39508, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.40494, val loss: 0.39375, in 0.016s 1 tree, 159 leaves, max depth = 19, train loss: 0.40358, val loss: 0.39258, in 0.016s 1 tree, 105 leaves, max depth = 19, train loss: 0.40246, val loss: 0.39130, in 0.016s Fit 91 trees in 1.955 s, (10906 total leaves) Time spent computing histograms: 0.610s Time spent finding best splits: 0.180s Time spent applying splits: 0.219s Time spent predicting: 0.000s Trial 96, Fold 5: Log loss = 0.4107916499987752, Average precision = 0.9446465371422839, ROC-AUC = 0.941807182802891, Elapsed Time = 1.9732861000011326 seconds
Optimization Progress: 97%|#########7| 97/100 [20:16<00:42, 14.27s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 97, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 13, train loss: 0.67810, val loss: 0.67770, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.66417, val loss: 0.66339, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.65109, val loss: 0.64994, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.63895, val loss: 0.63747, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.62738, val loss: 0.62555, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.61649, val loss: 0.61432, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.60624, val loss: 0.60373, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.59657, val loss: 0.59375, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.58745, val loss: 0.58432, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.57884, val loss: 0.57541, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.57071, val loss: 0.56699, in 0.000s 1 tree, 34 leaves, max depth = 13, train loss: 0.56304, val loss: 0.55903, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.55586, val loss: 0.55154, in 0.000s 1 tree, 36 leaves, max depth = 13, train loss: 0.54899, val loss: 0.54440, in 0.016s 1 tree, 35 leaves, max depth = 13, train loss: 0.54249, val loss: 0.53764, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.53560, val loss: 0.53120, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.52967, val loss: 0.52497, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.52327, val loss: 0.51901, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.51779, val loss: 0.51324, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.51183, val loss: 0.50770, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.50620, val loss: 0.50247, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.50087, val loss: 0.49753, in 0.000s 1 tree, 32 leaves, max depth = 10, train loss: 0.49600, val loss: 0.49240, in 0.016s 1 tree, 33 leaves, max depth = 13, train loss: 0.49139, val loss: 0.48757, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.48648, val loss: 0.48303, in 0.016s 1 tree, 56 leaves, max depth = 10, train loss: 0.48184, val loss: 0.47875, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.47743, val loss: 0.47469, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.47330, val loss: 0.47031, in 0.000s 1 tree, 56 leaves, max depth = 10, train loss: 0.46917, val loss: 0.46650, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.46532, val loss: 0.46240, in 0.000s 1 tree, 31 leaves, max depth = 13, train loss: 0.46166, val loss: 0.45857, in 0.016s 1 tree, 54 leaves, max depth = 10, train loss: 0.45783, val loss: 0.45505, in 0.000s 1 tree, 32 leaves, max depth = 9, train loss: 0.45430, val loss: 0.45135, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.45072, val loss: 0.44808, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.44732, val loss: 0.44499, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.44409, val loss: 0.44206, in 0.016s 1 tree, 31 leaves, max depth = 10, train loss: 0.44100, val loss: 0.43878, in 0.000s 1 tree, 52 leaves, max depth = 11, train loss: 0.43797, val loss: 0.43605, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.43498, val loss: 0.43289, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.43213, val loss: 0.43033, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.42939, val loss: 0.42737, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.42670, val loss: 0.42498, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.42413, val loss: 0.42219, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.42159, val loss: 0.41994, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.41916, val loss: 0.41733, in 0.000s 1 tree, 53 leaves, max depth = 10, train loss: 0.41677, val loss: 0.41522, in 0.016s 1 tree, 30 leaves, max depth = 12, train loss: 0.41452, val loss: 0.41282, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.41225, val loss: 0.41082, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.41011, val loss: 0.40847, in 0.000s 1 tree, 54 leaves, max depth = 10, train loss: 0.40796, val loss: 0.40660, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.40581, val loss: 0.40432, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40380, val loss: 0.40212, in 0.000s 1 tree, 52 leaves, max depth = 10, train loss: 0.40178, val loss: 0.40038, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.39982, val loss: 0.39826, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.39790, val loss: 0.39665, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39601, val loss: 0.39457, in 0.016s 1 tree, 55 leaves, max depth = 10, train loss: 0.39420, val loss: 0.39302, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39241, val loss: 0.39106, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39071, val loss: 0.38919, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38911, val loss: 0.38742, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38758, val loss: 0.38574, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.38586, val loss: 0.38434, in 0.000s 1 tree, 31 leaves, max depth = 10, train loss: 0.38408, val loss: 0.38245, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38263, val loss: 0.38085, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.38099, val loss: 0.37953, in 0.016s 1 tree, 35 leaves, max depth = 9, train loss: 0.37929, val loss: 0.37773, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.37791, val loss: 0.37621, in 0.000s 1 tree, 31 leaves, max depth = 9, train loss: 0.37633, val loss: 0.37455, in 0.016s 1 tree, 53 leaves, max depth = 11, train loss: 0.37477, val loss: 0.37330, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37353, val loss: 0.37184, in 0.016s Fit 70 trees in 0.798 s, (2569 total leaves) Time spent computing histograms: 0.274s Time spent finding best splits: 0.038s Time spent applying splits: 0.041s Time spent predicting: 0.016s Trial 97, Fold 1: Log loss = 0.3761059926294201, Average precision = 0.9467077987386714, ROC-AUC = 0.943012434698862, Elapsed Time = 0.7945130000007339 seconds Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 97, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.142 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 11, train loss: 0.67829, val loss: 0.67754, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.66451, val loss: 0.66308, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.65148, val loss: 0.64937, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.63932, val loss: 0.63659, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.62780, val loss: 0.62444, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.61696, val loss: 0.61298, in 0.016s 1 tree, 32 leaves, max depth = 15, train loss: 0.60684, val loss: 0.60235, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.59719, val loss: 0.59215, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.58819, val loss: 0.58267, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.57960, val loss: 0.57355, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.57157, val loss: 0.56511, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.56392, val loss: 0.55697, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.55675, val loss: 0.54941, in 0.000s 1 tree, 34 leaves, max depth = 9, train loss: 0.54991, val loss: 0.54211, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.54348, val loss: 0.53530, in 0.000s 1 tree, 55 leaves, max depth = 14, train loss: 0.53650, val loss: 0.52854, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.53058, val loss: 0.52228, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.52409, val loss: 0.51601, in 0.016s 1 tree, 31 leaves, max depth = 15, train loss: 0.51862, val loss: 0.51022, in 0.000s 1 tree, 55 leaves, max depth = 14, train loss: 0.51258, val loss: 0.50439, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.50688, val loss: 0.49888, in 0.000s 1 tree, 55 leaves, max depth = 14, train loss: 0.50148, val loss: 0.49367, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.49636, val loss: 0.48875, in 0.016s 1 tree, 33 leaves, max depth = 14, train loss: 0.49162, val loss: 0.48367, in 0.000s 1 tree, 56 leaves, max depth = 14, train loss: 0.48685, val loss: 0.47908, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.48243, val loss: 0.47438, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.47796, val loss: 0.47010, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.47374, val loss: 0.46605, in 0.000s 1 tree, 33 leaves, max depth = 10, train loss: 0.46968, val loss: 0.46174, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.46572, val loss: 0.45796, in 0.016s 1 tree, 33 leaves, max depth = 15, train loss: 0.46195, val loss: 0.45390, in 0.000s 1 tree, 54 leaves, max depth = 15, train loss: 0.45823, val loss: 0.45036, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.45470, val loss: 0.44659, in 0.000s 1 tree, 54 leaves, max depth = 15, train loss: 0.45120, val loss: 0.44327, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.44790, val loss: 0.43975, in 0.000s 1 tree, 55 leaves, max depth = 11, train loss: 0.44461, val loss: 0.43665, in 0.016s 1 tree, 33 leaves, max depth = 14, train loss: 0.44155, val loss: 0.43333, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.43845, val loss: 0.43041, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.43556, val loss: 0.42731, in 0.016s 1 tree, 56 leaves, max depth = 15, train loss: 0.43264, val loss: 0.42453, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.42993, val loss: 0.42162, in 0.016s 1 tree, 57 leaves, max depth = 15, train loss: 0.42717, val loss: 0.41902, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.42463, val loss: 0.41628, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.42202, val loss: 0.41383, in 0.000s 1 tree, 55 leaves, max depth = 15, train loss: 0.41954, val loss: 0.41150, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.41718, val loss: 0.40895, in 0.000s 1 tree, 55 leaves, max depth = 13, train loss: 0.41484, val loss: 0.40675, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.41261, val loss: 0.40467, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41052, val loss: 0.40249, in 0.000s 1 tree, 32 leaves, max depth = 13, train loss: 0.40837, val loss: 0.40014, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.40627, val loss: 0.39818, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40431, val loss: 0.39614, in 0.016s [53/70] 1 tree, 32 leaves, max depth = 13, train loss: 0.40231, val loss: 0.39394, in 0.000s 1 tree, 55 leaves, max depth = 11, train loss: 0.40033, val loss: 0.39211, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.39845, val loss: 0.39036, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39661, val loss: 0.38845, in 0.000s 1 tree, 33 leaves, max depth = 12, train loss: 0.39475, val loss: 0.38641, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39302, val loss: 0.38461, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39138, val loss: 0.38290, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38982, val loss: 0.38127, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.38799, val loss: 0.37930, in 0.000s 1 tree, 56 leaves, max depth = 13, train loss: 0.38621, val loss: 0.37769, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.38452, val loss: 0.37615, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38304, val loss: 0.37461, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38164, val loss: 0.37314, in 0.000s 1 tree, 31 leaves, max depth = 8, train loss: 0.37994, val loss: 0.37132, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.37834, val loss: 0.36987, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37706, val loss: 0.36855, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.37553, val loss: 0.36717, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.37397, val loss: 0.36546, in 0.000s Fit 70 trees in 0.845 s, (2631 total leaves) Time spent computing histograms: 0.306s Time spent finding best splits: 0.044s Time spent applying splits: 0.047s Time spent predicting: 0.000s Trial 97, Fold 2: Log loss = 0.3764287364805963, Average precision = 0.9427200995296994, ROC-AUC = 0.9438946706481238, Elapsed Time = 0.8514697000009619 seconds Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 97, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67784, in 0.000s 1 tree, 32 leaves, max depth = 9, train loss: 0.66474, val loss: 0.66375, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.65192, val loss: 0.65044, in 0.000s 1 tree, 36 leaves, max depth = 12, train loss: 0.63986, val loss: 0.63796, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.62851, val loss: 0.62616, in 0.000s 1 tree, 32 leaves, max depth = 9, train loss: 0.61790, val loss: 0.61511, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.60771, val loss: 0.60455, in 0.000s 1 tree, 31 leaves, max depth = 13, train loss: 0.59825, val loss: 0.59470, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.58929, val loss: 0.58535, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.58068, val loss: 0.57642, in 0.000s 1 tree, 35 leaves, max depth = 12, train loss: 0.57270, val loss: 0.56812, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.56501, val loss: 0.56013, in 0.000s 1 tree, 35 leaves, max depth = 9, train loss: 0.55788, val loss: 0.55266, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.55101, val loss: 0.54552, in 0.000s 1 tree, 34 leaves, max depth = 9, train loss: 0.54451, val loss: 0.53875, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.53741, val loss: 0.53212, in 0.000s 1 tree, 31 leaves, max depth = 14, train loss: 0.53155, val loss: 0.52595, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.52495, val loss: 0.51981, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.51952, val loss: 0.51413, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.51338, val loss: 0.50841, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.50757, val loss: 0.50301, in 0.000s 1 tree, 52 leaves, max depth = 12, train loss: 0.50207, val loss: 0.49791, in 0.016s 1 tree, 51 leaves, max depth = 12, train loss: 0.49687, val loss: 0.49310, in 0.016s 1 tree, 31 leaves, max depth = 14, train loss: 0.49219, val loss: 0.48814, in 0.000s 1 tree, 50 leaves, max depth = 12, train loss: 0.48733, val loss: 0.48366, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.48271, val loss: 0.47941, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.47841, val loss: 0.47488, in 0.016s 1 tree, 52 leaves, max depth = 12, train loss: 0.47409, val loss: 0.47092, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.47009, val loss: 0.46669, in 0.000s 1 tree, 53 leaves, max depth = 12, train loss: 0.46604, val loss: 0.46298, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.46234, val loss: 0.45904, in 0.000s 1 tree, 51 leaves, max depth = 12, train loss: 0.45854, val loss: 0.45557, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.45506, val loss: 0.45188, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.45148, val loss: 0.44863, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.44809, val loss: 0.44553, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.44488, val loss: 0.44213, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.44168, val loss: 0.43922, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.43868, val loss: 0.43603, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.43584, val loss: 0.43300, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.43285, val loss: 0.43028, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.43012, val loss: 0.42730, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.42730, val loss: 0.42476, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.42480, val loss: 0.42209, in 0.000s 1 tree, 53 leaves, max depth = 11, train loss: 0.42213, val loss: 0.41968, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.41959, val loss: 0.41740, in 0.000s 1 tree, 55 leaves, max depth = 11, train loss: 0.41718, val loss: 0.41523, in 0.016s 1 tree, 31 leaves, max depth = 16, train loss: 0.41491, val loss: 0.41275, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.41263, val loss: 0.41074, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.41046, val loss: 0.40873, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.40829, val loss: 0.40682, in 0.000s 1 tree, 34 leaves, max depth = 9, train loss: 0.40613, val loss: 0.40444, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40410, val loss: 0.40256, in 0.000s 1 tree, 54 leaves, max depth = 11, train loss: 0.40206, val loss: 0.40077, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.40007, val loss: 0.39862, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.39813, val loss: 0.39692, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39623, val loss: 0.39516, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.39437, val loss: 0.39311, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39258, val loss: 0.39145, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39087, val loss: 0.38988, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38925, val loss: 0.38839, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38772, val loss: 0.38697, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.38588, val loss: 0.38539, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.38413, val loss: 0.38389, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38267, val loss: 0.38254, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.38101, val loss: 0.38113, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.37961, val loss: 0.37984, in 0.000s 1 tree, 35 leaves, max depth = 11, train loss: 0.37789, val loss: 0.37796, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37663, val loss: 0.37679, in 0.000s 1 tree, 34 leaves, max depth = 9, train loss: 0.37502, val loss: 0.37501, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.37342, val loss: 0.37367, in 0.000s Fit 70 trees in 0.907 s, (2565 total leaves) Time spent computing histograms: 0.315s Time spent finding best splits: 0.046s Time spent applying splits: 0.050s Time spent predicting: 0.016s Trial 97, Fold 3: Log loss = 0.3713377352370851, Average precision = 0.9484391318896641, ROC-AUC = 0.9480855609114812, Elapsed Time = 0.9261010999998689 seconds Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 97, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 33 leaves, max depth = 11, train loss: 0.67834, val loss: 0.67749, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.66456, val loss: 0.66287, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.65162, val loss: 0.64913, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.63954, val loss: 0.63634, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.62809, val loss: 0.62414, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.61732, val loss: 0.61263, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.60718, val loss: 0.60177, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.59765, val loss: 0.59165, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.58862, val loss: 0.58195, in 0.000s 1 tree, 34 leaves, max depth = 15, train loss: 0.58014, val loss: 0.57282, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.57209, val loss: 0.56412, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.56451, val loss: 0.55601, in 0.016s 1 tree, 31 leaves, max depth = 11, train loss: 0.55736, val loss: 0.54832, in 0.000s 1 tree, 31 leaves, max depth = 13, train loss: 0.55054, val loss: 0.54091, in 0.016s 1 tree, 31 leaves, max depth = 13, train loss: 0.54409, val loss: 0.53389, in 0.000s 1 tree, 56 leaves, max depth = 11, train loss: 0.53705, val loss: 0.52698, in 0.000s 1 tree, 31 leaves, max depth = 11, train loss: 0.53115, val loss: 0.52059, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.52461, val loss: 0.51417, in 0.000s 1 tree, 34 leaves, max depth = 15, train loss: 0.51916, val loss: 0.50819, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.51308, val loss: 0.50223, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.50733, val loss: 0.49659, in 0.000s 1 tree, 54 leaves, max depth = 12, train loss: 0.50189, val loss: 0.49124, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.49673, val loss: 0.48618, in 0.000s 1 tree, 32 leaves, max depth = 10, train loss: 0.49202, val loss: 0.48103, in 0.016s [25/70] 1 tree, 56 leaves, max depth = 12, train loss: 0.48720, val loss: 0.47631, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.48264, val loss: 0.47182, in 0.000s 1 tree, 34 leaves, max depth = 14, train loss: 0.47832, val loss: 0.46705, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.47405, val loss: 0.46286, in 0.000s 1 tree, 34 leaves, max depth = 14, train loss: 0.47002, val loss: 0.45841, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.46601, val loss: 0.45449, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.46227, val loss: 0.45036, in 0.001s 1 tree, 57 leaves, max depth = 13, train loss: 0.45851, val loss: 0.44669, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.45492, val loss: 0.44272, in 0.000s 1 tree, 57 leaves, max depth = 13, train loss: 0.45139, val loss: 0.43928, in 0.016s 1 tree, 34 leaves, max depth = 14, train loss: 0.44812, val loss: 0.43562, in 0.000s 1 tree, 57 leaves, max depth = 11, train loss: 0.44480, val loss: 0.43238, in 0.016s 1 tree, 56 leaves, max depth = 11, train loss: 0.44166, val loss: 0.42931, in 0.016s 1 tree, 35 leaves, max depth = 16, train loss: 0.43863, val loss: 0.42590, in 0.000s 1 tree, 33 leaves, max depth = 10, train loss: 0.43569, val loss: 0.42262, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.43274, val loss: 0.41976, in 0.000s 1 tree, 34 leaves, max depth = 13, train loss: 0.43006, val loss: 0.41672, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.42727, val loss: 0.41402, in 0.000s 1 tree, 35 leaves, max depth = 16, train loss: 0.42476, val loss: 0.41115, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.42213, val loss: 0.40858, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.41963, val loss: 0.40614, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.41726, val loss: 0.40383, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.41497, val loss: 0.40124, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.41272, val loss: 0.39905, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.41071, val loss: 0.39692, in 0.000s 1 tree, 32 leaves, max depth = 12, train loss: 0.40855, val loss: 0.39446, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.40643, val loss: 0.39239, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.40454, val loss: 0.39039, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.40254, val loss: 0.38845, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.40054, val loss: 0.38617, in 0.000s 1 tree, 58 leaves, max depth = 11, train loss: 0.39864, val loss: 0.38432, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.39687, val loss: 0.38244, in 0.000s 1 tree, 33 leaves, max depth = 12, train loss: 0.39500, val loss: 0.38031, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.39333, val loss: 0.37854, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.39175, val loss: 0.37686, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.39025, val loss: 0.37526, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38883, val loss: 0.37375, in 0.016s 1 tree, 56 leaves, max depth = 12, train loss: 0.38701, val loss: 0.37202, in 0.000s 1 tree, 56 leaves, max depth = 13, train loss: 0.38529, val loss: 0.37035, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38393, val loss: 0.36890, in 0.000s 1 tree, 33 leaves, max depth = 12, train loss: 0.38223, val loss: 0.36697, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38094, val loss: 0.36560, in 0.000s 1 tree, 56 leaves, max depth = 12, train loss: 0.37929, val loss: 0.36404, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.37769, val loss: 0.36222, in 0.000s 1 tree, 57 leaves, max depth = 12, train loss: 0.37612, val loss: 0.36073, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37489, val loss: 0.35942, in 0.000s Fit 70 trees in 0.908 s, (2623 total leaves) Time spent computing histograms: 0.323s Time spent finding best splits: 0.046s Time spent applying splits: 0.050s Time spent predicting: 0.000s Trial 97, Fold 4: Log loss = 0.37434479667596815, Average precision = 0.9483072080684369, ROC-AUC = 0.9461314143603464, Elapsed Time = 0.9189717000008386 seconds Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 97, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 34 leaves, max depth = 10, train loss: 0.67829, val loss: 0.67728, in 0.016s 1 tree, 31 leaves, max depth = 12, train loss: 0.66450, val loss: 0.66250, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.65143, val loss: 0.64849, in 0.016s 1 tree, 34 leaves, max depth = 10, train loss: 0.63924, val loss: 0.63539, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.62767, val loss: 0.62294, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.61688, val loss: 0.61127, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.60661, val loss: 0.60017, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.59700, val loss: 0.58983, in 0.016s 1 tree, 32 leaves, max depth = 11, train loss: 0.58786, val loss: 0.57990, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.57930, val loss: 0.57065, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.57122, val loss: 0.56189, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.56351, val loss: 0.55345, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.55629, val loss: 0.54552, in 0.000s 1 tree, 32 leaves, max depth = 11, train loss: 0.54939, val loss: 0.53793, in 0.016s 1 tree, 34 leaves, max depth = 12, train loss: 0.54292, val loss: 0.53086, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.53594, val loss: 0.52418, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.52998, val loss: 0.51764, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.52350, val loss: 0.51145, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.51799, val loss: 0.50537, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.51196, val loss: 0.49963, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.50625, val loss: 0.49420, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.50085, val loss: 0.48907, in 0.016s 1 tree, 57 leaves, max depth = 13, train loss: 0.49572, val loss: 0.48422, in 0.000s 1 tree, 34 leaves, max depth = 12, train loss: 0.49095, val loss: 0.47892, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.48616, val loss: 0.47440, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.48171, val loss: 0.46944, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.47723, val loss: 0.46524, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.47308, val loss: 0.46061, in 0.000s 1 tree, 56 leaves, max depth = 14, train loss: 0.46888, val loss: 0.45668, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.46502, val loss: 0.45236, in 0.000s 1 tree, 35 leaves, max depth = 13, train loss: 0.46137, val loss: 0.44826, in 0.016s 1 tree, 55 leaves, max depth = 13, train loss: 0.45747, val loss: 0.44463, in 0.000s 1 tree, 36 leaves, max depth = 10, train loss: 0.45405, val loss: 0.44078, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.45038, val loss: 0.43737, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.44690, val loss: 0.43413, in 0.000s 1 tree, 53 leaves, max depth = 13, train loss: 0.44359, val loss: 0.43107, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.44051, val loss: 0.42759, in 0.000s 1 tree, 54 leaves, max depth = 13, train loss: 0.43740, val loss: 0.42472, in 0.000s 1 tree, 34 leaves, max depth = 9, train loss: 0.43449, val loss: 0.42142, in 0.016s 1 tree, 53 leaves, max depth = 13, train loss: 0.43155, val loss: 0.41873, in 0.000s 1 tree, 33 leaves, max depth = 9, train loss: 0.42883, val loss: 0.41563, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.42606, val loss: 0.41310, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.42351, val loss: 0.41018, in 0.000s 1 tree, 53 leaves, max depth = 14, train loss: 0.42088, val loss: 0.40780, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.41838, val loss: 0.40554, in 0.000s 1 tree, 36 leaves, max depth = 9, train loss: 0.41601, val loss: 0.40280, in 0.016s 1 tree, 54 leaves, max depth = 14, train loss: 0.41365, val loss: 0.40068, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.41144, val loss: 0.39814, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40935, val loss: 0.39611, in 0.016s 1 tree, 54 leaves, max depth = 12, train loss: 0.40712, val loss: 0.39412, in 0.000s 1 tree, 55 leaves, max depth = 12, train loss: 0.40499, val loss: 0.39223, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.40302, val loss: 0.39033, in 0.000s 1 tree, 36 leaves, max depth = 12, train loss: 0.40100, val loss: 0.38799, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.39900, val loss: 0.38622, in 0.000s 1 tree, 36 leaves, max depth = 9, train loss: 0.39707, val loss: 0.38397, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39523, val loss: 0.38220, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.39333, val loss: 0.38053, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39159, val loss: 0.37885, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38994, val loss: 0.37727, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38844, val loss: 0.37578, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.38661, val loss: 0.37367, in 0.016s 1 tree, 55 leaves, max depth = 14, train loss: 0.38480, val loss: 0.37210, in 0.016s 1 tree, 56 leaves, max depth = 13, train loss: 0.38308, val loss: 0.37061, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38166, val loss: 0.36920, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38032, val loss: 0.36787, in 0.000s 1 tree, 33 leaves, max depth = 11, train loss: 0.37866, val loss: 0.36598, in 0.016s 1 tree, 54 leaves, max depth = 13, train loss: 0.37702, val loss: 0.36457, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37574, val loss: 0.36331, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.37414, val loss: 0.36145, in 0.000s 1 tree, 55 leaves, max depth = 13, train loss: 0.37257, val loss: 0.36011, in 0.016s Fit 70 trees in 0.923 s, (2641 total leaves) Time spent computing histograms: 0.330s Time spent finding best splits: 0.047s Time spent applying splits: 0.051s Time spent predicting: 0.000s Trial 97, Fold 5: Log loss = 0.37853788756599877, Average precision = 0.9464296262456326, ROC-AUC = 0.9434605280270516, Elapsed Time = 0.9309966000000713 seconds
Optimization Progress: 98%|#########8| 98/100 [20:27<00:26, 13.34s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 98, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.174 s 0.040 GB of training data: 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.67807, val loss: 0.67763, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.66412, val loss: 0.66325, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.65101, val loss: 0.64974, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.63882, val loss: 0.63720, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.62723, val loss: 0.62523, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.61632, val loss: 0.61395, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.60616, val loss: 0.60346, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.59647, val loss: 0.59341, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.58732, val loss: 0.58393, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.57869, val loss: 0.57497, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.57054, val loss: 0.56650, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.56284, val loss: 0.55849, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55572, val loss: 0.55108, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.54883, val loss: 0.54390, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.54232, val loss: 0.53710, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.53540, val loss: 0.53061, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.52939, val loss: 0.52432, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.52298, val loss: 0.51831, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.51754, val loss: 0.51259, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51156, val loss: 0.50701, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.50591, val loss: 0.50174, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50057, val loss: 0.49676, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.49564, val loss: 0.49157, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.49098, val loss: 0.48664, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48606, val loss: 0.48207, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.48140, val loss: 0.47777, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.47700, val loss: 0.47371, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.47289, val loss: 0.46936, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.46876, val loss: 0.46554, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.46487, val loss: 0.46140, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46120, val loss: 0.45751, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.45738, val loss: 0.45400, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45393, val loss: 0.45032, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.45033, val loss: 0.44703, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44692, val loss: 0.44393, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.44368, val loss: 0.44095, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.44062, val loss: 0.43770, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.43757, val loss: 0.43493, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.43460, val loss: 0.43181, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.43174, val loss: 0.42922, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.42903, val loss: 0.42629, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.42632, val loss: 0.42386, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.42377, val loss: 0.42112, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.42122, val loss: 0.41884, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.41880, val loss: 0.41665, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.41643, val loss: 0.41407, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.41414, val loss: 0.41204, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.41193, val loss: 0.40968, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40979, val loss: 0.40733, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.40762, val loss: 0.40541, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.40549, val loss: 0.40316, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.40346, val loss: 0.40137, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.40153, val loss: 0.39931, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.39966, val loss: 0.39730, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.39774, val loss: 0.39563, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39576, val loss: 0.39346, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.39394, val loss: 0.39188, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39207, val loss: 0.38984, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39030, val loss: 0.38789, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38862, val loss: 0.38605, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38703, val loss: 0.38430, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.38528, val loss: 0.38282, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.38364, val loss: 0.38145, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38213, val loss: 0.37978, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38069, val loss: 0.37820, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.37900, val loss: 0.37642, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.37764, val loss: 0.37492, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.37607, val loss: 0.37328, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.37449, val loss: 0.37198, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.37326, val loss: 0.37054, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.37175, val loss: 0.36894, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.37025, val loss: 0.36774, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36907, val loss: 0.36637, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.36768, val loss: 0.36491, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36656, val loss: 0.36359, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.36524, val loss: 0.36221, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36417, val loss: 0.36095, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.36289, val loss: 0.35967, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.36168, val loss: 0.35841, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.36066, val loss: 0.35721, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.35951, val loss: 0.35599, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.35803, val loss: 0.35484, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.35694, val loss: 0.35368, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.35553, val loss: 0.35259, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.35420, val loss: 0.35155, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35322, val loss: 0.35039, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.35194, val loss: 0.34940, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35100, val loss: 0.34827, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35011, val loss: 0.34721, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.34907, val loss: 0.34618, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.34807, val loss: 0.34503, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.34684, val loss: 0.34409, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.34597, val loss: 0.34306, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.34479, val loss: 0.34214, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.34396, val loss: 0.34114, in 0.016s Fit 95 trees in 1.064 s, (1953 total leaves) Time spent computing histograms: 0.433s Time spent finding best splits: 0.051s Time spent applying splits: 0.043s Time spent predicting: 0.000s Trial 98, Fold 1: Log loss = 0.34746178444472975, Average precision = 0.9454165208062872, ROC-AUC = 0.9436222057326394, Elapsed Time = 1.0571799000008468 seconds Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 98, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 10, train loss: 0.67829, val loss: 0.67754, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.66451, val loss: 0.66308, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.65148, val loss: 0.64937, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.63932, val loss: 0.63659, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.62780, val loss: 0.62443, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.61696, val loss: 0.61298, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.60685, val loss: 0.60237, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.59721, val loss: 0.59216, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.58812, val loss: 0.58252, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.57961, val loss: 0.57360, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.57159, val loss: 0.56516, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.56389, val loss: 0.55699, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.55673, val loss: 0.54945, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.54985, val loss: 0.54212, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.54334, val loss: 0.53517, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.53631, val loss: 0.52836, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.53041, val loss: 0.52212, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.52387, val loss: 0.51581, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.51842, val loss: 0.51004, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.51234, val loss: 0.50417, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50659, val loss: 0.49862, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50115, val loss: 0.49337, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.49599, val loss: 0.48841, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.49126, val loss: 0.48334, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48645, val loss: 0.47871, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.48189, val loss: 0.47433, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.47757, val loss: 0.46972, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.47347, val loss: 0.46535, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.46925, val loss: 0.46131, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.46524, val loss: 0.45748, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.46149, val loss: 0.45343, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.45773, val loss: 0.44985, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.45422, val loss: 0.44610, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.45069, val loss: 0.44274, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44741, val loss: 0.43923, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.44408, val loss: 0.43607, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44092, val loss: 0.43309, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.43790, val loss: 0.42984, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.43503, val loss: 0.42676, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.43207, val loss: 0.42395, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.42938, val loss: 0.42106, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.42658, val loss: 0.41841, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.42407, val loss: 0.41569, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.42143, val loss: 0.41319, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.41891, val loss: 0.41082, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.41652, val loss: 0.40859, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.41423, val loss: 0.40607, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.41197, val loss: 0.40395, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40987, val loss: 0.40178, in 0.016s 1 tree, 25 leaves, max depth = 17, train loss: 0.40773, val loss: 0.39943, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.40560, val loss: 0.39744, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.40359, val loss: 0.39524, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.40157, val loss: 0.39336, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.39966, val loss: 0.39127, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.39774, val loss: 0.38949, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39582, val loss: 0.38750, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.39400, val loss: 0.38582, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39219, val loss: 0.38394, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39046, val loss: 0.38215, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38883, val loss: 0.38046, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38728, val loss: 0.37884, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.38555, val loss: 0.37726, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.38380, val loss: 0.37535, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38233, val loss: 0.37382, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.38069, val loss: 0.37233, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37935, val loss: 0.37094, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.37778, val loss: 0.36952, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37651, val loss: 0.36819, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.37486, val loss: 0.36642, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.37332, val loss: 0.36473, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.37182, val loss: 0.36339, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.37034, val loss: 0.36179, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36913, val loss: 0.36054, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.36771, val loss: 0.35926, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36655, val loss: 0.35806, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.36518, val loss: 0.35656, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36408, val loss: 0.35542, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.36276, val loss: 0.35399, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.36149, val loss: 0.35263, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.36045, val loss: 0.35154, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.35924, val loss: 0.35019, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.35786, val loss: 0.34895, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.35672, val loss: 0.34767, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.35540, val loss: 0.34650, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35439, val loss: 0.34546, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35344, val loss: 0.34447, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.35217, val loss: 0.34335, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.35125, val loss: 0.34239, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.35020, val loss: 0.34124, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.34898, val loss: 0.34016, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.34794, val loss: 0.33898, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.34677, val loss: 0.33796, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34588, val loss: 0.33703, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.34489, val loss: 0.33590, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34404, val loss: 0.33502, in 0.000s Fit 95 trees in 1.111 s, (1972 total leaves) Time spent computing histograms: 0.440s Time spent finding best splits: 0.059s Time spent applying splits: 0.049s Time spent predicting: 0.000s Trial 98, Fold 2: Log loss = 0.34651320081061987, Average precision = 0.9441432964982537, ROC-AUC = 0.946327187649187, Elapsed Time = 1.1205337000010331 seconds Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 98, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67785, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.66474, val loss: 0.66376, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.65192, val loss: 0.65043, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.63987, val loss: 0.63795, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.62852, val loss: 0.62616, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.61790, val loss: 0.61510, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.60784, val loss: 0.60461, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.59835, val loss: 0.59476, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.58923, val loss: 0.58532, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.58067, val loss: 0.57642, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.57274, val loss: 0.56812, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.56505, val loss: 0.56014, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.55792, val loss: 0.55266, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.55105, val loss: 0.54552, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.54455, val loss: 0.53876, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.53745, val loss: 0.53212, in 0.000s 1 tree, 25 leaves, max depth = 15, train loss: 0.53158, val loss: 0.52595, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.52499, val loss: 0.51980, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.51959, val loss: 0.51411, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.51346, val loss: 0.50841, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.50767, val loss: 0.50301, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.50217, val loss: 0.49788, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.49697, val loss: 0.49304, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.49230, val loss: 0.48809, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.48744, val loss: 0.48357, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.48284, val loss: 0.47930, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.47854, val loss: 0.47476, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.47446, val loss: 0.47046, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.47019, val loss: 0.46653, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46614, val loss: 0.46280, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.46245, val loss: 0.45884, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.45864, val loss: 0.45533, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.45516, val loss: 0.45164, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45159, val loss: 0.44835, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.44819, val loss: 0.44524, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44499, val loss: 0.44184, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.44178, val loss: 0.43889, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.43879, val loss: 0.43571, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.43595, val loss: 0.43268, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.43295, val loss: 0.42996, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.43023, val loss: 0.42699, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.42741, val loss: 0.42441, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.42491, val loss: 0.42174, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.42224, val loss: 0.41932, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.41970, val loss: 0.41701, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.41728, val loss: 0.41481, in 0.016s 1 tree, 25 leaves, max depth = 16, train loss: 0.41503, val loss: 0.41235, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.41274, val loss: 0.41028, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.41057, val loss: 0.40827, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.40840, val loss: 0.40632, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.40627, val loss: 0.40404, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.40421, val loss: 0.40219, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.40222, val loss: 0.39999, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.40032, val loss: 0.39794, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.39837, val loss: 0.39620, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39638, val loss: 0.39436, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.39453, val loss: 0.39272, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39264, val loss: 0.39097, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39086, val loss: 0.38931, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38916, val loss: 0.38774, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38755, val loss: 0.38626, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.38579, val loss: 0.38472, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.38412, val loss: 0.38327, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38259, val loss: 0.38185, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.38114, val loss: 0.38051, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.37976, val loss: 0.37924, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.37804, val loss: 0.37736, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.37645, val loss: 0.37561, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.37485, val loss: 0.37422, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37360, val loss: 0.37307, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.37206, val loss: 0.37139, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.37052, val loss: 0.37007, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.36933, val loss: 0.36896, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.36792, val loss: 0.36738, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.36679, val loss: 0.36633, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.36545, val loss: 0.36485, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.36437, val loss: 0.36384, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.36307, val loss: 0.36242, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.36184, val loss: 0.36105, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36082, val loss: 0.36010, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.35963, val loss: 0.35882, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.35813, val loss: 0.35754, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.35701, val loss: 0.35632, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.35558, val loss: 0.35511, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.35422, val loss: 0.35396, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35322, val loss: 0.35304, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.35192, val loss: 0.35195, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.35097, val loss: 0.35106, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.35006, val loss: 0.35021, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.34901, val loss: 0.34905, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.34800, val loss: 0.34783, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.34674, val loss: 0.34679, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.34586, val loss: 0.34597, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.34465, val loss: 0.34497, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.34381, val loss: 0.34419, in 0.016s Fit 95 trees in 1.064 s, (1953 total leaves) Time spent computing histograms: 0.420s Time spent finding best splits: 0.053s Time spent applying splits: 0.044s Time spent predicting: 0.000s Trial 98, Fold 3: Log loss = 0.340985015897022, Average precision = 0.9499141215918279, ROC-AUC = 0.9504775939989021, Elapsed Time = 1.0630522000010387 seconds Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 98, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.189 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 9, train loss: 0.67834, val loss: 0.67748, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.66456, val loss: 0.66287, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.65162, val loss: 0.64913, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.63954, val loss: 0.63634, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.62810, val loss: 0.62414, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.61733, val loss: 0.61263, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.60723, val loss: 0.60192, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.59766, val loss: 0.59164, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.58863, val loss: 0.58194, in 0.016s 1 tree, 25 leaves, max depth = 15, train loss: 0.58015, val loss: 0.57280, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.57214, val loss: 0.56422, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.56452, val loss: 0.55598, in 0.016s 1 tree, 25 leaves, max depth = 15, train loss: 0.55737, val loss: 0.54822, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.55056, val loss: 0.54081, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.54415, val loss: 0.53389, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53706, val loss: 0.52691, in 0.000s 1 tree, 25 leaves, max depth = 13, train loss: 0.53112, val loss: 0.52042, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.52453, val loss: 0.51395, in 0.016s 1 tree, 25 leaves, max depth = 15, train loss: 0.51909, val loss: 0.50797, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.51296, val loss: 0.50194, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.50716, val loss: 0.49623, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.50167, val loss: 0.49081, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.49646, val loss: 0.48569, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.49176, val loss: 0.48056, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.48689, val loss: 0.47577, in 0.000s 1 tree, 25 leaves, max depth = 7, train loss: 0.48228, val loss: 0.47124, in 0.016s 1 tree, 25 leaves, max depth = 14, train loss: 0.47798, val loss: 0.46649, in 0.000s 1 tree, 25 leaves, max depth = 14, train loss: 0.47391, val loss: 0.46198, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46963, val loss: 0.45778, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.46558, val loss: 0.45380, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.46185, val loss: 0.44970, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.45805, val loss: 0.44595, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.45447, val loss: 0.44199, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45090, val loss: 0.43848, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44750, val loss: 0.43514, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.44430, val loss: 0.43155, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.44110, val loss: 0.42845, in 0.016s 1 tree, 25 leaves, max depth = 15, train loss: 0.43811, val loss: 0.42506, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.43518, val loss: 0.42180, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.43220, val loss: 0.41890, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.42954, val loss: 0.41589, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.42672, val loss: 0.41315, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.42422, val loss: 0.41032, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.42155, val loss: 0.40772, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.41902, val loss: 0.40526, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.41661, val loss: 0.40292, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.41434, val loss: 0.40036, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.41206, val loss: 0.39812, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40996, val loss: 0.39590, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.40779, val loss: 0.39377, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.40567, val loss: 0.39134, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.40361, val loss: 0.38933, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.40163, val loss: 0.38705, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.39973, val loss: 0.38485, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.39779, val loss: 0.38295, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39586, val loss: 0.38091, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.39401, val loss: 0.37911, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39219, val loss: 0.37718, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.39047, val loss: 0.37535, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38890, val loss: 0.37368, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38741, val loss: 0.37209, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.38564, val loss: 0.37044, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.38396, val loss: 0.36885, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38254, val loss: 0.36734, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.38119, val loss: 0.36591, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.37948, val loss: 0.36399, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37820, val loss: 0.36263, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.37662, val loss: 0.36083, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.37501, val loss: 0.35933, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37379, val loss: 0.35804, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.37227, val loss: 0.35630, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.37073, val loss: 0.35488, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36957, val loss: 0.35364, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.36817, val loss: 0.35204, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36707, val loss: 0.35087, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.36574, val loss: 0.34934, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.36445, val loss: 0.34786, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.36321, val loss: 0.34646, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.36204, val loss: 0.34514, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36100, val loss: 0.34402, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.35990, val loss: 0.34275, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.35839, val loss: 0.34137, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.35735, val loss: 0.34016, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.35591, val loss: 0.33886, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.35454, val loss: 0.33760, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35353, val loss: 0.33652, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.35222, val loss: 0.33533, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35125, val loss: 0.33429, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35033, val loss: 0.33330, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.34907, val loss: 0.33215, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.34804, val loss: 0.33108, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.34683, val loss: 0.32998, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34594, val loss: 0.32902, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.34496, val loss: 0.32800, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34411, val loss: 0.32709, in 0.000s Fit 95 trees in 1.236 s, (1965 total leaves) Time spent computing histograms: 0.507s Time spent finding best splits: 0.066s Time spent applying splits: 0.055s Time spent predicting: 0.031s Trial 98, Fold 4: Log loss = 0.34290472175752834, Average precision = 0.9498242747729755, ROC-AUC = 0.9485274481331263, Elapsed Time = 1.2537620999992214 seconds Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 98, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.174 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 25 leaves, max depth = 9, train loss: 0.67829, val loss: 0.67729, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.66451, val loss: 0.66249, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.65143, val loss: 0.64848, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.63925, val loss: 0.63538, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.62768, val loss: 0.62294, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.61688, val loss: 0.61135, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.60671, val loss: 0.60033, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59701, val loss: 0.58983, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.58787, val loss: 0.57991, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.57932, val loss: 0.57066, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.57124, val loss: 0.56191, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.56353, val loss: 0.55348, in 0.016s 1 tree, 25 leaves, max depth = 12, train loss: 0.55631, val loss: 0.54555, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.54941, val loss: 0.53796, in 0.000s 1 tree, 25 leaves, max depth = 12, train loss: 0.54295, val loss: 0.53088, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.53595, val loss: 0.52417, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.52999, val loss: 0.51764, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.52349, val loss: 0.51143, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.51799, val loss: 0.50538, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.51193, val loss: 0.49961, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.50620, val loss: 0.49417, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.50077, val loss: 0.48900, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.49563, val loss: 0.48411, in 0.016s 1 tree, 25 leaves, max depth = 11, train loss: 0.49086, val loss: 0.47883, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.48605, val loss: 0.47427, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.48161, val loss: 0.46933, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.47710, val loss: 0.46509, in 0.024s 1 tree, 25 leaves, max depth = 10, train loss: 0.47297, val loss: 0.46047, in 0.008s 1 tree, 25 leaves, max depth = 10, train loss: 0.46875, val loss: 0.45648, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.46490, val loss: 0.45216, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.46125, val loss: 0.44807, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.45732, val loss: 0.44438, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.45386, val loss: 0.44045, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.45017, val loss: 0.43700, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44666, val loss: 0.43372, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.44334, val loss: 0.43062, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.44027, val loss: 0.42714, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.43713, val loss: 0.42424, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.43424, val loss: 0.42095, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.43127, val loss: 0.41823, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.42856, val loss: 0.41514, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.42576, val loss: 0.41257, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.42322, val loss: 0.40966, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.42057, val loss: 0.40726, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.41805, val loss: 0.40495, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.41569, val loss: 0.40223, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.41331, val loss: 0.40008, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.41104, val loss: 0.39804, in 0.000s 1 tree, 3 leaves, max depth = 2, train loss: 0.40894, val loss: 0.39601, in 0.016s 1 tree, 25 leaves, max depth = 13, train loss: 0.40678, val loss: 0.39352, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.40463, val loss: 0.39160, in 0.000s 1 tree, 25 leaves, max depth = 11, train loss: 0.40260, val loss: 0.38926, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.40057, val loss: 0.38744, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.39863, val loss: 0.38518, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.39669, val loss: 0.38346, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39477, val loss: 0.38160, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.39293, val loss: 0.37998, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.39110, val loss: 0.37822, in 0.016s 1 tree, 3 leaves, max depth = 2, train loss: 0.38937, val loss: 0.37655, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38781, val loss: 0.37500, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38632, val loss: 0.37352, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.38457, val loss: 0.37199, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.38282, val loss: 0.36995, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.38141, val loss: 0.36855, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.37974, val loss: 0.36711, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37840, val loss: 0.36578, in 0.000s 1 tree, 25 leaves, max depth = 8, train loss: 0.37673, val loss: 0.36384, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.37546, val loss: 0.36259, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.37386, val loss: 0.36121, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.37228, val loss: 0.35938, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.37075, val loss: 0.35808, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.36926, val loss: 0.35631, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36805, val loss: 0.35512, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.36659, val loss: 0.35388, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.36544, val loss: 0.35274, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.36406, val loss: 0.35111, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.36297, val loss: 0.35003, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.36163, val loss: 0.34845, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.36036, val loss: 0.34696, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35932, val loss: 0.34593, in 0.016s 1 tree, 25 leaves, max depth = 7, train loss: 0.35811, val loss: 0.34451, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.35669, val loss: 0.34332, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.35554, val loss: 0.34197, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.35419, val loss: 0.34083, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.35319, val loss: 0.33984, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.35224, val loss: 0.33890, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.35093, val loss: 0.33781, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.34985, val loss: 0.33665, in 0.000s 1 tree, 2 leaves, max depth = 1, train loss: 0.34894, val loss: 0.33575, in 0.016s 1 tree, 25 leaves, max depth = 10, train loss: 0.34768, val loss: 0.33471, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.34660, val loss: 0.33352, in 0.000s 1 tree, 25 leaves, max depth = 10, train loss: 0.34540, val loss: 0.33252, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34452, val loss: 0.33165, in 0.000s 1 tree, 25 leaves, max depth = 9, train loss: 0.34348, val loss: 0.33052, in 0.016s 1 tree, 2 leaves, max depth = 1, train loss: 0.34264, val loss: 0.32969, in 0.016s Fit 95 trees in 1.253 s, (1965 total leaves) Time spent computing histograms: 0.505s Time spent finding best splits: 0.065s Time spent applying splits: 0.054s Time spent predicting: 0.016s Trial 98, Fold 5: Log loss = 0.34892619304748446, Average precision = 0.9478084946031027, ROC-AUC = 0.9457619233413225, Elapsed Time = 1.2518749000009848 seconds
Optimization Progress: 99%|#########9| 99/100 [20:40<00:13, 13.36s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 99, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0.142 s 0.040 GB of training data: 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 29 leaves, max depth = 9, train loss: 0.66366, val loss: 0.66376, in 0.016s 1 tree, 25 leaves, max depth = 8, train loss: 0.63715, val loss: 0.63719, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.61314, val loss: 0.61315, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.59124, val loss: 0.59133, in 0.016s 1 tree, 33 leaves, max depth = 8, train loss: 0.57128, val loss: 0.57141, in 0.016s 1 tree, 34 leaves, max depth = 9, train loss: 0.55304, val loss: 0.55316, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.53633, val loss: 0.53644, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.52098, val loss: 0.52109, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.50687, val loss: 0.50702, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.49385, val loss: 0.49405, in 0.016s 1 tree, 43 leaves, max depth = 9, train loss: 0.48181, val loss: 0.48197, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.47067, val loss: 0.47087, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.46035, val loss: 0.46062, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.45077, val loss: 0.45113, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.44161, val loss: 0.44177, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.42857, val loss: 0.42919, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.42078, val loss: 0.42149, in 0.031s 1 tree, 30 leaves, max depth = 8, train loss: 0.40928, val loss: 0.41041, in 0.016s 1 tree, 61 leaves, max depth = 11, train loss: 0.40240, val loss: 0.40375, in 0.000s 1 tree, 32 leaves, max depth = 8, train loss: 0.39223, val loss: 0.39399, in 0.031s 1 tree, 57 leaves, max depth = 11, train loss: 0.38616, val loss: 0.38807, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.37719, val loss: 0.37951, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.36892, val loss: 0.37163, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.36379, val loss: 0.36668, in 0.016s 1 tree, 57 leaves, max depth = 11, train loss: 0.35900, val loss: 0.36210, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.35222, val loss: 0.35538, in 0.016s 1 tree, 12 leaves, max depth = 5, train loss: 0.34591, val loss: 0.34917, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.34012, val loss: 0.34346, in 0.000s 1 tree, 47 leaves, max depth = 10, train loss: 0.33510, val loss: 0.33830, in 0.031s 1 tree, 17 leaves, max depth = 7, train loss: 0.32998, val loss: 0.33318, in 0.000s 1 tree, 47 leaves, max depth = 10, train loss: 0.32543, val loss: 0.32854, in 0.031s 1 tree, 34 leaves, max depth = 11, train loss: 0.32066, val loss: 0.32428, in 0.000s 1 tree, 34 leaves, max depth = 11, train loss: 0.31625, val loss: 0.32036, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.31212, val loss: 0.31622, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.30821, val loss: 0.31237, in 0.031s 1 tree, 36 leaves, max depth = 11, train loss: 0.30447, val loss: 0.30907, in 0.000s 1 tree, 18 leaves, max depth = 7, train loss: 0.30094, val loss: 0.30553, in 0.016s 1 tree, 21 leaves, max depth = 7, train loss: 0.29771, val loss: 0.30228, in 0.016s 1 tree, 40 leaves, max depth = 11, train loss: 0.29431, val loss: 0.29896, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.29118, val loss: 0.29624, in 0.031s 1 tree, 41 leaves, max depth = 10, train loss: 0.28829, val loss: 0.29372, in 0.000s 1 tree, 20 leaves, max depth = 7, train loss: 0.28562, val loss: 0.29102, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.28265, val loss: 0.28801, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.28018, val loss: 0.28592, in 0.016s 1 tree, 19 leaves, max depth = 7, train loss: 0.27789, val loss: 0.28352, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.27522, val loss: 0.28092, in 0.016s 1 tree, 41 leaves, max depth = 11, train loss: 0.27311, val loss: 0.27918, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.27114, val loss: 0.27712, in 0.000s Fit 48 trees in 1.049 s, (1707 total leaves) Time spent computing histograms: 0.295s Time spent finding best splits: 0.050s Time spent applying splits: 0.033s Time spent predicting: 0.016s Trial 99, Fold 1: Log loss = 0.27620585109547047, Average precision = 0.9619353206628051, ROC-AUC = 0.9561175456331283, Elapsed Time = 1.0621444000007614 seconds Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 99, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 Binning 0.040 GB of training data: 0.158 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 23 leaves, max depth = 9, train loss: 0.66346, val loss: 0.66287, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.63670, val loss: 0.63553, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.61216, val loss: 0.61065, in 0.016s 1 tree, 25 leaves, max depth = 9, train loss: 0.59016, val loss: 0.58822, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.57012, val loss: 0.56771, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.55204, val loss: 0.54935, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.53522, val loss: 0.53221, in 0.016s 1 tree, 30 leaves, max depth = 8, train loss: 0.51978, val loss: 0.51646, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.50525, val loss: 0.50186, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.49186, val loss: 0.48843, in 0.016s 1 tree, 46 leaves, max depth = 9, train loss: 0.47949, val loss: 0.47606, in 0.016s 1 tree, 40 leaves, max depth = 8, train loss: 0.46807, val loss: 0.46459, in 0.016s 1 tree, 47 leaves, max depth = 9, train loss: 0.45747, val loss: 0.45402, in 0.016s 1 tree, 41 leaves, max depth = 9, train loss: 0.44768, val loss: 0.44418, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.43855, val loss: 0.43513, in 0.016s 1 tree, 20 leaves, max depth = 6, train loss: 0.42572, val loss: 0.42248, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.41774, val loss: 0.41447, in 0.016s 1 tree, 28 leaves, max depth = 9, train loss: 0.40641, val loss: 0.40340, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.39950, val loss: 0.39646, in 0.016s 1 tree, 32 leaves, max depth = 9, train loss: 0.38957, val loss: 0.38688, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.38348, val loss: 0.38072, in 0.016s 1 tree, 27 leaves, max depth = 7, train loss: 0.37450, val loss: 0.37191, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.36912, val loss: 0.36648, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.36118, val loss: 0.35875, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.35623, val loss: 0.35389, in 0.016s 1 tree, 13 leaves, max depth = 6, train loss: 0.34953, val loss: 0.34731, in 0.016s 1 tree, 10 leaves, max depth = 4, train loss: 0.34340, val loss: 0.34125, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.33828, val loss: 0.33634, in 0.016s 1 tree, 28 leaves, max depth = 11, train loss: 0.33283, val loss: 0.33130, in 0.016s 1 tree, 11 leaves, max depth = 6, train loss: 0.32767, val loss: 0.32622, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.32281, val loss: 0.32180, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.31831, val loss: 0.31734, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.31406, val loss: 0.31326, in 0.016s 1 tree, 36 leaves, max depth = 12, train loss: 0.30987, val loss: 0.30947, in 0.016s 1 tree, 15 leaves, max depth = 7, train loss: 0.30604, val loss: 0.30567, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.30227, val loss: 0.30207, in 0.016s 1 tree, 41 leaves, max depth = 12, train loss: 0.29870, val loss: 0.29886, in 0.016s 1 tree, 12 leaves, max depth = 4, train loss: 0.29544, val loss: 0.29563, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.29199, val loss: 0.29243, in 0.000s 1 tree, 41 leaves, max depth = 12, train loss: 0.28892, val loss: 0.28968, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.28613, val loss: 0.28692, in 0.016s 1 tree, 52 leaves, max depth = 13, train loss: 0.28310, val loss: 0.28405, in 0.031s 1 tree, 41 leaves, max depth = 12, train loss: 0.28046, val loss: 0.28172, in 0.016s 1 tree, 17 leaves, max depth = 5, train loss: 0.27806, val loss: 0.27932, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.27526, val loss: 0.27675, in 0.016s 1 tree, 38 leaves, max depth = 12, train loss: 0.27298, val loss: 0.27474, in 0.016s 1 tree, 18 leaves, max depth = 5, train loss: 0.27092, val loss: 0.27270, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.26889, val loss: 0.27094, in 0.016s Fit 48 trees in 1.111 s, (1585 total leaves) Time spent computing histograms: 0.298s Time spent finding best splits: 0.053s Time spent applying splits: 0.034s Time spent predicting: 0.016s Trial 99, Fold 2: Log loss = 0.27319669112073436, Average precision = 0.9622910927957704, ROC-AUC = 0.9599752317264558, Elapsed Time = 1.1303246000006766 seconds Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 99, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 Binning 0.040 GB of training data: 0.173 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 31 leaves, max depth = 9, train loss: 0.66374, val loss: 0.66367, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.63724, val loss: 0.63709, in 0.031s 1 tree, 36 leaves, max depth = 8, train loss: 0.61291, val loss: 0.61281, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.59109, val loss: 0.59089, in 0.000s 1 tree, 37 leaves, max depth = 8, train loss: 0.57090, val loss: 0.57075, in 0.031s 1 tree, 31 leaves, max depth = 9, train loss: 0.55327, val loss: 0.55312, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.53624, val loss: 0.53614, in 0.016s 1 tree, 44 leaves, max depth = 8, train loss: 0.52056, val loss: 0.52051, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.50613, val loss: 0.50610, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.49284, val loss: 0.49281, in 0.016s 1 tree, 42 leaves, max depth = 10, train loss: 0.48055, val loss: 0.48053, in 0.016s 1 tree, 43 leaves, max depth = 10, train loss: 0.46919, val loss: 0.46922, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.45868, val loss: 0.45877, in 0.016s 1 tree, 44 leaves, max depth = 10, train loss: 0.44895, val loss: 0.44908, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.43545, val loss: 0.43652, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.42690, val loss: 0.42818, in 0.031s 1 tree, 53 leaves, max depth = 12, train loss: 0.41893, val loss: 0.42043, in 0.016s 1 tree, 28 leaves, max depth = 14, train loss: 0.40744, val loss: 0.40993, in 0.031s 1 tree, 53 leaves, max depth = 11, train loss: 0.40051, val loss: 0.40324, in 0.016s 1 tree, 36 leaves, max depth = 14, train loss: 0.39033, val loss: 0.39405, in 0.016s 1 tree, 32 leaves, max depth = 12, train loss: 0.38093, val loss: 0.38553, in 0.016s 1 tree, 56 leaves, max depth = 14, train loss: 0.37503, val loss: 0.37991, in 0.031s 1 tree, 58 leaves, max depth = 14, train loss: 0.36952, val loss: 0.37461, in 0.016s 1 tree, 40 leaves, max depth = 14, train loss: 0.36146, val loss: 0.36744, in 0.016s 1 tree, 63 leaves, max depth = 14, train loss: 0.35657, val loss: 0.36272, in 0.031s 1 tree, 9 leaves, max depth = 5, train loss: 0.34976, val loss: 0.35648, in 0.000s 1 tree, 13 leaves, max depth = 5, train loss: 0.34350, val loss: 0.35079, in 0.016s 1 tree, 13 leaves, max depth = 5, train loss: 0.33775, val loss: 0.34558, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.33223, val loss: 0.34110, in 0.016s 1 tree, 53 leaves, max depth = 12, train loss: 0.32752, val loss: 0.33622, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.32268, val loss: 0.33186, in 0.031s 1 tree, 62 leaves, max depth = 13, train loss: 0.31840, val loss: 0.32748, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.31385, val loss: 0.32382, in 0.016s 1 tree, 15 leaves, max depth = 5, train loss: 0.30975, val loss: 0.32009, in 0.016s 1 tree, 62 leaves, max depth = 13, train loss: 0.30594, val loss: 0.31612, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.30205, val loss: 0.31310, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.29846, val loss: 0.31029, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.29512, val loss: 0.30721, in 0.016s 1 tree, 36 leaves, max depth = 11, train loss: 0.29195, val loss: 0.30471, in 0.016s 1 tree, 16 leaves, max depth = 5, train loss: 0.28900, val loss: 0.30200, in 0.016s 1 tree, 57 leaves, max depth = 12, train loss: 0.28586, val loss: 0.29871, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.28317, val loss: 0.29664, in 0.016s 1 tree, 18 leaves, max depth = 5, train loss: 0.28066, val loss: 0.29431, in 0.016s 1 tree, 55 leaves, max depth = 12, train loss: 0.27786, val loss: 0.29137, in 0.031s 1 tree, 35 leaves, max depth = 11, train loss: 0.27555, val loss: 0.28964, in 0.016s 1 tree, 19 leaves, max depth = 6, train loss: 0.27339, val loss: 0.28765, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.27077, val loss: 0.28474, in 0.016s 1 tree, 40 leaves, max depth = 12, train loss: 0.26877, val loss: 0.28328, in 0.016s Fit 48 trees in 1.235 s, (1805 total leaves) Time spent computing histograms: 0.344s Time spent finding best splits: 0.060s Time spent applying splits: 0.040s Time spent predicting: 0.000s Trial 99, Fold 3: Log loss = 0.26933763869940724, Average precision = 0.9635613369023321, ROC-AUC = 0.9604339774489314, Elapsed Time = 1.2420631000004505 seconds Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 99, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 Binning 0.040 GB of training data: 0.158 s 0.000 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 35 leaves, max depth = 10, train loss: 0.66352, val loss: 0.66248, in 0.016s 1 tree, 33 leaves, max depth = 10, train loss: 0.63681, val loss: 0.63479, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.61259, val loss: 0.60969, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.59065, val loss: 0.58691, in 0.016s 1 tree, 35 leaves, max depth = 11, train loss: 0.57057, val loss: 0.56605, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.55223, val loss: 0.54695, in 0.016s 1 tree, 33 leaves, max depth = 11, train loss: 0.53543, val loss: 0.52944, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.52003, val loss: 0.51334, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.50562, val loss: 0.49832, in 0.016s 1 tree, 39 leaves, max depth = 10, train loss: 0.49231, val loss: 0.48444, in 0.016s 1 tree, 35 leaves, max depth = 10, train loss: 0.48001, val loss: 0.47152, in 0.000s 1 tree, 37 leaves, max depth = 10, train loss: 0.46864, val loss: 0.45958, in 0.031s 1 tree, 39 leaves, max depth = 10, train loss: 0.45812, val loss: 0.44857, in 0.016s 1 tree, 38 leaves, max depth = 10, train loss: 0.44835, val loss: 0.43830, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.43928, val loss: 0.42877, in 0.016s 1 tree, 26 leaves, max depth = 9, train loss: 0.42646, val loss: 0.41578, in 0.016s 1 tree, 57 leaves, max depth = 10, train loss: 0.41848, val loss: 0.40742, in 0.016s 1 tree, 33 leaves, max depth = 9, train loss: 0.40717, val loss: 0.39598, in 0.016s 1 tree, 54 leaves, max depth = 11, train loss: 0.40012, val loss: 0.38864, in 0.016s 1 tree, 34 leaves, max depth = 11, train loss: 0.39012, val loss: 0.37853, in 0.016s 1 tree, 58 leaves, max depth = 11, train loss: 0.38390, val loss: 0.37199, in 0.031s 1 tree, 57 leaves, max depth = 11, train loss: 0.37806, val loss: 0.36593, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.36948, val loss: 0.35731, in 0.016s 1 tree, 43 leaves, max depth = 12, train loss: 0.36160, val loss: 0.34940, in 0.016s 1 tree, 38 leaves, max depth = 9, train loss: 0.35425, val loss: 0.34196, in 0.016s 1 tree, 55 leaves, max depth = 11, train loss: 0.34943, val loss: 0.33692, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.34325, val loss: 0.33051, in 0.016s 1 tree, 13 leaves, max depth = 7, train loss: 0.33759, val loss: 0.32461, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.33274, val loss: 0.31967, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.32822, val loss: 0.31506, in 0.031s 1 tree, 33 leaves, max depth = 9, train loss: 0.32313, val loss: 0.31023, in 0.016s 1 tree, 16 leaves, max depth = 7, train loss: 0.31847, val loss: 0.30537, in 0.016s 1 tree, 36 leaves, max depth = 9, train loss: 0.31395, val loss: 0.30116, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.30988, val loss: 0.29691, in 0.000s 1 tree, 62 leaves, max depth = 13, train loss: 0.30588, val loss: 0.29271, in 0.031s 1 tree, 36 leaves, max depth = 9, train loss: 0.30203, val loss: 0.28913, in 0.016s 1 tree, 17 leaves, max depth = 7, train loss: 0.29856, val loss: 0.28552, in 0.016s 1 tree, 63 leaves, max depth = 13, train loss: 0.29500, val loss: 0.28177, in 0.016s 1 tree, 41 leaves, max depth = 10, train loss: 0.29170, val loss: 0.27874, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.28875, val loss: 0.27563, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.28572, val loss: 0.27255, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.28288, val loss: 0.26995, in 0.016s 1 tree, 39 leaves, max depth = 9, train loss: 0.28025, val loss: 0.26751, in 0.016s 1 tree, 18 leaves, max depth = 7, train loss: 0.27783, val loss: 0.26495, in 0.016s 1 tree, 40 leaves, max depth = 9, train loss: 0.27549, val loss: 0.26281, in 0.016s 1 tree, 24 leaves, max depth = 8, train loss: 0.27335, val loss: 0.26051, in 0.016s 1 tree, 61 leaves, max depth = 12, train loss: 0.27065, val loss: 0.25761, in 0.016s 1 tree, 37 leaves, max depth = 9, train loss: 0.26862, val loss: 0.25578, in 0.016s Fit 48 trees in 1.143 s, (1787 total leaves) Time spent computing histograms: 0.334s Time spent finding best splits: 0.057s Time spent applying splits: 0.038s Time spent predicting: 0.000s Trial 99, Fold 4: Log loss = 0.26912938002939735, Average precision = 0.9637844512354639, ROC-AUC = 0.9605351867350514, Elapsed Time = 1.1649399999987509 seconds Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 99, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 Binning 0.040 GB of training data: 0.157 s 0.016 s 0.004 GB of validation data: Fitting gradient boosted rounds: 1 tree, 28 leaves, max depth = 9, train loss: 0.66315, val loss: 0.66194, in 0.016s 1 tree, 30 leaves, max depth = 9, train loss: 0.63607, val loss: 0.63374, in 0.016s 1 tree, 31 leaves, max depth = 9, train loss: 0.61158, val loss: 0.60823, in 0.016s 1 tree, 32 leaves, max depth = 10, train loss: 0.58934, val loss: 0.58506, in 0.016s 1 tree, 36 leaves, max depth = 10, train loss: 0.56905, val loss: 0.56390, in 0.016s 1 tree, 37 leaves, max depth = 10, train loss: 0.55051, val loss: 0.54460, in 0.016s 1 tree, 40 leaves, max depth = 10, train loss: 0.53347, val loss: 0.52680, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.51779, val loss: 0.51051, in 0.016s 1 tree, 49 leaves, max depth = 11, train loss: 0.50336, val loss: 0.49553, in 0.016s 1 tree, 45 leaves, max depth = 10, train loss: 0.49010, val loss: 0.48174, in 0.016s 1 tree, 50 leaves, max depth = 11, train loss: 0.47781, val loss: 0.46896, in 0.016s 1 tree, 29 leaves, max depth = 9, train loss: 0.46695, val loss: 0.45763, in 0.016s 1 tree, 48 leaves, max depth = 10, train loss: 0.45638, val loss: 0.44664, in 0.016s 1 tree, 46 leaves, max depth = 11, train loss: 0.44656, val loss: 0.43644, in 0.016s 1 tree, 53 leaves, max depth = 10, train loss: 0.43745, val loss: 0.42701, in 0.016s 1 tree, 27 leaves, max depth = 9, train loss: 0.42455, val loss: 0.41420, in 0.016s 1 tree, 52 leaves, max depth = 11, train loss: 0.41654, val loss: 0.40594, in 0.016s 1 tree, 28 leaves, max depth = 8, train loss: 0.40517, val loss: 0.39463, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.39826, val loss: 0.38739, in 0.016s 1 tree, 33 leaves, max depth = 12, train loss: 0.38820, val loss: 0.37740, in 0.016s 1 tree, 58 leaves, max depth = 13, train loss: 0.38194, val loss: 0.37098, in 0.016s 1 tree, 46 leaves, max depth = 10, train loss: 0.37605, val loss: 0.36496, in 0.016s 1 tree, 36 leaves, max depth = 13, train loss: 0.36738, val loss: 0.35645, in 0.016s 1 tree, 32 leaves, max depth = 8, train loss: 0.35942, val loss: 0.34862, in 0.016s 1 tree, 60 leaves, max depth = 12, train loss: 0.35440, val loss: 0.34353, in 0.031s 1 tree, 10 leaves, max depth = 4, train loss: 0.34762, val loss: 0.33662, in 0.000s 1 tree, 10 leaves, max depth = 4, train loss: 0.34141, val loss: 0.33028, in 0.016s 1 tree, 47 leaves, max depth = 11, train loss: 0.33624, val loss: 0.32518, in 0.016s 1 tree, 50 leaves, max depth = 13, train loss: 0.33030, val loss: 0.31946, in 0.016s 1 tree, 39 leaves, max depth = 12, train loss: 0.32498, val loss: 0.31465, in 0.031s 1 tree, 9 leaves, max depth = 4, train loss: 0.32018, val loss: 0.30972, in 0.000s 1 tree, 44 leaves, max depth = 11, train loss: 0.31567, val loss: 0.30519, in 0.031s 1 tree, 37 leaves, max depth = 10, train loss: 0.31114, val loss: 0.30113, in 0.016s 1 tree, 9 leaves, max depth = 4, train loss: 0.30707, val loss: 0.29694, in 0.016s 1 tree, 46 leaves, max depth = 12, train loss: 0.30307, val loss: 0.29292, in 0.016s 1 tree, 39 leaves, max depth = 11, train loss: 0.29921, val loss: 0.28948, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.29576, val loss: 0.28583, in 0.016s 1 tree, 49 leaves, max depth = 12, train loss: 0.29230, val loss: 0.28248, in 0.016s 1 tree, 42 leaves, max depth = 12, train loss: 0.28900, val loss: 0.27947, in 0.016s 1 tree, 11 leaves, max depth = 4, train loss: 0.28605, val loss: 0.27643, in 0.016s 1 tree, 49 leaves, max depth = 10, train loss: 0.28284, val loss: 0.27322, in 0.016s 1 tree, 38 leaves, max depth = 11, train loss: 0.27998, val loss: 0.27067, in 0.016s 1 tree, 42 leaves, max depth = 11, train loss: 0.27734, val loss: 0.26830, in 0.016s 1 tree, 14 leaves, max depth = 5, train loss: 0.27492, val loss: 0.26581, in 0.016s 1 tree, 47 leaves, max depth = 13, train loss: 0.27215, val loss: 0.26320, in 0.016s 1 tree, 37 leaves, max depth = 11, train loss: 0.26986, val loss: 0.26118, in 0.000s 1 tree, 19 leaves, max depth = 8, train loss: 0.26777, val loss: 0.25908, in 0.016s 1 tree, 23 leaves, max depth = 9, train loss: 0.26578, val loss: 0.25709, in 0.016s Fit 48 trees in 1.111 s, (1757 total leaves) Time spent computing histograms: 0.302s Time spent finding best splits: 0.053s Time spent applying splits: 0.035s Time spent predicting: 0.031s Trial 99, Fold 5: Log loss = 0.2757542983872139, Average precision = 0.9617069684914026, ROC-AUC = 0.956636010627427, Elapsed Time = 1.1231758999983867 seconds
Optimization Progress: 100%|##########| 100/100 [20:54<00:00, 13.38s/it]
Optuna Optimization Elapsed Time: 1254.3631370999992 seconds
Optimization Progress: 100%|##########| 100/100 [20:54<00:00, 12.54s/it]
Training with Best Trial 69:
Full_params: {'loss': 'log_loss', 'verbose': 2, 'random_state': 42, 'categorical_features': 'from_dtype', 'max_depth': None, 'learning_rate': 0.09615350775493679, 'max_iter': 64, 'max_leaf_nodes': 81, 'min_samples_leaf': 47, 'l2_regularization': 1.1831955846640462e-05, 'class_weight': None, 'max_features': 0.7736302453809708, 'max_bins': 217, 'interaction_cst': 'pairwise', 'warm_start': False}
Binning 0.050 GB of training data: 0.236 s
0.016 s 0.006 GB of validation data:
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 12, train loss: 0.63949, val loss: 0.64037, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.59379, val loss: 0.59532, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.55562, val loss: 0.55774, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.52336, val loss: 0.52629, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.49575, val loss: 0.49935, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.47204, val loss: 0.47629, in 0.078s
1 tree, 81 leaves, max depth = 13, train loss: 0.45162, val loss: 0.45640, in 0.062s
1 tree, 81 leaves, max depth = 13, train loss: 0.43389, val loss: 0.43922, in 0.047s
1 tree, 81 leaves, max depth = 10, train loss: 0.41858, val loss: 0.42506, in 0.031s
1 tree, 81 leaves, max depth = 11, train loss: 0.39676, val loss: 0.40394, in 0.063s
1 tree, 81 leaves, max depth = 15, train loss: 0.37807, val loss: 0.38536, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.36692, val loss: 0.37464, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.35195, val loss: 0.35972, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.33931, val loss: 0.34788, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.32960, val loss: 0.33878, in 0.047s
1 tree, 81 leaves, max depth = 17, train loss: 0.31972, val loss: 0.32946, in 0.047s
1 tree, 81 leaves, max depth = 17, train loss: 0.31039, val loss: 0.31987, in 0.047s
1 tree, 81 leaves, max depth = 12, train loss: 0.30230, val loss: 0.31217, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.29508, val loss: 0.30544, in 0.063s
1 tree, 81 leaves, max depth = 13, train loss: 0.28860, val loss: 0.29949, in 0.031s
1 tree, 81 leaves, max depth = 24, train loss: 0.28268, val loss: 0.29397, in 0.047s
1 tree, 81 leaves, max depth = 13, train loss: 0.27669, val loss: 0.28837, in 0.047s
1 tree, 81 leaves, max depth = 22, train loss: 0.27160, val loss: 0.28329, in 0.047s
1 tree, 81 leaves, max depth = 20, train loss: 0.26719, val loss: 0.27917, in 0.031s
1 tree, 81 leaves, max depth = 19, train loss: 0.26227, val loss: 0.27397, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.25786, val loss: 0.26993, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.25383, val loss: 0.26561, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.24968, val loss: 0.26176, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.24599, val loss: 0.25848, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.24283, val loss: 0.25510, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.23968, val loss: 0.25236, in 0.047s
1 tree, 81 leaves, max depth = 16, train loss: 0.23722, val loss: 0.25021, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.23498, val loss: 0.24820, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.23270, val loss: 0.24695, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.22979, val loss: 0.24434, in 0.047s
1 tree, 81 leaves, max depth = 11, train loss: 0.22731, val loss: 0.24223, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.22489, val loss: 0.24001, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.22285, val loss: 0.23828, in 0.047s
1 tree, 81 leaves, max depth = 12, train loss: 0.22092, val loss: 0.23663, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.21934, val loss: 0.23550, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.21791, val loss: 0.23431, in 0.047s
1 tree, 81 leaves, max depth = 14, train loss: 0.21581, val loss: 0.23244, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.21437, val loss: 0.23194, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.21294, val loss: 0.23029, in 0.031s
1 tree, 81 leaves, max depth = 11, train loss: 0.21077, val loss: 0.22851, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.20965, val loss: 0.22791, in 0.031s
1 tree, 81 leaves, max depth = 25, train loss: 0.20836, val loss: 0.22675, in 0.031s
1 tree, 81 leaves, max depth = 19, train loss: 0.20725, val loss: 0.22551, in 0.047s
1 tree, 81 leaves, max depth = 16, train loss: 0.20525, val loss: 0.22395, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.20415, val loss: 0.22374, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.20240, val loss: 0.22248, in 0.047s
1 tree, 81 leaves, max depth = 23, train loss: 0.20149, val loss: 0.22146, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.20066, val loss: 0.22105, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.19913, val loss: 0.21995, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.19817, val loss: 0.21925, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.19729, val loss: 0.21914, in 0.032s
1 tree, 81 leaves, max depth = 14, train loss: 0.19584, val loss: 0.21804, in 0.047s
1 tree, 81 leaves, max depth = 17, train loss: 0.19456, val loss: 0.21737, in 0.031s
1 tree, 36 leaves, max depth = 8, train loss: 0.19387, val loss: 0.21670, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.19237, val loss: 0.21644, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.19123, val loss: 0.21586, in 0.047s
1 tree, 81 leaves, max depth = 19, train loss: 0.18998, val loss: 0.21554, in 0.047s
1 tree, 81 leaves, max depth = 27, train loss: 0.18925, val loss: 0.21481, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.18814, val loss: 0.21431, in 0.047s
Fit 64 trees in 3.112 s, (5139 total leaves)
Time spent computing histograms: 0.902s
Time spent finding best splits: 0.300s
Time spent applying splits: 0.238s
Time spent predicting: 0.031s
Training Elapsed Time: 3.1261192000001756 seconds
Log loss: (Train) 0.2112895608019953 vs (Test) 0.22135196393197867
PR-AUC: (Train) 0.9714329280365582 vs (Test) 0.9678213476186919
ROC-AUC: (Train) 0.9668834736715238 vs (Test) 0.9627144738851556
In [25]:
save_results(clf_name = "HistGradientBoostingClassifier",
best_trials = best_trials_hgbc,
exec_time = exec_time_hgbc,
lloss_auc_train = lloss_auc_train_hgbc,
lloss_auc_test = lloss_auc_test_hgbc,
df_metrics = df_metrics_hgbc,
cm_final = cm_final_hgbc,
cm_all = cm_hgbc_all,
cm_labels = cm_labels_hgbc_all)
Optuna with XGBoost¶
In [26]:
gc.collect();
In [27]:
X_df = clean_df.drop(columns = ["target", "anon_ssn"])
y_df = clean_df.target
anon_ssn = clean_df.anon_ssn;
In [28]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring that no anon_ssn (grouped by anon_ssn) appear in both sets
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = seed)
train_idx, test_idx = next(gss.split(X_df, y_df, groups = anon_ssn))
X_train, X_test = X_df.iloc[train_idx], X_df.iloc[test_idx]
y_train, y_test = y_df.iloc[train_idx], y_df.iloc[test_idx]
anon_ssn_train = anon_ssn[train_idx] # Keeping track of anon_ssn for cross-validation
In [29]:
# Estimate scale_pos_weight value to be used if data is imbalance only
estimate = Counter(y_train)[0] / Counter(y_train)[1]
In [30]:
del X_df, y_df, gss, train_idx, test_idx;
In [31]:
# Define the objective function
def objective(trial):
# https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters
# https://xgboost.readthedocs.io/en/latest/treemethod.html
# https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.training
# https://xgboost.readthedocs.io/en/stable/parameter.html#parameters-for-tree-booster
param = {
"objective": "binary:logistic",
"booster": trial.suggest_categorical("booster", ["gbtree", "dart"]),
"device": "cpu",
"verbosity": 2,
"validate_parameters": True,
"eval_metric": ["logloss", "auc", "aucpr"],
"seed": seed,
"eta": trial.suggest_float("eta", 1e-2, 1e-1, log = True),
"gamma": trial.suggest_float("gamma", 1e-8, 5e0, log = True),
"max_depth": 0, # No limit on depth
"min_child_weight": trial.suggest_float("min_child_weight", 1e-10, 1e1, log = True),
"max_delta_step": trial.suggest_float("max_delta_step", 1, 100),
"subsample": trial.suggest_float("subsample", 1e-1, 1.0),
"sampling_method": "uniform", # Only 'uniform' is supported on CPU
"colsample_bytree": trial.suggest_float("colsample_bytree", 1e-1, 1e0),
"colsample_bylevel": trial.suggest_float("colsample_bylevel", 1e-1, 1e0),
"colsample_bynode": trial.suggest_float("colsample_bynode", 1e-1, 1e0),
"lambda": trial.suggest_float("lambda", 1e-8, 1e1, log = True),
"alpha": trial.suggest_float("alpha", 1e-8, 1e1, log = True),
"tree_method": trial.suggest_categorical("tree_method", ["auto", "approx", "hist"]), # CPU-supported methods
"scale_pos_weight": trial.suggest_categorical("scale_pos_weight", [1, estimate]),
"grow_policy": trial.suggest_categorical("grow_policy", ["depthwise", "lossguide"]),
"max_leaves": trial.suggest_int("max_leaves", 2, 256),
"max_bin": trial.suggest_int("max_bin", 40, 255),
"num_parallel_tree": 1,
}
sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):
# Split data into training and validation sets
X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]
y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]
# Summarize the composition of classes in the train and validation sets
train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])
print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where 0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')
# Create XGBoost datasets
# https://xgboost.readthedocs.io/en/stable/python/python_api.html
dtrain_fold = xgb.DMatrix(X_train_fold, label = y_train_fold, enable_categorical = True, nthread = -1)
dvalid_fold = xgb.DMatrix(X_valid_fold, label = y_valid_fold, enable_categorical = True, nthread = -1)
num_round = trial.suggest_int("num_boost_round", 5, 100)
start_fold = time.perf_counter()
# Train the model with the specified parameters and data
# https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.train
clf = xgb.train(param,
dtrain_fold,
num_boost_round = num_round,
evals = [(dvalid_fold, "validation")], # ValueError: Must have at least 1 validation dataset for early stopping
early_stopping_rounds = 50
)
end_fold = time.perf_counter()
print(clf.attributes()) # Check model attributes
y_prob_fold = clf.predict(dvalid_fold)
y_pred_fold = np.rint(y_prob_fold) # Set y_pred_fold = 1 if y_prob_fold => 0.5 and 0 if y_prob_fold < 0.5
print(f'Trial {trial.number}, Fold {fold_idx}: '
f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
f'Elapsed Time = {end_fold - start_fold} seconds')
lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))
del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, dtrain_fold, dvalid_fold, clf, start_fold, end_fold
gc.collect()
mean_lloss = np.mean(lloss_scores)
mean_pr_auc = np.mean(pr_auc_scores)
mean_roc_auc = np.mean(roc_auc_scores)
del lloss_scores, pr_auc_scores, roc_auc_scores
gc.collect()
return mean_lloss, mean_pr_auc, mean_roc_auc
In [32]:
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
ascii = True, # Plain text mode
dynamic_ncols = True # Auto-fit width
)
def update_progress(study_xgb, trial):
trial_progress.update(1)
# Disable Optuna's stdout handler so notebook isn’t spammed
optuna.logging.disable_default_handler()
# Enable propagation to Python’s logging
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)
# Configure Python logging
logging.basicConfig(filename = "optuna_debug_XGBClassifier.log", filemode = "w", level = logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")
study_xgb = optuna.create_study(study_name = "Optuna with XGBClassifier",
directions=["minimize", "maximize", "maximize"],
sampler = module.AutoSampler(seed = seed))
start_optuna = time.perf_counter()
study_xgb.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')
gc.collect()
fig = plot_pareto_front(study_xgb, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)
fig.show()
trial_progress.close()
# Plot optimization history for each objective
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
optuna.visualization.plot_optimization_history(study_xgb,
target = lambda t: t.values[i], # Correctly target each objective
target_name = obj).show()
best_trials = study_xgb.best_trials
best_trials_xgb = {}
exec_time_xgb, lloss_auc_train_xgb, lloss_auc_test_xgb, all_metrics = [], [], [], []
cm_xgb_all, cm_labels_xgb_all = [], []
for i, trial in enumerate(best_trials):
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Training with Best Trial {trial.number} </span>"))
best_params = trial.params
# Non-optimized and best Optuna optimized parameters
full_params = {"objective": "binary:logistic",
"device": "cpu",
"verbosity": 1,
"validate_parameters": True,
"eval_metric": ["logloss", "auc", "aucpr"],
"seed": seed,
"max_depth": 0,
"sampling_method": "uniform", # Only 'uniform' is supported on CPU
"num_parallel_tree": 1,
**best_params
}
print("Full_params:", full_params)
best_trials_xgb[trial.number] = full_params
dtrain_all = xgb.DMatrix(X_train, label = y_train, enable_categorical = True, nthread = -1)
dtest_all = xgb.DMatrix(X_test, label = y_test, enable_categorical = True, nthread = -1)
# Extract `num_boost_round` separately (default to 10 if not found)
num_boost_round = full_params.pop("num_boost_round", 10)
start_train = time.perf_counter()
# https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster
final_xgb = xgb.train(params = full_params,
dtrain = dtrain_all,
num_boost_round = num_boost_round, # Pass explicitly
#early_stopping_rounds = 50 # ValueError: Must have at least 1 validation dataset for early stopping
)
end_train = time.perf_counter()
print(f'Training Elapsed Time: {end_train - start_train} seconds')
y_prob_all = final_xgb.predict(dtest_all)
y_pred_all = np.rint(y_prob_all)
print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
print(f'PR-AUC: (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
print(f'ROC-AUC: (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')
exec_time_xgb.append({"Classifier": "XGBClassifier",
"Best Trial": trial.number,
"Optimization Elapsed Time (s)": end_optuna - start_optuna,
"Training Elapsed Time (s)": end_train - start_train})
lloss_auc_train_xgb.append({"Classifier": "XGBClassifier",
"Best Trial": trial.number,
"Set": "Training",
"Log loss": trial.values[0],
"PR-AUC": trial.values[1],
"ROC-AUC": trial.values[2]})
lloss_auc_test_xgb.append({"Classifier": "XGBClassifier",
"Best Trial": trial.number,
"Set": "Test",
"Log loss": log_loss(y_test, y_prob_all),
"PR-AUC": average_precision_score(y_test, y_prob_all),
"ROC-AUC": roc_auc_score(y_test, y_prob_all)})
report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
all_metrics.append({"Classifier": "XGBClassifier",
"Trial": trial.number,
"Accuracy": accuracy_score(y_test, y_pred_all),
"Precision (Safe)": report["Safe"]["precision"],
"Recall (Safe)": report["Safe"]["recall"],
"F1-score (Safe)": report["Safe"]["f1-score"],
"Precision (Risky)": report["Risky"]["precision"],
"Recall (Risky)": report["Risky"]["recall"],
"F1-score (Risky)": report["Risky"]["f1-score"],
"Precision (Macro avg)": report["macro avg"]["precision"],
"Recall (Macro avg)": report["macro avg"]["recall"],
"F1-score (Macro avg)": report["macro avg"]["f1-score"],
"Precision (Weighted avg)": report["weighted avg"]["precision"],
"Recall (Weighted avg)": report["weighted avg"]["recall"],
"F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})
# Store confusion matrix
cm_final_xgb = confusion_matrix(y_test, y_pred_all)
cm_xgb_all.append(cm_final_xgb)
cm_labels_xgb_all.append(f'XGBoostClassifier Confusion Matrix for Best Trial {trial.number}') # Store label for subplots
df_metrics_xgb = pd.DataFrame(all_metrics)
gc.collect();
Optimization Progress: 0%| | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 0, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68714 validation-auc:0.91258 validation-aucpr:0.91700
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.68097 validation-auc:0.93166 validation-aucpr:0.93703
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67488 validation-auc:0.93897 validation-aucpr:0.94349
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66831 validation-auc:0.95137 validation-aucpr:0.95887
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.66292 validation-auc:0.94986 validation-aucpr:0.95779
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65783 validation-auc:0.94954 validation-aucpr:0.95684
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.65216 validation-auc:0.94990 validation-aucpr:0.95706
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64588 validation-auc:0.95255 validation-aucpr:0.95984
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.64091 validation-auc:0.95181 validation-aucpr:0.95890
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.63570 validation-auc:0.95195 validation-aucpr:0.95913
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.63098 validation-auc:0.95088 validation-aucpr:0.95800
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.62620 validation-auc:0.95117 validation-aucpr:0.95836
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.62129 validation-auc:0.95202 validation-aucpr:0.95919
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.61664 validation-auc:0.95205 validation-aucpr:0.95932
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.61216 validation-auc:0.95192 validation-aucpr:0.95920
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.60780 validation-auc:0.95137 validation-aucpr:0.95848
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.60433 validation-auc:0.95200 validation-aucpr:0.95910
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.59974 validation-auc:0.95261 validation-aucpr:0.95976
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.59532 validation-auc:0.95279 validation-aucpr:0.95995
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.59060 validation-auc:0.95323 validation-aucpr:0.96023
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.58650 validation-auc:0.95309 validation-aucpr:0.96014
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.58330 validation-auc:0.95252 validation-aucpr:0.95964
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.57924 validation-auc:0.95215 validation-aucpr:0.95931
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.57545 validation-auc:0.95220 validation-aucpr:0.95941
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.57116 validation-auc:0.95373 validation-aucpr:0.96117
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.56745 validation-auc:0.95357 validation-aucpr:0.96103
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.56402 validation-auc:0.95340 validation-aucpr:0.96080
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.55977 validation-auc:0.95381 validation-aucpr:0.96132
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.55635 validation-auc:0.95371 validation-aucpr:0.96118
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.55258 validation-auc:0.95400 validation-aucpr:0.96137
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[30] validation-logloss:0.54912 validation-auc:0.95382 validation-aucpr:0.96123
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.54541 validation-auc:0.95372 validation-aucpr:0.96111
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.54087 validation-auc:0.95456 validation-aucpr:0.96196
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.53757 validation-auc:0.95440 validation-aucpr:0.96183
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.53419 validation-auc:0.95467 validation-aucpr:0.96203
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.53114 validation-auc:0.95437 validation-aucpr:0.96176
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.52807 validation-auc:0.95439 validation-aucpr:0.96174
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.52492 validation-auc:0.95448 validation-aucpr:0.96185
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.52220 validation-auc:0.95425 validation-aucpr:0.96162
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.51813 validation-auc:0.95462 validation-aucpr:0.96209
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.51428 validation-auc:0.95468 validation-aucpr:0.96223
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.51146 validation-auc:0.95466 validation-aucpr:0.96218
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.50875 validation-auc:0.95450 validation-aucpr:0.96200
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.50558 validation-auc:0.95482 validation-aucpr:0.96226
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.50312 validation-auc:0.95481 validation-aucpr:0.96221
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.50034 validation-auc:0.95478 validation-aucpr:0.96217
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.49764 validation-auc:0.95476 validation-aucpr:0.96213
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.49491 validation-auc:0.95481 validation-aucpr:0.96211
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.49231 validation-auc:0.95483 validation-aucpr:0.96215
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.48995 validation-auc:0.95473 validation-aucpr:0.96202
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.48707 validation-auc:0.95480 validation-aucpr:0.96210
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.48439 validation-auc:0.95476 validation-aucpr:0.96203
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.48239 validation-auc:0.95473 validation-aucpr:0.96201
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.47984 validation-auc:0.95482 validation-aucpr:0.96206
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.47721 validation-auc:0.95483 validation-aucpr:0.96201
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.47483 validation-auc:0.95487 validation-aucpr:0.96206
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.47276 validation-auc:0.95491 validation-aucpr:0.96208
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.47081 validation-auc:0.95487 validation-aucpr:0.96208
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.46760 validation-auc:0.95534 validation-aucpr:0.96263
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.46544 validation-auc:0.95529 validation-aucpr:0.96259
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.46320 validation-auc:0.95531 validation-aucpr:0.96259
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.46132 validation-auc:0.95518 validation-aucpr:0.96247
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.46009 validation-auc:0.95528 validation-aucpr:0.96254
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.45799 validation-auc:0.95529 validation-aucpr:0.96252
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.45624 validation-auc:0.95521 validation-aucpr:0.96243
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.45419 validation-auc:0.95539 validation-aucpr:0.96258
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.45232 validation-auc:0.95529 validation-aucpr:0.96249
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.45039 validation-auc:0.95529 validation-aucpr:0.96247
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.44726 validation-auc:0.95554 validation-aucpr:0.96277
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.44505 validation-auc:0.95565 validation-aucpr:0.96283
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.44314 validation-auc:0.95574 validation-aucpr:0.96293
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.44129 validation-auc:0.95570 validation-aucpr:0.96291
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.44014 validation-auc:0.95576 validation-aucpr:0.96295
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.43846 validation-auc:0.95570 validation-aucpr:0.96289
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.43680 validation-auc:0.95564 validation-aucpr:0.96285
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.43506 validation-auc:0.95560 validation-aucpr:0.96280
[17:59:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.43289 validation-auc:0.95568 validation-aucpr:0.96284
[17:59:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.43186 validation-auc:0.95573 validation-aucpr:0.96286
[17:59:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.43003 validation-auc:0.95578 validation-aucpr:0.96291
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.42846 validation-auc:0.95584 validation-aucpr:0.96295
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.42673 validation-auc:0.95582 validation-aucpr:0.96295
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.42521 validation-auc:0.95579 validation-aucpr:0.96291
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.42352 validation-auc:0.95582 validation-aucpr:0.96295
[17:59:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.42182 validation-auc:0.95588 validation-aucpr:0.96302
[17:59:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.42030 validation-auc:0.95588 validation-aucpr:0.96301
[17:59:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.41853 validation-auc:0.95585 validation-aucpr:0.96301
[17:59:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.41705 validation-auc:0.95598 validation-aucpr:0.96309
[17:59:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.41571 validation-auc:0.95594 validation-aucpr:0.96305
[17:59:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.41413 validation-auc:0.95588 validation-aucpr:0.96303
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.41265 validation-auc:0.95577 validation-aucpr:0.96292
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.41126 validation-auc:0.95577 validation-aucpr:0.96289
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.40991 validation-auc:0.95572 validation-aucpr:0.96284
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.40839 validation-auc:0.95572 validation-aucpr:0.96288
{'best_iteration': '86', 'best_score': '0.9630916850735398'}
Trial 0, Fold 1: Log loss = 0.40839214376528166, Average precision = 0.9628826257850801, ROC-AUC = 0.9557242750558017, Elapsed Time = 16.0506733000002 seconds
Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 0, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68722 validation-auc:0.92238 validation-aucpr:0.91976
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.68057 validation-auc:0.93376 validation-aucpr:0.93009
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67468 validation-auc:0.93503 validation-aucpr:0.93226
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66906 validation-auc:0.93381 validation-aucpr:0.93174
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.66332 validation-auc:0.93762 validation-aucpr:0.93605
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65765 validation-auc:0.94463 validation-aucpr:0.94623
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.65242 validation-auc:0.94435 validation-aucpr:0.94558
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64731 validation-auc:0.94458 validation-aucpr:0.94603
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.64077 validation-auc:0.95344 validation-aucpr:0.95696
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.63520 validation-auc:0.95361 validation-aucpr:0.95701
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.63017 validation-auc:0.95375 validation-aucpr:0.95741
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.62523 validation-auc:0.95345 validation-aucpr:0.95719
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.62083 validation-auc:0.95319 validation-aucpr:0.95706
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.61576 validation-auc:0.95346 validation-aucpr:0.95717
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.61100 validation-auc:0.95345 validation-aucpr:0.95709
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.60754 validation-auc:0.95300 validation-aucpr:0.95655
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.60322 validation-auc:0.95284 validation-aucpr:0.95643
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.59908 validation-auc:0.95290 validation-aucpr:0.95649
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.59496 validation-auc:0.95294 validation-aucpr:0.95642
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.59093 validation-auc:0.95291 validation-aucpr:0.95655
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.58644 validation-auc:0.95290 validation-aucpr:0.95669
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.58271 validation-auc:0.95235 validation-aucpr:0.95609
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.57866 validation-auc:0.95271 validation-aucpr:0.95622
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.57452 validation-auc:0.95252 validation-aucpr:0.95595
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.56971 validation-auc:0.95413 validation-aucpr:0.95777
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.56637 validation-auc:0.95432 validation-aucpr:0.95801
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.56238 validation-auc:0.95442 validation-aucpr:0.95806
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.55875 validation-auc:0.95422 validation-aucpr:0.95785
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.55505 validation-auc:0.95399 validation-aucpr:0.95755
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.55035 validation-auc:0.95517 validation-aucpr:0.95909
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.54688 validation-auc:0.95529 validation-aucpr:0.95923
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.54329 validation-auc:0.95536 validation-aucpr:0.95934
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.53984 validation-auc:0.95520 validation-aucpr:0.95920
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.53650 validation-auc:0.95518 validation-aucpr:0.95918
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.53271 validation-auc:0.95553 validation-aucpr:0.95965
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.52927 validation-auc:0.95555 validation-aucpr:0.95974
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.52596 validation-auc:0.95544 validation-aucpr:0.95959
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.52265 validation-auc:0.95540 validation-aucpr:0.95949
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.51958 validation-auc:0.95541 validation-aucpr:0.95951
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.51728 validation-auc:0.95529 validation-aucpr:0.95943
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.51343 validation-auc:0.95596 validation-aucpr:0.96029
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.51084 validation-auc:0.95587 validation-aucpr:0.96015
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.50844 validation-auc:0.95598 validation-aucpr:0.96023
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.50514 validation-auc:0.95593 validation-aucpr:0.96008
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.50258 validation-auc:0.95596 validation-aucpr:0.96014
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.49968 validation-auc:0.95610 validation-aucpr:0.96021
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.49691 validation-auc:0.95609 validation-aucpr:0.96015
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.49417 validation-auc:0.95607 validation-aucpr:0.96012
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.49190 validation-auc:0.95602 validation-aucpr:0.96016
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.48934 validation-auc:0.95621 validation-aucpr:0.96034
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.48589 validation-auc:0.95663 validation-aucpr:0.96089
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.48337 validation-auc:0.95666 validation-aucpr:0.96105
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.48079 validation-auc:0.95662 validation-aucpr:0.96099
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.47823 validation-auc:0.95668 validation-aucpr:0.96102
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.47540 validation-auc:0.95658 validation-aucpr:0.96088
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.47293 validation-auc:0.95650 validation-aucpr:0.96073
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.47089 validation-auc:0.95642 validation-aucpr:0.96064
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.46923 validation-auc:0.95647 validation-aucpr:0.96064
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.46689 validation-auc:0.95647 validation-aucpr:0.96064
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.46462 validation-auc:0.95658 validation-aucpr:0.96082
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.46276 validation-auc:0.95661 validation-aucpr:0.96085
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.46060 validation-auc:0.95648 validation-aucpr:0.96070
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.45874 validation-auc:0.95646 validation-aucpr:0.96067
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.45647 validation-auc:0.95647 validation-aucpr:0.96072
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.45459 validation-auc:0.95642 validation-aucpr:0.96067
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.45263 validation-auc:0.95639 validation-aucpr:0.96059
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.45063 validation-auc:0.95639 validation-aucpr:0.96058
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.44853 validation-auc:0.95635 validation-aucpr:0.96076
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.44633 validation-auc:0.95640 validation-aucpr:0.96080
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.44443 validation-auc:0.95620 validation-aucpr:0.96058
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.44251 validation-auc:0.95616 validation-aucpr:0.96055
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.44064 validation-auc:0.95600 validation-aucpr:0.96039
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.43873 validation-auc:0.95605 validation-aucpr:0.96040
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.43696 validation-auc:0.95604 validation-aucpr:0.96037
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.43493 validation-auc:0.95604 validation-aucpr:0.96038
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.43312 validation-auc:0.95597 validation-aucpr:0.96033
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.43032 validation-auc:0.95652 validation-aucpr:0.96099
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.42902 validation-auc:0.95647 validation-aucpr:0.96103
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.42751 validation-auc:0.95635 validation-aucpr:0.96093
[17:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.42599 validation-auc:0.95623 validation-aucpr:0.96084
[17:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.42432 validation-auc:0.95618 validation-aucpr:0.96076
[17:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.42266 validation-auc:0.95616 validation-aucpr:0.96073
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.42067 validation-auc:0.95620 validation-aucpr:0.96074
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.41793 validation-auc:0.95660 validation-aucpr:0.96120
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.41665 validation-auc:0.95659 validation-aucpr:0.96119
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.41489 validation-auc:0.95673 validation-aucpr:0.96129
[17:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.41292 validation-auc:0.95690 validation-aucpr:0.96152
[17:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.41136 validation-auc:0.95689 validation-aucpr:0.96149
[17:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.40989 validation-auc:0.95687 validation-aucpr:0.96146
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.40852 validation-auc:0.95683 validation-aucpr:0.96143
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.40715 validation-auc:0.95677 validation-aucpr:0.96137
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.40541 validation-auc:0.95673 validation-aucpr:0.96130
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.40400 validation-auc:0.95674 validation-aucpr:0.96130
{'best_iteration': '86', 'best_score': '0.9615151857978635'}
Trial 0, Fold 2: Log loss = 0.40400137854890755, Average precision = 0.9613053529150961, ROC-AUC = 0.9567406518052979, Elapsed Time = 15.39552619999995 seconds
Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 0, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68770 validation-auc:0.89543 validation-aucpr:0.89711
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.68144 validation-auc:0.93117 validation-aucpr:0.93039
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67547 validation-auc:0.93600 validation-aucpr:0.93526
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66957 validation-auc:0.93921 validation-aucpr:0.94207
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.66413 validation-auc:0.94041 validation-aucpr:0.94403
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65897 validation-auc:0.94172 validation-aucpr:0.94548
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.65351 validation-auc:0.94416 validation-aucpr:0.94793
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64713 validation-auc:0.95457 validation-aucpr:0.95955
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.64069 validation-auc:0.95716 validation-aucpr:0.96258
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.63613 validation-auc:0.95675 validation-aucpr:0.96218
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.63105 validation-auc:0.95675 validation-aucpr:0.96225
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.62614 validation-auc:0.95662 validation-aucpr:0.96285
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.62136 validation-auc:0.95627 validation-aucpr:0.96246
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.61664 validation-auc:0.95638 validation-aucpr:0.96247
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.61182 validation-auc:0.95641 validation-aucpr:0.96236
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.60817 validation-auc:0.95643 validation-aucpr:0.96243
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.60358 validation-auc:0.95662 validation-aucpr:0.96268
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.59950 validation-auc:0.95638 validation-aucpr:0.96243
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.59486 validation-auc:0.95675 validation-aucpr:0.96272
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.59167 validation-auc:0.95675 validation-aucpr:0.96279
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.58842 validation-auc:0.95635 validation-aucpr:0.96225
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.58429 validation-auc:0.95622 validation-aucpr:0.96205
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.58023 validation-auc:0.95629 validation-aucpr:0.96204
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.57639 validation-auc:0.95604 validation-aucpr:0.96182
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.57262 validation-auc:0.95587 validation-aucpr:0.96165
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.56826 validation-auc:0.95614 validation-aucpr:0.96191
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.56421 validation-auc:0.95623 validation-aucpr:0.96200
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.56028 validation-auc:0.95642 validation-aucpr:0.96224
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.55638 validation-auc:0.95624 validation-aucpr:0.96202
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.55257 validation-auc:0.95632 validation-aucpr:0.96209
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.54904 validation-auc:0.95631 validation-aucpr:0.96207
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.54531 validation-auc:0.95648 validation-aucpr:0.96212
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.54160 validation-auc:0.95649 validation-aucpr:0.96209
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.53832 validation-auc:0.95624 validation-aucpr:0.96183
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.53525 validation-auc:0.95603 validation-aucpr:0.96162
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.53207 validation-auc:0.95611 validation-aucpr:0.96178
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.52874 validation-auc:0.95611 validation-aucpr:0.96181
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.52549 validation-auc:0.95616 validation-aucpr:0.96181
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.52114 validation-auc:0.95757 validation-aucpr:0.96337
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.51747 validation-auc:0.95767 validation-aucpr:0.96342
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.51414 validation-auc:0.95778 validation-aucpr:0.96351
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.51169 validation-auc:0.95772 validation-aucpr:0.96342
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.50916 validation-auc:0.95772 validation-aucpr:0.96336
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.50618 validation-auc:0.95787 validation-aucpr:0.96350
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.50318 validation-auc:0.95775 validation-aucpr:0.96339
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.50028 validation-auc:0.95764 validation-aucpr:0.96323
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.49787 validation-auc:0.95746 validation-aucpr:0.96304
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.49413 validation-auc:0.95840 validation-aucpr:0.96416
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.49162 validation-auc:0.95837 validation-aucpr:0.96413
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.48898 validation-auc:0.95841 validation-aucpr:0.96418
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.48659 validation-auc:0.95837 validation-aucpr:0.96417
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.48449 validation-auc:0.95844 validation-aucpr:0.96416
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.48238 validation-auc:0.95834 validation-aucpr:0.96410
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.47999 validation-auc:0.95823 validation-aucpr:0.96396
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.47735 validation-auc:0.95818 validation-aucpr:0.96388
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.47486 validation-auc:0.95817 validation-aucpr:0.96384
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.47259 validation-auc:0.95812 validation-aucpr:0.96379
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.47017 validation-auc:0.95807 validation-aucpr:0.96371
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.46814 validation-auc:0.95794 validation-aucpr:0.96356
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.46575 validation-auc:0.95794 validation-aucpr:0.96352
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.46349 validation-auc:0.95798 validation-aucpr:0.96354
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.46206 validation-auc:0.95799 validation-aucpr:0.96355
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.45945 validation-auc:0.95806 validation-aucpr:0.96358
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.45742 validation-auc:0.95801 validation-aucpr:0.96353
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.45519 validation-auc:0.95797 validation-aucpr:0.96348
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.45255 validation-auc:0.95839 validation-aucpr:0.96394
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.45061 validation-auc:0.95836 validation-aucpr:0.96391
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.44853 validation-auc:0.95836 validation-aucpr:0.96390
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.44627 validation-auc:0.95828 validation-aucpr:0.96377
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.44439 validation-auc:0.95822 validation-aucpr:0.96366
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.44215 validation-auc:0.95821 validation-aucpr:0.96362
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.44033 validation-auc:0.95813 validation-aucpr:0.96354
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.43840 validation-auc:0.95804 validation-aucpr:0.96345
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.43687 validation-auc:0.95811 validation-aucpr:0.96348
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.43507 validation-auc:0.95801 validation-aucpr:0.96338
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.43297 validation-auc:0.95808 validation-aucpr:0.96346
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.43071 validation-auc:0.95869 validation-aucpr:0.96418
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.42900 validation-auc:0.95864 validation-aucpr:0.96411
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.42733 validation-auc:0.95863 validation-aucpr:0.96410
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.42552 validation-auc:0.95862 validation-aucpr:0.96407
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.42382 validation-auc:0.95854 validation-aucpr:0.96399
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.42118 validation-auc:0.95896 validation-aucpr:0.96451
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.41929 validation-auc:0.95903 validation-aucpr:0.96455
[17:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.41760 validation-auc:0.95903 validation-aucpr:0.96455
[17:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.41632 validation-auc:0.95898 validation-aucpr:0.96447
[17:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.41392 validation-auc:0.95931 validation-aucpr:0.96487
[17:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.41240 validation-auc:0.95926 validation-aucpr:0.96483
[17:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.41087 validation-auc:0.95921 validation-aucpr:0.96478
[17:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.40843 validation-auc:0.95952 validation-aucpr:0.96515
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.40713 validation-auc:0.95953 validation-aucpr:0.96514
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.40576 validation-auc:0.95947 validation-aucpr:0.96509
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.40446 validation-auc:0.95940 validation-aucpr:0.96502
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.40206 validation-auc:0.95970 validation-aucpr:0.96535
{'best_iteration': '92', 'best_score': '0.9653474941847704'}
Trial 0, Fold 3: Log loss = 0.4020617406521426, Average precision = 0.9653535765256415, ROC-AUC = 0.9596951717213549, Elapsed Time = 15.429512600001544 seconds
Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 0, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68706 validation-auc:0.91637 validation-aucpr:0.91737
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.68094 validation-auc:0.93038 validation-aucpr:0.93196
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67547 validation-auc:0.93114 validation-aucpr:0.93244
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66815 validation-auc:0.95085 validation-aucpr:0.95608
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.66237 validation-auc:0.95106 validation-aucpr:0.95656
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65672 validation-auc:0.95187 validation-aucpr:0.95764
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.65127 validation-auc:0.95158 validation-aucpr:0.95733
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64621 validation-auc:0.95184 validation-aucpr:0.95763
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.64073 validation-auc:0.95207 validation-aucpr:0.95796
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.63549 validation-auc:0.95244 validation-aucpr:0.95862
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.63030 validation-auc:0.95252 validation-aucpr:0.95848
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.62549 validation-auc:0.95216 validation-aucpr:0.95814
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.62080 validation-auc:0.95190 validation-aucpr:0.95802
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.61613 validation-auc:0.95157 validation-aucpr:0.95759
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.61161 validation-auc:0.95102 validation-aucpr:0.95695
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.60661 validation-auc:0.95135 validation-aucpr:0.95713
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.60225 validation-auc:0.95106 validation-aucpr:0.95664
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.59829 validation-auc:0.95026 validation-aucpr:0.95568
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.59407 validation-auc:0.95008 validation-aucpr:0.95555
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.58988 validation-auc:0.94980 validation-aucpr:0.95523
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.58417 validation-auc:0.95299 validation-aucpr:0.95923
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.57998 validation-auc:0.95318 validation-aucpr:0.95940
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.57598 validation-auc:0.95300 validation-aucpr:0.95920
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.57200 validation-auc:0.95296 validation-aucpr:0.95918
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.56770 validation-auc:0.95286 validation-aucpr:0.95904
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.56367 validation-auc:0.95290 validation-aucpr:0.95904
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.56011 validation-auc:0.95311 validation-aucpr:0.95919
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.55663 validation-auc:0.95273 validation-aucpr:0.95873
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.55305 validation-auc:0.95267 validation-aucpr:0.95869
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.54972 validation-auc:0.95270 validation-aucpr:0.95876
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.54642 validation-auc:0.95251 validation-aucpr:0.95858
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.54329 validation-auc:0.95232 validation-aucpr:0.95841
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.53988 validation-auc:0.95186 validation-aucpr:0.95793
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.53667 validation-auc:0.95188 validation-aucpr:0.95794
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.53239 validation-auc:0.95344 validation-aucpr:0.95991
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.52889 validation-auc:0.95354 validation-aucpr:0.96005
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.52594 validation-auc:0.95348 validation-aucpr:0.95997
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.52259 validation-auc:0.95358 validation-aucpr:0.96007
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.51935 validation-auc:0.95370 validation-aucpr:0.96020
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.51657 validation-auc:0.95363 validation-aucpr:0.96009
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.51254 validation-auc:0.95470 validation-aucpr:0.96133
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.50989 validation-auc:0.95465 validation-aucpr:0.96126
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.50705 validation-auc:0.95464 validation-aucpr:0.96115
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.50419 validation-auc:0.95448 validation-aucpr:0.96105
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.50158 validation-auc:0.95437 validation-aucpr:0.96092
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.49795 validation-auc:0.95500 validation-aucpr:0.96172
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.49569 validation-auc:0.95496 validation-aucpr:0.96165
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.49339 validation-auc:0.95492 validation-aucpr:0.96159
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.49052 validation-auc:0.95500 validation-aucpr:0.96158
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.48725 validation-auc:0.95533 validation-aucpr:0.96194
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.48476 validation-auc:0.95521 validation-aucpr:0.96183
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.48142 validation-auc:0.95563 validation-aucpr:0.96236
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.47821 validation-auc:0.95585 validation-aucpr:0.96266
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.47579 validation-auc:0.95588 validation-aucpr:0.96266
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.47311 validation-auc:0.95590 validation-aucpr:0.96265
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.47112 validation-auc:0.95571 validation-aucpr:0.96250
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.46903 validation-auc:0.95563 validation-aucpr:0.96243
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.46685 validation-auc:0.95568 validation-aucpr:0.96243
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.46453 validation-auc:0.95568 validation-aucpr:0.96242
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.46145 validation-auc:0.95597 validation-aucpr:0.96277
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.45937 validation-auc:0.95591 validation-aucpr:0.96272
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.45696 validation-auc:0.95594 validation-aucpr:0.96271
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.45495 validation-auc:0.95598 validation-aucpr:0.96272
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.45292 validation-auc:0.95600 validation-aucpr:0.96271
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.45063 validation-auc:0.95605 validation-aucpr:0.96270
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.44889 validation-auc:0.95599 validation-aucpr:0.96262
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.44701 validation-auc:0.95597 validation-aucpr:0.96260
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.44505 validation-auc:0.95600 validation-aucpr:0.96263
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.44315 validation-auc:0.95605 validation-aucpr:0.96268
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.44145 validation-auc:0.95607 validation-aucpr:0.96271
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.43946 validation-auc:0.95610 validation-aucpr:0.96270
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.43774 validation-auc:0.95601 validation-aucpr:0.96261
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.43589 validation-auc:0.95609 validation-aucpr:0.96264
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.43404 validation-auc:0.95609 validation-aucpr:0.96266
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.43255 validation-auc:0.95604 validation-aucpr:0.96262
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.43070 validation-auc:0.95604 validation-aucpr:0.96259
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.42906 validation-auc:0.95600 validation-aucpr:0.96257
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.42709 validation-auc:0.95601 validation-aucpr:0.96259
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.42538 validation-auc:0.95600 validation-aucpr:0.96257
[17:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.42399 validation-auc:0.95598 validation-aucpr:0.96254
[17:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.42250 validation-auc:0.95595 validation-aucpr:0.96252
[17:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.41995 validation-auc:0.95634 validation-aucpr:0.96293
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.41745 validation-auc:0.95655 validation-aucpr:0.96320
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.41560 validation-auc:0.95658 validation-aucpr:0.96322
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.41410 validation-auc:0.95652 validation-aucpr:0.96316
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.41222 validation-auc:0.95659 validation-aucpr:0.96320
[17:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.41049 validation-auc:0.95647 validation-aucpr:0.96312
[17:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.40895 validation-auc:0.95647 validation-aucpr:0.96310
[17:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.40762 validation-auc:0.95650 validation-aucpr:0.96315
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.40614 validation-auc:0.95646 validation-aucpr:0.96309
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.40481 validation-auc:0.95641 validation-aucpr:0.96305
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.40334 validation-auc:0.95642 validation-aucpr:0.96304
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.40220 validation-auc:0.95632 validation-aucpr:0.96297
{'best_iteration': '83', 'best_score': '0.9632182894831647'}
Trial 0, Fold 4: Log loss = 0.4022018041340452, Average precision = 0.9629778336083984, ROC-AUC = 0.9563161226635765, Elapsed Time = 15.438011500000357 seconds
Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 0, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68753 validation-auc:0.89140 validation-aucpr:0.86380
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.68146 validation-auc:0.92771 validation-aucpr:0.93034
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67620 validation-auc:0.92589 validation-aucpr:0.93015
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.67043 validation-auc:0.93133 validation-aucpr:0.93622
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.66431 validation-auc:0.94552 validation-aucpr:0.95293
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.66011 validation-auc:0.94665 validation-aucpr:0.95325
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.65412 validation-auc:0.94997 validation-aucpr:0.95638
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64803 validation-auc:0.95180 validation-aucpr:0.95821
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.64283 validation-auc:0.95159 validation-aucpr:0.95799
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.63748 validation-auc:0.95198 validation-aucpr:0.95828
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.63273 validation-auc:0.95181 validation-aucpr:0.95816
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.62795 validation-auc:0.95149 validation-aucpr:0.95787
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.62317 validation-auc:0.95153 validation-aucpr:0.95789
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.61846 validation-auc:0.95144 validation-aucpr:0.95775
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.61508 validation-auc:0.95123 validation-aucpr:0.95739
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.61057 validation-auc:0.95109 validation-aucpr:0.95734
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.60444 validation-auc:0.95351 validation-aucpr:0.96023
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.60028 validation-auc:0.95374 validation-aucpr:0.96051
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.59644 validation-auc:0.95353 validation-aucpr:0.96025
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.59326 validation-auc:0.95312 validation-aucpr:0.95977
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.58928 validation-auc:0.95288 validation-aucpr:0.95952
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.58539 validation-auc:0.95273 validation-aucpr:0.95934
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.58042 validation-auc:0.95352 validation-aucpr:0.96027
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.57656 validation-auc:0.95350 validation-aucpr:0.96027
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.57352 validation-auc:0.95344 validation-aucpr:0.96021
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.56965 validation-auc:0.95341 validation-aucpr:0.96002
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.56559 validation-auc:0.95378 validation-aucpr:0.96048
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.56186 validation-auc:0.95373 validation-aucpr:0.96034
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.55839 validation-auc:0.95359 validation-aucpr:0.96008
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.55497 validation-auc:0.95371 validation-aucpr:0.96023
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.55070 validation-auc:0.95411 validation-aucpr:0.96057
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.54690 validation-auc:0.95416 validation-aucpr:0.96066
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.54243 validation-auc:0.95432 validation-aucpr:0.96094
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.53915 validation-auc:0.95438 validation-aucpr:0.96096
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.53610 validation-auc:0.95436 validation-aucpr:0.96095
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.53343 validation-auc:0.95426 validation-aucpr:0.96077
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.52925 validation-auc:0.95463 validation-aucpr:0.96114
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.52563 validation-auc:0.95476 validation-aucpr:0.96122
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.52269 validation-auc:0.95480 validation-aucpr:0.96118
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.51956 validation-auc:0.95483 validation-aucpr:0.96119
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.51683 validation-auc:0.95481 validation-aucpr:0.96109
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.51382 validation-auc:0.95477 validation-aucpr:0.96107
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.51128 validation-auc:0.95479 validation-aucpr:0.96111
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.50775 validation-auc:0.95485 validation-aucpr:0.96123
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.50486 validation-auc:0.95482 validation-aucpr:0.96119
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.50206 validation-auc:0.95476 validation-aucpr:0.96108
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.49940 validation-auc:0.95477 validation-aucpr:0.96111
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.49656 validation-auc:0.95488 validation-aucpr:0.96121
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.49429 validation-auc:0.95480 validation-aucpr:0.96113
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.49157 validation-auc:0.95470 validation-aucpr:0.96107
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.48906 validation-auc:0.95466 validation-aucpr:0.96099
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.48614 validation-auc:0.95480 validation-aucpr:0.96114
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.48386 validation-auc:0.95479 validation-aucpr:0.96108
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.48147 validation-auc:0.95477 validation-aucpr:0.96110
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.47917 validation-auc:0.95474 validation-aucpr:0.96109
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.47708 validation-auc:0.95469 validation-aucpr:0.96106
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.47488 validation-auc:0.95477 validation-aucpr:0.96111
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.47260 validation-auc:0.95482 validation-aucpr:0.96112
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.47038 validation-auc:0.95481 validation-aucpr:0.96108
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.46774 validation-auc:0.95493 validation-aucpr:0.96115
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.46438 validation-auc:0.95518 validation-aucpr:0.96139
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.46225 validation-auc:0.95516 validation-aucpr:0.96139
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.46016 validation-auc:0.95514 validation-aucpr:0.96135
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.45807 validation-auc:0.95516 validation-aucpr:0.96137
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.45603 validation-auc:0.95514 validation-aucpr:0.96143
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.45402 validation-auc:0.95504 validation-aucpr:0.96136
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.45191 validation-auc:0.95500 validation-aucpr:0.96129
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.44887 validation-auc:0.95519 validation-aucpr:0.96144
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.44610 validation-auc:0.95525 validation-aucpr:0.96155
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.44427 validation-auc:0.95520 validation-aucpr:0.96151
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.44184 validation-auc:0.95531 validation-aucpr:0.96159
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.43987 validation-auc:0.95529 validation-aucpr:0.96156
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.43790 validation-auc:0.95530 validation-aucpr:0.96160
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.43610 validation-auc:0.95524 validation-aucpr:0.96159
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.43426 validation-auc:0.95523 validation-aucpr:0.96157
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.43247 validation-auc:0.95528 validation-aucpr:0.96160
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.42998 validation-auc:0.95543 validation-aucpr:0.96173
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.42864 validation-auc:0.95538 validation-aucpr:0.96167
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.42688 validation-auc:0.95535 validation-aucpr:0.96162
[18:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.42583 validation-auc:0.95535 validation-aucpr:0.96159
[18:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.42420 validation-auc:0.95529 validation-aucpr:0.96152
[18:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.42243 validation-auc:0.95532 validation-aucpr:0.96155
[18:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.42065 validation-auc:0.95529 validation-aucpr:0.96160
[18:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.41924 validation-auc:0.95531 validation-aucpr:0.96161
[18:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.41796 validation-auc:0.95531 validation-aucpr:0.96161
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.41638 validation-auc:0.95526 validation-aucpr:0.96157
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.41508 validation-auc:0.95524 validation-aucpr:0.96153
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.41381 validation-auc:0.95531 validation-aucpr:0.96161
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.41228 validation-auc:0.95529 validation-aucpr:0.96163
[18:00:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.41099 validation-auc:0.95527 validation-aucpr:0.96169
[18:00:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.40904 validation-auc:0.95532 validation-aucpr:0.96178
[18:00:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.40759 validation-auc:0.95532 validation-aucpr:0.96178
[18:00:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.40635 validation-auc:0.95529 validation-aucpr:0.96174
{'best_iteration': '90', 'best_score': '0.9617848063106246'}
Trial 0, Fold 5: Log loss = 0.4063536315120741, Average precision = 0.961749182442062, ROC-AUC = 0.9552881110306002, Elapsed Time = 15.487014800000907 seconds
Optimization Progress: 1%|1 | 1/100 [01:28<2:25:19, 88.08s/it]
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 1, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:00:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63392 validation-auc:0.94384 validation-aucpr:0.94999
[18:00:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58832 validation-auc:0.94990 validation-aucpr:0.94299
[18:00:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54919 validation-auc:0.95494 validation-aucpr:0.94824
[18:00:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.51654 validation-auc:0.95652 validation-aucpr:0.95592
[18:00:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.48251 validation-auc:0.95997 validation-aucpr:0.96125
{'best_iteration': '4', 'best_score': '0.9612493689860694'}
Trial 1, Fold 1: Log loss = 0.4825125440000191, Average precision = 0.9622372581737065, ROC-AUC = 0.9599684756217052, Elapsed Time = 1.3910605000000942 seconds
Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 1, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:00:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63285 validation-auc:0.93630 validation-aucpr:0.94394
[18:00:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58116 validation-auc:0.95561 validation-aucpr:0.95181
[18:00:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54115 validation-auc:0.96053 validation-aucpr:0.95841
[18:00:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.50679 validation-auc:0.96251 validation-aucpr:0.96537
[18:00:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.47790 validation-auc:0.96320 validation-aucpr:0.96723
{'best_iteration': '4', 'best_score': '0.9672330098919696'}
Trial 1, Fold 2: Log loss = 0.4778954946598143, Average precision = 0.9670471551250688, ROC-AUC = 0.963202831249482, Elapsed Time = 1.3718076999994082 seconds
Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 1, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:00:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63230 validation-auc:0.93998 validation-aucpr:0.95407
[18:00:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58061 validation-auc:0.95589 validation-aucpr:0.94565
[18:00:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54078 validation-auc:0.96280 validation-aucpr:0.96040
[18:00:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.50346 validation-auc:0.96485 validation-aucpr:0.96751
[18:00:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.46977 validation-auc:0.96618 validation-aucpr:0.97034
{'best_iteration': '4', 'best_score': '0.9703432338893858'}
Trial 1, Fold 3: Log loss = 0.4697678746716907, Average precision = 0.9706581052575195, ROC-AUC = 0.9661814480983523, Elapsed Time = 1.581263799998851 seconds
Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 1, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:00:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63334 validation-auc:0.92408 validation-aucpr:0.91700
[18:00:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58359 validation-auc:0.94845 validation-aucpr:0.93471
[18:00:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54465 validation-auc:0.95682 validation-aucpr:0.95295
[18:00:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.50549 validation-auc:0.96154 validation-aucpr:0.96622
[18:00:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.47218 validation-auc:0.96303 validation-aucpr:0.96621
{'best_iteration': '3', 'best_score': '0.966221599980624'}
Trial 1, Fold 4: Log loss = 0.4721788659488989, Average precision = 0.9668029424338604, ROC-AUC = 0.9630322598720525, Elapsed Time = 1.341411400000652 seconds
Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 1, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:00:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63208 validation-auc:0.93269 validation-aucpr:0.94766
[18:00:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58183 validation-auc:0.95642 validation-aucpr:0.95017
[18:00:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53918 validation-auc:0.95850 validation-aucpr:0.95651
[18:00:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.50252 validation-auc:0.96053 validation-aucpr:0.96324
[18:00:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.47027 validation-auc:0.96212 validation-aucpr:0.96623
{'best_iteration': '4', 'best_score': '0.9662340562252816'}
Trial 1, Fold 5: Log loss = 0.47026629801259107, Average precision = 0.9665882911261429, ROC-AUC = 0.962115898407744, Elapsed Time = 1.2950529999998253 seconds
Optimization Progress: 2%|2 | 2/100 [01:43<1:13:57, 45.28s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 2, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68353 validation-auc:0.91262 validation-aucpr:0.91609
[1] validation-logloss:0.67234 validation-auc:0.94708 validation-aucpr:0.95478
[2] validation-logloss:0.66292 validation-auc:0.95054 validation-aucpr:0.95893
[3] validation-logloss:0.65436 validation-auc:0.94975 validation-aucpr:0.95791
[4] validation-logloss:0.64550 validation-auc:0.95050 validation-aucpr:0.95875
[5] validation-logloss:0.63603 validation-auc:0.95166 validation-aucpr:0.95911
[6] validation-logloss:0.62775 validation-auc:0.95168 validation-aucpr:0.95896
[7] validation-logloss:0.62005 validation-auc:0.95170 validation-aucpr:0.95889
[8] validation-logloss:0.61279 validation-auc:0.95124 validation-aucpr:0.95832
[9] validation-logloss:0.60512 validation-auc:0.95178 validation-aucpr:0.95881
[10] validation-logloss:0.59861 validation-auc:0.95132 validation-aucpr:0.95826
[11] validation-logloss:0.59203 validation-auc:0.95058 validation-aucpr:0.95749
[12] validation-logloss:0.58474 validation-auc:0.95131 validation-aucpr:0.95848
[13] validation-logloss:0.57802 validation-auc:0.95106 validation-aucpr:0.95827
[14] validation-logloss:0.57120 validation-auc:0.95149 validation-aucpr:0.95893
[15] validation-logloss:0.56375 validation-auc:0.95204 validation-aucpr:0.95958
[16] validation-logloss:0.55670 validation-auc:0.95233 validation-aucpr:0.96010
[17] validation-logloss:0.55124 validation-auc:0.95205 validation-aucpr:0.95977
[18] validation-logloss:0.54516 validation-auc:0.95276 validation-aucpr:0.96034
[19] validation-logloss:0.53921 validation-auc:0.95289 validation-aucpr:0.96018
[20] validation-logloss:0.53406 validation-auc:0.95284 validation-aucpr:0.96014
[21] validation-logloss:0.52856 validation-auc:0.95288 validation-aucpr:0.96005
{'best_iteration': '18', 'best_score': '0.9603435077097092'}
Trial 2, Fold 1: Log loss = 0.5285589149145459, Average precision = 0.9599034058322928, ROC-AUC = 0.9528846762784207, Elapsed Time = 1.5365930999996635 seconds
Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 2, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68363 validation-auc:0.91583 validation-aucpr:0.91126
[1] validation-logloss:0.67451 validation-auc:0.92725 validation-aucpr:0.92492
[2] validation-logloss:0.66501 validation-auc:0.93310 validation-aucpr:0.93311
[3] validation-logloss:0.65645 validation-auc:0.93276 validation-aucpr:0.93250
[4] validation-logloss:0.64741 validation-auc:0.93954 validation-aucpr:0.94073
[5] validation-logloss:0.63955 validation-auc:0.94139 validation-aucpr:0.94358
[6] validation-logloss:0.63133 validation-auc:0.94242 validation-aucpr:0.94462
[7] validation-logloss:0.62367 validation-auc:0.94334 validation-aucpr:0.94536
[8] validation-logloss:0.61641 validation-auc:0.94318 validation-aucpr:0.94531
[9] validation-logloss:0.60882 validation-auc:0.94351 validation-aucpr:0.94579
[10] validation-logloss:0.60229 validation-auc:0.94309 validation-aucpr:0.94541
[11] validation-logloss:0.59566 validation-auc:0.94290 validation-aucpr:0.94513
[12] validation-logloss:0.58841 validation-auc:0.94387 validation-aucpr:0.94651
[13] validation-logloss:0.58162 validation-auc:0.94394 validation-aucpr:0.94659
[14] validation-logloss:0.57482 validation-auc:0.94452 validation-aucpr:0.94742
[15] validation-logloss:0.56723 validation-auc:0.95083 validation-aucpr:0.95501
[16] validation-logloss:0.56008 validation-auc:0.95258 validation-aucpr:0.95742
[17] validation-logloss:0.55448 validation-auc:0.95270 validation-aucpr:0.95784
[18] validation-logloss:0.54846 validation-auc:0.95332 validation-aucpr:0.95830
[19] validation-logloss:0.54241 validation-auc:0.95380 validation-aucpr:0.95825
[20] validation-logloss:0.53708 validation-auc:0.95368 validation-aucpr:0.95823
[21] validation-logloss:0.53168 validation-auc:0.95359 validation-aucpr:0.95806
{'best_iteration': '18', 'best_score': '0.9582963343506642'}
Trial 2, Fold 2: Log loss = 0.531684667525449, Average precision = 0.957925966584543, ROC-AUC = 0.9535940855997118, Elapsed Time = 1.873345200001495 seconds
Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 2, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68134 validation-auc:0.94037 validation-aucpr:0.93717
[1] validation-logloss:0.67031 validation-auc:0.95352 validation-aucpr:0.95412
[2] validation-logloss:0.66074 validation-auc:0.95657 validation-aucpr:0.96157
[3] validation-logloss:0.65196 validation-auc:0.95585 validation-aucpr:0.96089
[4] validation-logloss:0.64278 validation-auc:0.95889 validation-aucpr:0.96347
[5] validation-logloss:0.63391 validation-auc:0.95837 validation-aucpr:0.96310
[6] validation-logloss:0.62601 validation-auc:0.95815 validation-aucpr:0.96281
[7] validation-logloss:0.62166 validation-auc:0.95836 validation-aucpr:0.96298
[8] validation-logloss:0.61422 validation-auc:0.95780 validation-aucpr:0.96302
[9] validation-logloss:0.60690 validation-auc:0.95724 validation-aucpr:0.96235
[10] validation-logloss:0.59931 validation-auc:0.95768 validation-aucpr:0.96253
[11] validation-logloss:0.59238 validation-auc:0.95781 validation-aucpr:0.96246
[12] validation-logloss:0.58582 validation-auc:0.95784 validation-aucpr:0.96261
[13] validation-logloss:0.57913 validation-auc:0.95781 validation-aucpr:0.96244
[14] validation-logloss:0.57286 validation-auc:0.95759 validation-aucpr:0.96229
[15] validation-logloss:0.56661 validation-auc:0.95782 validation-aucpr:0.96254
[16] validation-logloss:0.56078 validation-auc:0.95787 validation-aucpr:0.96279
[17] validation-logloss:0.55301 validation-auc:0.95892 validation-aucpr:0.96404
[18] validation-logloss:0.54702 validation-auc:0.95879 validation-aucpr:0.96390
[19] validation-logloss:0.54167 validation-auc:0.95887 validation-aucpr:0.96411
[20] validation-logloss:0.53642 validation-auc:0.95868 validation-aucpr:0.96390
[21] validation-logloss:0.52959 validation-auc:0.95933 validation-aucpr:0.96467
{'best_iteration': '21', 'best_score': '0.9646701594022623'}
Trial 2, Fold 3: Log loss = 0.5295945618795187, Average precision = 0.9644928261022662, ROC-AUC = 0.9593264836609491, Elapsed Time = 1.8513464000006934 seconds
Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 2, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68152 validation-auc:0.94070 validation-aucpr:0.94479
[1] validation-logloss:0.67048 validation-auc:0.94815 validation-aucpr:0.95350
[2] validation-logloss:0.66102 validation-auc:0.95153 validation-aucpr:0.95926
[3] validation-logloss:0.65216 validation-auc:0.95090 validation-aucpr:0.95798
[4] validation-logloss:0.64325 validation-auc:0.95219 validation-aucpr:0.95918
[5] validation-logloss:0.63529 validation-auc:0.95192 validation-aucpr:0.95929
[6] validation-logloss:0.62694 validation-auc:0.95320 validation-aucpr:0.96008
[7] validation-logloss:0.61915 validation-auc:0.95280 validation-aucpr:0.95979
[8] validation-logloss:0.61180 validation-auc:0.95276 validation-aucpr:0.95947
[9] validation-logloss:0.60462 validation-auc:0.95194 validation-aucpr:0.95871
[10] validation-logloss:0.59717 validation-auc:0.95284 validation-aucpr:0.95903
[11] validation-logloss:0.59054 validation-auc:0.95395 validation-aucpr:0.95991
[12] validation-logloss:0.58411 validation-auc:0.95353 validation-aucpr:0.95980
[13] validation-logloss:0.57772 validation-auc:0.95308 validation-aucpr:0.95942
[14] validation-logloss:0.57136 validation-auc:0.95375 validation-aucpr:0.96010
[15] validation-logloss:0.56518 validation-auc:0.95423 validation-aucpr:0.96066
[16] validation-logloss:0.55945 validation-auc:0.95389 validation-aucpr:0.96035
[17] validation-logloss:0.55176 validation-auc:0.95459 validation-aucpr:0.96128
[18] validation-logloss:0.54599 validation-auc:0.95468 validation-aucpr:0.96129
[19] validation-logloss:0.54079 validation-auc:0.95501 validation-aucpr:0.96150
[20] validation-logloss:0.53568 validation-auc:0.95469 validation-aucpr:0.96121
[21] validation-logloss:0.52909 validation-auc:0.95507 validation-aucpr:0.96164
{'best_iteration': '21', 'best_score': '0.9616435554777185'}
Trial 2, Fold 4: Log loss = 0.52909184951774, Average precision = 0.961437936932935, ROC-AUC = 0.9550681076847143, Elapsed Time = 1.8316604000010557 seconds
Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 2, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68143 validation-auc:0.93842 validation-aucpr:0.94365
[1] validation-logloss:0.67050 validation-auc:0.94675 validation-aucpr:0.95040
[2] validation-logloss:0.66113 validation-auc:0.94800 validation-aucpr:0.95419
[3] validation-logloss:0.65252 validation-auc:0.94793 validation-aucpr:0.95343
[4] validation-logloss:0.64373 validation-auc:0.95174 validation-aucpr:0.95681
[5] validation-logloss:0.63496 validation-auc:0.95247 validation-aucpr:0.95770
[6] validation-logloss:0.62714 validation-auc:0.95206 validation-aucpr:0.95746
[7] validation-logloss:0.62275 validation-auc:0.95204 validation-aucpr:0.95729
[8] validation-logloss:0.61510 validation-auc:0.95156 validation-aucpr:0.95661
[9] validation-logloss:0.60807 validation-auc:0.95122 validation-aucpr:0.95616
[10] validation-logloss:0.60071 validation-auc:0.95169 validation-aucpr:0.95680
[11] validation-logloss:0.59420 validation-auc:0.95135 validation-aucpr:0.95662
[12] validation-logloss:0.58782 validation-auc:0.95151 validation-aucpr:0.95707
[13] validation-logloss:0.58148 validation-auc:0.95131 validation-aucpr:0.95696
[14] validation-logloss:0.57524 validation-auc:0.95164 validation-aucpr:0.95725
[15] validation-logloss:0.56916 validation-auc:0.95224 validation-aucpr:0.95787
[16] validation-logloss:0.56337 validation-auc:0.95244 validation-aucpr:0.95802
[17] validation-logloss:0.55568 validation-auc:0.95336 validation-aucpr:0.95948
[18] validation-logloss:0.54982 validation-auc:0.95347 validation-aucpr:0.95946
[19] validation-logloss:0.54483 validation-auc:0.95392 validation-aucpr:0.95977
[20] validation-logloss:0.53981 validation-auc:0.95366 validation-aucpr:0.95942
[21] validation-logloss:0.53325 validation-auc:0.95402 validation-aucpr:0.96005
{'best_iteration': '21', 'best_score': '0.9600526891124913'}
Trial 2, Fold 5: Log loss = 0.5332477529706764, Average precision = 0.9599245768391352, ROC-AUC = 0.9540233729074932, Elapsed Time = 1.8385123999996722 seconds
Optimization Progress: 3%|3 | 3/100 [02:00<52:27, 32.45s/it]
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 3, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68648 validation-auc:0.92196 validation-aucpr:0.92137
[1] validation-logloss:0.67979 validation-auc:0.93993 validation-aucpr:0.94332
[2] validation-logloss:0.67481 validation-auc:0.93782 validation-aucpr:0.94263
[3] validation-logloss:0.67037 validation-auc:0.94815 validation-aucpr:0.95520
[4] validation-logloss:0.66325 validation-auc:0.95313 validation-aucpr:0.96064
[5] validation-logloss:0.65757 validation-auc:0.95367 validation-aucpr:0.96074
[6] validation-logloss:0.65164 validation-auc:0.95371 validation-aucpr:0.96076
[7] validation-logloss:0.64613 validation-auc:0.95296 validation-aucpr:0.95999
[8] validation-logloss:0.63929 validation-auc:0.95503 validation-aucpr:0.96216
[9] validation-logloss:0.63400 validation-auc:0.95465 validation-aucpr:0.96159
[10] validation-logloss:0.62856 validation-auc:0.95467 validation-aucpr:0.96143
[11] validation-logloss:0.62336 validation-auc:0.95547 validation-aucpr:0.96198
[12] validation-logloss:0.61810 validation-auc:0.95542 validation-aucpr:0.96221
[13] validation-logloss:0.61454 validation-auc:0.95524 validation-aucpr:0.96216
[14] validation-logloss:0.60945 validation-auc:0.95545 validation-aucpr:0.96244
[15] validation-logloss:0.60455 validation-auc:0.95551 validation-aucpr:0.96232
[16] validation-logloss:0.59971 validation-auc:0.95561 validation-aucpr:0.96230
[17] validation-logloss:0.59507 validation-auc:0.95588 validation-aucpr:0.96268
[18] validation-logloss:0.59096 validation-auc:0.95576 validation-aucpr:0.96253
[19] validation-logloss:0.58663 validation-auc:0.95559 validation-aucpr:0.96248
[20] validation-logloss:0.58216 validation-auc:0.95558 validation-aucpr:0.96244
[21] validation-logloss:0.57705 validation-auc:0.95626 validation-aucpr:0.96306
[22] validation-logloss:0.57264 validation-auc:0.95622 validation-aucpr:0.96300
[23] validation-logloss:0.56832 validation-auc:0.95613 validation-aucpr:0.96291
[24] validation-logloss:0.56404 validation-auc:0.95620 validation-aucpr:0.96287
[25] validation-logloss:0.56015 validation-auc:0.95604 validation-aucpr:0.96269
[26] validation-logloss:0.55595 validation-auc:0.95590 validation-aucpr:0.96254
[27] validation-logloss:0.55209 validation-auc:0.95562 validation-aucpr:0.96222
[28] validation-logloss:0.54827 validation-auc:0.95578 validation-aucpr:0.96249
[29] validation-logloss:0.54546 validation-auc:0.95586 validation-aucpr:0.96257
[30] validation-logloss:0.54056 validation-auc:0.95655 validation-aucpr:0.96335
[31] validation-logloss:0.53725 validation-auc:0.95638 validation-aucpr:0.96318
[32] validation-logloss:0.53406 validation-auc:0.95641 validation-aucpr:0.96320
[33] validation-logloss:0.53054 validation-auc:0.95637 validation-aucpr:0.96316
[34] validation-logloss:0.52707 validation-auc:0.95630 validation-aucpr:0.96310
[35] validation-logloss:0.52277 validation-auc:0.95666 validation-aucpr:0.96350
[36] validation-logloss:0.51957 validation-auc:0.95649 validation-aucpr:0.96334
[37] validation-logloss:0.51637 validation-auc:0.95649 validation-aucpr:0.96334
[38] validation-logloss:0.51229 validation-auc:0.95659 validation-aucpr:0.96342
[39] validation-logloss:0.50904 validation-auc:0.95681 validation-aucpr:0.96358
[40] validation-logloss:0.50655 validation-auc:0.95683 validation-aucpr:0.96355
[41] validation-logloss:0.50273 validation-auc:0.95698 validation-aucpr:0.96370
[42] validation-logloss:0.49980 validation-auc:0.95690 validation-aucpr:0.96363
[43] validation-logloss:0.49695 validation-auc:0.95687 validation-aucpr:0.96359
[44] validation-logloss:0.49428 validation-auc:0.95680 validation-aucpr:0.96350
[45] validation-logloss:0.49116 validation-auc:0.95685 validation-aucpr:0.96354
[46] validation-logloss:0.48822 validation-auc:0.95672 validation-aucpr:0.96342
[47] validation-logloss:0.48540 validation-auc:0.95663 validation-aucpr:0.96334
[48] validation-logloss:0.48298 validation-auc:0.95654 validation-aucpr:0.96326
[49] validation-logloss:0.48015 validation-auc:0.95654 validation-aucpr:0.96329
[50] validation-logloss:0.47744 validation-auc:0.95641 validation-aucpr:0.96319
[51] validation-logloss:0.47453 validation-auc:0.95648 validation-aucpr:0.96323
[52] validation-logloss:0.47209 validation-auc:0.95646 validation-aucpr:0.96319
[53] validation-logloss:0.46993 validation-auc:0.95650 validation-aucpr:0.96322
[54] validation-logloss:0.46865 validation-auc:0.95653 validation-aucpr:0.96326
[55] validation-logloss:0.46506 validation-auc:0.95667 validation-aucpr:0.96344
[56] validation-logloss:0.46277 validation-auc:0.95667 validation-aucpr:0.96344
[57] validation-logloss:0.46066 validation-auc:0.95663 validation-aucpr:0.96343
[58] validation-logloss:0.45836 validation-auc:0.95651 validation-aucpr:0.96332
[59] validation-logloss:0.45611 validation-auc:0.95654 validation-aucpr:0.96333
[60] validation-logloss:0.45582 validation-auc:0.95657 validation-aucpr:0.96336
[61] validation-logloss:0.45406 validation-auc:0.95660 validation-aucpr:0.96342
[62] validation-logloss:0.45193 validation-auc:0.95664 validation-aucpr:0.96341
[63] validation-logloss:0.44985 validation-auc:0.95661 validation-aucpr:0.96335
[64] validation-logloss:0.44796 validation-auc:0.95671 validation-aucpr:0.96346
[65] validation-logloss:0.44593 validation-auc:0.95667 validation-aucpr:0.96343
[66] validation-logloss:0.44410 validation-auc:0.95673 validation-aucpr:0.96346
[67] validation-logloss:0.44186 validation-auc:0.95676 validation-aucpr:0.96345
[68] validation-logloss:0.43990 validation-auc:0.95669 validation-aucpr:0.96339
[69] validation-logloss:0.43801 validation-auc:0.95665 validation-aucpr:0.96336
[70] validation-logloss:0.43578 validation-auc:0.95671 validation-aucpr:0.96341
[71] validation-logloss:0.43273 validation-auc:0.95693 validation-aucpr:0.96363
[72] validation-logloss:0.43068 validation-auc:0.95696 validation-aucpr:0.96366
[73] validation-logloss:0.42894 validation-auc:0.95691 validation-aucpr:0.96359
[74] validation-logloss:0.42739 validation-auc:0.95698 validation-aucpr:0.96364
[75] validation-logloss:0.42569 validation-auc:0.95700 validation-aucpr:0.96364
[76] validation-logloss:0.42396 validation-auc:0.95705 validation-aucpr:0.96369
[77] validation-logloss:0.42233 validation-auc:0.95705 validation-aucpr:0.96369
[78] validation-logloss:0.42070 validation-auc:0.95705 validation-aucpr:0.96367
[79] validation-logloss:0.41917 validation-auc:0.95703 validation-aucpr:0.96366
[80] validation-logloss:0.41726 validation-auc:0.95706 validation-aucpr:0.96368
[81] validation-logloss:0.41545 validation-auc:0.95709 validation-aucpr:0.96368
[82] validation-logloss:0.41369 validation-auc:0.95701 validation-aucpr:0.96362
[83] validation-logloss:0.41209 validation-auc:0.95696 validation-aucpr:0.96359
[84] validation-logloss:0.41013 validation-auc:0.95701 validation-aucpr:0.96363
[85] validation-logloss:0.40853 validation-auc:0.95708 validation-aucpr:0.96368
[86] validation-logloss:0.40618 validation-auc:0.95720 validation-aucpr:0.96385
[87] validation-logloss:0.40457 validation-auc:0.95721 validation-aucpr:0.96385
[88] validation-logloss:0.40296 validation-auc:0.95719 validation-aucpr:0.96383
[89] validation-logloss:0.40150 validation-auc:0.95715 validation-aucpr:0.96378
[90] validation-logloss:0.40007 validation-auc:0.95706 validation-aucpr:0.96372
[91] validation-logloss:0.39855 validation-auc:0.95708 validation-aucpr:0.96374
[92] validation-logloss:0.39658 validation-auc:0.95715 validation-aucpr:0.96376
[93] validation-logloss:0.39545 validation-auc:0.95710 validation-aucpr:0.96370
[94] validation-logloss:0.39386 validation-auc:0.95710 validation-aucpr:0.96368
{'best_iteration': '87', 'best_score': '0.9638543290754388'}
Trial 3, Fold 1: Log loss = 0.3938565476281746, Average precision = 0.9636843615734073, ROC-AUC = 0.957102141017001, Elapsed Time = 2.7186390999995638 seconds
Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 3, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68674 validation-auc:0.91252 validation-aucpr:0.91239
[1] validation-logloss:0.68039 validation-auc:0.92215 validation-aucpr:0.92220
[2] validation-logloss:0.67433 validation-auc:0.92483 validation-aucpr:0.92480
[3] validation-logloss:0.66802 validation-auc:0.92934 validation-aucpr:0.92810
[4] validation-logloss:0.66176 validation-auc:0.93242 validation-aucpr:0.93143
[5] validation-logloss:0.65601 validation-auc:0.93170 validation-aucpr:0.93022
[6] validation-logloss:0.64917 validation-auc:0.94908 validation-aucpr:0.95162
[7] validation-logloss:0.64310 validation-auc:0.95003 validation-aucpr:0.95237
[8] validation-logloss:0.63788 validation-auc:0.94970 validation-aucpr:0.95273
[9] validation-logloss:0.63344 validation-auc:0.94830 validation-aucpr:0.95040
[10] validation-logloss:0.62848 validation-auc:0.94864 validation-aucpr:0.95117
[11] validation-logloss:0.62404 validation-auc:0.94934 validation-aucpr:0.95200
[12] validation-logloss:0.61965 validation-auc:0.94871 validation-aucpr:0.95112
[13] validation-logloss:0.61347 validation-auc:0.95379 validation-aucpr:0.95793
[14] validation-logloss:0.60851 validation-auc:0.95415 validation-aucpr:0.95834
[15] validation-logloss:0.60376 validation-auc:0.95383 validation-aucpr:0.95810
[16] validation-logloss:0.59908 validation-auc:0.95385 validation-aucpr:0.95820
[17] validation-logloss:0.59466 validation-auc:0.95366 validation-aucpr:0.95813
[18] validation-logloss:0.59066 validation-auc:0.95372 validation-aucpr:0.95829
[19] validation-logloss:0.58502 validation-auc:0.95545 validation-aucpr:0.96043
[20] validation-logloss:0.57953 validation-auc:0.95649 validation-aucpr:0.96159
[21] validation-logloss:0.57560 validation-auc:0.95649 validation-aucpr:0.96162
[22] validation-logloss:0.57118 validation-auc:0.95677 validation-aucpr:0.96178
[23] validation-logloss:0.56723 validation-auc:0.95678 validation-aucpr:0.96165
[24] validation-logloss:0.56202 validation-auc:0.95735 validation-aucpr:0.96245
[25] validation-logloss:0.55826 validation-auc:0.95720 validation-aucpr:0.96226
[26] validation-logloss:0.55383 validation-auc:0.95742 validation-aucpr:0.96257
[27] validation-logloss:0.55008 validation-auc:0.95733 validation-aucpr:0.96242
[28] validation-logloss:0.54654 validation-auc:0.95729 validation-aucpr:0.96233
[29] validation-logloss:0.54262 validation-auc:0.95732 validation-aucpr:0.96231
[30] validation-logloss:0.53918 validation-auc:0.95710 validation-aucpr:0.96213
[31] validation-logloss:0.53458 validation-auc:0.95760 validation-aucpr:0.96258
[32] validation-logloss:0.53096 validation-auc:0.95760 validation-aucpr:0.96257
[33] validation-logloss:0.52740 validation-auc:0.95779 validation-aucpr:0.96271
[34] validation-logloss:0.52421 validation-auc:0.95769 validation-aucpr:0.96261
[35] validation-logloss:0.52063 validation-auc:0.95777 validation-aucpr:0.96267
[36] validation-logloss:0.51691 validation-auc:0.95793 validation-aucpr:0.96275
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[39] validation-logloss:0.50749 validation-auc:0.95781 validation-aucpr:0.96254
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[89] validation-logloss:0.40013 validation-auc:0.95768 validation-aucpr:0.96193
[90] validation-logloss:0.39886 validation-auc:0.95765 validation-aucpr:0.96189
[91] validation-logloss:0.39738 validation-auc:0.95765 validation-aucpr:0.96185
[92] validation-logloss:0.39623 validation-auc:0.95759 validation-aucpr:0.96178
[93] validation-logloss:0.39481 validation-auc:0.95764 validation-aucpr:0.96184
[94] validation-logloss:0.39321 validation-auc:0.95762 validation-aucpr:0.96178
{'best_iteration': '54', 'best_score': '0.9628922856828451'}
Trial 3, Fold 2: Log loss = 0.3932079132355882, Average precision = 0.9617895947599515, ROC-AUC = 0.9576229134300127, Elapsed Time = 2.8691792999998142 seconds
Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 3, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68637 validation-auc:0.91589 validation-aucpr:0.90796
[1] validation-logloss:0.68078 validation-auc:0.92850 validation-aucpr:0.92889
[2] validation-logloss:0.67416 validation-auc:0.94698 validation-aucpr:0.95374
[3] validation-logloss:0.66803 validation-auc:0.94846 validation-aucpr:0.95492
[4] validation-logloss:0.66274 validation-auc:0.94739 validation-aucpr:0.95377
[5] validation-logloss:0.65644 validation-auc:0.95036 validation-aucpr:0.95687
[6] validation-logloss:0.64935 validation-auc:0.95312 validation-aucpr:0.95948
[7] validation-logloss:0.64360 validation-auc:0.95329 validation-aucpr:0.95977
[8] validation-logloss:0.63840 validation-auc:0.95338 validation-aucpr:0.95971
[9] validation-logloss:0.63291 validation-auc:0.95463 validation-aucpr:0.96039
[10] validation-logloss:0.62713 validation-auc:0.95512 validation-aucpr:0.96082
[11] validation-logloss:0.62123 validation-auc:0.95592 validation-aucpr:0.96243
[12] validation-logloss:0.61643 validation-auc:0.95651 validation-aucpr:0.96287
[13] validation-logloss:0.61044 validation-auc:0.95703 validation-aucpr:0.96339
[14] validation-logloss:0.60546 validation-auc:0.95734 validation-aucpr:0.96357
[15] validation-logloss:0.59992 validation-auc:0.95813 validation-aucpr:0.96408
[16] validation-logloss:0.59505 validation-auc:0.95837 validation-aucpr:0.96429
[17] validation-logloss:0.59001 validation-auc:0.95859 validation-aucpr:0.96458
[18] validation-logloss:0.58534 validation-auc:0.95865 validation-aucpr:0.96474
[19] validation-logloss:0.58115 validation-auc:0.95868 validation-aucpr:0.96485
[20] validation-logloss:0.57679 validation-auc:0.95845 validation-aucpr:0.96465
[21] validation-logloss:0.57199 validation-auc:0.95867 validation-aucpr:0.96477
[22] validation-logloss:0.56769 validation-auc:0.95889 validation-aucpr:0.96495
[23] validation-logloss:0.56219 validation-auc:0.95890 validation-aucpr:0.96512
[24] validation-logloss:0.55829 validation-auc:0.95871 validation-aucpr:0.96503
[25] validation-logloss:0.55504 validation-auc:0.95859 validation-aucpr:0.96491
[26] validation-logloss:0.55004 validation-auc:0.95866 validation-aucpr:0.96497
[27] validation-logloss:0.54646 validation-auc:0.95853 validation-aucpr:0.96485
[28] validation-logloss:0.54217 validation-auc:0.95849 validation-aucpr:0.96480
[29] validation-logloss:0.53876 validation-auc:0.95843 validation-aucpr:0.96472
[30] validation-logloss:0.53531 validation-auc:0.95841 validation-aucpr:0.96462
[31] validation-logloss:0.53165 validation-auc:0.95828 validation-aucpr:0.96454
[32] validation-logloss:0.52858 validation-auc:0.95841 validation-aucpr:0.96457
[33] validation-logloss:0.52439 validation-auc:0.95842 validation-aucpr:0.96464
[34] validation-logloss:0.52065 validation-auc:0.95837 validation-aucpr:0.96457
[35] validation-logloss:0.51740 validation-auc:0.95830 validation-aucpr:0.96447
[36] validation-logloss:0.51431 validation-auc:0.95839 validation-aucpr:0.96454
[37] validation-logloss:0.51143 validation-auc:0.95831 validation-aucpr:0.96443
[38] validation-logloss:0.50802 validation-auc:0.95834 validation-aucpr:0.96448
[39] validation-logloss:0.50473 validation-auc:0.95837 validation-aucpr:0.96446
[40] validation-logloss:0.50267 validation-auc:0.95846 validation-aucpr:0.96443
[41] validation-logloss:0.49981 validation-auc:0.95834 validation-aucpr:0.96438
[42] validation-logloss:0.49679 validation-auc:0.95853 validation-aucpr:0.96443
[43] validation-logloss:0.49455 validation-auc:0.95843 validation-aucpr:0.96437
[44] validation-logloss:0.49165 validation-auc:0.95856 validation-aucpr:0.96444
[45] validation-logloss:0.48878 validation-auc:0.95850 validation-aucpr:0.96440
[46] validation-logloss:0.48590 validation-auc:0.95840 validation-aucpr:0.96431
[47] validation-logloss:0.48340 validation-auc:0.95843 validation-aucpr:0.96451
[48] validation-logloss:0.48075 validation-auc:0.95839 validation-aucpr:0.96451
[49] validation-logloss:0.47740 validation-auc:0.95836 validation-aucpr:0.96454
[50] validation-logloss:0.47456 validation-auc:0.95845 validation-aucpr:0.96456
[51] validation-logloss:0.47229 validation-auc:0.95846 validation-aucpr:0.96457
[52] validation-logloss:0.46974 validation-auc:0.95845 validation-aucpr:0.96453
[53] validation-logloss:0.46706 validation-auc:0.95865 validation-aucpr:0.96462
[54] validation-logloss:0.46457 validation-auc:0.95873 validation-aucpr:0.96470
[55] validation-logloss:0.46209 validation-auc:0.95877 validation-aucpr:0.96475
[56] validation-logloss:0.45970 validation-auc:0.95901 validation-aucpr:0.96510
[57] validation-logloss:0.45769 validation-auc:0.95917 validation-aucpr:0.96515
[58] validation-logloss:0.45617 validation-auc:0.95916 validation-aucpr:0.96514
[59] validation-logloss:0.45401 validation-auc:0.95921 validation-aucpr:0.96518
[60] validation-logloss:0.45198 validation-auc:0.95925 validation-aucpr:0.96521
[61] validation-logloss:0.44992 validation-auc:0.95923 validation-aucpr:0.96518
[62] validation-logloss:0.44763 validation-auc:0.95917 validation-aucpr:0.96515
[63] validation-logloss:0.44532 validation-auc:0.95920 validation-aucpr:0.96518
[64] validation-logloss:0.44328 validation-auc:0.95921 validation-aucpr:0.96520
[65] validation-logloss:0.44101 validation-auc:0.95928 validation-aucpr:0.96522
[66] validation-logloss:0.43898 validation-auc:0.95940 validation-aucpr:0.96530
[67] validation-logloss:0.43675 validation-auc:0.95949 validation-aucpr:0.96540
[68] validation-logloss:0.43503 validation-auc:0.95946 validation-aucpr:0.96537
[69] validation-logloss:0.43310 validation-auc:0.95946 validation-aucpr:0.96537
[70] validation-logloss:0.43136 validation-auc:0.95937 validation-aucpr:0.96529
[71] validation-logloss:0.42923 validation-auc:0.95935 validation-aucpr:0.96529
[72] validation-logloss:0.42712 validation-auc:0.95945 validation-aucpr:0.96535
[73] validation-logloss:0.42516 validation-auc:0.95952 validation-aucpr:0.96545
[74] validation-logloss:0.42318 validation-auc:0.95959 validation-aucpr:0.96549
[75] validation-logloss:0.42158 validation-auc:0.95962 validation-aucpr:0.96554
[76] validation-logloss:0.41998 validation-auc:0.95962 validation-aucpr:0.96552
[77] validation-logloss:0.41806 validation-auc:0.95964 validation-aucpr:0.96552
[78] validation-logloss:0.41618 validation-auc:0.95956 validation-aucpr:0.96545
[79] validation-logloss:0.41455 validation-auc:0.95957 validation-aucpr:0.96542
[80] validation-logloss:0.41264 validation-auc:0.95960 validation-aucpr:0.96544
[81] validation-logloss:0.41101 validation-auc:0.95956 validation-aucpr:0.96541
[82] validation-logloss:0.40966 validation-auc:0.95954 validation-aucpr:0.96543
[83] validation-logloss:0.40819 validation-auc:0.95948 validation-aucpr:0.96539
[84] validation-logloss:0.40642 validation-auc:0.95944 validation-aucpr:0.96535
[85] validation-logloss:0.40475 validation-auc:0.95948 validation-aucpr:0.96536
[86] validation-logloss:0.40294 validation-auc:0.95958 validation-aucpr:0.96542
[87] validation-logloss:0.40129 validation-auc:0.95960 validation-aucpr:0.96544
[88] validation-logloss:0.39988 validation-auc:0.95959 validation-aucpr:0.96541
[89] validation-logloss:0.39816 validation-auc:0.95958 validation-aucpr:0.96539
[90] validation-logloss:0.39683 validation-auc:0.95957 validation-aucpr:0.96536
[91] validation-logloss:0.39561 validation-auc:0.95949 validation-aucpr:0.96531
[92] validation-logloss:0.39507 validation-auc:0.95949 validation-aucpr:0.96530
[93] validation-logloss:0.39366 validation-auc:0.95937 validation-aucpr:0.96518
[94] validation-logloss:0.39203 validation-auc:0.95937 validation-aucpr:0.96519
{'best_iteration': '75', 'best_score': '0.965536071811244'}
Trial 3, Fold 3: Log loss = 0.3920261647812823, Average precision = 0.9651939229642484, ROC-AUC = 0.9593688451666651, Elapsed Time = 2.9667830999987927 seconds
Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 3, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68650 validation-auc:0.91122 validation-aucpr:0.91672
[1] validation-logloss:0.68023 validation-auc:0.92379 validation-aucpr:0.92782
[2] validation-logloss:0.67405 validation-auc:0.93039 validation-aucpr:0.93561
[3] validation-logloss:0.66843 validation-auc:0.93237 validation-aucpr:0.93702
[4] validation-logloss:0.66248 validation-auc:0.93462 validation-aucpr:0.93935
[5] validation-logloss:0.65544 validation-auc:0.94953 validation-aucpr:0.95605
[6] validation-logloss:0.64954 validation-auc:0.94976 validation-aucpr:0.95695
[7] validation-logloss:0.64419 validation-auc:0.94895 validation-aucpr:0.95615
[8] validation-logloss:0.63847 validation-auc:0.94925 validation-aucpr:0.95641
[9] validation-logloss:0.63183 validation-auc:0.95105 validation-aucpr:0.95855
[10] validation-logloss:0.62670 validation-auc:0.95077 validation-aucpr:0.95831
[11] validation-logloss:0.62140 validation-auc:0.95096 validation-aucpr:0.95844
[12] validation-logloss:0.61612 validation-auc:0.95237 validation-aucpr:0.95971
[13] validation-logloss:0.61166 validation-auc:0.95254 validation-aucpr:0.95998
[14] validation-logloss:0.60652 validation-auc:0.95265 validation-aucpr:0.96018
[15] validation-logloss:0.60154 validation-auc:0.95301 validation-aucpr:0.96063
[16] validation-logloss:0.59947 validation-auc:0.95334 validation-aucpr:0.96095
[17] validation-logloss:0.59541 validation-auc:0.95316 validation-aucpr:0.96075
[18] validation-logloss:0.59134 validation-auc:0.95304 validation-aucpr:0.96057
[19] validation-logloss:0.58703 validation-auc:0.95311 validation-aucpr:0.96066
[20] validation-logloss:0.58293 validation-auc:0.95311 validation-aucpr:0.96062
[21] validation-logloss:0.57890 validation-auc:0.95247 validation-aucpr:0.95995
[22] validation-logloss:0.57513 validation-auc:0.95225 validation-aucpr:0.95962
[23] validation-logloss:0.57089 validation-auc:0.95245 validation-aucpr:0.95964
[24] validation-logloss:0.56692 validation-auc:0.95286 validation-aucpr:0.96010
[25] validation-logloss:0.56284 validation-auc:0.95297 validation-aucpr:0.96017
[26] validation-logloss:0.55955 validation-auc:0.95299 validation-aucpr:0.96020
[27] validation-logloss:0.55590 validation-auc:0.95314 validation-aucpr:0.96029
[28] validation-logloss:0.55205 validation-auc:0.95270 validation-aucpr:0.95979
[29] validation-logloss:0.54865 validation-auc:0.95251 validation-aucpr:0.95960
[30] validation-logloss:0.54525 validation-auc:0.95196 validation-aucpr:0.95898
[31] validation-logloss:0.54125 validation-auc:0.95293 validation-aucpr:0.96022
[32] validation-logloss:0.53716 validation-auc:0.95347 validation-aucpr:0.96075
[33] validation-logloss:0.53490 validation-auc:0.95360 validation-aucpr:0.96095
[34] validation-logloss:0.53023 validation-auc:0.95417 validation-aucpr:0.96154
[35] validation-logloss:0.52720 validation-auc:0.95439 validation-aucpr:0.96166
[36] validation-logloss:0.52432 validation-auc:0.95413 validation-aucpr:0.96142
[37] validation-logloss:0.52094 validation-auc:0.95420 validation-aucpr:0.96152
[38] validation-logloss:0.51744 validation-auc:0.95434 validation-aucpr:0.96165
[39] validation-logloss:0.51438 validation-auc:0.95434 validation-aucpr:0.96167
[40] validation-logloss:0.51183 validation-auc:0.95432 validation-aucpr:0.96159
[41] validation-logloss:0.50924 validation-auc:0.95416 validation-aucpr:0.96143
[42] validation-logloss:0.50608 validation-auc:0.95418 validation-aucpr:0.96136
[43] validation-logloss:0.50337 validation-auc:0.95412 validation-aucpr:0.96129
[44] validation-logloss:0.50054 validation-auc:0.95399 validation-aucpr:0.96115
[45] validation-logloss:0.49782 validation-auc:0.95399 validation-aucpr:0.96112
[46] validation-logloss:0.49540 validation-auc:0.95387 validation-aucpr:0.96096
[47] validation-logloss:0.49263 validation-auc:0.95406 validation-aucpr:0.96111
[48] validation-logloss:0.48973 validation-auc:0.95401 validation-aucpr:0.96096
[49] validation-logloss:0.48696 validation-auc:0.95406 validation-aucpr:0.96102
[50] validation-logloss:0.48424 validation-auc:0.95416 validation-aucpr:0.96105
[51] validation-logloss:0.48085 validation-auc:0.95453 validation-aucpr:0.96152
[52] validation-logloss:0.47813 validation-auc:0.95452 validation-aucpr:0.96151
[53] validation-logloss:0.47535 validation-auc:0.95460 validation-aucpr:0.96156
[54] validation-logloss:0.47301 validation-auc:0.95452 validation-aucpr:0.96145
[55] validation-logloss:0.47037 validation-auc:0.95455 validation-aucpr:0.96146
[56] validation-logloss:0.46813 validation-auc:0.95452 validation-aucpr:0.96143
[57] validation-logloss:0.46603 validation-auc:0.95443 validation-aucpr:0.96134
[58] validation-logloss:0.46372 validation-auc:0.95441 validation-aucpr:0.96133
[59] validation-logloss:0.46170 validation-auc:0.95433 validation-aucpr:0.96124
[60] validation-logloss:0.45951 validation-auc:0.95433 validation-aucpr:0.96119
[61] validation-logloss:0.45728 validation-auc:0.95439 validation-aucpr:0.96126
[62] validation-logloss:0.45506 validation-auc:0.95440 validation-aucpr:0.96121
[63] validation-logloss:0.45301 validation-auc:0.95436 validation-aucpr:0.96119
[64] validation-logloss:0.45152 validation-auc:0.95429 validation-aucpr:0.96113
[65] validation-logloss:0.44944 validation-auc:0.95432 validation-aucpr:0.96112
[66] validation-logloss:0.44780 validation-auc:0.95425 validation-aucpr:0.96101
[67] validation-logloss:0.44602 validation-auc:0.95427 validation-aucpr:0.96101
[68] validation-logloss:0.44356 validation-auc:0.95448 validation-aucpr:0.96119
[69] validation-logloss:0.44159 validation-auc:0.95451 validation-aucpr:0.96118
[70] validation-logloss:0.44149 validation-auc:0.95448 validation-aucpr:0.96115
[71] validation-logloss:0.43888 validation-auc:0.95490 validation-aucpr:0.96170
[72] validation-logloss:0.43678 validation-auc:0.95492 validation-aucpr:0.96170
[73] validation-logloss:0.43445 validation-auc:0.95495 validation-aucpr:0.96166
[74] validation-logloss:0.43259 validation-auc:0.95497 validation-aucpr:0.96171
[75] validation-logloss:0.43105 validation-auc:0.95487 validation-aucpr:0.96165
[76] validation-logloss:0.42931 validation-auc:0.95490 validation-aucpr:0.96169
[77] validation-logloss:0.42642 validation-auc:0.95528 validation-aucpr:0.96215
[78] validation-logloss:0.42448 validation-auc:0.95540 validation-aucpr:0.96223
[79] validation-logloss:0.42247 validation-auc:0.95547 validation-aucpr:0.96229
[80] validation-logloss:0.42078 validation-auc:0.95543 validation-aucpr:0.96224
[81] validation-logloss:0.41919 validation-auc:0.95541 validation-aucpr:0.96219
[82] validation-logloss:0.41773 validation-auc:0.95535 validation-aucpr:0.96212
[83] validation-logloss:0.41615 validation-auc:0.95537 validation-aucpr:0.96216
[84] validation-logloss:0.41496 validation-auc:0.95534 validation-aucpr:0.96213
[85] validation-logloss:0.41365 validation-auc:0.95530 validation-aucpr:0.96211
[86] validation-logloss:0.41200 validation-auc:0.95525 validation-aucpr:0.96204
[87] validation-logloss:0.41045 validation-auc:0.95520 validation-aucpr:0.96200
[88] validation-logloss:0.40904 validation-auc:0.95529 validation-aucpr:0.96209
[89] validation-logloss:0.40787 validation-auc:0.95536 validation-aucpr:0.96215
[90] validation-logloss:0.40625 validation-auc:0.95540 validation-aucpr:0.96216
[91] validation-logloss:0.40489 validation-auc:0.95534 validation-aucpr:0.96209
[92] validation-logloss:0.40348 validation-auc:0.95528 validation-aucpr:0.96199
[93] validation-logloss:0.40213 validation-auc:0.95527 validation-aucpr:0.96199
[94] validation-logloss:0.40036 validation-auc:0.95535 validation-aucpr:0.96205
{'best_iteration': '79', 'best_score': '0.9622923502065677'}
Trial 3, Fold 4: Log loss = 0.4003591014363517, Average precision = 0.9620560602631516, ROC-AUC = 0.9553464130131547, Elapsed Time = 2.7846226999990904 seconds
Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 3, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68639 validation-auc:0.91537 validation-aucpr:0.91728
[1] validation-logloss:0.68013 validation-auc:0.92791 validation-aucpr:0.93193
[2] validation-logloss:0.67394 validation-auc:0.92814 validation-aucpr:0.93182
[3] validation-logloss:0.66757 validation-auc:0.93667 validation-aucpr:0.94082
[4] validation-logloss:0.66294 validation-auc:0.93631 validation-aucpr:0.94060
[5] validation-logloss:0.65729 validation-auc:0.93803 validation-aucpr:0.94266
[6] validation-logloss:0.65090 validation-auc:0.94229 validation-aucpr:0.94764
[7] validation-logloss:0.64525 validation-auc:0.94258 validation-aucpr:0.94772
[8] validation-logloss:0.64001 validation-auc:0.94279 validation-aucpr:0.94788
[9] validation-logloss:0.63478 validation-auc:0.94305 validation-aucpr:0.94816
[10] validation-logloss:0.62928 validation-auc:0.94329 validation-aucpr:0.94860
[11] validation-logloss:0.62416 validation-auc:0.94347 validation-aucpr:0.94872
[12] validation-logloss:0.61951 validation-auc:0.94328 validation-aucpr:0.94861
[13] validation-logloss:0.61508 validation-auc:0.94369 validation-aucpr:0.94924
[14] validation-logloss:0.61004 validation-auc:0.94355 validation-aucpr:0.94898
[15] validation-logloss:0.60580 validation-auc:0.94325 validation-aucpr:0.94858
[16] validation-logloss:0.60117 validation-auc:0.94387 validation-aucpr:0.94913
[17] validation-logloss:0.59639 validation-auc:0.94382 validation-aucpr:0.94923
[18] validation-logloss:0.59094 validation-auc:0.94798 validation-aucpr:0.95403
[19] validation-logloss:0.58653 validation-auc:0.94794 validation-aucpr:0.95395
[20] validation-logloss:0.58221 validation-auc:0.94815 validation-aucpr:0.95410
[21] validation-logloss:0.57830 validation-auc:0.94783 validation-aucpr:0.95375
[22] validation-logloss:0.57425 validation-auc:0.94773 validation-aucpr:0.95358
[23] validation-logloss:0.57052 validation-auc:0.94753 validation-aucpr:0.95340
[24] validation-logloss:0.56631 validation-auc:0.94774 validation-aucpr:0.95349
[25] validation-logloss:0.56246 validation-auc:0.94786 validation-aucpr:0.95352
[26] validation-logloss:0.55857 validation-auc:0.94782 validation-aucpr:0.95345
[27] validation-logloss:0.55479 validation-auc:0.94783 validation-aucpr:0.95346
[28] validation-logloss:0.55109 validation-auc:0.94787 validation-aucpr:0.95352
[29] validation-logloss:0.54746 validation-auc:0.94802 validation-aucpr:0.95368
[30] validation-logloss:0.54482 validation-auc:0.94811 validation-aucpr:0.95360
[31] validation-logloss:0.54094 validation-auc:0.94971 validation-aucpr:0.95558
[32] validation-logloss:0.53799 validation-auc:0.94961 validation-aucpr:0.95547
[33] validation-logloss:0.53478 validation-auc:0.94953 validation-aucpr:0.95531
[34] validation-logloss:0.53144 validation-auc:0.94939 validation-aucpr:0.95509
[35] validation-logloss:0.52803 validation-auc:0.94945 validation-aucpr:0.95516
[36] validation-logloss:0.52445 validation-auc:0.95022 validation-aucpr:0.95612
[37] validation-logloss:0.52171 validation-auc:0.95008 validation-aucpr:0.95599
[38] validation-logloss:0.51859 validation-auc:0.95005 validation-aucpr:0.95598
[39] validation-logloss:0.51538 validation-auc:0.95003 validation-aucpr:0.95589
[40] validation-logloss:0.51260 validation-auc:0.94990 validation-aucpr:0.95570
[41] validation-logloss:0.50974 validation-auc:0.94986 validation-aucpr:0.95564
[42] validation-logloss:0.50683 validation-auc:0.94989 validation-aucpr:0.95562
[43] validation-logloss:0.50397 validation-auc:0.95003 validation-aucpr:0.95576
[44] validation-logloss:0.50139 validation-auc:0.94988 validation-aucpr:0.95554
[45] validation-logloss:0.49949 validation-auc:0.94990 validation-aucpr:0.95555
[46] validation-logloss:0.49699 validation-auc:0.94984 validation-aucpr:0.95545
[47] validation-logloss:0.49415 validation-auc:0.94988 validation-aucpr:0.95545
[48] validation-logloss:0.49145 validation-auc:0.94993 validation-aucpr:0.95542
[49] validation-logloss:0.48879 validation-auc:0.94966 validation-aucpr:0.95512
[50] validation-logloss:0.48580 validation-auc:0.94956 validation-aucpr:0.95505
[51] validation-logloss:0.48293 validation-auc:0.94975 validation-aucpr:0.95519
[52] validation-logloss:0.48027 validation-auc:0.94980 validation-aucpr:0.95520
[53] validation-logloss:0.47698 validation-auc:0.95132 validation-aucpr:0.95702
[54] validation-logloss:0.47454 validation-auc:0.95129 validation-aucpr:0.95694
[55] validation-logloss:0.47208 validation-auc:0.95111 validation-aucpr:0.95676
[56] validation-logloss:0.46927 validation-auc:0.95125 validation-aucpr:0.95686
[57] validation-logloss:0.46717 validation-auc:0.95133 validation-aucpr:0.95695
[58] validation-logloss:0.46452 validation-auc:0.95133 validation-aucpr:0.95695
[59] validation-logloss:0.46226 validation-auc:0.95141 validation-aucpr:0.95708
[60] validation-logloss:0.46015 validation-auc:0.95136 validation-aucpr:0.95705
[61] validation-logloss:0.45794 validation-auc:0.95143 validation-aucpr:0.95712
[62] validation-logloss:0.45539 validation-auc:0.95179 validation-aucpr:0.95758
[63] validation-logloss:0.45343 validation-auc:0.95176 validation-aucpr:0.95754
[64] validation-logloss:0.45153 validation-auc:0.95173 validation-aucpr:0.95749
[65] validation-logloss:0.44959 validation-auc:0.95158 validation-aucpr:0.95744
[66] validation-logloss:0.44747 validation-auc:0.95158 validation-aucpr:0.95743
[67] validation-logloss:0.44579 validation-auc:0.95147 validation-aucpr:0.95736
[68] validation-logloss:0.44361 validation-auc:0.95152 validation-aucpr:0.95743
[69] validation-logloss:0.44161 validation-auc:0.95151 validation-aucpr:0.95741
[70] validation-logloss:0.44023 validation-auc:0.95147 validation-aucpr:0.95739
[71] validation-logloss:0.43895 validation-auc:0.95141 validation-aucpr:0.95718
[72] validation-logloss:0.43726 validation-auc:0.95139 validation-aucpr:0.95713
[73] validation-logloss:0.43548 validation-auc:0.95131 validation-aucpr:0.95704
[74] validation-logloss:0.43390 validation-auc:0.95138 validation-aucpr:0.95707
[75] validation-logloss:0.43229 validation-auc:0.95122 validation-aucpr:0.95687
[76] validation-logloss:0.43072 validation-auc:0.95118 validation-aucpr:0.95686
[77] validation-logloss:0.42927 validation-auc:0.95108 validation-aucpr:0.95678
[78] validation-logloss:0.42760 validation-auc:0.95109 validation-aucpr:0.95677
[79] validation-logloss:0.42490 validation-auc:0.95201 validation-aucpr:0.95782
[80] validation-logloss:0.42348 validation-auc:0.95203 validation-aucpr:0.95780
[81] validation-logloss:0.42184 validation-auc:0.95205 validation-aucpr:0.95791
[82] validation-logloss:0.42022 validation-auc:0.95193 validation-aucpr:0.95775
[83] validation-logloss:0.41852 validation-auc:0.95199 validation-aucpr:0.95785
[84] validation-logloss:0.41677 validation-auc:0.95202 validation-aucpr:0.95785
[85] validation-logloss:0.41538 validation-auc:0.95203 validation-aucpr:0.95812
[86] validation-logloss:0.41238 validation-auc:0.95273 validation-aucpr:0.95894
[87] validation-logloss:0.41136 validation-auc:0.95283 validation-aucpr:0.95910
[88] validation-logloss:0.40900 validation-auc:0.95323 validation-aucpr:0.95961
[89] validation-logloss:0.40767 validation-auc:0.95322 validation-aucpr:0.95964
[90] validation-logloss:0.40615 validation-auc:0.95326 validation-aucpr:0.95964
[91] validation-logloss:0.40460 validation-auc:0.95326 validation-aucpr:0.95963
[92] validation-logloss:0.40333 validation-auc:0.95321 validation-aucpr:0.95956
[93] validation-logloss:0.40207 validation-auc:0.95319 validation-aucpr:0.95952
[94] validation-logloss:0.40052 validation-auc:0.95326 validation-aucpr:0.95957
{'best_iteration': '89', 'best_score': '0.959639811688674'}
Trial 3, Fold 5: Log loss = 0.4005184527327331, Average precision = 0.959576679824926, ROC-AUC = 0.9532623599576391, Elapsed Time = 2.835710700001073 seconds
Optimization Progress: 4%|4 | 4/100 [02:22<45:23, 28.37s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 4, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65332 validation-auc:0.91488 validation-aucpr:0.93009
[1] validation-logloss:0.61677 validation-auc:0.95002 validation-aucpr:0.93873
[2] validation-logloss:0.58489 validation-auc:0.95826 validation-aucpr:0.95774
[3] validation-logloss:0.55534 validation-auc:0.96160 validation-aucpr:0.96131
[4] validation-logloss:0.52807 validation-auc:0.96369 validation-aucpr:0.96267
[5] validation-logloss:0.50386 validation-auc:0.96427 validation-aucpr:0.96573
[6] validation-logloss:0.48156 validation-auc:0.96485 validation-aucpr:0.96500
[7] validation-logloss:0.46084 validation-auc:0.96574 validation-aucpr:0.96729
[8] validation-logloss:0.44250 validation-auc:0.96652 validation-aucpr:0.96887
[9] validation-logloss:0.42517 validation-auc:0.96669 validation-aucpr:0.96917
[10] validation-logloss:0.40921 validation-auc:0.96710 validation-aucpr:0.96945
[11] validation-logloss:0.39461 validation-auc:0.96725 validation-aucpr:0.96953
[12] validation-logloss:0.38065 validation-auc:0.96765 validation-aucpr:0.96960
[13] validation-logloss:0.36827 validation-auc:0.96782 validation-aucpr:0.96967
[14] validation-logloss:0.35661 validation-auc:0.96827 validation-aucpr:0.96992
[15] validation-logloss:0.34547 validation-auc:0.96881 validation-aucpr:0.97045
[16] validation-logloss:0.33553 validation-auc:0.96893 validation-aucpr:0.97055
[17] validation-logloss:0.32616 validation-auc:0.96908 validation-aucpr:0.97072
[18] validation-logloss:0.31781 validation-auc:0.96892 validation-aucpr:0.97072
[19] validation-logloss:0.30997 validation-auc:0.96933 validation-aucpr:0.97087
[20] validation-logloss:0.30243 validation-auc:0.96962 validation-aucpr:0.97249
[21] validation-logloss:0.29536 validation-auc:0.96956 validation-aucpr:0.97243
[22] validation-logloss:0.28905 validation-auc:0.96962 validation-aucpr:0.97110
[23] validation-logloss:0.28345 validation-auc:0.96963 validation-aucpr:0.97091
[24] validation-logloss:0.27766 validation-auc:0.96971 validation-aucpr:0.97091
[25] validation-logloss:0.27273 validation-auc:0.96978 validation-aucpr:0.97101
[26] validation-logloss:0.26762 validation-auc:0.97000 validation-aucpr:0.97103
[27] validation-logloss:0.26281 validation-auc:0.97032 validation-aucpr:0.97310
[28] validation-logloss:0.25878 validation-auc:0.97042 validation-aucpr:0.97476
[29] validation-logloss:0.25450 validation-auc:0.97054 validation-aucpr:0.97483
[30] validation-logloss:0.25040 validation-auc:0.97052 validation-aucpr:0.97414
[31] validation-logloss:0.24737 validation-auc:0.97026 validation-aucpr:0.97382
[32] validation-logloss:0.24373 validation-auc:0.97055 validation-aucpr:0.97407
[33] validation-logloss:0.24097 validation-auc:0.97051 validation-aucpr:0.97405
[34] validation-logloss:0.23806 validation-auc:0.97053 validation-aucpr:0.97415
[35] validation-logloss:0.23527 validation-auc:0.97051 validation-aucpr:0.97410
[36] validation-logloss:0.23253 validation-auc:0.97077 validation-aucpr:0.97494
[37] validation-logloss:0.22996 validation-auc:0.97074 validation-aucpr:0.97427
[38] validation-logloss:0.22786 validation-auc:0.97066 validation-aucpr:0.97432
[39] validation-logloss:0.22586 validation-auc:0.97066 validation-aucpr:0.97429
[40] validation-logloss:0.22382 validation-auc:0.97062 validation-aucpr:0.97426
[41] validation-logloss:0.22164 validation-auc:0.97085 validation-aucpr:0.97495
[42] validation-logloss:0.21978 validation-auc:0.97095 validation-aucpr:0.97497
[43] validation-logloss:0.21841 validation-auc:0.97083 validation-aucpr:0.97492
[44] validation-logloss:0.21652 validation-auc:0.97105 validation-aucpr:0.97512
[45] validation-logloss:0.21482 validation-auc:0.97118 validation-aucpr:0.97526
[46] validation-logloss:0.21414 validation-auc:0.97092 validation-aucpr:0.97505
[47] validation-logloss:0.21301 validation-auc:0.97088 validation-aucpr:0.97502
[48] validation-logloss:0.21179 validation-auc:0.97094 validation-aucpr:0.97506
[49] validation-logloss:0.21025 validation-auc:0.97118 validation-aucpr:0.97526
[50] validation-logloss:0.20964 validation-auc:0.97106 validation-aucpr:0.97517
[51] validation-logloss:0.20835 validation-auc:0.97126 validation-aucpr:0.97531
[52] validation-logloss:0.20737 validation-auc:0.97133 validation-aucpr:0.97532
[53] validation-logloss:0.20654 validation-auc:0.97132 validation-aucpr:0.97532
[54] validation-logloss:0.20595 validation-auc:0.97119 validation-aucpr:0.97523
[55] validation-logloss:0.20518 validation-auc:0.97122 validation-aucpr:0.97526
[56] validation-logloss:0.20436 validation-auc:0.97116 validation-aucpr:0.97508
[57] validation-logloss:0.20369 validation-auc:0.97115 validation-aucpr:0.97483
[58] validation-logloss:0.20300 validation-auc:0.97119 validation-aucpr:0.97485
[59] validation-logloss:0.20215 validation-auc:0.97139 validation-aucpr:0.97556
[60] validation-logloss:0.20155 validation-auc:0.97144 validation-aucpr:0.97560
[61] validation-logloss:0.20131 validation-auc:0.97135 validation-aucpr:0.97545
[62] validation-logloss:0.20093 validation-auc:0.97127 validation-aucpr:0.97539
[63] validation-logloss:0.20046 validation-auc:0.97129 validation-aucpr:0.97535
[64] validation-logloss:0.20002 validation-auc:0.97130 validation-aucpr:0.97532
[65] validation-logloss:0.19932 validation-auc:0.97143 validation-aucpr:0.97542
[66] validation-logloss:0.19880 validation-auc:0.97149 validation-aucpr:0.97482
[67] validation-logloss:0.19855 validation-auc:0.97152 validation-aucpr:0.97564
[68] validation-logloss:0.19820 validation-auc:0.97155 validation-aucpr:0.97560
[69] validation-logloss:0.19818 validation-auc:0.97153 validation-aucpr:0.97556
[70] validation-logloss:0.19783 validation-auc:0.97158 validation-aucpr:0.97558
[71] validation-logloss:0.19749 validation-auc:0.97162 validation-aucpr:0.97556
[72] validation-logloss:0.19719 validation-auc:0.97169 validation-aucpr:0.97568
[73] validation-logloss:0.19713 validation-auc:0.97165 validation-aucpr:0.97564
[74] validation-logloss:0.19710 validation-auc:0.97168 validation-aucpr:0.97571
[75] validation-logloss:0.19696 validation-auc:0.97165 validation-aucpr:0.97568
[76] validation-logloss:0.19691 validation-auc:0.97165 validation-aucpr:0.97564
[77] validation-logloss:0.19696 validation-auc:0.97163 validation-aucpr:0.97562
[78] validation-logloss:0.19695 validation-auc:0.97164 validation-aucpr:0.97572
[79] validation-logloss:0.19706 validation-auc:0.97160 validation-aucpr:0.97568
[80] validation-logloss:0.19694 validation-auc:0.97154 validation-aucpr:0.97564
[81] validation-logloss:0.19649 validation-auc:0.97166 validation-aucpr:0.97570
[82] validation-logloss:0.19645 validation-auc:0.97165 validation-aucpr:0.97566
[83] validation-logloss:0.19653 validation-auc:0.97172 validation-aucpr:0.97571
[84] validation-logloss:0.19673 validation-auc:0.97160 validation-aucpr:0.97561
[85] validation-logloss:0.19689 validation-auc:0.97157 validation-aucpr:0.97563
[86] validation-logloss:0.19673 validation-auc:0.97159 validation-aucpr:0.97564
[87] validation-logloss:0.19684 validation-auc:0.97154 validation-aucpr:0.97560
[88] validation-logloss:0.19681 validation-auc:0.97153 validation-aucpr:0.97551
{'best_iteration': '78', 'best_score': '0.97572330288339'}
Trial 4, Fold 1: Log loss = 0.1968148526228069, Average precision = 0.97551845493452, ROC-AUC = 0.9715284349712985, Elapsed Time = 2.971843600000284 seconds
Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 4, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65239 validation-auc:0.94171 validation-aucpr:0.94549
[1] validation-logloss:0.61997 validation-auc:0.95250 validation-aucpr:0.94399
[2] validation-logloss:0.58764 validation-auc:0.95756 validation-aucpr:0.95015
[3] validation-logloss:0.55752 validation-auc:0.96201 validation-aucpr:0.95601
[4] validation-logloss:0.53032 validation-auc:0.96465 validation-aucpr:0.96392
[5] validation-logloss:0.50606 validation-auc:0.96637 validation-aucpr:0.96873
[6] validation-logloss:0.48325 validation-auc:0.96693 validation-aucpr:0.97139
[7] validation-logloss:0.46264 validation-auc:0.96691 validation-aucpr:0.97143
[8] validation-logloss:0.44603 validation-auc:0.96767 validation-aucpr:0.97161
[9] validation-logloss:0.42815 validation-auc:0.96824 validation-aucpr:0.97206
[10] validation-logloss:0.41171 validation-auc:0.96856 validation-aucpr:0.97239
[11] validation-logloss:0.39777 validation-auc:0.96910 validation-aucpr:0.97267
[12] validation-logloss:0.38349 validation-auc:0.96958 validation-aucpr:0.97314
[13] validation-logloss:0.37060 validation-auc:0.96991 validation-aucpr:0.97338
[14] validation-logloss:0.35853 validation-auc:0.97003 validation-aucpr:0.97324
[15] validation-logloss:0.34755 validation-auc:0.97012 validation-aucpr:0.97344
[16] validation-logloss:0.33644 validation-auc:0.97082 validation-aucpr:0.97402
[17] validation-logloss:0.32598 validation-auc:0.97129 validation-aucpr:0.97429
[18] validation-logloss:0.31719 validation-auc:0.97150 validation-aucpr:0.97447
[19] validation-logloss:0.30889 validation-auc:0.97152 validation-aucpr:0.97435
[20] validation-logloss:0.30109 validation-auc:0.97161 validation-aucpr:0.97455
[21] validation-logloss:0.29421 validation-auc:0.97162 validation-aucpr:0.97438
[22] validation-logloss:0.28789 validation-auc:0.97162 validation-aucpr:0.97443
[23] validation-logloss:0.28159 validation-auc:0.97135 validation-aucpr:0.97405
[24] validation-logloss:0.27544 validation-auc:0.97165 validation-aucpr:0.97443
[25] validation-logloss:0.26978 validation-auc:0.97163 validation-aucpr:0.97455
[26] validation-logloss:0.26426 validation-auc:0.97180 validation-aucpr:0.97431
[27] validation-logloss:0.25919 validation-auc:0.97204 validation-aucpr:0.97508
[28] validation-logloss:0.25451 validation-auc:0.97201 validation-aucpr:0.97521
[29] validation-logloss:0.25009 validation-auc:0.97204 validation-aucpr:0.97532
[30] validation-logloss:0.24625 validation-auc:0.97185 validation-aucpr:0.97518
[31] validation-logloss:0.24240 validation-auc:0.97202 validation-aucpr:0.97533
[32] validation-logloss:0.23860 validation-auc:0.97199 validation-aucpr:0.97529
[33] validation-logloss:0.23521 validation-auc:0.97216 validation-aucpr:0.97539
[34] validation-logloss:0.23179 validation-auc:0.97230 validation-aucpr:0.97546
[35] validation-logloss:0.22864 validation-auc:0.97253 validation-aucpr:0.97563
[36] validation-logloss:0.22609 validation-auc:0.97264 validation-aucpr:0.97564
[37] validation-logloss:0.22347 validation-auc:0.97259 validation-aucpr:0.97556
[38] validation-logloss:0.22138 validation-auc:0.97258 validation-aucpr:0.97558
[39] validation-logloss:0.21909 validation-auc:0.97254 validation-aucpr:0.97552
[40] validation-logloss:0.21672 validation-auc:0.97263 validation-aucpr:0.97561
[41] validation-logloss:0.21448 validation-auc:0.97273 validation-aucpr:0.97563
[42] validation-logloss:0.21266 validation-auc:0.97269 validation-aucpr:0.97558
[43] validation-logloss:0.21132 validation-auc:0.97265 validation-aucpr:0.97553
[44] validation-logloss:0.20944 validation-auc:0.97273 validation-aucpr:0.97556
[45] validation-logloss:0.20739 validation-auc:0.97292 validation-aucpr:0.97573
[46] validation-logloss:0.20574 validation-auc:0.97304 validation-aucpr:0.97578
[47] validation-logloss:0.20423 validation-auc:0.97302 validation-aucpr:0.97578
[48] validation-logloss:0.20299 validation-auc:0.97308 validation-aucpr:0.97581
[49] validation-logloss:0.20187 validation-auc:0.97303 validation-aucpr:0.97575
[50] validation-logloss:0.20080 validation-auc:0.97287 validation-aucpr:0.97563
[51] validation-logloss:0.19983 validation-auc:0.97292 validation-aucpr:0.97565
[52] validation-logloss:0.19840 validation-auc:0.97305 validation-aucpr:0.97574
[53] validation-logloss:0.19747 validation-auc:0.97305 validation-aucpr:0.97573
[54] validation-logloss:0.19642 validation-auc:0.97305 validation-aucpr:0.97556
[55] validation-logloss:0.19518 validation-auc:0.97322 validation-aucpr:0.97568
[56] validation-logloss:0.19419 validation-auc:0.97330 validation-aucpr:0.97575
[57] validation-logloss:0.19317 validation-auc:0.97333 validation-aucpr:0.97556
[58] validation-logloss:0.19238 validation-auc:0.97339 validation-aucpr:0.97559
[59] validation-logloss:0.19142 validation-auc:0.97348 validation-aucpr:0.97571
[60] validation-logloss:0.19042 validation-auc:0.97358 validation-aucpr:0.97580
[61] validation-logloss:0.18957 validation-auc:0.97362 validation-aucpr:0.97584
[62] validation-logloss:0.18885 validation-auc:0.97366 validation-aucpr:0.97604
[63] validation-logloss:0.18810 validation-auc:0.97382 validation-aucpr:0.97622
[64] validation-logloss:0.18724 validation-auc:0.97397 validation-aucpr:0.97629
[65] validation-logloss:0.18666 validation-auc:0.97400 validation-aucpr:0.97629
[66] validation-logloss:0.18650 validation-auc:0.97398 validation-aucpr:0.97626
[67] validation-logloss:0.18604 validation-auc:0.97403 validation-aucpr:0.97628
[68] validation-logloss:0.18576 validation-auc:0.97395 validation-aucpr:0.97611
[69] validation-logloss:0.18522 validation-auc:0.97402 validation-aucpr:0.97611
[70] validation-logloss:0.18485 validation-auc:0.97410 validation-aucpr:0.97581
[71] validation-logloss:0.18459 validation-auc:0.97412 validation-aucpr:0.97572
[72] validation-logloss:0.18446 validation-auc:0.97403 validation-aucpr:0.97566
[73] validation-logloss:0.18390 validation-auc:0.97421 validation-aucpr:0.97653
[74] validation-logloss:0.18348 validation-auc:0.97426 validation-aucpr:0.97657
[75] validation-logloss:0.18344 validation-auc:0.97425 validation-aucpr:0.97666
[76] validation-logloss:0.18301 validation-auc:0.97434 validation-aucpr:0.97672
[77] validation-logloss:0.18272 validation-auc:0.97429 validation-aucpr:0.97667
[78] validation-logloss:0.18223 validation-auc:0.97447 validation-aucpr:0.97680
[79] validation-logloss:0.18196 validation-auc:0.97450 validation-aucpr:0.97681
[80] validation-logloss:0.18192 validation-auc:0.97449 validation-aucpr:0.97682
[81] validation-logloss:0.18180 validation-auc:0.97444 validation-aucpr:0.97674
[82] validation-logloss:0.18151 validation-auc:0.97460 validation-aucpr:0.97692
[83] validation-logloss:0.18131 validation-auc:0.97468 validation-aucpr:0.97695
[84] validation-logloss:0.18141 validation-auc:0.97464 validation-aucpr:0.97697
[85] validation-logloss:0.18153 validation-auc:0.97464 validation-aucpr:0.97717
[86] validation-logloss:0.18159 validation-auc:0.97462 validation-aucpr:0.97715
[87] validation-logloss:0.18154 validation-auc:0.97455 validation-aucpr:0.97707
[88] validation-logloss:0.18135 validation-auc:0.97457 validation-aucpr:0.97706
{'best_iteration': '85', 'best_score': '0.9771727738107567'}
Trial 4, Fold 2: Log loss = 0.18135254688458874, Average precision = 0.9770647111537439, ROC-AUC = 0.974569954363371, Elapsed Time = 3.2144864999991114 seconds
Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 4, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65257 validation-auc:0.94874 validation-aucpr:0.95522
[1] validation-logloss:0.61594 validation-auc:0.95668 validation-aucpr:0.95192
[2] validation-logloss:0.58267 validation-auc:0.96405 validation-aucpr:0.96123
[3] validation-logloss:0.55282 validation-auc:0.96524 validation-aucpr:0.96189
[4] validation-logloss:0.52565 validation-auc:0.96659 validation-aucpr:0.96425
[5] validation-logloss:0.50078 validation-auc:0.96730 validation-aucpr:0.96627
[6] validation-logloss:0.47832 validation-auc:0.96850 validation-aucpr:0.97182
[7] validation-logloss:0.45804 validation-auc:0.96910 validation-aucpr:0.97369
[8] validation-logloss:0.43904 validation-auc:0.96973 validation-aucpr:0.97414
[9] validation-logloss:0.42171 validation-auc:0.96970 validation-aucpr:0.97278
[10] validation-logloss:0.40580 validation-auc:0.96998 validation-aucpr:0.97426
[11] validation-logloss:0.39125 validation-auc:0.96989 validation-aucpr:0.97399
[12] validation-logloss:0.37749 validation-auc:0.97049 validation-aucpr:0.97433
[13] validation-logloss:0.36449 validation-auc:0.97102 validation-aucpr:0.97474
[14] validation-logloss:0.35295 validation-auc:0.97095 validation-aucpr:0.97468
[15] validation-logloss:0.34236 validation-auc:0.97109 validation-aucpr:0.97499
[16] validation-logloss:0.33249 validation-auc:0.97092 validation-aucpr:0.97462
[17] validation-logloss:0.32348 validation-auc:0.97061 validation-aucpr:0.97440
[18] validation-logloss:0.31490 validation-auc:0.97077 validation-aucpr:0.97490
[19] validation-logloss:0.30675 validation-auc:0.97069 validation-aucpr:0.97487
[20] validation-logloss:0.29915 validation-auc:0.97105 validation-aucpr:0.97505
[21] validation-logloss:0.29200 validation-auc:0.97122 validation-aucpr:0.97514
[22] validation-logloss:0.28552 validation-auc:0.97105 validation-aucpr:0.97505
[23] validation-logloss:0.27946 validation-auc:0.97113 validation-aucpr:0.97503
[24] validation-logloss:0.27368 validation-auc:0.97105 validation-aucpr:0.97495
[25] validation-logloss:0.26799 validation-auc:0.97116 validation-aucpr:0.97502
[26] validation-logloss:0.26261 validation-auc:0.97149 validation-aucpr:0.97528
[27] validation-logloss:0.25793 validation-auc:0.97132 validation-aucpr:0.97507
[28] validation-logloss:0.25362 validation-auc:0.97127 validation-aucpr:0.97507
[29] validation-logloss:0.24973 validation-auc:0.97119 validation-aucpr:0.97495
[30] validation-logloss:0.24571 validation-auc:0.97113 validation-aucpr:0.97491
[31] validation-logloss:0.24218 validation-auc:0.97104 validation-aucpr:0.97489
[32] validation-logloss:0.23851 validation-auc:0.97124 validation-aucpr:0.97508
[33] validation-logloss:0.23542 validation-auc:0.97117 validation-aucpr:0.97408
[34] validation-logloss:0.23267 validation-auc:0.97099 validation-aucpr:0.97393
[35] validation-logloss:0.22966 validation-auc:0.97107 validation-aucpr:0.97396
[36] validation-logloss:0.22675 validation-auc:0.97121 validation-aucpr:0.97409
[37] validation-logloss:0.22434 validation-auc:0.97121 validation-aucpr:0.97399
[38] validation-logloss:0.22197 validation-auc:0.97123 validation-aucpr:0.97396
[39] validation-logloss:0.21961 validation-auc:0.97156 validation-aucpr:0.97434
[40] validation-logloss:0.21773 validation-auc:0.97155 validation-aucpr:0.97431
[41] validation-logloss:0.21536 validation-auc:0.97181 validation-aucpr:0.97441
[42] validation-logloss:0.21351 validation-auc:0.97181 validation-aucpr:0.97440
[43] validation-logloss:0.21176 validation-auc:0.97183 validation-aucpr:0.97444
[44] validation-logloss:0.21005 validation-auc:0.97198 validation-aucpr:0.97550
[45] validation-logloss:0.20869 validation-auc:0.97189 validation-aucpr:0.97495
[46] validation-logloss:0.20768 validation-auc:0.97174 validation-aucpr:0.97472
[47] validation-logloss:0.20620 validation-auc:0.97194 validation-aucpr:0.97516
[48] validation-logloss:0.20471 validation-auc:0.97212 validation-aucpr:0.97525
[49] validation-logloss:0.20322 validation-auc:0.97236 validation-aucpr:0.97558
[50] validation-logloss:0.20205 validation-auc:0.97242 validation-aucpr:0.97590
[51] validation-logloss:0.20088 validation-auc:0.97252 validation-aucpr:0.97598
[52] validation-logloss:0.20008 validation-auc:0.97256 validation-aucpr:0.97624
[53] validation-logloss:0.19906 validation-auc:0.97258 validation-aucpr:0.97624
[54] validation-logloss:0.19863 validation-auc:0.97244 validation-aucpr:0.97612
[55] validation-logloss:0.19790 validation-auc:0.97242 validation-aucpr:0.97608
[56] validation-logloss:0.19694 validation-auc:0.97237 validation-aucpr:0.97610
[57] validation-logloss:0.19640 validation-auc:0.97239 validation-aucpr:0.97608
[58] validation-logloss:0.19572 validation-auc:0.97243 validation-aucpr:0.97611
[59] validation-logloss:0.19525 validation-auc:0.97247 validation-aucpr:0.97612
[60] validation-logloss:0.19455 validation-auc:0.97248 validation-aucpr:0.97614
[61] validation-logloss:0.19392 validation-auc:0.97271 validation-aucpr:0.97622
[62] validation-logloss:0.19347 validation-auc:0.97278 validation-aucpr:0.97608
[63] validation-logloss:0.19313 validation-auc:0.97281 validation-aucpr:0.97609
[64] validation-logloss:0.19293 validation-auc:0.97280 validation-aucpr:0.97624
[65] validation-logloss:0.19259 validation-auc:0.97282 validation-aucpr:0.97607
[66] validation-logloss:0.19227 validation-auc:0.97288 validation-aucpr:0.97603
[67] validation-logloss:0.19212 validation-auc:0.97293 validation-aucpr:0.97615
[68] validation-logloss:0.19177 validation-auc:0.97287 validation-aucpr:0.97607
[69] validation-logloss:0.19117 validation-auc:0.97299 validation-aucpr:0.97613
[70] validation-logloss:0.19101 validation-auc:0.97302 validation-aucpr:0.97632
[71] validation-logloss:0.19080 validation-auc:0.97303 validation-aucpr:0.97634
[72] validation-logloss:0.19057 validation-auc:0.97306 validation-aucpr:0.97638
[73] validation-logloss:0.19022 validation-auc:0.97311 validation-aucpr:0.97637
[74] validation-logloss:0.19005 validation-auc:0.97310 validation-aucpr:0.97631
[75] validation-logloss:0.18975 validation-auc:0.97311 validation-aucpr:0.97641
[76] validation-logloss:0.18942 validation-auc:0.97324 validation-aucpr:0.97659
[77] validation-logloss:0.18960 validation-auc:0.97313 validation-aucpr:0.97647
[78] validation-logloss:0.18948 validation-auc:0.97305 validation-aucpr:0.97640
[79] validation-logloss:0.18917 validation-auc:0.97310 validation-aucpr:0.97641
[80] validation-logloss:0.18942 validation-auc:0.97302 validation-aucpr:0.97634
[81] validation-logloss:0.18926 validation-auc:0.97309 validation-aucpr:0.97637
[82] validation-logloss:0.18941 validation-auc:0.97299 validation-aucpr:0.97619
[83] validation-logloss:0.18964 validation-auc:0.97291 validation-aucpr:0.97616
[84] validation-logloss:0.18960 validation-auc:0.97292 validation-aucpr:0.97622
[85] validation-logloss:0.18950 validation-auc:0.97294 validation-aucpr:0.97617
[86] validation-logloss:0.18940 validation-auc:0.97294 validation-aucpr:0.97610
[87] validation-logloss:0.18945 validation-auc:0.97295 validation-aucpr:0.97600
[88] validation-logloss:0.18922 validation-auc:0.97301 validation-aucpr:0.97602
{'best_iteration': '76', 'best_score': '0.9765877426803053'}
Trial 4, Fold 3: Log loss = 0.18921685039483901, Average precision = 0.9760259892551311, ROC-AUC = 0.9730138397973631, Elapsed Time = 3.222346100001232 seconds
Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 4, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65218 validation-auc:0.93087 validation-aucpr:0.92133
[1] validation-logloss:0.61615 validation-auc:0.95108 validation-aucpr:0.93605
[2] validation-logloss:0.58374 validation-auc:0.96028 validation-aucpr:0.95399
[3] validation-logloss:0.55701 validation-auc:0.96363 validation-aucpr:0.96372
[4] validation-logloss:0.52946 validation-auc:0.96594 validation-aucpr:0.96859
[5] validation-logloss:0.50448 validation-auc:0.96686 validation-aucpr:0.97103
[6] validation-logloss:0.48197 validation-auc:0.96722 validation-aucpr:0.96976
[7] validation-logloss:0.46334 validation-auc:0.96722 validation-aucpr:0.96986
[8] validation-logloss:0.44405 validation-auc:0.96798 validation-aucpr:0.97051
[9] validation-logloss:0.42880 validation-auc:0.96722 validation-aucpr:0.96973
[10] validation-logloss:0.41224 validation-auc:0.96813 validation-aucpr:0.97165
[11] validation-logloss:0.39697 validation-auc:0.96840 validation-aucpr:0.97174
[12] validation-logloss:0.38253 validation-auc:0.96924 validation-aucpr:0.97242
[13] validation-logloss:0.36932 validation-auc:0.96970 validation-aucpr:0.97278
[14] validation-logloss:0.35877 validation-auc:0.96980 validation-aucpr:0.97287
[15] validation-logloss:0.34759 validation-auc:0.96979 validation-aucpr:0.97297
[16] validation-logloss:0.33695 validation-auc:0.97002 validation-aucpr:0.97318
[17] validation-logloss:0.32702 validation-auc:0.97042 validation-aucpr:0.97351
[18] validation-logloss:0.31823 validation-auc:0.97069 validation-aucpr:0.97546
[19] validation-logloss:0.30953 validation-auc:0.97097 validation-aucpr:0.97570
[20] validation-logloss:0.30191 validation-auc:0.97083 validation-aucpr:0.97557
[21] validation-logloss:0.29446 validation-auc:0.97089 validation-aucpr:0.97564
[22] validation-logloss:0.28791 validation-auc:0.97082 validation-aucpr:0.97556
[23] validation-logloss:0.28090 validation-auc:0.97121 validation-aucpr:0.97585
[24] validation-logloss:0.27475 validation-auc:0.97140 validation-aucpr:0.97598
[25] validation-logloss:0.26969 validation-auc:0.97143 validation-aucpr:0.97596
[26] validation-logloss:0.26453 validation-auc:0.97147 validation-aucpr:0.97598
[27] validation-logloss:0.25957 validation-auc:0.97152 validation-aucpr:0.97603
[28] validation-logloss:0.25493 validation-auc:0.97175 validation-aucpr:0.97620
[29] validation-logloss:0.25018 validation-auc:0.97219 validation-aucpr:0.97642
[30] validation-logloss:0.24673 validation-auc:0.97213 validation-aucpr:0.97636
[31] validation-logloss:0.24274 validation-auc:0.97233 validation-aucpr:0.97649
[32] validation-logloss:0.23901 validation-auc:0.97248 validation-aucpr:0.97657
[33] validation-logloss:0.23534 validation-auc:0.97253 validation-aucpr:0.97661
[34] validation-logloss:0.23205 validation-auc:0.97275 validation-aucpr:0.97689
[35] validation-logloss:0.22914 validation-auc:0.97285 validation-aucpr:0.97699
[36] validation-logloss:0.22629 validation-auc:0.97281 validation-aucpr:0.97698
[37] validation-logloss:0.22385 validation-auc:0.97266 validation-aucpr:0.97688
[38] validation-logloss:0.22127 validation-auc:0.97271 validation-aucpr:0.97695
[39] validation-logloss:0.21895 validation-auc:0.97274 validation-aucpr:0.97699
[40] validation-logloss:0.21709 validation-auc:0.97253 validation-aucpr:0.97686
[41] validation-logloss:0.21538 validation-auc:0.97239 validation-aucpr:0.97674
[42] validation-logloss:0.21353 validation-auc:0.97238 validation-aucpr:0.97676
[43] validation-logloss:0.21158 validation-auc:0.97245 validation-aucpr:0.97681
[44] validation-logloss:0.20968 validation-auc:0.97248 validation-aucpr:0.97683
[45] validation-logloss:0.20810 validation-auc:0.97250 validation-aucpr:0.97682
[46] validation-logloss:0.20676 validation-auc:0.97238 validation-aucpr:0.97676
[47] validation-logloss:0.20568 validation-auc:0.97222 validation-aucpr:0.97662
[48] validation-logloss:0.20416 validation-auc:0.97230 validation-aucpr:0.97667
[49] validation-logloss:0.20300 validation-auc:0.97237 validation-aucpr:0.97673
[50] validation-logloss:0.20175 validation-auc:0.97250 validation-aucpr:0.97681
[51] validation-logloss:0.20079 validation-auc:0.97246 validation-aucpr:0.97675
[52] validation-logloss:0.20006 validation-auc:0.97233 validation-aucpr:0.97663
[53] validation-logloss:0.19926 validation-auc:0.97233 validation-aucpr:0.97663
[54] validation-logloss:0.19870 validation-auc:0.97217 validation-aucpr:0.97651
[55] validation-logloss:0.19805 validation-auc:0.97209 validation-aucpr:0.97642
[56] validation-logloss:0.19706 validation-auc:0.97216 validation-aucpr:0.97646
[57] validation-logloss:0.19626 validation-auc:0.97214 validation-aucpr:0.97645
[58] validation-logloss:0.19579 validation-auc:0.97204 validation-aucpr:0.97640
[59] validation-logloss:0.19544 validation-auc:0.97190 validation-aucpr:0.97628
[60] validation-logloss:0.19504 validation-auc:0.97178 validation-aucpr:0.97619
[61] validation-logloss:0.19453 validation-auc:0.97181 validation-aucpr:0.97621
[62] validation-logloss:0.19392 validation-auc:0.97189 validation-aucpr:0.97626
[63] validation-logloss:0.19362 validation-auc:0.97180 validation-aucpr:0.97623
[64] validation-logloss:0.19309 validation-auc:0.97193 validation-aucpr:0.97632
[65] validation-logloss:0.19237 validation-auc:0.97208 validation-aucpr:0.97643
[66] validation-logloss:0.19205 validation-auc:0.97215 validation-aucpr:0.97644
[67] validation-logloss:0.19177 validation-auc:0.97219 validation-aucpr:0.97647
[68] validation-logloss:0.19152 validation-auc:0.97207 validation-aucpr:0.97638
[69] validation-logloss:0.19132 validation-auc:0.97200 validation-aucpr:0.97633
[70] validation-logloss:0.19122 validation-auc:0.97195 validation-aucpr:0.97631
[71] validation-logloss:0.19092 validation-auc:0.97192 validation-aucpr:0.97629
[72] validation-logloss:0.19062 validation-auc:0.97196 validation-aucpr:0.97632
[73] validation-logloss:0.19051 validation-auc:0.97194 validation-aucpr:0.97631
[74] validation-logloss:0.19048 validation-auc:0.97181 validation-aucpr:0.97621
[75] validation-logloss:0.19040 validation-auc:0.97170 validation-aucpr:0.97613
[76] validation-logloss:0.19033 validation-auc:0.97171 validation-aucpr:0.97611
[77] validation-logloss:0.19027 validation-auc:0.97176 validation-aucpr:0.97615
[78] validation-logloss:0.19046 validation-auc:0.97163 validation-aucpr:0.97606
[79] validation-logloss:0.19064 validation-auc:0.97155 validation-aucpr:0.97598
[80] validation-logloss:0.19064 validation-auc:0.97155 validation-aucpr:0.97596
[81] validation-logloss:0.19054 validation-auc:0.97157 validation-aucpr:0.97598
[82] validation-logloss:0.19054 validation-auc:0.97148 validation-aucpr:0.97592
[83] validation-logloss:0.19041 validation-auc:0.97157 validation-aucpr:0.97598
[84] validation-logloss:0.19043 validation-auc:0.97158 validation-aucpr:0.97599
[85] validation-logloss:0.19021 validation-auc:0.97170 validation-aucpr:0.97608
[86] validation-logloss:0.19001 validation-auc:0.97179 validation-aucpr:0.97614
[87] validation-logloss:0.18979 validation-auc:0.97182 validation-aucpr:0.97619
[88] validation-logloss:0.18988 validation-auc:0.97183 validation-aucpr:0.97619
{'best_iteration': '39', 'best_score': '0.9769865175482205'}
Trial 4, Fold 4: Log loss = 0.1898793489588116, Average precision = 0.9761969319498418, ROC-AUC = 0.9718313280447124, Elapsed Time = 3.1994386999995186 seconds
Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 4, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65278 validation-auc:0.93567 validation-aucpr:0.93547
[1] validation-logloss:0.62061 validation-auc:0.94959 validation-aucpr:0.92957
[2] validation-logloss:0.58765 validation-auc:0.95713 validation-aucpr:0.94956
[3] validation-logloss:0.55951 validation-auc:0.96044 validation-aucpr:0.96057
[4] validation-logloss:0.53240 validation-auc:0.96270 validation-aucpr:0.96259
[5] validation-logloss:0.50762 validation-auc:0.96477 validation-aucpr:0.96957
[6] validation-logloss:0.48488 validation-auc:0.96603 validation-aucpr:0.97029
[7] validation-logloss:0.46434 validation-auc:0.96644 validation-aucpr:0.97096
[8] validation-logloss:0.44581 validation-auc:0.96622 validation-aucpr:0.97089
[9] validation-logloss:0.42847 validation-auc:0.96638 validation-aucpr:0.97110
[10] validation-logloss:0.41269 validation-auc:0.96628 validation-aucpr:0.97105
[11] validation-logloss:0.39811 validation-auc:0.96664 validation-aucpr:0.97119
[12] validation-logloss:0.38447 validation-auc:0.96702 validation-aucpr:0.97145
[13] validation-logloss:0.37203 validation-auc:0.96718 validation-aucpr:0.97154
[14] validation-logloss:0.35984 validation-auc:0.96761 validation-aucpr:0.97146
[15] validation-logloss:0.34851 validation-auc:0.96822 validation-aucpr:0.97228
[16] validation-logloss:0.33808 validation-auc:0.96879 validation-aucpr:0.97307
[17] validation-logloss:0.32890 validation-auc:0.96890 validation-aucpr:0.97314
[18] validation-logloss:0.32097 validation-auc:0.96865 validation-aucpr:0.97290
[19] validation-logloss:0.31286 validation-auc:0.96905 validation-aucpr:0.97307
[20] validation-logloss:0.30531 validation-auc:0.96916 validation-aucpr:0.97320
[21] validation-logloss:0.29899 validation-auc:0.96920 validation-aucpr:0.97338
[22] validation-logloss:0.29218 validation-auc:0.96935 validation-aucpr:0.97344
[23] validation-logloss:0.28713 validation-auc:0.96918 validation-aucpr:0.97322
[24] validation-logloss:0.28154 validation-auc:0.96919 validation-aucpr:0.97322
[25] validation-logloss:0.27590 validation-auc:0.96930 validation-aucpr:0.97327
[26] validation-logloss:0.27055 validation-auc:0.96942 validation-aucpr:0.97344
[27] validation-logloss:0.26589 validation-auc:0.96929 validation-aucpr:0.97329
[28] validation-logloss:0.26135 validation-auc:0.96930 validation-aucpr:0.97329
[29] validation-logloss:0.25704 validation-auc:0.96963 validation-aucpr:0.97376
[30] validation-logloss:0.25315 validation-auc:0.96962 validation-aucpr:0.97368
[31] validation-logloss:0.24938 validation-auc:0.96977 validation-aucpr:0.97384
[32] validation-logloss:0.24572 validation-auc:0.96986 validation-aucpr:0.97389
[33] validation-logloss:0.24250 validation-auc:0.96993 validation-aucpr:0.97397
[34] validation-logloss:0.23956 validation-auc:0.97011 validation-aucpr:0.97426
[35] validation-logloss:0.23685 validation-auc:0.97014 validation-aucpr:0.97426
[36] validation-logloss:0.23399 validation-auc:0.97034 validation-aucpr:0.97441
[37] validation-logloss:0.23188 validation-auc:0.97010 validation-aucpr:0.97420
[38] validation-logloss:0.22930 validation-auc:0.97022 validation-aucpr:0.97431
[39] validation-logloss:0.22710 validation-auc:0.97034 validation-aucpr:0.97437
[40] validation-logloss:0.22502 validation-auc:0.97028 validation-aucpr:0.97432
[41] validation-logloss:0.22324 validation-auc:0.97023 validation-aucpr:0.97428
[42] validation-logloss:0.22138 validation-auc:0.97032 validation-aucpr:0.97435
[43] validation-logloss:0.21987 validation-auc:0.97022 validation-aucpr:0.97427
[44] validation-logloss:0.21803 validation-auc:0.97038 validation-aucpr:0.97434
[45] validation-logloss:0.21674 validation-auc:0.97037 validation-aucpr:0.97431
[46] validation-logloss:0.21561 validation-auc:0.97029 validation-aucpr:0.97424
[47] validation-logloss:0.21408 validation-auc:0.97039 validation-aucpr:0.97432
[48] validation-logloss:0.21273 validation-auc:0.97051 validation-aucpr:0.97439
[49] validation-logloss:0.21134 validation-auc:0.97055 validation-aucpr:0.97451
[50] validation-logloss:0.21020 validation-auc:0.97060 validation-aucpr:0.97457
[51] validation-logloss:0.20920 validation-auc:0.97064 validation-aucpr:0.97459
[52] validation-logloss:0.20822 validation-auc:0.97069 validation-aucpr:0.97463
[53] validation-logloss:0.20716 validation-auc:0.97085 validation-aucpr:0.97484
[54] validation-logloss:0.20613 validation-auc:0.97100 validation-aucpr:0.97490
[55] validation-logloss:0.20481 validation-auc:0.97128 validation-aucpr:0.97510
[56] validation-logloss:0.20399 validation-auc:0.97136 validation-aucpr:0.97515
[57] validation-logloss:0.20360 validation-auc:0.97126 validation-aucpr:0.97499
[58] validation-logloss:0.20262 validation-auc:0.97145 validation-aucpr:0.97512
[59] validation-logloss:0.20217 validation-auc:0.97145 validation-aucpr:0.97511
[60] validation-logloss:0.20139 validation-auc:0.97158 validation-aucpr:0.97518
[61] validation-logloss:0.20084 validation-auc:0.97151 validation-aucpr:0.97510
[62] validation-logloss:0.20007 validation-auc:0.97168 validation-aucpr:0.97523
[63] validation-logloss:0.19965 validation-auc:0.97175 validation-aucpr:0.97521
[64] validation-logloss:0.19924 validation-auc:0.97177 validation-aucpr:0.97519
[65] validation-logloss:0.19871 validation-auc:0.97188 validation-aucpr:0.97524
[66] validation-logloss:0.19829 validation-auc:0.97193 validation-aucpr:0.97528
[67] validation-logloss:0.19776 validation-auc:0.97197 validation-aucpr:0.97531
[68] validation-logloss:0.19735 validation-auc:0.97202 validation-aucpr:0.97536
[69] validation-logloss:0.19713 validation-auc:0.97198 validation-aucpr:0.97532
[70] validation-logloss:0.19700 validation-auc:0.97195 validation-aucpr:0.97527
[71] validation-logloss:0.19694 validation-auc:0.97187 validation-aucpr:0.97509
[72] validation-logloss:0.19695 validation-auc:0.97180 validation-aucpr:0.97499
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[74] validation-logloss:0.19684 validation-auc:0.97177 validation-aucpr:0.97479
[75] validation-logloss:0.19669 validation-auc:0.97175 validation-aucpr:0.97476
[76] validation-logloss:0.19639 validation-auc:0.97184 validation-aucpr:0.97506
[77] validation-logloss:0.19636 validation-auc:0.97179 validation-aucpr:0.97495
[78] validation-logloss:0.19651 validation-auc:0.97173 validation-aucpr:0.97495
[79] validation-logloss:0.19639 validation-auc:0.97179 validation-aucpr:0.97522
[80] validation-logloss:0.19611 validation-auc:0.97185 validation-aucpr:0.97527
[81] validation-logloss:0.19611 validation-auc:0.97189 validation-aucpr:0.97540
[82] validation-logloss:0.19577 validation-auc:0.97196 validation-aucpr:0.97546
[83] validation-logloss:0.19592 validation-auc:0.97180 validation-aucpr:0.97533
[84] validation-logloss:0.19609 validation-auc:0.97172 validation-aucpr:0.97526
[85] validation-logloss:0.19604 validation-auc:0.97172 validation-aucpr:0.97521
[86] validation-logloss:0.19610 validation-auc:0.97166 validation-aucpr:0.97521
[87] validation-logloss:0.19595 validation-auc:0.97169 validation-aucpr:0.97523
[88] validation-logloss:0.19613 validation-auc:0.97160 validation-aucpr:0.97525
{'best_iteration': '82', 'best_score': '0.9754620734892333'}
Trial 4, Fold 5: Log loss = 0.19613147834100697, Average precision = 0.9752497992069998, ROC-AUC = 0.9716035895435038, Elapsed Time = 3.3549910000001546 seconds
Optimization Progress: 5%|5 | 5/100 [02:47<42:36, 26.91s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 5, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68411 validation-auc:0.92495 validation-aucpr:0.92667
[1] validation-logloss:0.67398 validation-auc:0.95069 validation-aucpr:0.95639
[2] validation-logloss:0.66382 validation-auc:0.95533 validation-aucpr:0.96185
[3] validation-logloss:0.65600 validation-auc:0.95352 validation-aucpr:0.95998
[4] validation-logloss:0.64691 validation-auc:0.95499 validation-aucpr:0.96191
[5] validation-logloss:0.63940 validation-auc:0.95475 validation-aucpr:0.96156
[6] validation-logloss:0.63110 validation-auc:0.95566 validation-aucpr:0.96202
[7] validation-logloss:0.62281 validation-auc:0.95646 validation-aucpr:0.96287
[8] validation-logloss:0.61529 validation-auc:0.95652 validation-aucpr:0.96296
[9] validation-logloss:0.60838 validation-auc:0.95570 validation-aucpr:0.96218
[10] validation-logloss:0.60150 validation-auc:0.95552 validation-aucpr:0.96184
[11] validation-logloss:0.59507 validation-auc:0.95605 validation-aucpr:0.96273
[12] validation-logloss:0.58692 validation-auc:0.95727 validation-aucpr:0.96403
[13] validation-logloss:0.57943 validation-auc:0.95806 validation-aucpr:0.96461
[14] validation-logloss:0.57197 validation-auc:0.95849 validation-aucpr:0.96513
[15] validation-logloss:0.56615 validation-auc:0.95846 validation-aucpr:0.96538
[16] validation-logloss:0.55961 validation-auc:0.95860 validation-aucpr:0.96549
[17] validation-logloss:0.55281 validation-auc:0.95902 validation-aucpr:0.96586
{'best_iteration': '17', 'best_score': '0.9658596269830234'}
Trial 5, Fold 1: Log loss = 0.552814188535648, Average precision = 0.9658570155340371, ROC-AUC = 0.9590218929085236, Elapsed Time = 2.8525215000008757 seconds
Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 5, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68338 validation-auc:0.93597 validation-aucpr:0.93907
[1] validation-logloss:0.67327 validation-auc:0.95048 validation-aucpr:0.95611
[2] validation-logloss:0.66397 validation-auc:0.95365 validation-aucpr:0.96019
[3] validation-logloss:0.65405 validation-auc:0.95823 validation-aucpr:0.96382
[4] validation-logloss:0.64544 validation-auc:0.95959 validation-aucpr:0.96459
[5] validation-logloss:0.63633 validation-auc:0.96052 validation-aucpr:0.96537
[6] validation-logloss:0.62716 validation-auc:0.96203 validation-aucpr:0.96648
[7] validation-logloss:0.61860 validation-auc:0.96195 validation-aucpr:0.96676
[8] validation-logloss:0.61064 validation-auc:0.96220 validation-aucpr:0.96676
[9] validation-logloss:0.60334 validation-auc:0.96178 validation-aucpr:0.96629
[10] validation-logloss:0.59626 validation-auc:0.96172 validation-aucpr:0.96615
[11] validation-logloss:0.58932 validation-auc:0.96150 validation-aucpr:0.96585
[12] validation-logloss:0.58274 validation-auc:0.96117 validation-aucpr:0.96546
[13] validation-logloss:0.57548 validation-auc:0.96186 validation-aucpr:0.96596
[14] validation-logloss:0.56897 validation-auc:0.96226 validation-aucpr:0.96625
[15] validation-logloss:0.56278 validation-auc:0.96213 validation-aucpr:0.96607
[16] validation-logloss:0.55681 validation-auc:0.96160 validation-aucpr:0.96556
[17] validation-logloss:0.55075 validation-auc:0.96167 validation-aucpr:0.96557
{'best_iteration': '8', 'best_score': '0.9667615147131858'}
Trial 5, Fold 2: Log loss = 0.5507514087219968, Average precision = 0.9655134610344999, ROC-AUC = 0.9616703303640338, Elapsed Time = 3.293433200000436 seconds
Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 5, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68452 validation-auc:0.91430 validation-aucpr:0.89637
[1] validation-logloss:0.67552 validation-auc:0.93844 validation-aucpr:0.94050
[2] validation-logloss:0.66523 validation-auc:0.95468 validation-aucpr:0.95576
[3] validation-logloss:0.65679 validation-auc:0.95555 validation-aucpr:0.95943
[4] validation-logloss:0.64875 validation-auc:0.95470 validation-aucpr:0.95925
[5] validation-logloss:0.63933 validation-auc:0.95731 validation-aucpr:0.96235
[6] validation-logloss:0.63020 validation-auc:0.95787 validation-aucpr:0.96329
[7] validation-logloss:0.62145 validation-auc:0.95855 validation-aucpr:0.96401
[8] validation-logloss:0.61409 validation-auc:0.95906 validation-aucpr:0.96401
[9] validation-logloss:0.60568 validation-auc:0.95958 validation-aucpr:0.96450
[10] validation-logloss:0.59785 validation-auc:0.95937 validation-aucpr:0.96426
[11] validation-logloss:0.59003 validation-auc:0.95997 validation-aucpr:0.96469
[12] validation-logloss:0.58349 validation-auc:0.95958 validation-aucpr:0.96488
[13] validation-logloss:0.57626 validation-auc:0.96036 validation-aucpr:0.96628
[14] validation-logloss:0.56964 validation-auc:0.96067 validation-aucpr:0.96649
[15] validation-logloss:0.56273 validation-auc:0.96066 validation-aucpr:0.96657
[16] validation-logloss:0.55549 validation-auc:0.96143 validation-aucpr:0.96721
[17] validation-logloss:0.54823 validation-auc:0.96246 validation-aucpr:0.96796
{'best_iteration': '17', 'best_score': '0.9679629925673398'}
Trial 5, Fold 3: Log loss = 0.5482264868254652, Average precision = 0.967963204762582, ROC-AUC = 0.9624570967485625, Elapsed Time = 3.393403899999612 seconds
Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 5, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68407 validation-auc:0.92159 validation-aucpr:0.89391
[1] validation-logloss:0.67386 validation-auc:0.95333 validation-aucpr:0.95828
[2] validation-logloss:0.66508 validation-auc:0.95452 validation-aucpr:0.96148
[3] validation-logloss:0.65704 validation-auc:0.95389 validation-aucpr:0.96080
[4] validation-logloss:0.64866 validation-auc:0.95435 validation-aucpr:0.96096
[5] validation-logloss:0.63980 validation-auc:0.95546 validation-aucpr:0.96243
[6] validation-logloss:0.63050 validation-auc:0.95681 validation-aucpr:0.96371
[7] validation-logloss:0.62330 validation-auc:0.95603 validation-aucpr:0.96301
[8] validation-logloss:0.61628 validation-auc:0.95542 validation-aucpr:0.96259
[9] validation-logloss:0.60927 validation-auc:0.95504 validation-aucpr:0.96231
[10] validation-logloss:0.60115 validation-auc:0.95644 validation-aucpr:0.96353
[11] validation-logloss:0.59427 validation-auc:0.95678 validation-aucpr:0.96361
[12] validation-logloss:0.58693 validation-auc:0.95685 validation-aucpr:0.96361
[13] validation-logloss:0.57984 validation-auc:0.95708 validation-aucpr:0.96395
[14] validation-logloss:0.57315 validation-auc:0.95754 validation-aucpr:0.96420
[15] validation-logloss:0.56671 validation-auc:0.95756 validation-aucpr:0.96425
[16] validation-logloss:0.55984 validation-auc:0.95781 validation-aucpr:0.96465
[17] validation-logloss:0.55305 validation-auc:0.95809 validation-aucpr:0.96504
{'best_iteration': '17', 'best_score': '0.9650388062047341'}
Trial 5, Fold 4: Log loss = 0.5530506863842228, Average precision = 0.965038797513921, ROC-AUC = 0.9580878136393007, Elapsed Time = 3.3468467999991844 seconds
Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 5, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68377 validation-auc:0.92746 validation-aucpr:0.92197
[1] validation-logloss:0.67479 validation-auc:0.94111 validation-aucpr:0.94618
[2] validation-logloss:0.66475 validation-auc:0.95406 validation-aucpr:0.95977
[3] validation-logloss:0.65642 validation-auc:0.95316 validation-aucpr:0.95912
[4] validation-logloss:0.64750 validation-auc:0.95445 validation-aucpr:0.96017
[5] validation-logloss:0.63949 validation-auc:0.95412 validation-aucpr:0.96001
[6] validation-logloss:0.63138 validation-auc:0.95485 validation-aucpr:0.96028
[7] validation-logloss:0.62322 validation-auc:0.95570 validation-aucpr:0.96099
[8] validation-logloss:0.61643 validation-auc:0.95518 validation-aucpr:0.96037
[9] validation-logloss:0.60766 validation-auc:0.95737 validation-aucpr:0.96278
[10] validation-logloss:0.59931 validation-auc:0.95828 validation-aucpr:0.96403
[11] validation-logloss:0.59182 validation-auc:0.95888 validation-aucpr:0.96353
[12] validation-logloss:0.58400 validation-auc:0.95903 validation-aucpr:0.96385
[13] validation-logloss:0.57641 validation-auc:0.95936 validation-aucpr:0.96457
[14] validation-logloss:0.57065 validation-auc:0.95904 validation-aucpr:0.96416
[15] validation-logloss:0.56348 validation-auc:0.95951 validation-aucpr:0.96463
[16] validation-logloss:0.55800 validation-auc:0.95910 validation-aucpr:0.96442
[17] validation-logloss:0.55198 validation-auc:0.95917 validation-aucpr:0.96378
{'best_iteration': '15', 'best_score': '0.9646284351019664'}
Trial 5, Fold 5: Log loss = 0.551975421982808, Average precision = 0.9641766244099718, ROC-AUC = 0.9591747022648309, Elapsed Time = 3.3459750000001804 seconds
Optimization Progress: 6%|6 | 6/100 [03:11<41:00, 26.18s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 6, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66868 validation-auc:0.92656 validation-aucpr:0.89275
[1] validation-logloss:0.64642 validation-auc:0.94301 validation-aucpr:0.93566
[2] validation-logloss:0.62475 validation-auc:0.95046 validation-aucpr:0.95092
[3] validation-logloss:0.60542 validation-auc:0.95212 validation-aucpr:0.95692
[4] validation-logloss:0.58687 validation-auc:0.95403 validation-aucpr:0.95893
[5] validation-logloss:0.56954 validation-auc:0.95452 validation-aucpr:0.95945
[6] validation-logloss:0.54956 validation-auc:0.96044 validation-aucpr:0.96491
[7] validation-logloss:0.53461 validation-auc:0.96030 validation-aucpr:0.96489
[8] validation-logloss:0.51724 validation-auc:0.96261 validation-aucpr:0.96724
[9] validation-logloss:0.50330 validation-auc:0.96294 validation-aucpr:0.96786
[10] validation-logloss:0.49033 validation-auc:0.96300 validation-aucpr:0.96842
[11] validation-logloss:0.47571 validation-auc:0.96388 validation-aucpr:0.96945
[12] validation-logloss:0.46129 validation-auc:0.96477 validation-aucpr:0.97030
[13] validation-logloss:0.45096 validation-auc:0.96456 validation-aucpr:0.97006
[14] validation-logloss:0.43848 validation-auc:0.96512 validation-aucpr:0.97062
[15] validation-logloss:0.42864 validation-auc:0.96520 validation-aucpr:0.97064
[16] validation-logloss:0.41941 validation-auc:0.96511 validation-aucpr:0.97053
[17] validation-logloss:0.40882 validation-auc:0.96548 validation-aucpr:0.97091
[18] validation-logloss:0.39866 validation-auc:0.96566 validation-aucpr:0.97107
[19] validation-logloss:0.39133 validation-auc:0.96546 validation-aucpr:0.97088
[20] validation-logloss:0.38188 validation-auc:0.96588 validation-aucpr:0.97126
[21] validation-logloss:0.37457 validation-auc:0.96611 validation-aucpr:0.97142
[22] validation-logloss:0.36786 validation-auc:0.96617 validation-aucpr:0.97146
[23] validation-logloss:0.36001 validation-auc:0.96653 validation-aucpr:0.97178
[24] validation-logloss:0.35400 validation-auc:0.96645 validation-aucpr:0.97169
[25] validation-logloss:0.34681 validation-auc:0.96670 validation-aucpr:0.97189
[26] validation-logloss:0.34125 validation-auc:0.96685 validation-aucpr:0.97196
[27] validation-logloss:0.33596 validation-auc:0.96682 validation-aucpr:0.97192
[28] validation-logloss:0.33077 validation-auc:0.96693 validation-aucpr:0.97197
[29] validation-logloss:0.32597 validation-auc:0.96695 validation-aucpr:0.97199
[30] validation-logloss:0.31973 validation-auc:0.96723 validation-aucpr:0.97224
[31] validation-logloss:0.31548 validation-auc:0.96742 validation-aucpr:0.97236
[32] validation-logloss:0.30959 validation-auc:0.96778 validation-aucpr:0.97266
[33] validation-logloss:0.30441 validation-auc:0.96795 validation-aucpr:0.97280
[34] validation-logloss:0.30063 validation-auc:0.96797 validation-aucpr:0.97283
[35] validation-logloss:0.29591 validation-auc:0.96806 validation-aucpr:0.97292
[36] validation-logloss:0.29211 validation-auc:0.96818 validation-aucpr:0.97298
[37] validation-logloss:0.28771 validation-auc:0.96837 validation-aucpr:0.97312
[38] validation-logloss:0.28446 validation-auc:0.96833 validation-aucpr:0.97308
[39] validation-logloss:0.28170 validation-auc:0.96830 validation-aucpr:0.97300
[40] validation-logloss:0.27742 validation-auc:0.96855 validation-aucpr:0.97324
[41] validation-logloss:0.27466 validation-auc:0.96849 validation-aucpr:0.97315
[42] validation-logloss:0.27089 validation-auc:0.96868 validation-aucpr:0.97330
[43] validation-logloss:0.26827 validation-auc:0.96872 validation-aucpr:0.97333
[44] validation-logloss:0.26586 validation-auc:0.96876 validation-aucpr:0.97331
[45] validation-logloss:0.26238 validation-auc:0.96897 validation-aucpr:0.97359
[46] validation-logloss:0.25999 validation-auc:0.96908 validation-aucpr:0.97365
[47] validation-logloss:0.25790 validation-auc:0.96897 validation-aucpr:0.97357
[48] validation-logloss:0.25562 validation-auc:0.96914 validation-aucpr:0.97370
[49] validation-logloss:0.25375 validation-auc:0.96907 validation-aucpr:0.97360
[50] validation-logloss:0.25073 validation-auc:0.96929 validation-aucpr:0.97382
[51] validation-logloss:0.24888 validation-auc:0.96934 validation-aucpr:0.97386
{'best_iteration': '51', 'best_score': '0.9738558070482932'}
Trial 6, Fold 1: Log loss = 0.24888475472946808, Average precision = 0.9738601882875679, ROC-AUC = 0.9693418475741195, Elapsed Time = 1.6845219000006182 seconds
Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 6, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66788 validation-auc:0.93467 validation-aucpr:0.91059
[1] validation-logloss:0.64453 validation-auc:0.95338 validation-aucpr:0.95073
[2] validation-logloss:0.62375 validation-auc:0.95511 validation-aucpr:0.95729
[3] validation-logloss:0.60063 validation-auc:0.96283 validation-aucpr:0.96462
[4] validation-logloss:0.57891 validation-auc:0.96523 validation-aucpr:0.96826
[5] validation-logloss:0.56156 validation-auc:0.96566 validation-aucpr:0.96894
[6] validation-logloss:0.54520 validation-auc:0.96588 validation-aucpr:0.96890
[7] validation-logloss:0.52685 validation-auc:0.96672 validation-aucpr:0.96999
[8] validation-logloss:0.51011 validation-auc:0.96704 validation-aucpr:0.97042
[9] validation-logloss:0.49444 validation-auc:0.96748 validation-aucpr:0.97081
[10] validation-logloss:0.47964 validation-auc:0.96765 validation-aucpr:0.97100
[11] validation-logloss:0.46581 validation-auc:0.96809 validation-aucpr:0.97139
[12] validation-logloss:0.45496 validation-auc:0.96793 validation-aucpr:0.97124
[13] validation-logloss:0.44395 validation-auc:0.96799 validation-aucpr:0.97125
[14] validation-logloss:0.43378 validation-auc:0.96798 validation-aucpr:0.97133
[15] validation-logloss:0.42528 validation-auc:0.96748 validation-aucpr:0.97077
[16] validation-logloss:0.41465 validation-auc:0.96762 validation-aucpr:0.97090
[17] validation-logloss:0.40565 validation-auc:0.96757 validation-aucpr:0.97028
[18] validation-logloss:0.39571 validation-auc:0.96767 validation-aucpr:0.97044
[19] validation-logloss:0.38781 validation-auc:0.96774 validation-aucpr:0.97046
[20] validation-logloss:0.38028 validation-auc:0.96781 validation-aucpr:0.97050
[21] validation-logloss:0.37130 validation-auc:0.96824 validation-aucpr:0.97085
[22] validation-logloss:0.36476 validation-auc:0.96815 validation-aucpr:0.97078
[23] validation-logloss:0.35668 validation-auc:0.96842 validation-aucpr:0.97101
[24] validation-logloss:0.35027 validation-auc:0.96858 validation-aucpr:0.97114
[25] validation-logloss:0.34288 validation-auc:0.96874 validation-aucpr:0.97130
[26] validation-logloss:0.33586 validation-auc:0.96892 validation-aucpr:0.97144
[27] validation-logloss:0.33056 validation-auc:0.96895 validation-aucpr:0.97150
[28] validation-logloss:0.32402 validation-auc:0.96926 validation-aucpr:0.97168
[29] validation-logloss:0.31906 validation-auc:0.96920 validation-aucpr:0.97165
[30] validation-logloss:0.31305 validation-auc:0.96948 validation-aucpr:0.97205
[31] validation-logloss:0.30734 validation-auc:0.96974 validation-aucpr:0.97234
[32] validation-logloss:0.30292 validation-auc:0.96984 validation-aucpr:0.97240
[33] validation-logloss:0.29776 validation-auc:0.96992 validation-aucpr:0.97251
[34] validation-logloss:0.29272 validation-auc:0.96999 validation-aucpr:0.97261
[35] validation-logloss:0.28898 validation-auc:0.97005 validation-aucpr:0.97264
[36] validation-logloss:0.28518 validation-auc:0.97010 validation-aucpr:0.97267
[37] validation-logloss:0.28164 validation-auc:0.97020 validation-aucpr:0.97269
[38] validation-logloss:0.27842 validation-auc:0.97032 validation-aucpr:0.97276
[39] validation-logloss:0.27516 validation-auc:0.97038 validation-aucpr:0.97280
[40] validation-logloss:0.27111 validation-auc:0.97049 validation-aucpr:0.97291
[41] validation-logloss:0.26738 validation-auc:0.97060 validation-aucpr:0.97299
[42] validation-logloss:0.26459 validation-auc:0.97062 validation-aucpr:0.97301
[43] validation-logloss:0.26107 validation-auc:0.97082 validation-aucpr:0.97323
[44] validation-logloss:0.25834 validation-auc:0.97089 validation-aucpr:0.97393
[45] validation-logloss:0.25590 validation-auc:0.97095 validation-aucpr:0.97396
[46] validation-logloss:0.25344 validation-auc:0.97103 validation-aucpr:0.97399
[47] validation-logloss:0.25101 validation-auc:0.97109 validation-aucpr:0.97403
[48] validation-logloss:0.24893 validation-auc:0.97107 validation-aucpr:0.97399
[49] validation-logloss:0.24667 validation-auc:0.97108 validation-aucpr:0.97400
[50] validation-logloss:0.24461 validation-auc:0.97118 validation-aucpr:0.97406
[51] validation-logloss:0.24163 validation-auc:0.97143 validation-aucpr:0.97427
{'best_iteration': '51', 'best_score': '0.9742744916003644'}
Trial 6, Fold 2: Log loss = 0.2416254566667652, Average precision = 0.974289737756206, ROC-AUC = 0.9714251332573178, Elapsed Time = 1.8510905000002822 seconds
Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 6, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66832 validation-auc:0.92346 validation-aucpr:0.88843
[1] validation-logloss:0.64380 validation-auc:0.95272 validation-aucpr:0.95000
[2] validation-logloss:0.62157 validation-auc:0.95911 validation-aucpr:0.96307
[3] validation-logloss:0.59860 validation-auc:0.96419 validation-aucpr:0.96711
[4] validation-logloss:0.57704 validation-auc:0.96581 validation-aucpr:0.96937
[5] validation-logloss:0.55930 validation-auc:0.96675 validation-aucpr:0.97002
[6] validation-logloss:0.54293 validation-auc:0.96719 validation-aucpr:0.97030
[7] validation-logloss:0.52767 validation-auc:0.96737 validation-aucpr:0.96963
[8] validation-logloss:0.51299 validation-auc:0.96722 validation-aucpr:0.96950
[9] validation-logloss:0.50055 validation-auc:0.96708 validation-aucpr:0.96921
[10] validation-logloss:0.48829 validation-auc:0.96690 validation-aucpr:0.96913
[11] validation-logloss:0.47310 validation-auc:0.96760 validation-aucpr:0.97226
[12] validation-logloss:0.45907 validation-auc:0.96797 validation-aucpr:0.97263
[13] validation-logloss:0.44667 validation-auc:0.96809 validation-aucpr:0.97279
[14] validation-logloss:0.43562 validation-auc:0.96826 validation-aucpr:0.97294
[15] validation-logloss:0.42385 validation-auc:0.96841 validation-aucpr:0.97310
[16] validation-logloss:0.41436 validation-auc:0.96874 validation-aucpr:0.97338
[17] validation-logloss:0.40374 validation-auc:0.96897 validation-aucpr:0.97358
[18] validation-logloss:0.39549 validation-auc:0.96893 validation-aucpr:0.97354
[19] validation-logloss:0.38716 validation-auc:0.96896 validation-aucpr:0.97357
[20] validation-logloss:0.37922 validation-auc:0.96919 validation-aucpr:0.97372
[21] validation-logloss:0.37037 validation-auc:0.96939 validation-aucpr:0.97387
[22] validation-logloss:0.36176 validation-auc:0.96949 validation-aucpr:0.97397
[23] validation-logloss:0.35367 validation-auc:0.96976 validation-aucpr:0.97421
[24] validation-logloss:0.34586 validation-auc:0.96988 validation-aucpr:0.97433
[25] validation-logloss:0.33841 validation-auc:0.97016 validation-aucpr:0.97457
[26] validation-logloss:0.33269 validation-auc:0.97030 validation-aucpr:0.97463
[27] validation-logloss:0.32732 validation-auc:0.97022 validation-aucpr:0.97457
[28] validation-logloss:0.32193 validation-auc:0.97045 validation-aucpr:0.97475
[29] validation-logloss:0.31622 validation-auc:0.97057 validation-aucpr:0.97485
[30] validation-logloss:0.31038 validation-auc:0.97082 validation-aucpr:0.97507
[31] validation-logloss:0.30544 validation-auc:0.97110 validation-aucpr:0.97523
[32] validation-logloss:0.30030 validation-auc:0.97105 validation-aucpr:0.97521
[33] validation-logloss:0.29628 validation-auc:0.97101 validation-aucpr:0.97515
[34] validation-logloss:0.29245 validation-auc:0.97107 validation-aucpr:0.97520
[35] validation-logloss:0.28883 validation-auc:0.97104 validation-aucpr:0.97519
[36] validation-logloss:0.28389 validation-auc:0.97127 validation-aucpr:0.97537
[37] validation-logloss:0.28063 validation-auc:0.97119 validation-aucpr:0.97530
[38] validation-logloss:0.27750 validation-auc:0.97116 validation-aucpr:0.97526
[39] validation-logloss:0.27325 validation-auc:0.97123 validation-aucpr:0.97533
[40] validation-logloss:0.27030 validation-auc:0.97116 validation-aucpr:0.97527
[41] validation-logloss:0.26646 validation-auc:0.97130 validation-aucpr:0.97537
[42] validation-logloss:0.26342 validation-auc:0.97149 validation-aucpr:0.97549
[43] validation-logloss:0.26088 validation-auc:0.97148 validation-aucpr:0.97547
[44] validation-logloss:0.25833 validation-auc:0.97154 validation-aucpr:0.97552
[45] validation-logloss:0.25584 validation-auc:0.97153 validation-aucpr:0.97548
[46] validation-logloss:0.25349 validation-auc:0.97150 validation-aucpr:0.97543
[47] validation-logloss:0.25019 validation-auc:0.97160 validation-aucpr:0.97553
[48] validation-logloss:0.24808 validation-auc:0.97167 validation-aucpr:0.97563
[49] validation-logloss:0.24598 validation-auc:0.97165 validation-aucpr:0.97560
[50] validation-logloss:0.24396 validation-auc:0.97163 validation-aucpr:0.97559
[51] validation-logloss:0.24117 validation-auc:0.97175 validation-aucpr:0.97569
{'best_iteration': '51', 'best_score': '0.9756914292499006'}
Trial 6, Fold 3: Log loss = 0.2411696065616846, Average precision = 0.9756956642097862, ROC-AUC = 0.9717455528695669, Elapsed Time = 1.7352725000000646 seconds
Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 6, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66848 validation-auc:0.92531 validation-aucpr:0.88966
[1] validation-logloss:0.64595 validation-auc:0.94503 validation-aucpr:0.93222
[2] validation-logloss:0.62460 validation-auc:0.95218 validation-aucpr:0.95507
[3] validation-logloss:0.60120 validation-auc:0.96106 validation-aucpr:0.96514
[4] validation-logloss:0.58273 validation-auc:0.96159 validation-aucpr:0.96633
[5] validation-logloss:0.56187 validation-auc:0.96428 validation-aucpr:0.96930
[6] validation-logloss:0.54578 validation-auc:0.96443 validation-aucpr:0.96933
[7] validation-logloss:0.52969 validation-auc:0.96527 validation-aucpr:0.97000
[8] validation-logloss:0.51239 validation-auc:0.96593 validation-aucpr:0.97084
[9] validation-logloss:0.49680 validation-auc:0.96612 validation-aucpr:0.97112
[10] validation-logloss:0.48098 validation-auc:0.96711 validation-aucpr:0.97201
[11] validation-logloss:0.46852 validation-auc:0.96726 validation-aucpr:0.97218
[12] validation-logloss:0.45710 validation-auc:0.96716 validation-aucpr:0.97206
[13] validation-logloss:0.44662 validation-auc:0.96680 validation-aucpr:0.97177
[14] validation-logloss:0.43433 validation-auc:0.96699 validation-aucpr:0.97206
[15] validation-logloss:0.42454 validation-auc:0.96679 validation-aucpr:0.97190
[16] validation-logloss:0.41541 validation-auc:0.96685 validation-aucpr:0.97198
[17] validation-logloss:0.40681 validation-auc:0.96682 validation-aucpr:0.97196
[18] validation-logloss:0.39676 validation-auc:0.96712 validation-aucpr:0.97225
[19] validation-logloss:0.38865 validation-auc:0.96741 validation-aucpr:0.97245
[20] validation-logloss:0.38153 validation-auc:0.96710 validation-aucpr:0.97219
[21] validation-logloss:0.37415 validation-auc:0.96708 validation-aucpr:0.97212
[22] validation-logloss:0.36591 validation-auc:0.96724 validation-aucpr:0.97234
[23] validation-logloss:0.35958 validation-auc:0.96712 validation-aucpr:0.97222
[24] validation-logloss:0.35205 validation-auc:0.96713 validation-aucpr:0.97230
[25] validation-logloss:0.34635 validation-auc:0.96702 validation-aucpr:0.97222
[26] validation-logloss:0.34088 validation-auc:0.96699 validation-aucpr:0.97220
[27] validation-logloss:0.33484 validation-auc:0.96711 validation-aucpr:0.97231
[28] validation-logloss:0.32844 validation-auc:0.96722 validation-aucpr:0.97246
[29] validation-logloss:0.32361 validation-auc:0.96730 validation-aucpr:0.97246
[30] validation-logloss:0.31878 validation-auc:0.96740 validation-aucpr:0.97252
[31] validation-logloss:0.31279 validation-auc:0.96761 validation-aucpr:0.97270
[32] validation-logloss:0.30723 validation-auc:0.96787 validation-aucpr:0.97293
[33] validation-logloss:0.30309 validation-auc:0.96795 validation-aucpr:0.97296
[34] validation-logloss:0.29896 validation-auc:0.96813 validation-aucpr:0.97308
[35] validation-logloss:0.29486 validation-auc:0.96828 validation-aucpr:0.97318
[36] validation-logloss:0.29129 validation-auc:0.96829 validation-aucpr:0.97317
[37] validation-logloss:0.28664 validation-auc:0.96853 validation-aucpr:0.97340
[38] validation-logloss:0.28238 validation-auc:0.96861 validation-aucpr:0.97350
[39] validation-logloss:0.27820 validation-auc:0.96871 validation-aucpr:0.97360
[40] validation-logloss:0.27520 validation-auc:0.96873 validation-aucpr:0.97359
[41] validation-logloss:0.27256 validation-auc:0.96860 validation-aucpr:0.97348
[42] validation-logloss:0.27001 validation-auc:0.96859 validation-aucpr:0.97349
[43] validation-logloss:0.26621 validation-auc:0.96885 validation-aucpr:0.97370
[44] validation-logloss:0.26271 validation-auc:0.96896 validation-aucpr:0.97380
[45] validation-logloss:0.26011 validation-auc:0.96904 validation-aucpr:0.97385
[46] validation-logloss:0.25675 validation-auc:0.96924 validation-aucpr:0.97402
[47] validation-logloss:0.25437 validation-auc:0.96928 validation-aucpr:0.97407
[48] validation-logloss:0.25136 validation-auc:0.96942 validation-aucpr:0.97420
[49] validation-logloss:0.24929 validation-auc:0.96944 validation-aucpr:0.97421
[50] validation-logloss:0.24666 validation-auc:0.96955 validation-aucpr:0.97432
[51] validation-logloss:0.24464 validation-auc:0.96963 validation-aucpr:0.97439
{'best_iteration': '51', 'best_score': '0.9743919537729241'}
Trial 6, Fold 4: Log loss = 0.24464498648864635, Average precision = 0.9743956571983957, ROC-AUC = 0.9696342434625445, Elapsed Time = 1.8642994000001636 seconds
Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 6, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66837 validation-auc:0.92013 validation-aucpr:0.88776
[1] validation-logloss:0.64569 validation-auc:0.94533 validation-aucpr:0.93629
[2] validation-logloss:0.62488 validation-auc:0.94923 validation-aucpr:0.94806
[3] validation-logloss:0.60140 validation-auc:0.95964 validation-aucpr:0.96366
[4] validation-logloss:0.57992 validation-auc:0.96210 validation-aucpr:0.96599
[5] validation-logloss:0.56293 validation-auc:0.96179 validation-aucpr:0.96558
[6] validation-logloss:0.54669 validation-auc:0.96209 validation-aucpr:0.96568
[7] validation-logloss:0.53101 validation-auc:0.96269 validation-aucpr:0.96581
[8] validation-logloss:0.51657 validation-auc:0.96266 validation-aucpr:0.96579
[9] validation-logloss:0.50308 validation-auc:0.96248 validation-aucpr:0.96588
[10] validation-logloss:0.48987 validation-auc:0.96302 validation-aucpr:0.96623
[11] validation-logloss:0.47787 validation-auc:0.96293 validation-aucpr:0.96608
[12] validation-logloss:0.46411 validation-auc:0.96347 validation-aucpr:0.96622
[13] validation-logloss:0.45101 validation-auc:0.96426 validation-aucpr:0.96697
[14] validation-logloss:0.44053 validation-auc:0.96436 validation-aucpr:0.96700
[15] validation-logloss:0.42948 validation-auc:0.96442 validation-aucpr:0.96680
[16] validation-logloss:0.42044 validation-auc:0.96439 validation-aucpr:0.96674
[17] validation-logloss:0.40995 validation-auc:0.96452 validation-aucpr:0.96692
[18] validation-logloss:0.40207 validation-auc:0.96447 validation-aucpr:0.96670
[19] validation-logloss:0.39202 validation-auc:0.96500 validation-aucpr:0.96742
[20] validation-logloss:0.38485 validation-auc:0.96500 validation-aucpr:0.96747
[21] validation-logloss:0.37770 validation-auc:0.96507 validation-aucpr:0.96747
[22] validation-logloss:0.36904 validation-auc:0.96559 validation-aucpr:0.96790
[23] validation-logloss:0.36094 validation-auc:0.96599 validation-aucpr:0.96941
[24] validation-logloss:0.35346 validation-auc:0.96619 validation-aucpr:0.96930
[25] validation-logloss:0.34712 validation-auc:0.96634 validation-aucpr:0.96941
[26] validation-logloss:0.34005 validation-auc:0.96682 validation-aucpr:0.96981
[27] validation-logloss:0.33429 validation-auc:0.96689 validation-aucpr:0.97002
[28] validation-logloss:0.32918 validation-auc:0.96692 validation-aucpr:0.97000
[29] validation-logloss:0.32433 validation-auc:0.96702 validation-aucpr:0.97116
[30] validation-logloss:0.31817 validation-auc:0.96735 validation-aucpr:0.97145
[31] validation-logloss:0.31389 validation-auc:0.96757 validation-aucpr:0.97154
[32] validation-logloss:0.30980 validation-auc:0.96744 validation-aucpr:0.97142
[33] validation-logloss:0.30554 validation-auc:0.96755 validation-aucpr:0.97149
[34] validation-logloss:0.30047 validation-auc:0.96774 validation-aucpr:0.97166
[35] validation-logloss:0.29599 validation-auc:0.96783 validation-aucpr:0.97181
[36] validation-logloss:0.29258 validation-auc:0.96781 validation-aucpr:0.97175
[37] validation-logloss:0.28796 validation-auc:0.96815 validation-aucpr:0.97205
[38] validation-logloss:0.28344 validation-auc:0.96839 validation-aucpr:0.97225
[39] validation-logloss:0.27925 validation-auc:0.96858 validation-aucpr:0.97243
[40] validation-logloss:0.27545 validation-auc:0.96865 validation-aucpr:0.97249
[41] validation-logloss:0.27171 validation-auc:0.96880 validation-aucpr:0.97265
[42] validation-logloss:0.26805 validation-auc:0.96892 validation-aucpr:0.97276
[43] validation-logloss:0.26589 validation-auc:0.96875 validation-aucpr:0.97263
[44] validation-logloss:0.26353 validation-auc:0.96876 validation-aucpr:0.97262
[45] validation-logloss:0.26108 validation-auc:0.96885 validation-aucpr:0.97268
[46] validation-logloss:0.25851 validation-auc:0.96891 validation-aucpr:0.97274
[47] validation-logloss:0.25536 validation-auc:0.96917 validation-aucpr:0.97297
[48] validation-logloss:0.25252 validation-auc:0.96929 validation-aucpr:0.97306
[49] validation-logloss:0.24971 validation-auc:0.96947 validation-aucpr:0.97316
[50] validation-logloss:0.24772 validation-auc:0.96953 validation-aucpr:0.97317
[51] validation-logloss:0.24576 validation-auc:0.96963 validation-aucpr:0.97323
{'best_iteration': '51', 'best_score': '0.9732328723738558'}
Trial 6, Fold 5: Log loss = 0.24575918207555797, Average precision = 0.9732383941377283, ROC-AUC = 0.9696304553815285, Elapsed Time = 1.744874300000447 seconds
Optimization Progress: 7%|7 | 7/100 [03:29<36:20, 23.45s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 7, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68016 validation-auc:0.89251 validation-aucpr:0.87031
[1] validation-logloss:0.66712 validation-auc:0.90934 validation-aucpr:0.90403
[2] validation-logloss:0.65493 validation-auc:0.91940 validation-aucpr:0.91914
[3] validation-logloss:0.64218 validation-auc:0.92620 validation-aucpr:0.93081
[4] validation-logloss:0.63544 validation-auc:0.92475 validation-aucpr:0.93087
[5] validation-logloss:0.62441 validation-auc:0.93475 validation-aucpr:0.94099
[6] validation-logloss:0.61526 validation-auc:0.93665 validation-aucpr:0.94386
[7] validation-logloss:0.60932 validation-auc:0.93241 validation-aucpr:0.94166
[8] validation-logloss:0.59916 validation-auc:0.93500 validation-aucpr:0.94389
[9] validation-logloss:0.58964 validation-auc:0.93711 validation-aucpr:0.94473
[10] validation-logloss:0.58046 validation-auc:0.93881 validation-aucpr:0.94572
[11] validation-logloss:0.57157 validation-auc:0.94023 validation-aucpr:0.94657
[12] validation-logloss:0.56514 validation-auc:0.94241 validation-aucpr:0.94895
[13] validation-logloss:0.55683 validation-auc:0.94293 validation-aucpr:0.94978
[14] validation-logloss:0.54973 validation-auc:0.94335 validation-aucpr:0.95017
[15] validation-logloss:0.54508 validation-auc:0.94246 validation-aucpr:0.94980
[16] validation-logloss:0.53825 validation-auc:0.94314 validation-aucpr:0.95041
[17] validation-logloss:0.53180 validation-auc:0.94337 validation-aucpr:0.95039
[18] validation-logloss:0.52467 validation-auc:0.94405 validation-aucpr:0.95080
[19] validation-logloss:0.51864 validation-auc:0.94388 validation-aucpr:0.95064
[20] validation-logloss:0.51293 validation-auc:0.94378 validation-aucpr:0.95054
[21] validation-logloss:0.50736 validation-auc:0.94372 validation-aucpr:0.95017
[22] validation-logloss:0.50184 validation-auc:0.94344 validation-aucpr:0.94997
[23] validation-logloss:0.49344 validation-auc:0.94956 validation-aucpr:0.95698
[24] validation-logloss:0.48983 validation-auc:0.94934 validation-aucpr:0.95664
[25] validation-logloss:0.48617 validation-auc:0.95008 validation-aucpr:0.95737
[26] validation-logloss:0.48121 validation-auc:0.94982 validation-aucpr:0.95701
[27] validation-logloss:0.47813 validation-auc:0.95035 validation-aucpr:0.95764
[28] validation-logloss:0.47385 validation-auc:0.95076 validation-aucpr:0.95807
[29] validation-logloss:0.46691 validation-auc:0.95212 validation-aucpr:0.95960
[30] validation-logloss:0.46231 validation-auc:0.95230 validation-aucpr:0.95991
[31] validation-logloss:0.45990 validation-auc:0.95229 validation-aucpr:0.96008
[32] validation-logloss:0.45711 validation-auc:0.95246 validation-aucpr:0.96015
[33] validation-logloss:0.45468 validation-auc:0.95230 validation-aucpr:0.95999
[34] validation-logloss:0.45072 validation-auc:0.95221 validation-aucpr:0.95995
[35] validation-logloss:0.44696 validation-auc:0.95222 validation-aucpr:0.95985
[36] validation-logloss:0.44315 validation-auc:0.95207 validation-aucpr:0.95965
[37] validation-logloss:0.44108 validation-auc:0.95219 validation-aucpr:0.95974
[38] validation-logloss:0.43709 validation-auc:0.95222 validation-aucpr:0.95972
[39] validation-logloss:0.43289 validation-auc:0.95231 validation-aucpr:0.95966
[40] validation-logloss:0.42983 validation-auc:0.95198 validation-aucpr:0.95931
[41] validation-logloss:0.42831 validation-auc:0.95212 validation-aucpr:0.95946
[42] validation-logloss:0.42538 validation-auc:0.95195 validation-aucpr:0.95925
[43] validation-logloss:0.42330 validation-auc:0.95198 validation-aucpr:0.95940
[44] validation-logloss:0.41957 validation-auc:0.95204 validation-aucpr:0.95948
[45] validation-logloss:0.41739 validation-auc:0.95186 validation-aucpr:0.95931
[46] validation-logloss:0.41432 validation-auc:0.95198 validation-aucpr:0.95948
[47] validation-logloss:0.41163 validation-auc:0.95179 validation-aucpr:0.95919
[48] validation-logloss:0.41000 validation-auc:0.95197 validation-aucpr:0.95943
[49] validation-logloss:0.40495 validation-auc:0.95289 validation-aucpr:0.96051
[50] validation-logloss:0.40270 validation-auc:0.95280 validation-aucpr:0.96035
[51] validation-logloss:0.40179 validation-auc:0.95277 validation-aucpr:0.96034
[52] validation-logloss:0.39913 validation-auc:0.95276 validation-aucpr:0.96032
[53] validation-logloss:0.39708 validation-auc:0.95267 validation-aucpr:0.96016
[54] validation-logloss:0.39336 validation-auc:0.95303 validation-aucpr:0.96045
{'best_iteration': '49', 'best_score': '0.9605111595823358'}
Trial 7, Fold 1: Log loss = 0.39335688845107086, Average precision = 0.9604510688194967, ROC-AUC = 0.9530320220530248, Elapsed Time = 0.7336209999994026 seconds
Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 7, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67933 validation-auc:0.90882 validation-aucpr:0.89431
[1] validation-logloss:0.66675 validation-auc:0.91501 validation-aucpr:0.89901
[2] validation-logloss:0.65206 validation-auc:0.93963 validation-aucpr:0.93515
[3] validation-logloss:0.63992 validation-auc:0.94320 validation-aucpr:0.94373
[4] validation-logloss:0.62894 validation-auc:0.94160 validation-aucpr:0.94018
[5] validation-logloss:0.61858 validation-auc:0.94205 validation-aucpr:0.94202
[6] validation-logloss:0.60899 validation-auc:0.94013 validation-aucpr:0.94055
[7] validation-logloss:0.59955 validation-auc:0.94683 validation-aucpr:0.95021
[8] validation-logloss:0.58795 validation-auc:0.95064 validation-aucpr:0.95505
[9] validation-logloss:0.57983 validation-auc:0.95104 validation-aucpr:0.95535
[10] validation-logloss:0.57015 validation-auc:0.95200 validation-aucpr:0.95618
[11] validation-logloss:0.56186 validation-auc:0.95137 validation-aucpr:0.95500
[12] validation-logloss:0.55357 validation-auc:0.95168 validation-aucpr:0.95523
[13] validation-logloss:0.54688 validation-auc:0.95224 validation-aucpr:0.95573
[14] validation-logloss:0.54133 validation-auc:0.95203 validation-aucpr:0.95582
[15] validation-logloss:0.53531 validation-auc:0.95226 validation-aucpr:0.95626
[16] validation-logloss:0.52876 validation-auc:0.95188 validation-aucpr:0.95567
[17] validation-logloss:0.52642 validation-auc:0.95163 validation-aucpr:0.95592
[18] validation-logloss:0.52025 validation-auc:0.95145 validation-aucpr:0.95577
[19] validation-logloss:0.51251 validation-auc:0.95244 validation-aucpr:0.95678
[20] validation-logloss:0.50681 validation-auc:0.95245 validation-aucpr:0.95640
[21] validation-logloss:0.49955 validation-auc:0.95320 validation-aucpr:0.95719
[22] validation-logloss:0.49401 validation-auc:0.95304 validation-aucpr:0.95700
[23] validation-logloss:0.49148 validation-auc:0.95294 validation-aucpr:0.95727
[24] validation-logloss:0.48907 validation-auc:0.95270 validation-aucpr:0.95711
[25] validation-logloss:0.48413 validation-auc:0.95268 validation-aucpr:0.95688
[26] validation-logloss:0.47927 validation-auc:0.95270 validation-aucpr:0.95669
[27] validation-logloss:0.47472 validation-auc:0.95235 validation-aucpr:0.95614
[28] validation-logloss:0.46771 validation-auc:0.95332 validation-aucpr:0.95745
[29] validation-logloss:0.46268 validation-auc:0.95332 validation-aucpr:0.95756
[30] validation-logloss:0.45885 validation-auc:0.95332 validation-aucpr:0.95740
[31] validation-logloss:0.45480 validation-auc:0.95336 validation-aucpr:0.95773
[32] validation-logloss:0.45011 validation-auc:0.95325 validation-aucpr:0.95745
[33] validation-logloss:0.44529 validation-auc:0.95321 validation-aucpr:0.95728
[34] validation-logloss:0.44151 validation-auc:0.95325 validation-aucpr:0.95725
[35] validation-logloss:0.43920 validation-auc:0.95310 validation-aucpr:0.95713
[36] validation-logloss:0.43571 validation-auc:0.95319 validation-aucpr:0.95722
[37] validation-logloss:0.43194 validation-auc:0.95306 validation-aucpr:0.95703
[38] validation-logloss:0.42812 validation-auc:0.95290 validation-aucpr:0.95683
[39] validation-logloss:0.42485 validation-auc:0.95285 validation-aucpr:0.95678
[40] validation-logloss:0.42238 validation-auc:0.95290 validation-aucpr:0.95677
[41] validation-logloss:0.41903 validation-auc:0.95285 validation-aucpr:0.95671
[42] validation-logloss:0.41634 validation-auc:0.95306 validation-aucpr:0.95691
[43] validation-logloss:0.41369 validation-auc:0.95307 validation-aucpr:0.95686
[44] validation-logloss:0.41114 validation-auc:0.95309 validation-aucpr:0.95680
[45] validation-logloss:0.40835 validation-auc:0.95294 validation-aucpr:0.95656
[46] validation-logloss:0.40635 validation-auc:0.95315 validation-aucpr:0.95684
[47] validation-logloss:0.40329 validation-auc:0.95309 validation-aucpr:0.95681
[48] validation-logloss:0.40126 validation-auc:0.95306 validation-aucpr:0.95681
[49] validation-logloss:0.39889 validation-auc:0.95332 validation-aucpr:0.95720
[50] validation-logloss:0.39657 validation-auc:0.95334 validation-aucpr:0.95723
[51] validation-logloss:0.39397 validation-auc:0.95336 validation-aucpr:0.95724
[52] validation-logloss:0.39086 validation-auc:0.95348 validation-aucpr:0.95742
[53] validation-logloss:0.38884 validation-auc:0.95351 validation-aucpr:0.95744
[54] validation-logloss:0.38677 validation-auc:0.95351 validation-aucpr:0.95742
{'best_iteration': '31', 'best_score': '0.9577280678288306'}
Trial 7, Fold 2: Log loss = 0.38676745688883635, Average precision = 0.9574293626158382, ROC-AUC = 0.9535117624260951, Elapsed Time = 1.0004017999999633 seconds
Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 7, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67935 validation-auc:0.90470 validation-aucpr:0.89052
[1] validation-logloss:0.66670 validation-auc:0.91107 validation-aucpr:0.89678
[2] validation-logloss:0.65748 validation-auc:0.91284 validation-aucpr:0.90760
[3] validation-logloss:0.64437 validation-auc:0.92801 validation-aucpr:0.92905
[4] validation-logloss:0.63204 validation-auc:0.93349 validation-aucpr:0.93578
[5] validation-logloss:0.62087 validation-auc:0.93476 validation-aucpr:0.93718
[6] validation-logloss:0.61630 validation-auc:0.93486 validation-aucpr:0.93826
[7] validation-logloss:0.61212 validation-auc:0.93174 validation-aucpr:0.93799
[8] validation-logloss:0.60159 validation-auc:0.93401 validation-aucpr:0.93934
[9] validation-logloss:0.59161 validation-auc:0.93668 validation-aucpr:0.94126
[10] validation-logloss:0.58263 validation-auc:0.93978 validation-aucpr:0.94483
[11] validation-logloss:0.57186 validation-auc:0.95108 validation-aucpr:0.95796
[12] validation-logloss:0.56858 validation-auc:0.94955 validation-aucpr:0.95721
[13] validation-logloss:0.56002 validation-auc:0.94974 validation-aucpr:0.95730
[14] validation-logloss:0.55234 validation-auc:0.94992 validation-aucpr:0.95716
[15] validation-logloss:0.54406 validation-auc:0.95155 validation-aucpr:0.95845
[16] validation-logloss:0.53695 validation-auc:0.95150 validation-aucpr:0.95833
[17] validation-logloss:0.52953 validation-auc:0.95245 validation-aucpr:0.95905
[18] validation-logloss:0.52296 validation-auc:0.95230 validation-aucpr:0.95877
[19] validation-logloss:0.51900 validation-auc:0.95238 validation-aucpr:0.95908
[20] validation-logloss:0.51275 validation-auc:0.95292 validation-aucpr:0.95949
[21] validation-logloss:0.50723 validation-auc:0.95304 validation-aucpr:0.95942
[22] validation-logloss:0.50095 validation-auc:0.95318 validation-aucpr:0.95929
[23] validation-logloss:0.49563 validation-auc:0.95320 validation-aucpr:0.95926
[24] validation-logloss:0.49035 validation-auc:0.95288 validation-aucpr:0.95887
[25] validation-logloss:0.48681 validation-auc:0.95267 validation-aucpr:0.95863
[26] validation-logloss:0.48187 validation-auc:0.95262 validation-aucpr:0.95845
[27] validation-logloss:0.47603 validation-auc:0.95327 validation-aucpr:0.95908
[28] validation-logloss:0.47167 validation-auc:0.95319 validation-aucpr:0.95900
[29] validation-logloss:0.46714 validation-auc:0.95359 validation-aucpr:0.95937
[30] validation-logloss:0.46427 validation-auc:0.95360 validation-aucpr:0.95939
[31] validation-logloss:0.46030 validation-auc:0.95307 validation-aucpr:0.95879
[32] validation-logloss:0.45621 validation-auc:0.95313 validation-aucpr:0.95888
[33] validation-logloss:0.45440 validation-auc:0.95291 validation-aucpr:0.95856
[34] validation-logloss:0.45190 validation-auc:0.95242 validation-aucpr:0.95793
[35] validation-logloss:0.44799 validation-auc:0.95217 validation-aucpr:0.95766
[36] validation-logloss:0.44177 validation-auc:0.95468 validation-aucpr:0.96061
[37] validation-logloss:0.43795 validation-auc:0.95492 validation-aucpr:0.96086
[38] validation-logloss:0.43421 validation-auc:0.95481 validation-aucpr:0.96068
[39] validation-logloss:0.43090 validation-auc:0.95464 validation-aucpr:0.96048
[40] validation-logloss:0.42521 validation-auc:0.95571 validation-aucpr:0.96178
[41] validation-logloss:0.42179 validation-auc:0.95555 validation-aucpr:0.96157
[42] validation-logloss:0.41843 validation-auc:0.95546 validation-aucpr:0.96149
[43] validation-logloss:0.41547 validation-auc:0.95558 validation-aucpr:0.96161
[44] validation-logloss:0.41066 validation-auc:0.95638 validation-aucpr:0.96243
[45] validation-logloss:0.40819 validation-auc:0.95618 validation-aucpr:0.96220
[46] validation-logloss:0.40649 validation-auc:0.95614 validation-aucpr:0.96203
[47] validation-logloss:0.40335 validation-auc:0.95655 validation-aucpr:0.96242
[48] validation-logloss:0.40051 validation-auc:0.95652 validation-aucpr:0.96234
[49] validation-logloss:0.39855 validation-auc:0.95658 validation-aucpr:0.96240
[50] validation-logloss:0.39698 validation-auc:0.95674 validation-aucpr:0.96260
[51] validation-logloss:0.39445 validation-auc:0.95666 validation-aucpr:0.96255
[52] validation-logloss:0.39174 validation-auc:0.95661 validation-aucpr:0.96253
[53] validation-logloss:0.38659 validation-auc:0.95728 validation-aucpr:0.96340
[54] validation-logloss:0.38234 validation-auc:0.95745 validation-aucpr:0.96364
{'best_iteration': '54', 'best_score': '0.9636424415346243'}
Trial 7, Fold 3: Log loss = 0.38234323236366546, Average precision = 0.9636474357242567, ROC-AUC = 0.9574512910402482, Elapsed Time = 1.0445481999995536 seconds
Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 7, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67951 validation-auc:0.90264 validation-aucpr:0.89429
[1] validation-logloss:0.66617 validation-auc:0.92506 validation-aucpr:0.92731
[2] validation-logloss:0.65515 validation-auc:0.92324 validation-aucpr:0.92434
[3] validation-logloss:0.64695 validation-auc:0.92645 validation-aucpr:0.92960
[4] validation-logloss:0.63759 validation-auc:0.92542 validation-aucpr:0.92953
[5] validation-logloss:0.62642 validation-auc:0.93200 validation-aucpr:0.93649
[6] validation-logloss:0.61601 validation-auc:0.93403 validation-aucpr:0.93867
[7] validation-logloss:0.60945 validation-auc:0.93368 validation-aucpr:0.93902
[8] validation-logloss:0.59785 validation-auc:0.94521 validation-aucpr:0.95217
[9] validation-logloss:0.58813 validation-auc:0.94676 validation-aucpr:0.95335
[10] validation-logloss:0.57837 validation-auc:0.94708 validation-aucpr:0.95317
[11] validation-logloss:0.57021 validation-auc:0.94647 validation-aucpr:0.95165
[12] validation-logloss:0.56092 validation-auc:0.94800 validation-aucpr:0.95338
[13] validation-logloss:0.55357 validation-auc:0.94763 validation-aucpr:0.95298
[14] validation-logloss:0.54679 validation-auc:0.94728 validation-aucpr:0.95234
[15] validation-logloss:0.54011 validation-auc:0.94694 validation-aucpr:0.95171
[16] validation-logloss:0.53152 validation-auc:0.94949 validation-aucpr:0.95486
[17] validation-logloss:0.52481 validation-auc:0.94942 validation-aucpr:0.95486
[18] validation-logloss:0.51691 validation-auc:0.95087 validation-aucpr:0.95629
[19] validation-logloss:0.51071 validation-auc:0.95076 validation-aucpr:0.95625
[20] validation-logloss:0.50306 validation-auc:0.95188 validation-aucpr:0.95777
[21] validation-logloss:0.49896 validation-auc:0.95225 validation-aucpr:0.95844
[22] validation-logloss:0.49325 validation-auc:0.95265 validation-aucpr:0.95920
[23] validation-logloss:0.48753 validation-auc:0.95283 validation-aucpr:0.95948
[24] validation-logloss:0.48229 validation-auc:0.95292 validation-aucpr:0.95950
[25] validation-logloss:0.47732 validation-auc:0.95277 validation-aucpr:0.95923
[26] validation-logloss:0.47215 validation-auc:0.95295 validation-aucpr:0.95949
[27] validation-logloss:0.46876 validation-auc:0.95282 validation-aucpr:0.95943
[28] validation-logloss:0.46398 validation-auc:0.95274 validation-aucpr:0.95929
[29] validation-logloss:0.46021 validation-auc:0.95252 validation-aucpr:0.95900
[30] validation-logloss:0.45739 validation-auc:0.95277 validation-aucpr:0.95928
[31] validation-logloss:0.45329 validation-auc:0.95253 validation-aucpr:0.95902
[32] validation-logloss:0.44984 validation-auc:0.95281 validation-aucpr:0.95929
[33] validation-logloss:0.44446 validation-auc:0.95361 validation-aucpr:0.96008
[34] validation-logloss:0.44069 validation-auc:0.95327 validation-aucpr:0.95968
[35] validation-logloss:0.43709 validation-auc:0.95317 validation-aucpr:0.95950
[36] validation-logloss:0.43309 validation-auc:0.95322 validation-aucpr:0.95956
[37] validation-logloss:0.42951 validation-auc:0.95313 validation-aucpr:0.95954
[38] validation-logloss:0.42588 validation-auc:0.95328 validation-aucpr:0.95963
[39] validation-logloss:0.42408 validation-auc:0.95332 validation-aucpr:0.95964
[40] validation-logloss:0.42050 validation-auc:0.95307 validation-aucpr:0.95938
[41] validation-logloss:0.41780 validation-auc:0.95288 validation-aucpr:0.95918
[42] validation-logloss:0.41334 validation-auc:0.95339 validation-aucpr:0.95982
[43] validation-logloss:0.41064 validation-auc:0.95358 validation-aucpr:0.96018
[44] validation-logloss:0.40806 validation-auc:0.95328 validation-aucpr:0.95988
[45] validation-logloss:0.40514 validation-auc:0.95336 validation-aucpr:0.95999
[46] validation-logloss:0.39938 validation-auc:0.95438 validation-aucpr:0.96106
[47] validation-logloss:0.39676 validation-auc:0.95454 validation-aucpr:0.96116
[48] validation-logloss:0.39263 validation-auc:0.95484 validation-aucpr:0.96146
[49] validation-logloss:0.39008 validation-auc:0.95483 validation-aucpr:0.96139
[50] validation-logloss:0.38875 validation-auc:0.95489 validation-aucpr:0.96144
[51] validation-logloss:0.38713 validation-auc:0.95470 validation-aucpr:0.96126
[52] validation-logloss:0.38499 validation-auc:0.95483 validation-aucpr:0.96144
[53] validation-logloss:0.38412 validation-auc:0.95494 validation-aucpr:0.96161
[54] validation-logloss:0.38293 validation-auc:0.95493 validation-aucpr:0.96159
{'best_iteration': '53', 'best_score': '0.9616095000793439'}
Trial 7, Fold 4: Log loss = 0.3829302825616808, Average precision = 0.9615912528910233, ROC-AUC = 0.9549325688915593, Elapsed Time = 1.014082800000324 seconds
Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 7, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68005 validation-auc:0.89139 validation-aucpr:0.87633
[1] validation-logloss:0.66708 validation-auc:0.91207 validation-aucpr:0.91324
[2] validation-logloss:0.65532 validation-auc:0.92129 validation-aucpr:0.92471
[3] validation-logloss:0.64280 validation-auc:0.92677 validation-aucpr:0.93153
[4] validation-logloss:0.63319 validation-auc:0.92862 validation-aucpr:0.93410
[5] validation-logloss:0.62247 validation-auc:0.93553 validation-aucpr:0.93941
[6] validation-logloss:0.61248 validation-auc:0.93679 validation-aucpr:0.94078
[7] validation-logloss:0.60286 validation-auc:0.93738 validation-aucpr:0.94164
[8] validation-logloss:0.59772 validation-auc:0.93772 validation-aucpr:0.94272
[9] validation-logloss:0.59146 validation-auc:0.93922 validation-aucpr:0.94386
[10] validation-logloss:0.58172 validation-auc:0.94040 validation-aucpr:0.94490
[11] validation-logloss:0.57129 validation-auc:0.94791 validation-aucpr:0.95330
[12] validation-logloss:0.56417 validation-auc:0.94962 validation-aucpr:0.95564
[13] validation-logloss:0.55580 validation-auc:0.95008 validation-aucpr:0.95600
[14] validation-logloss:0.54835 validation-auc:0.95015 validation-aucpr:0.95582
[15] validation-logloss:0.54062 validation-auc:0.95095 validation-aucpr:0.95681
[16] validation-logloss:0.53470 validation-auc:0.95068 validation-aucpr:0.95659
[17] validation-logloss:0.52802 validation-auc:0.95077 validation-aucpr:0.95659
[18] validation-logloss:0.52146 validation-auc:0.95062 validation-aucpr:0.95642
[19] validation-logloss:0.51544 validation-auc:0.95013 validation-aucpr:0.95571
[20] validation-logloss:0.50931 validation-auc:0.95011 validation-aucpr:0.95570
[21] validation-logloss:0.50313 validation-auc:0.94994 validation-aucpr:0.95555
[22] validation-logloss:0.49776 validation-auc:0.95177 validation-aucpr:0.95766
[23] validation-logloss:0.49294 validation-auc:0.95146 validation-aucpr:0.95734
[24] validation-logloss:0.48717 validation-auc:0.95138 validation-aucpr:0.95731
[25] validation-logloss:0.48162 validation-auc:0.95117 validation-aucpr:0.95692
[26] validation-logloss:0.47693 validation-auc:0.95116 validation-aucpr:0.95705
[27] validation-logloss:0.47241 validation-auc:0.95078 validation-aucpr:0.95657
[28] validation-logloss:0.46850 validation-auc:0.95059 validation-aucpr:0.95630
[29] validation-logloss:0.46486 validation-auc:0.95053 validation-aucpr:0.95640
[30] validation-logloss:0.46017 validation-auc:0.95115 validation-aucpr:0.95714
[31] validation-logloss:0.45638 validation-auc:0.95109 validation-aucpr:0.95702
[32] validation-logloss:0.44950 validation-auc:0.95194 validation-aucpr:0.95815
[33] validation-logloss:0.44490 validation-auc:0.95257 validation-aucpr:0.95875
[34] validation-logloss:0.44167 validation-auc:0.95259 validation-aucpr:0.95883
[35] validation-logloss:0.43810 validation-auc:0.95267 validation-aucpr:0.95885
[36] validation-logloss:0.43474 validation-auc:0.95252 validation-aucpr:0.95874
[37] validation-logloss:0.43075 validation-auc:0.95248 validation-aucpr:0.95859
[38] validation-logloss:0.42759 validation-auc:0.95219 validation-aucpr:0.95824
[39] validation-logloss:0.42523 validation-auc:0.95230 validation-aucpr:0.95827
[40] validation-logloss:0.42350 validation-auc:0.95242 validation-aucpr:0.95846
[41] validation-logloss:0.42053 validation-auc:0.95239 validation-aucpr:0.95845
[42] validation-logloss:0.41733 validation-auc:0.95235 validation-aucpr:0.95830
[43] validation-logloss:0.41358 validation-auc:0.95262 validation-aucpr:0.95879
[44] validation-logloss:0.41037 validation-auc:0.95255 validation-aucpr:0.95862
[45] validation-logloss:0.40747 validation-auc:0.95235 validation-aucpr:0.95837
[46] validation-logloss:0.40442 validation-auc:0.95244 validation-aucpr:0.95849
[47] validation-logloss:0.40226 validation-auc:0.95230 validation-aucpr:0.95827
[48] validation-logloss:0.39997 validation-auc:0.95230 validation-aucpr:0.95836
[49] validation-logloss:0.39775 validation-auc:0.95232 validation-aucpr:0.95838
[50] validation-logloss:0.39405 validation-auc:0.95272 validation-aucpr:0.95875
[51] validation-logloss:0.38953 validation-auc:0.95320 validation-aucpr:0.95935
[52] validation-logloss:0.38533 validation-auc:0.95370 validation-aucpr:0.95999
[53] validation-logloss:0.38300 validation-auc:0.95371 validation-aucpr:0.95999
[54] validation-logloss:0.38231 validation-auc:0.95377 validation-aucpr:0.96003
{'best_iteration': '54', 'best_score': '0.9600339440211474'}
Trial 7, Fold 5: Log loss = 0.38230679109129057, Average precision = 0.9600364880023089, ROC-AUC = 0.9537678687549933, Elapsed Time = 1.1196113000005425 seconds
Optimization Progress: 8%|8 | 8/100 [03:43<31:07, 20.30s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 8, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67257 validation-auc:0.92769 validation-aucpr:0.91050
[1] validation-logloss:0.65027 validation-auc:0.95658 validation-aucpr:0.95645
[2] validation-logloss:0.63157 validation-auc:0.95832 validation-aucpr:0.96167
[3] validation-logloss:0.61415 validation-auc:0.95866 validation-aucpr:0.96361
[4] validation-logloss:0.59720 validation-auc:0.95935 validation-aucpr:0.96394
[5] validation-logloss:0.58111 validation-auc:0.95989 validation-aucpr:0.96423
[6] validation-logloss:0.56754 validation-auc:0.95943 validation-aucpr:0.96397
[7] validation-logloss:0.55336 validation-auc:0.95981 validation-aucpr:0.96470
[8] validation-logloss:0.53679 validation-auc:0.96220 validation-aucpr:0.96740
[9] validation-logloss:0.52404 validation-auc:0.96238 validation-aucpr:0.96753
[10] validation-logloss:0.51220 validation-auc:0.96238 validation-aucpr:0.96753
[11] validation-logloss:0.50134 validation-auc:0.96252 validation-aucpr:0.96761
[12] validation-logloss:0.48980 validation-auc:0.96292 validation-aucpr:0.96809
[13] validation-logloss:0.47960 validation-auc:0.96277 validation-aucpr:0.96795
[14] validation-logloss:0.46999 validation-auc:0.96253 validation-aucpr:0.96735
[15] validation-logloss:0.46119 validation-auc:0.96221 validation-aucpr:0.96701
[16] validation-logloss:0.45209 validation-auc:0.96214 validation-aucpr:0.96691
[17] validation-logloss:0.44408 validation-auc:0.96217 validation-aucpr:0.96738
[18] validation-logloss:0.43600 validation-auc:0.96214 validation-aucpr:0.96728
{'best_iteration': '12', 'best_score': '0.968090333653044'}
Trial 8, Fold 1: Log loss = 0.43599817143202385, Average precision = 0.9672858086737529, ROC-AUC = 0.9621420685106321, Elapsed Time = 1.3593856999996206 seconds
Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 8, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67242 validation-auc:0.93086 validation-aucpr:0.90704
[1] validation-logloss:0.64977 validation-auc:0.95785 validation-aucpr:0.95825
[2] validation-logloss:0.63079 validation-auc:0.96015 validation-aucpr:0.96284
[3] validation-logloss:0.61379 validation-auc:0.96007 validation-aucpr:0.96319
[4] validation-logloss:0.59733 validation-auc:0.96125 validation-aucpr:0.96473
[5] validation-logloss:0.58147 validation-auc:0.96153 validation-aucpr:0.96471
[6] validation-logloss:0.56832 validation-auc:0.95991 validation-aucpr:0.96315
[7] validation-logloss:0.55353 validation-auc:0.96076 validation-aucpr:0.96411
[8] validation-logloss:0.53987 validation-auc:0.96089 validation-aucpr:0.96424
[9] validation-logloss:0.52758 validation-auc:0.96092 validation-aucpr:0.96421
[10] validation-logloss:0.51678 validation-auc:0.96033 validation-aucpr:0.96356
[11] validation-logloss:0.50381 validation-auc:0.96201 validation-aucpr:0.96547
[12] validation-logloss:0.49091 validation-auc:0.96305 validation-aucpr:0.96657
[13] validation-logloss:0.48081 validation-auc:0.96311 validation-aucpr:0.96657
[14] validation-logloss:0.47140 validation-auc:0.96313 validation-aucpr:0.96652
[15] validation-logloss:0.46246 validation-auc:0.96288 validation-aucpr:0.96627
[16] validation-logloss:0.45431 validation-auc:0.96259 validation-aucpr:0.96598
[17] validation-logloss:0.44543 validation-auc:0.96309 validation-aucpr:0.96644
[18] validation-logloss:0.43829 validation-auc:0.96283 validation-aucpr:0.96614
{'best_iteration': '12', 'best_score': '0.9665736485651384'}
Trial 8, Fold 2: Log loss = 0.43828518469363265, Average precision = 0.9661639868587157, ROC-AUC = 0.9628348428697207, Elapsed Time = 1.6450736000006145 seconds
Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 8, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67217 validation-auc:0.92719 validation-aucpr:0.90401
[1] validation-logloss:0.65099 validation-auc:0.95515 validation-aucpr:0.95623
[2] validation-logloss:0.63246 validation-auc:0.95664 validation-aucpr:0.95948
[3] validation-logloss:0.61522 validation-auc:0.95661 validation-aucpr:0.95932
[4] validation-logloss:0.59891 validation-auc:0.95667 validation-aucpr:0.96017
[5] validation-logloss:0.58324 validation-auc:0.95751 validation-aucpr:0.96076
[6] validation-logloss:0.56526 validation-auc:0.96327 validation-aucpr:0.96676
[7] validation-logloss:0.55152 validation-auc:0.96316 validation-aucpr:0.96697
[8] validation-logloss:0.53550 validation-auc:0.96447 validation-aucpr:0.96839
[9] validation-logloss:0.52326 validation-auc:0.96469 validation-aucpr:0.96847
[10] validation-logloss:0.51114 validation-auc:0.96436 validation-aucpr:0.96815
[11] validation-logloss:0.50023 validation-auc:0.96424 validation-aucpr:0.96799
[12] validation-logloss:0.48964 validation-auc:0.96447 validation-aucpr:0.96855
[13] validation-logloss:0.47940 validation-auc:0.96487 validation-aucpr:0.96880
[14] validation-logloss:0.46980 validation-auc:0.96490 validation-aucpr:0.96904
[15] validation-logloss:0.45893 validation-auc:0.96545 validation-aucpr:0.96963
[16] validation-logloss:0.44967 validation-auc:0.96560 validation-aucpr:0.96969
[17] validation-logloss:0.43885 validation-auc:0.96601 validation-aucpr:0.97015
[18] validation-logloss:0.43158 validation-auc:0.96593 validation-aucpr:0.97017
{'best_iteration': '18', 'best_score': '0.9701717611710687'}
Trial 8, Fold 3: Log loss = 0.431576279013927, Average precision = 0.9701762699819079, ROC-AUC = 0.9659336746732672, Elapsed Time = 1.5622930000008637 seconds
Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 8, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67264 validation-auc:0.92282 validation-aucpr:0.88926
[1] validation-logloss:0.64939 validation-auc:0.95910 validation-aucpr:0.96190
[2] validation-logloss:0.62990 validation-auc:0.96036 validation-aucpr:0.96612
[3] validation-logloss:0.61286 validation-auc:0.96062 validation-aucpr:0.96666
[4] validation-logloss:0.59632 validation-auc:0.96039 validation-aucpr:0.96651
[5] validation-logloss:0.58025 validation-auc:0.96145 validation-aucpr:0.96740
[6] validation-logloss:0.56505 validation-auc:0.96239 validation-aucpr:0.96798
[7] validation-logloss:0.54853 validation-auc:0.96344 validation-aucpr:0.96900
[8] validation-logloss:0.53268 validation-auc:0.96437 validation-aucpr:0.96997
[9] validation-logloss:0.51970 validation-auc:0.96438 validation-aucpr:0.96997
[10] validation-logloss:0.50779 validation-auc:0.96433 validation-aucpr:0.96981
[11] validation-logloss:0.49657 validation-auc:0.96465 validation-aucpr:0.96999
[12] validation-logloss:0.48608 validation-auc:0.96456 validation-aucpr:0.96987
[13] validation-logloss:0.47570 validation-auc:0.96473 validation-aucpr:0.97002
[14] validation-logloss:0.46410 validation-auc:0.96502 validation-aucpr:0.97034
[15] validation-logloss:0.45355 validation-auc:0.96522 validation-aucpr:0.97061
[16] validation-logloss:0.44299 validation-auc:0.96538 validation-aucpr:0.97085
[17] validation-logloss:0.43307 validation-auc:0.96547 validation-aucpr:0.97101
[18] validation-logloss:0.42449 validation-auc:0.96570 validation-aucpr:0.97117
{'best_iteration': '18', 'best_score': '0.9711688641698892'}
Trial 8, Fold 4: Log loss = 0.4244943068705463, Average precision = 0.9711685110879875, ROC-AUC = 0.965697731949422, Elapsed Time = 1.530232099999921 seconds
Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 8, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67267 validation-auc:0.91918 validation-aucpr:0.89168
[1] validation-logloss:0.65222 validation-auc:0.94691 validation-aucpr:0.94239
[2] validation-logloss:0.63352 validation-auc:0.95275 validation-aucpr:0.95577
[3] validation-logloss:0.61526 validation-auc:0.95581 validation-aucpr:0.95973
[4] validation-logloss:0.59935 validation-auc:0.95742 validation-aucpr:0.96176
[5] validation-logloss:0.58507 validation-auc:0.95652 validation-aucpr:0.96055
[6] validation-logloss:0.56745 validation-auc:0.96048 validation-aucpr:0.96483
[7] validation-logloss:0.55348 validation-auc:0.96071 validation-aucpr:0.96524
[8] validation-logloss:0.54116 validation-auc:0.95976 validation-aucpr:0.96425
[9] validation-logloss:0.52659 validation-auc:0.96123 validation-aucpr:0.96573
[10] validation-logloss:0.51486 validation-auc:0.96119 validation-aucpr:0.96572
[11] validation-logloss:0.50349 validation-auc:0.96180 validation-aucpr:0.96615
[12] validation-logloss:0.49053 validation-auc:0.96247 validation-aucpr:0.96682
[13] validation-logloss:0.48153 validation-auc:0.96217 validation-aucpr:0.96645
[14] validation-logloss:0.47185 validation-auc:0.96227 validation-aucpr:0.96528
[15] validation-logloss:0.46302 validation-auc:0.96222 validation-aucpr:0.96533
[16] validation-logloss:0.45214 validation-auc:0.96264 validation-aucpr:0.96645
[17] validation-logloss:0.44099 validation-auc:0.96332 validation-aucpr:0.96712
[18] validation-logloss:0.43070 validation-auc:0.96375 validation-aucpr:0.96754
{'best_iteration': '18', 'best_score': '0.9675360413317013'}
Trial 8, Fold 5: Log loss = 0.4307014960639307, Average precision = 0.9676823377355989, ROC-AUC = 0.9637460565185887, Elapsed Time = 1.5349166999985755 seconds
Optimization Progress: 9%|9 | 9/100 [03:59<28:51, 19.02s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 9, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68501 validation-auc:0.94432 validation-aucpr:0.94674
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67707 validation-auc:0.94745 validation-aucpr:0.94975
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66938 validation-auc:0.95070 validation-aucpr:0.95346
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66194 validation-auc:0.95101 validation-aucpr:0.95378
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65456 validation-auc:0.95241 validation-aucpr:0.95538
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64747 validation-auc:0.95343 validation-aucpr:0.95627
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64064 validation-auc:0.95419 validation-aucpr:0.95732
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63369 validation-auc:0.95621 validation-aucpr:0.95883
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62576 validation-auc:0.95991 validation-aucpr:0.96309
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61929 validation-auc:0.95976 validation-aucpr:0.96283
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61323 validation-auc:0.95899 validation-aucpr:0.96439
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60576 validation-auc:0.96066 validation-aucpr:0.96632
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59948 validation-auc:0.96075 validation-aucpr:0.96633
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59338 validation-auc:0.96074 validation-aucpr:0.96625
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58762 validation-auc:0.96072 validation-aucpr:0.96622
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58217 validation-auc:0.96056 validation-aucpr:0.96605
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.57660 validation-auc:0.96031 validation-aucpr:0.96572
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57127 validation-auc:0.96009 validation-aucpr:0.96555
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.56595 validation-auc:0.95984 validation-aucpr:0.96519
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56068 validation-auc:0.95990 validation-aucpr:0.96527
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.55583 validation-auc:0.95964 validation-aucpr:0.96508
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54961 validation-auc:0.96057 validation-aucpr:0.96616
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54489 validation-auc:0.96054 validation-aucpr:0.96619
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54019 validation-auc:0.96063 validation-aucpr:0.96625
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53530 validation-auc:0.96079 validation-aucpr:0.96633
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53078 validation-auc:0.96078 validation-aucpr:0.96630
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52624 validation-auc:0.96074 validation-aucpr:0.96624
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52187 validation-auc:0.96077 validation-aucpr:0.96626
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.51647 validation-auc:0.96121 validation-aucpr:0.96682
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51230 validation-auc:0.96126 validation-aucpr:0.96689
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.50817 validation-auc:0.96124 validation-aucpr:0.96683
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50290 validation-auc:0.96195 validation-aucpr:0.96762
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.49898 validation-auc:0.96196 validation-aucpr:0.96761
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49482 validation-auc:0.96199 validation-aucpr:0.96763
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49060 validation-auc:0.96218 validation-aucpr:0.96777
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.48710 validation-auc:0.96207 validation-aucpr:0.96773
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.48225 validation-auc:0.96254 validation-aucpr:0.96822
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.47737 validation-auc:0.96288 validation-aucpr:0.96857
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47302 validation-auc:0.96314 validation-aucpr:0.96887
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.46920 validation-auc:0.96312 validation-aucpr:0.96886
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.46567 validation-auc:0.96311 validation-aucpr:0.96884
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46212 validation-auc:0.96317 validation-aucpr:0.96886
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.45886 validation-auc:0.96317 validation-aucpr:0.96885
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45557 validation-auc:0.96314 validation-aucpr:0.96881
{'best_iteration': '38', 'best_score': '0.9688745689451286'}
Trial 9, Fold 1: Log loss = 0.45556608645567226, Average precision = 0.9688078257615644, ROC-AUC = 0.9631374926513816, Elapsed Time = 3.3502621999996336 seconds
Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 9, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68465 validation-auc:0.94008 validation-aucpr:0.93101
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67570 validation-auc:0.95996 validation-aucpr:0.96218
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66799 validation-auc:0.96088 validation-aucpr:0.96512
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66046 validation-auc:0.96114 validation-aucpr:0.96514
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65341 validation-auc:0.96049 validation-aucpr:0.96416
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64647 validation-auc:0.95997 validation-aucpr:0.96386
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63951 validation-auc:0.95993 validation-aucpr:0.96369
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63254 validation-auc:0.95985 validation-aucpr:0.96364
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62567 validation-auc:0.95979 validation-aucpr:0.96331
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61934 validation-auc:0.95941 validation-aucpr:0.96278
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61296 validation-auc:0.95898 validation-aucpr:0.96214
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60568 validation-auc:0.96132 validation-aucpr:0.96500
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59944 validation-auc:0.96086 validation-aucpr:0.96426
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59360 validation-auc:0.96056 validation-aucpr:0.96395
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58697 validation-auc:0.96170 validation-aucpr:0.96540
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57996 validation-auc:0.96238 validation-aucpr:0.96532
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.57448 validation-auc:0.96241 validation-aucpr:0.96524
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56913 validation-auc:0.96252 validation-aucpr:0.96523
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.56370 validation-auc:0.96231 validation-aucpr:0.96500
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55846 validation-auc:0.96207 validation-aucpr:0.96474
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.55244 validation-auc:0.96242 validation-aucpr:0.96517
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54729 validation-auc:0.96234 validation-aucpr:0.96510
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54102 validation-auc:0.96296 validation-aucpr:0.96593
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.53594 validation-auc:0.96304 validation-aucpr:0.96594
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53105 validation-auc:0.96306 validation-aucpr:0.96594
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.52511 validation-auc:0.96353 validation-aucpr:0.96644
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52067 validation-auc:0.96357 validation-aucpr:0.96651
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.51522 validation-auc:0.96390 validation-aucpr:0.96686
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.51083 validation-auc:0.96392 validation-aucpr:0.96680
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.50692 validation-auc:0.96380 validation-aucpr:0.96657
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.50292 validation-auc:0.96373 validation-aucpr:0.96652
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49876 validation-auc:0.96375 validation-aucpr:0.96651
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.49363 validation-auc:0.96398 validation-aucpr:0.96681
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48974 validation-auc:0.96388 validation-aucpr:0.96789
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.48612 validation-auc:0.96376 validation-aucpr:0.96768
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.48118 validation-auc:0.96396 validation-aucpr:0.96792
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.47757 validation-auc:0.96384 validation-aucpr:0.96779
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.47382 validation-auc:0.96376 validation-aucpr:0.96771
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47025 validation-auc:0.96382 validation-aucpr:0.96772
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.46678 validation-auc:0.96386 validation-aucpr:0.96775
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.46325 validation-auc:0.96390 validation-aucpr:0.96808
[18:03:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.45910 validation-auc:0.96403 validation-aucpr:0.96820
[18:03:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.45468 validation-auc:0.96429 validation-aucpr:0.96849
[18:03:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45118 validation-auc:0.96433 validation-aucpr:0.96849
{'best_iteration': '43', 'best_score': '0.968494347322956'}
Trial 9, Fold 2: Log loss = 0.4511816000390403, Average precision = 0.9684326083583713, ROC-AUC = 0.9643344144857222, Elapsed Time = 3.7610129000004235 seconds
Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 9, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68485 validation-auc:0.93746 validation-aucpr:0.93170
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67589 validation-auc:0.96026 validation-aucpr:0.96209
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66736 validation-auc:0.96233 validation-aucpr:0.96690
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65984 validation-auc:0.96268 validation-aucpr:0.96592
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65244 validation-auc:0.96265 validation-aucpr:0.96576
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64516 validation-auc:0.96318 validation-aucpr:0.96805
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63828 validation-auc:0.96350 validation-aucpr:0.96829
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63102 validation-auc:0.96374 validation-aucpr:0.96851
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62320 validation-auc:0.96444 validation-aucpr:0.96958
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61684 validation-auc:0.96404 validation-aucpr:0.96919
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61013 validation-auc:0.96431 validation-aucpr:0.96933
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60364 validation-auc:0.96439 validation-aucpr:0.96935
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59775 validation-auc:0.96442 validation-aucpr:0.96937
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59040 validation-auc:0.96509 validation-aucpr:0.97008
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58452 validation-auc:0.96505 validation-aucpr:0.96993
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57766 validation-auc:0.96530 validation-aucpr:0.97024
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.57112 validation-auc:0.96545 validation-aucpr:0.97049
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56568 validation-auc:0.96535 validation-aucpr:0.97046
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55962 validation-auc:0.96550 validation-aucpr:0.97058
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55413 validation-auc:0.96548 validation-aucpr:0.97052
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54897 validation-auc:0.96533 validation-aucpr:0.97047
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54385 validation-auc:0.96543 validation-aucpr:0.97051
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53892 validation-auc:0.96548 validation-aucpr:0.97054
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.53407 validation-auc:0.96543 validation-aucpr:0.97046
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52943 validation-auc:0.96538 validation-aucpr:0.97033
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.52382 validation-auc:0.96540 validation-aucpr:0.97040
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.51921 validation-auc:0.96546 validation-aucpr:0.97042
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.51482 validation-auc:0.96553 validation-aucpr:0.97044
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.51019 validation-auc:0.96557 validation-aucpr:0.97048
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.50575 validation-auc:0.96553 validation-aucpr:0.97041
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.50157 validation-auc:0.96546 validation-aucpr:0.97034
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49742 validation-auc:0.96547 validation-aucpr:0.97030
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.49254 validation-auc:0.96563 validation-aucpr:0.97050
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48860 validation-auc:0.96569 validation-aucpr:0.97050
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.48478 validation-auc:0.96560 validation-aucpr:0.97042
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.48097 validation-auc:0.96560 validation-aucpr:0.97037
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.47692 validation-auc:0.96568 validation-aucpr:0.97041
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.47330 validation-auc:0.96561 validation-aucpr:0.97035
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.46881 validation-auc:0.96583 validation-aucpr:0.97058
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.46512 validation-auc:0.96582 validation-aucpr:0.97057
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.46165 validation-auc:0.96572 validation-aucpr:0.97050
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.45812 validation-auc:0.96583 validation-aucpr:0.97057
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.45483 validation-auc:0.96578 validation-aucpr:0.97051
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45114 validation-auc:0.96586 validation-aucpr:0.97059
{'best_iteration': '43', 'best_score': '0.9705907319091631'}
Trial 9, Fold 3: Log loss = 0.4511384331227501, Average precision = 0.9705902080277841, ROC-AUC = 0.9658637744267127, Elapsed Time = 3.7800318999998126 seconds
Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 9, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68470 validation-auc:0.93733 validation-aucpr:0.92477
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67582 validation-auc:0.95888 validation-aucpr:0.96391
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66734 validation-auc:0.96074 validation-aucpr:0.96742
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66009 validation-auc:0.95988 validation-aucpr:0.96691
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65142 validation-auc:0.96155 validation-aucpr:0.96835
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64336 validation-auc:0.96232 validation-aucpr:0.96862
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63653 validation-auc:0.96233 validation-aucpr:0.96885
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62993 validation-auc:0.96176 validation-aucpr:0.96822
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62347 validation-auc:0.96147 validation-aucpr:0.96783
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61582 validation-auc:0.96180 validation-aucpr:0.96816
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60928 validation-auc:0.96169 validation-aucpr:0.96808
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60193 validation-auc:0.96192 validation-aucpr:0.96841
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59594 validation-auc:0.96197 validation-aucpr:0.96842
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58973 validation-auc:0.96194 validation-aucpr:0.96842
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58288 validation-auc:0.96236 validation-aucpr:0.96885
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57617 validation-auc:0.96261 validation-aucpr:0.96910
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.57105 validation-auc:0.96250 validation-aucpr:0.96894
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56549 validation-auc:0.96264 validation-aucpr:0.96896
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.56007 validation-auc:0.96270 validation-aucpr:0.96891
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55458 validation-auc:0.96291 validation-aucpr:0.96902
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54950 validation-auc:0.96280 validation-aucpr:0.96888
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54438 validation-auc:0.96281 validation-aucpr:0.96883
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53832 validation-auc:0.96317 validation-aucpr:0.96919
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.53360 validation-auc:0.96311 validation-aucpr:0.96912
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52847 validation-auc:0.96306 validation-aucpr:0.96912
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.52378 validation-auc:0.96298 validation-aucpr:0.96905
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.51926 validation-auc:0.96282 validation-aucpr:0.96895
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.51500 validation-auc:0.96270 validation-aucpr:0.96881
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.51068 validation-auc:0.96275 validation-aucpr:0.96880
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.50545 validation-auc:0.96291 validation-aucpr:0.96899
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.50115 validation-auc:0.96288 validation-aucpr:0.96891
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49703 validation-auc:0.96291 validation-aucpr:0.96885
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.49225 validation-auc:0.96305 validation-aucpr:0.96902
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48842 validation-auc:0.96293 validation-aucpr:0.96892
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.48453 validation-auc:0.96287 validation-aucpr:0.96884
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.48077 validation-auc:0.96280 validation-aucpr:0.96877
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.47676 validation-auc:0.96285 validation-aucpr:0.96879
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.47212 validation-auc:0.96302 validation-aucpr:0.96896
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.46775 validation-auc:0.96327 validation-aucpr:0.96921
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.46411 validation-auc:0.96327 validation-aucpr:0.96923
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.45991 validation-auc:0.96338 validation-aucpr:0.96935
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.45653 validation-auc:0.96335 validation-aucpr:0.96930
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.45237 validation-auc:0.96353 validation-aucpr:0.96947
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.44910 validation-auc:0.96354 validation-aucpr:0.96947
{'best_iteration': '43', 'best_score': '0.9694712773591828'}
Trial 9, Fold 4: Log loss = 0.44909951162714856, Average precision = 0.9694630808116731, ROC-AUC = 0.9635393940260997, Elapsed Time = 3.8774806000001263 seconds
Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 9, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68496 validation-auc:0.93507 validation-aucpr:0.91961
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67608 validation-auc:0.95637 validation-aucpr:0.95470
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66752 validation-auc:0.95770 validation-aucpr:0.96311
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66012 validation-auc:0.95890 validation-aucpr:0.96433
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65289 validation-auc:0.95894 validation-aucpr:0.96422
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64577 validation-auc:0.95921 validation-aucpr:0.96419
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63898 validation-auc:0.95908 validation-aucpr:0.96409
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63252 validation-auc:0.95892 validation-aucpr:0.96428
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62585 validation-auc:0.95907 validation-aucpr:0.96428
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61939 validation-auc:0.95925 validation-aucpr:0.96438
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61322 validation-auc:0.95923 validation-aucpr:0.96435
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60581 validation-auc:0.95997 validation-aucpr:0.96513
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59985 validation-auc:0.95981 validation-aucpr:0.96495
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59410 validation-auc:0.95966 validation-aucpr:0.96477
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58839 validation-auc:0.95953 validation-aucpr:0.96466
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58279 validation-auc:0.95919 validation-aucpr:0.96439
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.57699 validation-auc:0.95944 validation-aucpr:0.96463
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57138 validation-auc:0.95951 validation-aucpr:0.96466
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.56655 validation-auc:0.95930 validation-aucpr:0.96441
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56020 validation-auc:0.95991 validation-aucpr:0.96499
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.55529 validation-auc:0.95978 validation-aucpr:0.96491
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54914 validation-auc:0.96006 validation-aucpr:0.96524
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54430 validation-auc:0.96008 validation-aucpr:0.96524
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.53950 validation-auc:0.96031 validation-aucpr:0.96534
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53471 validation-auc:0.96036 validation-aucpr:0.96538
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.52985 validation-auc:0.96067 validation-aucpr:0.96561
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52514 validation-auc:0.96069 validation-aucpr:0.96559
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52092 validation-auc:0.96063 validation-aucpr:0.96551
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.51662 validation-auc:0.96059 validation-aucpr:0.96543
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51124 validation-auc:0.96092 validation-aucpr:0.96580
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.50625 validation-auc:0.96117 validation-aucpr:0.96651
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50111 validation-auc:0.96133 validation-aucpr:0.96665
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.49721 validation-auc:0.96123 validation-aucpr:0.96655
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49206 validation-auc:0.96159 validation-aucpr:0.96689
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.48823 validation-auc:0.96150 validation-aucpr:0.96680
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.48387 validation-auc:0.96153 validation-aucpr:0.96689
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.47921 validation-auc:0.96168 validation-aucpr:0.96703
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.47458 validation-auc:0.96188 validation-aucpr:0.96722
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47117 validation-auc:0.96183 validation-aucpr:0.96716
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.46784 validation-auc:0.96178 validation-aucpr:0.96709
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.46448 validation-auc:0.96165 validation-aucpr:0.96701
[18:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46101 validation-auc:0.96172 validation-aucpr:0.96705
[18:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.45752 validation-auc:0.96177 validation-aucpr:0.96708
[18:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45425 validation-auc:0.96174 validation-aucpr:0.96706
{'best_iteration': '37', 'best_score': '0.9672193418676625'}
Trial 9, Fold 5: Log loss = 0.4542515284001841, Average precision = 0.9670490035762956, ROC-AUC = 0.96174490459469, Elapsed Time = 3.735684199999014 seconds
Optimization Progress: 10%|# | 10/100 [04:26<32:11, 21.47s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 10, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67854 validation-auc:0.91581 validation-aucpr:0.90795
[1] validation-logloss:0.66452 validation-auc:0.92989 validation-aucpr:0.92944
[2] validation-logloss:0.64978 validation-auc:0.94898 validation-aucpr:0.95368
[3] validation-logloss:0.63678 validation-auc:0.95179 validation-aucpr:0.95817
[4] validation-logloss:0.62092 validation-auc:0.95804 validation-aucpr:0.96407
[5] validation-logloss:0.60858 validation-auc:0.95759 validation-aucpr:0.96377
[6] validation-logloss:0.59778 validation-auc:0.95701 validation-aucpr:0.96330
[7] validation-logloss:0.58770 validation-auc:0.95669 validation-aucpr:0.96292
[8] validation-logloss:0.57914 validation-auc:0.95677 validation-aucpr:0.96342
[9] validation-logloss:0.57009 validation-auc:0.95599 validation-aucpr:0.96237
[10] validation-logloss:0.56079 validation-auc:0.95652 validation-aucpr:0.96275
[11] validation-logloss:0.55168 validation-auc:0.95649 validation-aucpr:0.96271
[12] validation-logloss:0.54383 validation-auc:0.95613 validation-aucpr:0.96226
[13] validation-logloss:0.54043 validation-auc:0.95609 validation-aucpr:0.96140
[14] validation-logloss:0.53295 validation-auc:0.95645 validation-aucpr:0.96288
[15] validation-logloss:0.52513 validation-auc:0.95649 validation-aucpr:0.96301
[16] validation-logloss:0.51814 validation-auc:0.95663 validation-aucpr:0.96321
[17] validation-logloss:0.51001 validation-auc:0.95686 validation-aucpr:0.96336
[18] validation-logloss:0.50350 validation-auc:0.95659 validation-aucpr:0.96309
[19] validation-logloss:0.49708 validation-auc:0.95698 validation-aucpr:0.96351
[20] validation-logloss:0.49048 validation-auc:0.95701 validation-aucpr:0.96349
[21] validation-logloss:0.48409 validation-auc:0.95708 validation-aucpr:0.96362
[22] validation-logloss:0.47753 validation-auc:0.95737 validation-aucpr:0.96386
[23] validation-logloss:0.47173 validation-auc:0.95735 validation-aucpr:0.96386
[24] validation-logloss:0.46604 validation-auc:0.95718 validation-aucpr:0.96367
[25] validation-logloss:0.46019 validation-auc:0.95728 validation-aucpr:0.96374
[26] validation-logloss:0.45554 validation-auc:0.95704 validation-aucpr:0.96347
{'best_iteration': '4', 'best_score': '0.9640740072621098'}
Trial 10, Fold 1: Log loss = 0.45554417321152957, Average precision = 0.9634563201492339, ROC-AUC = 0.9570427695568843, Elapsed Time = 1.0330285999989428 seconds
Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 10, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67846 validation-auc:0.91873 validation-aucpr:0.90311
[1] validation-logloss:0.66542 validation-auc:0.92942 validation-aucpr:0.92406
[2] validation-logloss:0.65286 validation-auc:0.93128 validation-aucpr:0.92687
[3] validation-logloss:0.63966 validation-auc:0.94142 validation-aucpr:0.94299
[4] validation-logloss:0.62541 validation-auc:0.95174 validation-aucpr:0.95526
[5] validation-logloss:0.61327 validation-auc:0.95274 validation-aucpr:0.95579
[6] validation-logloss:0.60389 validation-auc:0.95350 validation-aucpr:0.95655
[7] validation-logloss:0.59448 validation-auc:0.95282 validation-aucpr:0.95564
[8] validation-logloss:0.58427 validation-auc:0.95225 validation-aucpr:0.95570
[9] validation-logloss:0.57413 validation-auc:0.95236 validation-aucpr:0.95580
[10] validation-logloss:0.56522 validation-auc:0.95168 validation-aucpr:0.95499
[11] validation-logloss:0.55616 validation-auc:0.95163 validation-aucpr:0.95484
[12] validation-logloss:0.54743 validation-auc:0.95179 validation-aucpr:0.95475
[13] validation-logloss:0.53862 validation-auc:0.95147 validation-aucpr:0.95429
[14] validation-logloss:0.52693 validation-auc:0.95532 validation-aucpr:0.95882
[15] validation-logloss:0.51872 validation-auc:0.95564 validation-aucpr:0.95889
[16] validation-logloss:0.51264 validation-auc:0.95537 validation-aucpr:0.95863
[17] validation-logloss:0.50538 validation-auc:0.95548 validation-aucpr:0.95895
[18] validation-logloss:0.49840 validation-auc:0.95544 validation-aucpr:0.95900
[19] validation-logloss:0.49135 validation-auc:0.95560 validation-aucpr:0.95919
[20] validation-logloss:0.48570 validation-auc:0.95557 validation-aucpr:0.95908
[21] validation-logloss:0.47965 validation-auc:0.95575 validation-aucpr:0.95911
[22] validation-logloss:0.47324 validation-auc:0.95585 validation-aucpr:0.95914
[23] validation-logloss:0.46620 validation-auc:0.95592 validation-aucpr:0.95905
[24] validation-logloss:0.45986 validation-auc:0.95604 validation-aucpr:0.95914
[25] validation-logloss:0.45508 validation-auc:0.95576 validation-aucpr:0.95883
[26] validation-logloss:0.45213 validation-auc:0.95586 validation-aucpr:0.95888
{'best_iteration': '19', 'best_score': '0.9591881059143724'}
Trial 10, Fold 2: Log loss = 0.4521302113765439, Average precision = 0.9588842977089528, ROC-AUC = 0.9558649912092507, Elapsed Time = 1.3426713000008021 seconds
Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 10, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67796 validation-auc:0.92452 validation-aucpr:0.91870
[1] validation-logloss:0.66517 validation-auc:0.92814 validation-aucpr:0.92500
[2] validation-logloss:0.65165 validation-auc:0.93558 validation-aucpr:0.93512
[3] validation-logloss:0.63856 validation-auc:0.93893 validation-aucpr:0.93850
[4] validation-logloss:0.62334 validation-auc:0.95315 validation-aucpr:0.95637
[5] validation-logloss:0.61199 validation-auc:0.95358 validation-aucpr:0.95676
[6] validation-logloss:0.60083 validation-auc:0.95417 validation-aucpr:0.95926
[7] validation-logloss:0.59101 validation-auc:0.95335 validation-aucpr:0.95878
[8] validation-logloss:0.58087 validation-auc:0.95383 validation-aucpr:0.95922
[9] validation-logloss:0.57160 validation-auc:0.95411 validation-aucpr:0.95874
[10] validation-logloss:0.56290 validation-auc:0.95434 validation-aucpr:0.95876
[11] validation-logloss:0.55485 validation-auc:0.95375 validation-aucpr:0.95838
[12] validation-logloss:0.54498 validation-auc:0.95525 validation-aucpr:0.96000
[13] validation-logloss:0.53740 validation-auc:0.95480 validation-aucpr:0.95952
[14] validation-logloss:0.52926 validation-auc:0.95498 validation-aucpr:0.95972
[15] validation-logloss:0.52178 validation-auc:0.95594 validation-aucpr:0.96058
[16] validation-logloss:0.51499 validation-auc:0.95565 validation-aucpr:0.96027
[17] validation-logloss:0.50855 validation-auc:0.95539 validation-aucpr:0.96004
[18] validation-logloss:0.50149 validation-auc:0.95589 validation-aucpr:0.96079
[19] validation-logloss:0.49457 validation-auc:0.95565 validation-aucpr:0.96060
[20] validation-logloss:0.48845 validation-auc:0.95571 validation-aucpr:0.96059
[21] validation-logloss:0.48232 validation-auc:0.95581 validation-aucpr:0.96062
[22] validation-logloss:0.47589 validation-auc:0.95606 validation-aucpr:0.96076
[23] validation-logloss:0.46998 validation-auc:0.95658 validation-aucpr:0.96112
[24] validation-logloss:0.46504 validation-auc:0.95606 validation-aucpr:0.96074
[25] validation-logloss:0.45942 validation-auc:0.95658 validation-aucpr:0.96120
[26] validation-logloss:0.45406 validation-auc:0.95634 validation-aucpr:0.96099
{'best_iteration': '25', 'best_score': '0.9612033851979362'}
Trial 10, Fold 3: Log loss = 0.45405906982495015, Average precision = 0.96099287969551, ROC-AUC = 0.9563417657058105, Elapsed Time = 1.3540023999994446 seconds
Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 10, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67799 validation-auc:0.92305 validation-aucpr:0.91630
[1] validation-logloss:0.66305 validation-auc:0.94321 validation-aucpr:0.94644
[2] validation-logloss:0.64946 validation-auc:0.95125 validation-aucpr:0.95569
[3] validation-logloss:0.64038 validation-auc:0.94774 validation-aucpr:0.95305
[4] validation-logloss:0.62896 validation-auc:0.94848 validation-aucpr:0.95454
[5] validation-logloss:0.61711 validation-auc:0.94967 validation-aucpr:0.95597
[6] validation-logloss:0.60580 validation-auc:0.95091 validation-aucpr:0.95730
[7] validation-logloss:0.59591 validation-auc:0.95098 validation-aucpr:0.95725
[8] validation-logloss:0.58589 validation-auc:0.95133 validation-aucpr:0.95743
[9] validation-logloss:0.57581 validation-auc:0.95200 validation-aucpr:0.95819
[10] validation-logloss:0.56749 validation-auc:0.95131 validation-aucpr:0.95759
[11] validation-logloss:0.55838 validation-auc:0.95126 validation-aucpr:0.95739
[12] validation-logloss:0.55005 validation-auc:0.95090 validation-aucpr:0.95701
[13] validation-logloss:0.54130 validation-auc:0.95096 validation-aucpr:0.95711
[14] validation-logloss:0.53400 validation-auc:0.95069 validation-aucpr:0.95697
[15] validation-logloss:0.52682 validation-auc:0.95051 validation-aucpr:0.95688
[16] validation-logloss:0.52040 validation-auc:0.95115 validation-aucpr:0.95756
[17] validation-logloss:0.51365 validation-auc:0.95147 validation-aucpr:0.95781
[18] validation-logloss:0.50591 validation-auc:0.95208 validation-aucpr:0.95832
[19] validation-logloss:0.50168 validation-auc:0.95202 validation-aucpr:0.95812
[20] validation-logloss:0.49618 validation-auc:0.95213 validation-aucpr:0.95807
[21] validation-logloss:0.48888 validation-auc:0.95337 validation-aucpr:0.95958
[22] validation-logloss:0.48316 validation-auc:0.95353 validation-aucpr:0.95981
[23] validation-logloss:0.47711 validation-auc:0.95381 validation-aucpr:0.95994
[24] validation-logloss:0.47135 validation-auc:0.95404 validation-aucpr:0.96020
[25] validation-logloss:0.46577 validation-auc:0.95380 validation-aucpr:0.95987
[26] validation-logloss:0.46036 validation-auc:0.95368 validation-aucpr:0.95968
{'best_iteration': '24', 'best_score': '0.9601950676419457'}
Trial 10, Fold 4: Log loss = 0.46035901083769054, Average precision = 0.9596825375211024, ROC-AUC = 0.9536800086315633, Elapsed Time = 1.4137474999988626 seconds
Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 10, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67936 validation-auc:0.89905 validation-aucpr:0.87397
[1] validation-logloss:0.66630 validation-auc:0.91514 validation-aucpr:0.90211
[2] validation-logloss:0.65378 validation-auc:0.92234 validation-aucpr:0.91901
[3] validation-logloss:0.64099 validation-auc:0.92810 validation-aucpr:0.92915
[4] validation-logloss:0.63089 validation-auc:0.93158 validation-aucpr:0.93389
[5] validation-logloss:0.62011 validation-auc:0.93258 validation-aucpr:0.93401
[6] validation-logloss:0.60970 validation-auc:0.93295 validation-aucpr:0.93483
[7] validation-logloss:0.59977 validation-auc:0.93444 validation-aucpr:0.93635
[8] validation-logloss:0.59187 validation-auc:0.93689 validation-aucpr:0.93846
[9] validation-logloss:0.58184 validation-auc:0.93884 validation-aucpr:0.94017
[10] validation-logloss:0.57209 validation-auc:0.93993 validation-aucpr:0.94129
[11] validation-logloss:0.56341 validation-auc:0.94012 validation-aucpr:0.94165
[12] validation-logloss:0.55469 validation-auc:0.94069 validation-aucpr:0.94226
[13] validation-logloss:0.54692 validation-auc:0.94100 validation-aucpr:0.94318
[14] validation-logloss:0.53946 validation-auc:0.94182 validation-aucpr:0.94460
[15] validation-logloss:0.52867 validation-auc:0.94978 validation-aucpr:0.95396
[16] validation-logloss:0.51929 validation-auc:0.95258 validation-aucpr:0.95744
[17] validation-logloss:0.51175 validation-auc:0.95309 validation-aucpr:0.95803
[18] validation-logloss:0.50566 validation-auc:0.95257 validation-aucpr:0.95752
[19] validation-logloss:0.50021 validation-auc:0.95318 validation-aucpr:0.95823
[20] validation-logloss:0.49218 validation-auc:0.95354 validation-aucpr:0.95875
[21] validation-logloss:0.48564 validation-auc:0.95378 validation-aucpr:0.95901
[22] validation-logloss:0.47926 validation-auc:0.95465 validation-aucpr:0.95992
[23] validation-logloss:0.47286 validation-auc:0.95483 validation-aucpr:0.96016
[24] validation-logloss:0.46804 validation-auc:0.95459 validation-aucpr:0.95992
[25] validation-logloss:0.46235 validation-auc:0.95459 validation-aucpr:0.95995
[26] validation-logloss:0.45748 validation-auc:0.95450 validation-aucpr:0.95988
{'best_iteration': '23', 'best_score': '0.9601604895372757'}
Trial 10, Fold 5: Log loss = 0.4574784116641232, Average precision = 0.9598834535675733, ROC-AUC = 0.9544994519071773, Elapsed Time = 1.380747799999881 seconds
Optimization Progress: 11%|#1 | 11/100 [04:40<28:38, 19.30s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 11, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:03:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68589 validation-auc:0.95478 validation-aucpr:0.95734
[18:03:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67891 validation-auc:0.96195 validation-aucpr:0.96755
[18:03:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67204 validation-auc:0.96271 validation-aucpr:0.96876
[18:03:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66515 validation-auc:0.96429 validation-aucpr:0.97006
[18:03:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65841 validation-auc:0.96486 validation-aucpr:0.97045
[18:03:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65255 validation-auc:0.96552 validation-aucpr:0.97108
[18:03:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64623 validation-auc:0.96555 validation-aucpr:0.97143
[18:03:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63986 validation-auc:0.96620 validation-aucpr:0.97186
[18:04:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63365 validation-auc:0.96602 validation-aucpr:0.97177
[18:04:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62839 validation-auc:0.96572 validation-aucpr:0.97153
[18:04:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62228 validation-auc:0.96636 validation-aucpr:0.97200
[18:04:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61638 validation-auc:0.96624 validation-aucpr:0.97198
[18:04:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61120 validation-auc:0.96622 validation-aucpr:0.97190
[18:04:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60609 validation-auc:0.96657 validation-aucpr:0.97210
[18:04:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.60036 validation-auc:0.96679 validation-aucpr:0.97231
[18:04:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59477 validation-auc:0.96682 validation-aucpr:0.97233
[18:04:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58931 validation-auc:0.96673 validation-aucpr:0.97262
[18:04:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58387 validation-auc:0.96676 validation-aucpr:0.97268
[18:04:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57862 validation-auc:0.96697 validation-aucpr:0.97287
[18:04:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57395 validation-auc:0.96687 validation-aucpr:0.97279
[18:04:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56873 validation-auc:0.96706 validation-aucpr:0.97295
[18:04:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56362 validation-auc:0.96719 validation-aucpr:0.97304
[18:04:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55870 validation-auc:0.96739 validation-aucpr:0.97318
[18:04:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55431 validation-auc:0.96745 validation-aucpr:0.97317
[18:04:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.55003 validation-auc:0.96727 validation-aucpr:0.97303
[18:04:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54538 validation-auc:0.96734 validation-aucpr:0.97309
[18:04:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54060 validation-auc:0.96750 validation-aucpr:0.97322
[18:04:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53602 validation-auc:0.96753 validation-aucpr:0.97333
[18:04:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53154 validation-auc:0.96757 validation-aucpr:0.97338
[18:05:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52722 validation-auc:0.96760 validation-aucpr:0.97338
[18:05:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52286 validation-auc:0.96775 validation-aucpr:0.97351
[18:05:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51868 validation-auc:0.96766 validation-aucpr:0.97343
[18:05:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51432 validation-auc:0.96785 validation-aucpr:0.97357
[18:05:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51021 validation-auc:0.96781 validation-aucpr:0.97353
[18:05:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50624 validation-auc:0.96780 validation-aucpr:0.97355
[18:05:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50223 validation-auc:0.96782 validation-aucpr:0.97358
[18:05:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49878 validation-auc:0.96778 validation-aucpr:0.97353
[18:05:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49536 validation-auc:0.96780 validation-aucpr:0.97346
[18:05:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49167 validation-auc:0.96770 validation-aucpr:0.97345
[18:05:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48823 validation-auc:0.96780 validation-aucpr:0.97353
[18:05:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48452 validation-auc:0.96785 validation-aucpr:0.97357
[18:05:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48094 validation-auc:0.96788 validation-aucpr:0.97363
[18:05:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47732 validation-auc:0.96793 validation-aucpr:0.97368
[18:05:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47374 validation-auc:0.96801 validation-aucpr:0.97372
[18:05:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47023 validation-auc:0.96806 validation-aucpr:0.97375
[18:05:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46676 validation-auc:0.96804 validation-aucpr:0.97375
[18:05:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46385 validation-auc:0.96791 validation-aucpr:0.97365
[18:05:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.46048 validation-auc:0.96796 validation-aucpr:0.97370
[18:06:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45756 validation-auc:0.96801 validation-aucpr:0.97371
[18:06:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45427 validation-auc:0.96808 validation-aucpr:0.97375
[18:06:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45105 validation-auc:0.96803 validation-aucpr:0.97372
[18:06:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44786 validation-auc:0.96808 validation-aucpr:0.97374
[18:06:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44473 validation-auc:0.96808 validation-aucpr:0.97376
[18:06:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44177 validation-auc:0.96808 validation-aucpr:0.97377
[18:06:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43878 validation-auc:0.96804 validation-aucpr:0.97374
[18:06:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43619 validation-auc:0.96797 validation-aucpr:0.97368
[18:06:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43320 validation-auc:0.96793 validation-aucpr:0.97365
[18:06:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.43062 validation-auc:0.96795 validation-aucpr:0.97366
[18:06:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42764 validation-auc:0.96805 validation-aucpr:0.97373
[18:06:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42489 validation-auc:0.96799 validation-aucpr:0.97369
[18:06:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42205 validation-auc:0.96802 validation-aucpr:0.97370
[18:06:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41960 validation-auc:0.96799 validation-aucpr:0.97366
[18:06:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41683 validation-auc:0.96801 validation-aucpr:0.97367
[18:06:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41416 validation-auc:0.96809 validation-aucpr:0.97371
[18:06:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41149 validation-auc:0.96818 validation-aucpr:0.97376
[18:06:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40925 validation-auc:0.96812 validation-aucpr:0.97371
[18:06:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40670 validation-auc:0.96812 validation-aucpr:0.97371
[18:06:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40411 validation-auc:0.96812 validation-aucpr:0.97371
[18:06:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40163 validation-auc:0.96822 validation-aucpr:0.97379
[18:07:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39914 validation-auc:0.96816 validation-aucpr:0.97374
[18:07:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39700 validation-auc:0.96808 validation-aucpr:0.97368
[18:07:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39455 validation-auc:0.96812 validation-aucpr:0.97370
[18:07:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39219 validation-auc:0.96811 validation-aucpr:0.97369
[18:07:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38980 validation-auc:0.96818 validation-aucpr:0.97375
[18:07:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38783 validation-auc:0.96823 validation-aucpr:0.97376
[18:07:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38563 validation-auc:0.96821 validation-aucpr:0.97375
[18:07:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38363 validation-auc:0.96823 validation-aucpr:0.97376
[18:07:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.38133 validation-auc:0.96831 validation-aucpr:0.97385
[18:07:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37900 validation-auc:0.96842 validation-aucpr:0.97394
[18:07:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37683 validation-auc:0.96847 validation-aucpr:0.97398
[18:07:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37474 validation-auc:0.96841 validation-aucpr:0.97394
[18:07:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37263 validation-auc:0.96851 validation-aucpr:0.97398
[18:07:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.37055 validation-auc:0.96847 validation-aucpr:0.97395
[18:07:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36848 validation-auc:0.96849 validation-aucpr:0.97397
[18:07:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36677 validation-auc:0.96848 validation-aucpr:0.97398
[18:07:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36478 validation-auc:0.96849 validation-aucpr:0.97399
[18:07:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36281 validation-auc:0.96850 validation-aucpr:0.97399
[18:07:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.36084 validation-auc:0.96849 validation-aucpr:0.97398
[18:07:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.35887 validation-auc:0.96856 validation-aucpr:0.97403
{'best_iteration': '88', 'best_score': '0.9740344937804971'}
Trial 11, Fold 1: Log loss = 0.3588683607750186, Average precision = 0.9740402771464329, ROC-AUC = 0.968557994938415, Elapsed Time = 261.71760129999893 seconds
Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 11, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:08:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68605 validation-auc:0.95120 validation-aucpr:0.95669
[18:08:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67888 validation-auc:0.95967 validation-aucpr:0.96385
[18:08:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67196 validation-auc:0.96205 validation-aucpr:0.96544
[18:08:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66513 validation-auc:0.96373 validation-aucpr:0.96652
[18:08:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65840 validation-auc:0.96515 validation-aucpr:0.96802
[18:08:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65198 validation-auc:0.96542 validation-aucpr:0.96811
[18:08:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64553 validation-auc:0.96568 validation-aucpr:0.96830
[18:08:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63912 validation-auc:0.96595 validation-aucpr:0.96854
[18:08:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63350 validation-auc:0.96573 validation-aucpr:0.96839
[18:08:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62746 validation-auc:0.96573 validation-aucpr:0.96841
[18:08:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62149 validation-auc:0.96630 validation-aucpr:0.96980
[18:08:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61558 validation-auc:0.96682 validation-aucpr:0.97041
[18:08:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60987 validation-auc:0.96690 validation-aucpr:0.97063
[18:08:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60397 validation-auc:0.96736 validation-aucpr:0.97100
[18:08:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59839 validation-auc:0.96785 validation-aucpr:0.97142
[18:08:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59283 validation-auc:0.96819 validation-aucpr:0.97176
[18:08:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58795 validation-auc:0.96816 validation-aucpr:0.97165
[18:08:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58260 validation-auc:0.96815 validation-aucpr:0.97161
[18:08:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57792 validation-auc:0.96839 validation-aucpr:0.97182
[18:08:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57335 validation-auc:0.96844 validation-aucpr:0.97209
[18:08:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56832 validation-auc:0.96839 validation-aucpr:0.97209
[18:09:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56393 validation-auc:0.96827 validation-aucpr:0.97198
[18:09:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55896 validation-auc:0.96843 validation-aucpr:0.97209
[18:09:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55416 validation-auc:0.96851 validation-aucpr:0.97220
[18:09:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54926 validation-auc:0.96872 validation-aucpr:0.97236
[18:09:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54457 validation-auc:0.96877 validation-aucpr:0.97237
[18:09:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53980 validation-auc:0.96895 validation-aucpr:0.97251
[18:09:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53522 validation-auc:0.96895 validation-aucpr:0.97249
[18:09:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53128 validation-auc:0.96899 validation-aucpr:0.97253
[18:09:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52734 validation-auc:0.96907 validation-aucpr:0.97260
[18:09:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52304 validation-auc:0.96913 validation-aucpr:0.97267
[18:09:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51917 validation-auc:0.96914 validation-aucpr:0.97266
[18:09:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51536 validation-auc:0.96912 validation-aucpr:0.97260
[18:09:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51167 validation-auc:0.96909 validation-aucpr:0.97255
[18:09:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50755 validation-auc:0.96922 validation-aucpr:0.97269
[18:09:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50343 validation-auc:0.96927 validation-aucpr:0.97274
[18:09:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49936 validation-auc:0.96927 validation-aucpr:0.97274
[18:09:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49548 validation-auc:0.96940 validation-aucpr:0.97289
[18:09:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49206 validation-auc:0.96928 validation-aucpr:0.97279
[18:09:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48823 validation-auc:0.96940 validation-aucpr:0.97287
[18:10:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48445 validation-auc:0.96935 validation-aucpr:0.97284
[18:10:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48077 validation-auc:0.96935 validation-aucpr:0.97282
[18:10:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47710 validation-auc:0.96940 validation-aucpr:0.97286
[18:10:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47347 validation-auc:0.96933 validation-aucpr:0.97281
[18:10:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47000 validation-auc:0.96948 validation-aucpr:0.97292
[18:10:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46645 validation-auc:0.96960 validation-aucpr:0.97301
[18:10:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46303 validation-auc:0.96970 validation-aucpr:0.97308
[18:10:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45960 validation-auc:0.96966 validation-aucpr:0.97305
[18:10:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45628 validation-auc:0.96968 validation-aucpr:0.97304
[18:10:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45312 validation-auc:0.96964 validation-aucpr:0.97301
[18:10:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44995 validation-auc:0.96958 validation-aucpr:0.97298
[18:10:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44679 validation-auc:0.96962 validation-aucpr:0.97304
[18:10:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44367 validation-auc:0.96973 validation-aucpr:0.97311
[18:10:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44063 validation-auc:0.96960 validation-aucpr:0.97301
[18:10:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43754 validation-auc:0.96967 validation-aucpr:0.97307
[18:10:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43452 validation-auc:0.96971 validation-aucpr:0.97311
[18:10:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43162 validation-auc:0.96971 validation-aucpr:0.97310
[18:10:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42860 validation-auc:0.96975 validation-aucpr:0.97314
[18:10:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42565 validation-auc:0.96980 validation-aucpr:0.97318
[18:10:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42284 validation-auc:0.96977 validation-aucpr:0.97317
[18:10:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42045 validation-auc:0.96976 validation-aucpr:0.97313
[18:10:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41758 validation-auc:0.96981 validation-aucpr:0.97317
[18:11:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41484 validation-auc:0.96981 validation-aucpr:0.97318
[18:11:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41213 validation-auc:0.96985 validation-aucpr:0.97320
[18:11:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40939 validation-auc:0.96990 validation-aucpr:0.97326
[18:11:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40675 validation-auc:0.96998 validation-aucpr:0.97332
[18:11:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40416 validation-auc:0.97004 validation-aucpr:0.97337
[18:11:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40165 validation-auc:0.97004 validation-aucpr:0.97338
[18:11:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.39909 validation-auc:0.97003 validation-aucpr:0.97336
[18:11:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39663 validation-auc:0.97011 validation-aucpr:0.97343
[18:11:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39414 validation-auc:0.97016 validation-aucpr:0.97346
[18:11:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39202 validation-auc:0.97019 validation-aucpr:0.97346
[18:11:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.38962 validation-auc:0.97026 validation-aucpr:0.97351
[18:11:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38721 validation-auc:0.97030 validation-aucpr:0.97353
[18:11:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38483 validation-auc:0.97031 validation-aucpr:0.97352
[18:11:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38248 validation-auc:0.97033 validation-aucpr:0.97353
[18:11:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38017 validation-auc:0.97036 validation-aucpr:0.97355
[18:11:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37805 validation-auc:0.97027 validation-aucpr:0.97350
[18:11:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37585 validation-auc:0.97032 validation-aucpr:0.97353
[18:11:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37397 validation-auc:0.97035 validation-aucpr:0.97356
[18:11:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37186 validation-auc:0.97032 validation-aucpr:0.97354
[18:11:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.36963 validation-auc:0.97033 validation-aucpr:0.97356
[18:12:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.36759 validation-auc:0.97034 validation-aucpr:0.97357
[18:12:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36561 validation-auc:0.97030 validation-aucpr:0.97360
[18:12:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36353 validation-auc:0.97035 validation-aucpr:0.97364
[18:12:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36150 validation-auc:0.97038 validation-aucpr:0.97367
[18:12:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.35960 validation-auc:0.97042 validation-aucpr:0.97402
[18:12:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.35755 validation-auc:0.97044 validation-aucpr:0.97402
[18:12:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.35560 validation-auc:0.97039 validation-aucpr:0.97400
{'best_iteration': '87', 'best_score': '0.9740248478874732'}
Trial 11, Fold 2: Log loss = 0.3556043571620501, Average precision = 0.9739869661255756, ROC-AUC = 0.970393172442238, Elapsed Time = 260.57359840000026 seconds
Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 11, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:12:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68585 validation-auc:0.95527 validation-aucpr:0.95682
[18:12:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67966 validation-auc:0.95976 validation-aucpr:0.96432
[18:12:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67349 validation-auc:0.96038 validation-aucpr:0.96523
[18:12:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66654 validation-auc:0.96394 validation-aucpr:0.96873
[18:12:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65969 validation-auc:0.96518 validation-aucpr:0.96994
[18:12:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65297 validation-auc:0.96646 validation-aucpr:0.97094
[18:12:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64634 validation-auc:0.96700 validation-aucpr:0.97141
[18:12:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64009 validation-auc:0.96719 validation-aucpr:0.97153
[18:12:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63443 validation-auc:0.96754 validation-aucpr:0.97205
[18:12:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62826 validation-auc:0.96759 validation-aucpr:0.97219
[18:12:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62217 validation-auc:0.96747 validation-aucpr:0.97208
[18:12:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61698 validation-auc:0.96731 validation-aucpr:0.97195
[18:13:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61169 validation-auc:0.96739 validation-aucpr:0.97202
[18:13:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60665 validation-auc:0.96744 validation-aucpr:0.97203
[18:13:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.60168 validation-auc:0.96733 validation-aucpr:0.97192
[18:13:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59610 validation-auc:0.96735 validation-aucpr:0.97197
[18:13:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.59069 validation-auc:0.96750 validation-aucpr:0.97210
[18:13:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58588 validation-auc:0.96730 validation-aucpr:0.97193
[18:13:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.58053 validation-auc:0.96760 validation-aucpr:0.97213
[18:13:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57605 validation-auc:0.96754 validation-aucpr:0.97208
[18:13:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.57083 validation-auc:0.96780 validation-aucpr:0.97226
[18:13:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56577 validation-auc:0.96805 validation-aucpr:0.97254
[18:13:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.56074 validation-auc:0.96822 validation-aucpr:0.97267
[18:13:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55597 validation-auc:0.96849 validation-aucpr:0.97326
[18:13:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.55108 validation-auc:0.96868 validation-aucpr:0.97340
[18:13:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54651 validation-auc:0.96870 validation-aucpr:0.97343
[18:13:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54245 validation-auc:0.96864 validation-aucpr:0.97333
[18:13:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53792 validation-auc:0.96863 validation-aucpr:0.97337
[18:13:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53334 validation-auc:0.96878 validation-aucpr:0.97349
[18:13:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52938 validation-auc:0.96878 validation-aucpr:0.97347
[18:13:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52496 validation-auc:0.96885 validation-aucpr:0.97353
[18:13:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.52056 validation-auc:0.96896 validation-aucpr:0.97359
[18:14:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51635 validation-auc:0.96894 validation-aucpr:0.97357
[18:14:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51217 validation-auc:0.96890 validation-aucpr:0.97355
[18:14:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50795 validation-auc:0.96893 validation-aucpr:0.97359
[18:14:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50385 validation-auc:0.96893 validation-aucpr:0.97361
[18:14:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49988 validation-auc:0.96900 validation-aucpr:0.97366
[18:14:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49635 validation-auc:0.96907 validation-aucpr:0.97368
[18:14:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49252 validation-auc:0.96912 validation-aucpr:0.97372
[18:14:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48855 validation-auc:0.96924 validation-aucpr:0.97383
[18:14:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48472 validation-auc:0.96938 validation-aucpr:0.97394
[18:14:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48109 validation-auc:0.96939 validation-aucpr:0.97395
[18:14:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47732 validation-auc:0.96939 validation-aucpr:0.97395
[18:14:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47373 validation-auc:0.96943 validation-aucpr:0.97399
[18:14:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47018 validation-auc:0.96941 validation-aucpr:0.97398
[18:14:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46656 validation-auc:0.96955 validation-aucpr:0.97409
[18:14:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46312 validation-auc:0.96969 validation-aucpr:0.97420
[18:14:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45983 validation-auc:0.96959 validation-aucpr:0.97413
[18:14:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45645 validation-auc:0.96963 validation-aucpr:0.97418
[18:14:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45359 validation-auc:0.96963 validation-aucpr:0.97414
[18:14:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45036 validation-auc:0.96967 validation-aucpr:0.97418
[18:14:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44709 validation-auc:0.96969 validation-aucpr:0.97420
[18:14:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44437 validation-auc:0.96971 validation-aucpr:0.97421
[18:14:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44121 validation-auc:0.96974 validation-aucpr:0.97424
[18:15:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43816 validation-auc:0.96978 validation-aucpr:0.97431
[18:15:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43505 validation-auc:0.96987 validation-aucpr:0.97439
[18:15:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43199 validation-auc:0.96992 validation-aucpr:0.97442
[18:15:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42895 validation-auc:0.97004 validation-aucpr:0.97450
[18:15:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42626 validation-auc:0.96999 validation-aucpr:0.97445
[18:15:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42335 validation-auc:0.97000 validation-aucpr:0.97447
[18:15:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42094 validation-auc:0.97001 validation-aucpr:0.97445
[18:15:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41811 validation-auc:0.97004 validation-aucpr:0.97447
[18:15:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41569 validation-auc:0.97011 validation-aucpr:0.97450
[18:15:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41305 validation-auc:0.97012 validation-aucpr:0.97452
[18:15:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41034 validation-auc:0.97018 validation-aucpr:0.97466
[18:15:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40797 validation-auc:0.97022 validation-aucpr:0.97466
[18:15:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40528 validation-auc:0.97021 validation-aucpr:0.97466
[18:15:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40267 validation-auc:0.97019 validation-aucpr:0.97464
[18:15:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40052 validation-auc:0.97022 validation-aucpr:0.97464
[18:15:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39794 validation-auc:0.97026 validation-aucpr:0.97467
[18:15:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39541 validation-auc:0.97030 validation-aucpr:0.97469
[18:15:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39303 validation-auc:0.97031 validation-aucpr:0.97475
[18:15:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39093 validation-auc:0.97022 validation-aucpr:0.97467
[18:15:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38849 validation-auc:0.97022 validation-aucpr:0.97468
[18:15:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38595 validation-auc:0.97032 validation-aucpr:0.97476
[18:16:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38358 validation-auc:0.97035 validation-aucpr:0.97477
[18:16:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38128 validation-auc:0.97037 validation-aucpr:0.97479
[18:16:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37900 validation-auc:0.97037 validation-aucpr:0.97479
[18:16:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37675 validation-auc:0.97041 validation-aucpr:0.97481
[18:16:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37486 validation-auc:0.97044 validation-aucpr:0.97480
[18:16:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37270 validation-auc:0.97050 validation-aucpr:0.97485
[18:16:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37052 validation-auc:0.97054 validation-aucpr:0.97488
[18:16:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.36838 validation-auc:0.97055 validation-aucpr:0.97487
[18:16:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36627 validation-auc:0.97057 validation-aucpr:0.97488
[18:16:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36419 validation-auc:0.97060 validation-aucpr:0.97490
[18:16:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36208 validation-auc:0.97062 validation-aucpr:0.97493
[18:16:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36020 validation-auc:0.97063 validation-aucpr:0.97492
[18:16:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.35815 validation-auc:0.97064 validation-aucpr:0.97493
[18:16:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.35644 validation-auc:0.97065 validation-aucpr:0.97494
{'best_iteration': '88', 'best_score': '0.9749374341359066'}
Trial 11, Fold 3: Log loss = 0.35644221608984444, Average precision = 0.974941946430545, ROC-AUC = 0.9706546124230722, Elapsed Time = 259.26046209999913 seconds
Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 11, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:16:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68580 validation-auc:0.95624 validation-aucpr:0.96239
[18:16:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67874 validation-auc:0.96047 validation-aucpr:0.96517
[18:16:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67178 validation-auc:0.96229 validation-aucpr:0.96916
[18:16:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66502 validation-auc:0.96249 validation-aucpr:0.96931
[18:16:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65826 validation-auc:0.96430 validation-aucpr:0.97046
[18:16:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65177 validation-auc:0.96519 validation-aucpr:0.97103
[18:16:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64614 validation-auc:0.96584 validation-aucpr:0.97162
[18:17:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63979 validation-auc:0.96654 validation-aucpr:0.97205
[18:17:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63369 validation-auc:0.96690 validation-aucpr:0.97234
[18:17:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62759 validation-auc:0.96692 validation-aucpr:0.97240
[18:17:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62149 validation-auc:0.96723 validation-aucpr:0.97265
[18:17:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61549 validation-auc:0.96761 validation-aucpr:0.97290
[18:17:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60978 validation-auc:0.96769 validation-aucpr:0.97291
[18:17:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60410 validation-auc:0.96752 validation-aucpr:0.97279
[18:17:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59843 validation-auc:0.96795 validation-aucpr:0.97313
[18:17:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59280 validation-auc:0.96817 validation-aucpr:0.97332
[18:17:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58747 validation-auc:0.96810 validation-aucpr:0.97323
[18:17:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58209 validation-auc:0.96830 validation-aucpr:0.97331
[18:17:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57700 validation-auc:0.96816 validation-aucpr:0.97320
[18:17:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57189 validation-auc:0.96802 validation-aucpr:0.97311
[18:17:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56677 validation-auc:0.96811 validation-aucpr:0.97315
[18:17:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56177 validation-auc:0.96818 validation-aucpr:0.97325
[18:17:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55678 validation-auc:0.96857 validation-aucpr:0.97354
[18:17:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55201 validation-auc:0.96879 validation-aucpr:0.97368
[18:17:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54726 validation-auc:0.96885 validation-aucpr:0.97372
[18:17:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54265 validation-auc:0.96883 validation-aucpr:0.97375
[18:17:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53791 validation-auc:0.96906 validation-aucpr:0.97390
[18:17:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53336 validation-auc:0.96929 validation-aucpr:0.97408
[18:18:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52941 validation-auc:0.96919 validation-aucpr:0.97405
[18:18:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52496 validation-auc:0.96938 validation-aucpr:0.97419
[18:18:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52055 validation-auc:0.96943 validation-aucpr:0.97423
[18:18:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51628 validation-auc:0.96956 validation-aucpr:0.97432
[18:18:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51214 validation-auc:0.96953 validation-aucpr:0.97429
[18:18:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50799 validation-auc:0.96961 validation-aucpr:0.97436
[18:18:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50436 validation-auc:0.96956 validation-aucpr:0.97430
[18:18:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50038 validation-auc:0.96948 validation-aucpr:0.97424
[18:18:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49638 validation-auc:0.96962 validation-aucpr:0.97432
[18:18:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49243 validation-auc:0.96968 validation-aucpr:0.97437
[18:18:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48905 validation-auc:0.96969 validation-aucpr:0.97437
[18:18:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48535 validation-auc:0.96960 validation-aucpr:0.97431
[18:18:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48155 validation-auc:0.96967 validation-aucpr:0.97436
[18:18:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47826 validation-auc:0.96959 validation-aucpr:0.97429
[18:18:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47473 validation-auc:0.96965 validation-aucpr:0.97431
[18:18:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47116 validation-auc:0.96954 validation-aucpr:0.97423
[18:18:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46763 validation-auc:0.96961 validation-aucpr:0.97428
[18:18:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46451 validation-auc:0.96955 validation-aucpr:0.97422
[18:18:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46116 validation-auc:0.96948 validation-aucpr:0.97416
[18:18:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45782 validation-auc:0.96953 validation-aucpr:0.97418
[18:18:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45459 validation-auc:0.96962 validation-aucpr:0.97424
[18:19:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45132 validation-auc:0.96958 validation-aucpr:0.97423
[18:19:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44817 validation-auc:0.96958 validation-aucpr:0.97423
[18:19:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44508 validation-auc:0.96953 validation-aucpr:0.97420
[18:19:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44200 validation-auc:0.96942 validation-aucpr:0.97410
[18:19:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.43899 validation-auc:0.96946 validation-aucpr:0.97412
[18:19:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43639 validation-auc:0.96932 validation-aucpr:0.97402
[18:19:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43342 validation-auc:0.96930 validation-aucpr:0.97400
[18:19:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43050 validation-auc:0.96931 validation-aucpr:0.97401
[18:19:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42803 validation-auc:0.96926 validation-aucpr:0.97399
[18:19:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42554 validation-auc:0.96918 validation-aucpr:0.97392
[18:19:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42309 validation-auc:0.96925 validation-aucpr:0.97398
[18:19:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42033 validation-auc:0.96923 validation-aucpr:0.97397
[18:19:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41786 validation-auc:0.96913 validation-aucpr:0.97389
[18:19:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41516 validation-auc:0.96921 validation-aucpr:0.97395
[18:19:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41246 validation-auc:0.96916 validation-aucpr:0.97391
[18:19:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40977 validation-auc:0.96918 validation-aucpr:0.97394
[18:19:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40722 validation-auc:0.96916 validation-aucpr:0.97393
[18:19:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40468 validation-auc:0.96922 validation-aucpr:0.97396
[18:19:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40259 validation-auc:0.96913 validation-aucpr:0.97389
[18:19:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40005 validation-auc:0.96921 validation-aucpr:0.97395
[18:20:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39753 validation-auc:0.96932 validation-aucpr:0.97403
[18:20:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39508 validation-auc:0.96932 validation-aucpr:0.97404
[18:20:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39265 validation-auc:0.96933 validation-aucpr:0.97404
[18:20:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39066 validation-auc:0.96924 validation-aucpr:0.97399
[18:20:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38865 validation-auc:0.96917 validation-aucpr:0.97394
[18:20:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38637 validation-auc:0.96913 validation-aucpr:0.97392
[18:20:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38406 validation-auc:0.96917 validation-aucpr:0.97396
[18:20:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38174 validation-auc:0.96923 validation-aucpr:0.97401
[18:20:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37942 validation-auc:0.96932 validation-aucpr:0.97407
[18:20:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37723 validation-auc:0.96938 validation-aucpr:0.97411
[18:20:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37508 validation-auc:0.96946 validation-aucpr:0.97416
[18:20:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37330 validation-auc:0.96939 validation-aucpr:0.97410
[18:20:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37112 validation-auc:0.96945 validation-aucpr:0.97414
[18:20:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.36905 validation-auc:0.96940 validation-aucpr:0.97412
[18:20:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36702 validation-auc:0.96939 validation-aucpr:0.97410
[18:20:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36513 validation-auc:0.96938 validation-aucpr:0.97410
[18:20:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36314 validation-auc:0.96951 validation-aucpr:0.97420
[18:20:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36123 validation-auc:0.96946 validation-aucpr:0.97414
[18:20:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.35923 validation-auc:0.96951 validation-aucpr:0.97419
[18:20:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
{'best_iteration': '38', 'best_score': '0.9743741208236099'}
Trial 11, Fold 4: Log loss = 0.3572366804694858, Average precision = 0.9742509040078975, ROC-AUC = 0.9695805942426042, Elapsed Time = 257.6183670999999 seconds
Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 11, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:20:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68595 validation-auc:0.95108 validation-aucpr:0.95716
[18:21:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67893 validation-auc:0.95631 validation-aucpr:0.96264
[18:21:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67196 validation-auc:0.95890 validation-aucpr:0.96451
[18:21:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66515 validation-auc:0.95991 validation-aucpr:0.96521
[18:21:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65859 validation-auc:0.96067 validation-aucpr:0.96532
[18:21:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65196 validation-auc:0.96175 validation-aucpr:0.96615
[18:21:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64622 validation-auc:0.96169 validation-aucpr:0.96649
[18:21:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64001 validation-auc:0.96280 validation-aucpr:0.96741
[18:21:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63383 validation-auc:0.96339 validation-aucpr:0.96790
[18:21:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62765 validation-auc:0.96424 validation-aucpr:0.96853
[18:21:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62190 validation-auc:0.96418 validation-aucpr:0.96849
[18:21:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61609 validation-auc:0.96437 validation-aucpr:0.96869
[18:21:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61026 validation-auc:0.96433 validation-aucpr:0.96872
[18:21:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60456 validation-auc:0.96490 validation-aucpr:0.96917
[18:21:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59899 validation-auc:0.96494 validation-aucpr:0.96921
[18:21:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59346 validation-auc:0.96512 validation-aucpr:0.96929
[18:21:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58858 validation-auc:0.96519 validation-aucpr:0.96947
[18:21:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58324 validation-auc:0.96531 validation-aucpr:0.96953
[18:21:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57809 validation-auc:0.96556 validation-aucpr:0.96969
[18:21:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57299 validation-auc:0.96549 validation-aucpr:0.96962
[18:21:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56791 validation-auc:0.96553 validation-aucpr:0.96967
[18:21:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56309 validation-auc:0.96586 validation-aucpr:0.96984
[18:21:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55889 validation-auc:0.96575 validation-aucpr:0.96977
[18:22:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55406 validation-auc:0.96578 validation-aucpr:0.96980
[18:22:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54919 validation-auc:0.96611 validation-aucpr:0.97002
[18:22:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54460 validation-auc:0.96612 validation-aucpr:0.97005
[18:22:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54003 validation-auc:0.96605 validation-aucpr:0.97001
[18:22:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53542 validation-auc:0.96626 validation-aucpr:0.97018
[18:22:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53119 validation-auc:0.96621 validation-aucpr:0.97015
[18:22:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52677 validation-auc:0.96626 validation-aucpr:0.97057
[18:22:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52297 validation-auc:0.96637 validation-aucpr:0.97060
[18:22:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51876 validation-auc:0.96637 validation-aucpr:0.97062
[18:22:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51445 validation-auc:0.96656 validation-aucpr:0.97073
[18:22:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51036 validation-auc:0.96658 validation-aucpr:0.97075
[18:22:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50697 validation-auc:0.96648 validation-aucpr:0.97063
[18:22:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50303 validation-auc:0.96650 validation-aucpr:0.97063
[18:22:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49920 validation-auc:0.96635 validation-aucpr:0.97054
[18:22:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49585 validation-auc:0.96631 validation-aucpr:0.97047
[18:22:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49203 validation-auc:0.96638 validation-aucpr:0.97052
[18:22:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48826 validation-auc:0.96636 validation-aucpr:0.97045
[18:22:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48457 validation-auc:0.96640 validation-aucpr:0.97048
[18:22:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48087 validation-auc:0.96659 validation-aucpr:0.97066
[18:22:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47729 validation-auc:0.96670 validation-aucpr:0.97074
[18:22:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47381 validation-auc:0.96670 validation-aucpr:0.97075
[18:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47039 validation-auc:0.96667 validation-aucpr:0.96921
[18:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46701 validation-auc:0.96663 validation-aucpr:0.96918
[18:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46358 validation-auc:0.96676 validation-aucpr:0.96927
[18:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.46038 validation-auc:0.96672 validation-aucpr:0.96923
[18:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45756 validation-auc:0.96664 validation-aucpr:0.96916
[18:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45441 validation-auc:0.96667 validation-aucpr:0.96915
[18:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45118 validation-auc:0.96667 validation-aucpr:0.96915
[18:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44797 validation-auc:0.96678 validation-aucpr:0.96924
[18:23:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44522 validation-auc:0.96683 validation-aucpr:0.96928
[18:23:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44225 validation-auc:0.96676 validation-aucpr:0.96923
[18:23:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43926 validation-auc:0.96695 validation-aucpr:0.96939
[18:23:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43669 validation-auc:0.96685 validation-aucpr:0.96932
[18:23:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43404 validation-auc:0.96683 validation-aucpr:0.96931
[18:23:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.43149 validation-auc:0.96680 validation-aucpr:0.96929
[18:23:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42896 validation-auc:0.96668 validation-aucpr:0.96920
[18:23:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42614 validation-auc:0.96672 validation-aucpr:0.96925
[18:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42331 validation-auc:0.96681 validation-aucpr:0.96932
[18:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.42057 validation-auc:0.96685 validation-aucpr:0.96963
[18:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41784 validation-auc:0.96689 validation-aucpr:0.96972
[18:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41511 validation-auc:0.96699 validation-aucpr:0.96979
[18:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41280 validation-auc:0.96698 validation-aucpr:0.96977
[18:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.41016 validation-auc:0.96697 validation-aucpr:0.96978
[18:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40761 validation-auc:0.96701 validation-aucpr:0.96981
[18:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40508 validation-auc:0.96705 validation-aucpr:0.96983
[18:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40253 validation-auc:0.96706 validation-aucpr:0.96984
[18:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.40007 validation-auc:0.96702 validation-aucpr:0.96981
[18:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39791 validation-auc:0.96703 validation-aucpr:0.96982
[18:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39584 validation-auc:0.96701 validation-aucpr:0.96979
[18:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39346 validation-auc:0.96707 validation-aucpr:0.96984
[18:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.39144 validation-auc:0.96709 validation-aucpr:0.96982
[18:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38910 validation-auc:0.96708 validation-aucpr:0.96982
[18:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38681 validation-auc:0.96710 validation-aucpr:0.96983
[18:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38457 validation-auc:0.96707 validation-aucpr:0.96981
[18:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.38239 validation-auc:0.96709 validation-aucpr:0.96982
[18:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.38023 validation-auc:0.96715 validation-aucpr:0.96987
[18:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37804 validation-auc:0.96719 validation-aucpr:0.96991
[18:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37607 validation-auc:0.96724 validation-aucpr:0.96995
[18:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37407 validation-auc:0.96726 validation-aucpr:0.96997
[18:24:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.37201 validation-auc:0.96724 validation-aucpr:0.96958
[18:24:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36998 validation-auc:0.96716 validation-aucpr:0.96952
{'best_iteration': '33', 'best_score': '0.9707520012467902'}
Trial 11, Fold 5: Log loss = 0.3699763639546245, Average precision = 0.9705531018910141, ROC-AUC = 0.9671560485294391, Elapsed Time = 228.57969969999976 seconds
Optimization Progress: 12%|#2 | 12/100 [25:57<9:49:25, 401.88s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 12, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:24:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66259 validation-auc:0.94294 validation-aucpr:0.93924
[18:24:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63428 validation-auc:0.95463 validation-aucpr:0.95805
[18:24:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60932 validation-auc:0.95643 validation-aucpr:0.96112
[18:24:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58603 validation-auc:0.95797 validation-aucpr:0.96251
[18:25:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56475 validation-auc:0.95805 validation-aucpr:0.96270
[18:25:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.54351 validation-auc:0.95997 validation-aucpr:0.96492
[18:25:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.52625 validation-auc:0.96042 validation-aucpr:0.96503
[18:25:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.50972 validation-auc:0.96048 validation-aucpr:0.96531
[18:25:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.48994 validation-auc:0.96277 validation-aucpr:0.96772
[18:25:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.47233 validation-auc:0.96360 validation-aucpr:0.96885
[18:25:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.45930 validation-auc:0.96345 validation-aucpr:0.96876
[18:25:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.44865 validation-auc:0.96304 validation-aucpr:0.96829
[18:25:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.43703 validation-auc:0.96304 validation-aucpr:0.96818
[18:25:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.42596 validation-auc:0.96324 validation-aucpr:0.96849
[18:25:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.41246 validation-auc:0.96400 validation-aucpr:0.96934
[18:25:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.40000 validation-auc:0.96474 validation-aucpr:0.97009
[18:25:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.39074 validation-auc:0.96472 validation-aucpr:0.97005
[18:25:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.37932 validation-auc:0.96527 validation-aucpr:0.97058
[18:25:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.36852 validation-auc:0.96575 validation-aucpr:0.97102
[18:25:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.36046 validation-auc:0.96597 validation-aucpr:0.97116
[18:25:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.35358 validation-auc:0.96593 validation-aucpr:0.97116
[18:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.34714 validation-auc:0.96585 validation-aucpr:0.97111
[18:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.34141 validation-auc:0.96571 validation-aucpr:0.97095
[18:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.33502 validation-auc:0.96588 validation-aucpr:0.97107
[18:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.32988 validation-auc:0.96588 validation-aucpr:0.97102
[18:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.32296 validation-auc:0.96599 validation-aucpr:0.97115
[18:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.31652 validation-auc:0.96616 validation-aucpr:0.97133
[18:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.31225 validation-auc:0.96613 validation-aucpr:0.97130
[18:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.30603 validation-auc:0.96639 validation-aucpr:0.97156
{'best_iteration': '28', 'best_score': '0.9715565489584634'}
Trial 12, Fold 1: Log loss = 0.30603410223586613, Average precision = 0.9715614797671308, ROC-AUC = 0.9663893310008937, Elapsed Time = 53.693495299999995 seconds
Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 12, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66405 validation-auc:0.93662 validation-aucpr:0.93297
[18:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63717 validation-auc:0.94590 validation-aucpr:0.94476
[18:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60878 validation-auc:0.95973 validation-aucpr:0.96166
[18:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58238 validation-auc:0.96332 validation-aucpr:0.96699
[18:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56224 validation-auc:0.96292 validation-aucpr:0.96663
[18:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.54181 validation-auc:0.96346 validation-aucpr:0.96689
[18:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.52407 validation-auc:0.96363 validation-aucpr:0.96678
[18:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.50769 validation-auc:0.96352 validation-aucpr:0.96665
[18:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.49264 validation-auc:0.96327 validation-aucpr:0.96630
[18:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.47488 validation-auc:0.96469 validation-aucpr:0.96778
[18:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.46040 validation-auc:0.96491 validation-aucpr:0.96777
[18:26:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.44749 validation-auc:0.96491 validation-aucpr:0.96812
[18:26:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.43521 validation-auc:0.96518 validation-aucpr:0.96834
[18:26:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.42396 validation-auc:0.96546 validation-aucpr:0.96847
[18:26:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.41358 validation-auc:0.96565 validation-aucpr:0.96863
[18:26:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.40227 validation-auc:0.96600 validation-aucpr:0.96902
[18:26:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.39100 validation-auc:0.96636 validation-aucpr:0.96935
[18:26:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.38242 validation-auc:0.96643 validation-aucpr:0.96933
[18:26:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.37471 validation-auc:0.96632 validation-aucpr:0.96921
[18:26:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.36568 validation-auc:0.96664 validation-aucpr:0.96946
[18:26:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.35859 validation-auc:0.96659 validation-aucpr:0.96938
[18:26:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.35133 validation-auc:0.96676 validation-aucpr:0.96950
[18:26:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.34315 validation-auc:0.96710 validation-aucpr:0.96979
[18:26:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.33454 validation-auc:0.96747 validation-aucpr:0.97014
[18:26:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.32775 validation-auc:0.96769 validation-aucpr:0.97030
[18:26:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.32063 validation-auc:0.96793 validation-aucpr:0.97049
[18:26:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.31557 validation-auc:0.96796 validation-aucpr:0.97048
[18:26:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.30904 validation-auc:0.96819 validation-aucpr:0.97073
[18:26:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.30284 validation-auc:0.96846 validation-aucpr:0.97135
{'best_iteration': '28', 'best_score': '0.9713477996479326'}
Trial 12, Fold 2: Log loss = 0.3028431411702671, Average precision = 0.971361292061751, ROC-AUC = 0.9684592986642249, Elapsed Time = 53.507002000000284 seconds
Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 12, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:26:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66239 validation-auc:0.94767 validation-aucpr:0.95016
[18:26:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63434 validation-auc:0.95690 validation-aucpr:0.95905
[18:26:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61081 validation-auc:0.95657 validation-aucpr:0.96020
[18:26:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58871 validation-auc:0.95568 validation-aucpr:0.95868
[18:26:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56640 validation-auc:0.96000 validation-aucpr:0.96273
[18:26:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.54629 validation-auc:0.96183 validation-aucpr:0.96466
[18:26:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.52812 validation-auc:0.96205 validation-aucpr:0.96552
[18:26:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51158 validation-auc:0.96192 validation-aucpr:0.96532
[18:26:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.49587 validation-auc:0.96204 validation-aucpr:0.96580
[18:26:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.47702 validation-auc:0.96506 validation-aucpr:0.96934
[18:26:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.46401 validation-auc:0.96487 validation-aucpr:0.96903
[18:27:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.44886 validation-auc:0.96570 validation-aucpr:0.96999
[18:27:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.43399 validation-auc:0.96645 validation-aucpr:0.97098
[18:27:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.42069 validation-auc:0.96685 validation-aucpr:0.97142
[18:27:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.40991 validation-auc:0.96716 validation-aucpr:0.97171
[18:27:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.39989 validation-auc:0.96705 validation-aucpr:0.97163
[18:27:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.39035 validation-auc:0.96719 validation-aucpr:0.97173
[18:27:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.37903 validation-auc:0.96745 validation-aucpr:0.97202
[18:27:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.37144 validation-auc:0.96731 validation-aucpr:0.97186
[18:27:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.36378 validation-auc:0.96719 validation-aucpr:0.97176
[18:27:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.35450 validation-auc:0.96738 validation-aucpr:0.97199
[18:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.34755 validation-auc:0.96757 validation-aucpr:0.97209
[18:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.33920 validation-auc:0.96771 validation-aucpr:0.97225
[18:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.33335 validation-auc:0.96767 validation-aucpr:0.97225
[18:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.32542 validation-auc:0.96801 validation-aucpr:0.97254
[18:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.31965 validation-auc:0.96814 validation-aucpr:0.97263
[18:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.31198 validation-auc:0.96867 validation-aucpr:0.97311
[18:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.30669 validation-auc:0.96896 validation-aucpr:0.97333
[18:27:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.30179 validation-auc:0.96896 validation-aucpr:0.97333
{'best_iteration': '27', 'best_score': '0.9733342783045276'}
Trial 12, Fold 3: Log loss = 0.3017870133683276, Average precision = 0.9733305895492752, ROC-AUC = 0.9689629361654997, Elapsed Time = 52.86153060000106 seconds
Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 12, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:27:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65952 validation-auc:0.94855 validation-aucpr:0.94885
[18:27:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63334 validation-auc:0.95400 validation-aucpr:0.96011
[18:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60796 validation-auc:0.95741 validation-aucpr:0.96390
[18:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58166 validation-auc:0.96230 validation-aucpr:0.96786
[18:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56142 validation-auc:0.96212 validation-aucpr:0.96781
[18:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.54217 validation-auc:0.96172 validation-aucpr:0.96766
[18:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.52369 validation-auc:0.96255 validation-aucpr:0.96829
[18:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.50683 validation-auc:0.96240 validation-aucpr:0.96800
[18:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.49166 validation-auc:0.96181 validation-aucpr:0.96748
[18:27:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.47382 validation-auc:0.96299 validation-aucpr:0.96874
[18:27:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.45801 validation-auc:0.96368 validation-aucpr:0.96947
[18:27:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.44566 validation-auc:0.96360 validation-aucpr:0.96937
[18:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.43217 validation-auc:0.96402 validation-aucpr:0.96983
[18:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.42100 validation-auc:0.96409 validation-aucpr:0.96984
[18:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.40792 validation-auc:0.96486 validation-aucpr:0.97057
[18:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.39699 validation-auc:0.96519 validation-aucpr:0.97090
[18:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.38804 validation-auc:0.96508 validation-aucpr:0.97077
[18:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.37930 validation-auc:0.96500 validation-aucpr:0.97066
[18:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.36885 validation-auc:0.96538 validation-aucpr:0.97105
[18:28:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.36095 validation-auc:0.96549 validation-aucpr:0.97112
[18:28:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.35323 validation-auc:0.96555 validation-aucpr:0.97118
[18:28:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.34611 validation-auc:0.96571 validation-aucpr:0.97128
[18:28:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.33928 validation-auc:0.96608 validation-aucpr:0.97152
[18:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.33346 validation-auc:0.96604 validation-aucpr:0.97148
[18:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.32800 validation-auc:0.96606 validation-aucpr:0.97149
[18:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.32263 validation-auc:0.96615 validation-aucpr:0.97152
[18:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.31779 validation-auc:0.96603 validation-aucpr:0.97137
[18:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.31307 validation-auc:0.96601 validation-aucpr:0.97135
[18:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.30850 validation-auc:0.96609 validation-aucpr:0.97139
{'best_iteration': '22', 'best_score': '0.9715216628471062'}
Trial 12, Fold 4: Log loss = 0.3085016825154917, Average precision = 0.9713912806763556, ROC-AUC = 0.9660949597153969, Elapsed Time = 53.205132799999774 seconds
Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 12, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:28:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66283 validation-auc:0.94372 validation-aucpr:0.94356
[18:28:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63360 validation-auc:0.95657 validation-aucpr:0.95950
[18:28:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60862 validation-auc:0.95788 validation-aucpr:0.96140
[18:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58644 validation-auc:0.95707 validation-aucpr:0.96084
[18:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56481 validation-auc:0.95857 validation-aucpr:0.96289
[18:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.54563 validation-auc:0.95935 validation-aucpr:0.96323
[18:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.52717 validation-auc:0.96000 validation-aucpr:0.96396
[18:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51130 validation-auc:0.95970 validation-aucpr:0.96365
[18:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.49270 validation-auc:0.96179 validation-aucpr:0.96598
[18:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.47617 validation-auc:0.96227 validation-aucpr:0.96667
[18:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.46293 validation-auc:0.96210 validation-aucpr:0.96650
[18:28:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.44688 validation-auc:0.96312 validation-aucpr:0.96744
[18:28:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.43315 validation-auc:0.96336 validation-aucpr:0.96809
[18:28:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.42298 validation-auc:0.96317 validation-aucpr:0.96784
[18:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.41097 validation-auc:0.96328 validation-aucpr:0.96801
[18:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.40214 validation-auc:0.96300 validation-aucpr:0.96767
[18:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.39274 validation-auc:0.96279 validation-aucpr:0.96753
[18:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.38429 validation-auc:0.96301 validation-aucpr:0.96765
[18:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.37446 validation-auc:0.96312 validation-aucpr:0.96781
[18:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.36749 validation-auc:0.96309 validation-aucpr:0.96781
[18:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.35817 validation-auc:0.96325 validation-aucpr:0.96804
[18:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.35118 validation-auc:0.96347 validation-aucpr:0.96821
[18:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.34505 validation-auc:0.96344 validation-aucpr:0.96811
[18:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.33953 validation-auc:0.96322 validation-aucpr:0.96794
[18:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.33427 validation-auc:0.96332 validation-aucpr:0.96796
[18:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.32729 validation-auc:0.96351 validation-aucpr:0.96826
[18:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.32167 validation-auc:0.96378 validation-aucpr:0.96858
[18:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.31664 validation-auc:0.96398 validation-aucpr:0.96874
[18:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.31166 validation-auc:0.96412 validation-aucpr:0.96891
{'best_iteration': '28', 'best_score': '0.9689149458783484'}
Trial 12, Fold 5: Log loss = 0.31166072012417156, Average precision = 0.9689226422619246, ROC-AUC = 0.9641158612489085, Elapsed Time = 53.00868619999892 seconds
Optimization Progress: 13%|#3 | 13/100 [30:31<8:46:37, 363.19s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 13, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68199 validation-auc:0.91934 validation-aucpr:0.89907
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67118 validation-auc:0.94339 validation-aucpr:0.94700
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65875 validation-auc:0.96044 validation-aucpr:0.96344
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64739 validation-auc:0.96276 validation-aucpr:0.96840
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63735 validation-auc:0.96386 validation-aucpr:0.96919
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62614 validation-auc:0.96518 validation-aucpr:0.97051
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.61654 validation-auc:0.96538 validation-aucpr:0.97058
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.60583 validation-auc:0.96625 validation-aucpr:0.97137
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.59564 validation-auc:0.96707 validation-aucpr:0.97211
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.58711 validation-auc:0.96692 validation-aucpr:0.97190
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.57876 validation-auc:0.96711 validation-aucpr:0.97180
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.56969 validation-auc:0.96741 validation-aucpr:0.97215
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56191 validation-auc:0.96713 validation-aucpr:0.97187
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.55418 validation-auc:0.96706 validation-aucpr:0.97146
{'best_iteration': '11', 'best_score': '0.9721533735935723'}
Trial 13, Fold 1: Log loss = 0.5541760171341904, Average precision = 0.9715588809569982, ROC-AUC = 0.9670561583812487, Elapsed Time = 8.191645800001425 seconds
Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 13, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68205 validation-auc:0.92486 validation-aucpr:0.89257
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67062 validation-auc:0.95107 validation-aucpr:0.95183
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65970 validation-auc:0.95674 validation-aucpr:0.95885
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64923 validation-auc:0.95845 validation-aucpr:0.96106
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63893 validation-auc:0.95881 validation-aucpr:0.96155
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62746 validation-auc:0.96518 validation-aucpr:0.96826
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.61787 validation-auc:0.96484 validation-aucpr:0.96790
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.60742 validation-auc:0.96667 validation-aucpr:0.96988
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.59728 validation-auc:0.96769 validation-aucpr:0.97111
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.58740 validation-auc:0.96849 validation-aucpr:0.97196
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.57921 validation-auc:0.96842 validation-aucpr:0.97186
[18:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.57127 validation-auc:0.96836 validation-aucpr:0.97166
[18:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56223 validation-auc:0.96892 validation-aucpr:0.97213
[18:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.55369 validation-auc:0.96885 validation-aucpr:0.97222
{'best_iteration': '13', 'best_score': '0.9722168506448429'}
Trial 13, Fold 2: Log loss = 0.5536905013799721, Average precision = 0.9722019875011716, ROC-AUC = 0.9688539187803363, Elapsed Time = 8.690538099999685 seconds
Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 13, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68175 validation-auc:0.92494 validation-aucpr:0.89388
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67063 validation-auc:0.94710 validation-aucpr:0.94695
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66008 validation-auc:0.95246 validation-aucpr:0.95365
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64965 validation-auc:0.95434 validation-aucpr:0.95691
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63927 validation-auc:0.95700 validation-aucpr:0.95967
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62977 validation-auc:0.95784 validation-aucpr:0.96078
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.62003 validation-auc:0.95862 validation-aucpr:0.96179
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.61077 validation-auc:0.95928 validation-aucpr:0.96292
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60170 validation-auc:0.95979 validation-aucpr:0.96332
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.59295 validation-auc:0.95998 validation-aucpr:0.96332
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.58471 validation-auc:0.96018 validation-aucpr:0.96353
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.57661 validation-auc:0.96033 validation-aucpr:0.96365
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56744 validation-auc:0.96288 validation-aucpr:0.96652
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.55985 validation-auc:0.96291 validation-aucpr:0.96644
{'best_iteration': '12', 'best_score': '0.966518950749605'}
Trial 13, Fold 3: Log loss = 0.5598497446494258, Average precision = 0.9669748866732013, ROC-AUC = 0.9629090029711743, Elapsed Time = 7.858257300000332 seconds
Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 13, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68161 validation-auc:0.92751 validation-aucpr:0.90009
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67016 validation-auc:0.94928 validation-aucpr:0.94291
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65784 validation-auc:0.96084 validation-aucpr:0.96151
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64629 validation-auc:0.96445 validation-aucpr:0.96949
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63616 validation-auc:0.96470 validation-aucpr:0.96962
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62502 validation-auc:0.96526 validation-aucpr:0.97043
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.61541 validation-auc:0.96534 validation-aucpr:0.97049
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.60513 validation-auc:0.96583 validation-aucpr:0.97104
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.59581 validation-auc:0.96634 validation-aucpr:0.97149
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.58747 validation-auc:0.96614 validation-aucpr:0.97126
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.57793 validation-auc:0.96669 validation-aucpr:0.97190
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.56905 validation-auc:0.96678 validation-aucpr:0.97209
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56007 validation-auc:0.96690 validation-aucpr:0.97232
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.55237 validation-auc:0.96658 validation-aucpr:0.97213
{'best_iteration': '12', 'best_score': '0.972317800443495'}
Trial 13, Fold 4: Log loss = 0.5523687742806942, Average precision = 0.972130547603141, ROC-AUC = 0.966584583360158, Elapsed Time = 8.161774599999262 seconds
Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 13, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68195 validation-auc:0.92479 validation-aucpr:0.90042
[18:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66939 validation-auc:0.95639 validation-aucpr:0.95941
[18:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65735 validation-auc:0.96104 validation-aucpr:0.96365
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64698 validation-auc:0.96273 validation-aucpr:0.96809
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63647 validation-auc:0.96369 validation-aucpr:0.96885
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62690 validation-auc:0.96339 validation-aucpr:0.96840
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.61614 validation-auc:0.96454 validation-aucpr:0.96974
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.60590 validation-auc:0.96481 validation-aucpr:0.97010
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.59584 validation-auc:0.96517 validation-aucpr:0.97051
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.58610 validation-auc:0.96540 validation-aucpr:0.97072
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.57786 validation-auc:0.96531 validation-aucpr:0.97044
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.56890 validation-auc:0.96535 validation-aucpr:0.97056
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56080 validation-auc:0.96556 validation-aucpr:0.97075
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.55342 validation-auc:0.96535 validation-aucpr:0.97050
{'best_iteration': '12', 'best_score': '0.9707524225232216'}
Trial 13, Fold 5: Log loss = 0.5534183540087733, Average precision = 0.9705031427069372, ROC-AUC = 0.9653473793731304, Elapsed Time = 8.009748399999808 seconds
Optimization Progress: 14%|#4 | 14/100 [31:20<6:24:32, 268.29s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 14, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67300 validation-auc:0.93535 validation-aucpr:0.91414
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65352 validation-auc:0.95997 validation-aucpr:0.94955
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63501 validation-auc:0.96534 validation-aucpr:0.96582
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61730 validation-auc:0.96661 validation-aucpr:0.96679
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60074 validation-auc:0.96710 validation-aucpr:0.96875
{'best_iteration': '4', 'best_score': '0.9687460586423775'}
Trial 14, Fold 1: Log loss = 0.6007382625759794, Average precision = 0.9697246970219232, ROC-AUC = 0.9671048504466526, Elapsed Time = 9.356031400000575 seconds
Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 14, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:30:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67308 validation-auc:0.93724 validation-aucpr:0.90542
[18:30:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65404 validation-auc:0.95966 validation-aucpr:0.95575
[18:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63566 validation-auc:0.96497 validation-aucpr:0.96601
[18:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61960 validation-auc:0.96509 validation-aucpr:0.96872
[18:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60307 validation-auc:0.96593 validation-aucpr:0.96962
{'best_iteration': '4', 'best_score': '0.9696233259644926'}
Trial 14, Fold 2: Log loss = 0.6030703772186887, Average precision = 0.969516017292491, ROC-AUC = 0.9659294354587835, Elapsed Time = 9.748138599999947 seconds
Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 14, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67279 validation-auc:0.94268 validation-aucpr:0.92792
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65354 validation-auc:0.96313 validation-aucpr:0.96026
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63494 validation-auc:0.96576 validation-aucpr:0.96564
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61735 validation-auc:0.96730 validation-aucpr:0.96911
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60225 validation-auc:0.96770 validation-aucpr:0.96910
{'best_iteration': '3', 'best_score': '0.9691086296590194'}
Trial 14, Fold 3: Log loss = 0.6022545490642423, Average precision = 0.9701395087255767, ROC-AUC = 0.9677014963016826, Elapsed Time = 9.65295689999948 seconds
Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 14, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67389 validation-auc:0.93183 validation-aucpr:0.91325
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65492 validation-auc:0.95639 validation-aucpr:0.95643
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63656 validation-auc:0.96061 validation-aucpr:0.96473
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61956 validation-auc:0.96282 validation-aucpr:0.96665
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60402 validation-auc:0.96411 validation-aucpr:0.96995
{'best_iteration': '4', 'best_score': '0.9699524125024998'}
Trial 14, Fold 4: Log loss = 0.6040156415532489, Average precision = 0.969877524685255, ROC-AUC = 0.964110087603215, Elapsed Time = 9.464738699998634 seconds
Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 14, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67263 validation-auc:0.93698 validation-aucpr:0.92727
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65439 validation-auc:0.95989 validation-aucpr:0.96219
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63584 validation-auc:0.96387 validation-aucpr:0.96903
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61880 validation-auc:0.96427 validation-aucpr:0.96953
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60297 validation-auc:0.96474 validation-aucpr:0.96920
{'best_iteration': '3', 'best_score': '0.9695329455063676'}
Trial 14, Fold 5: Log loss = 0.6029668724141746, Average precision = 0.969375726079295, ROC-AUC = 0.964739832413652, Elapsed Time = 9.787574100000711 seconds
Optimization Progress: 15%|#5 | 15/100 [32:16<4:49:20, 204.24s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 15, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65835 validation-auc:0.95231 validation-aucpr:0.95569
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62991 validation-auc:0.95311 validation-aucpr:0.95398
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60171 validation-auc:0.95713 validation-aucpr:0.96099
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57811 validation-auc:0.95772 validation-aucpr:0.96117
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55292 validation-auc:0.96075 validation-aucpr:0.96445
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53003 validation-auc:0.96176 validation-aucpr:0.96522
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51238 validation-auc:0.96237 validation-aucpr:0.96548
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49493 validation-auc:0.96314 validation-aucpr:0.96610
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47646 validation-auc:0.96344 validation-aucpr:0.96656
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46201 validation-auc:0.96369 validation-aucpr:0.96711
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44873 validation-auc:0.96369 validation-aucpr:0.96715
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43649 validation-auc:0.96322 validation-aucpr:0.96663
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42217 validation-auc:0.96347 validation-aucpr:0.96688
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40893 validation-auc:0.96388 validation-aucpr:0.96714
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39879 validation-auc:0.96403 validation-aucpr:0.96723
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38702 validation-auc:0.96432 validation-aucpr:0.96744
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37732 validation-auc:0.96432 validation-aucpr:0.96751
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36847 validation-auc:0.96437 validation-aucpr:0.96740
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35939 validation-auc:0.96526 validation-aucpr:0.96962
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.35032 validation-auc:0.96539 validation-aucpr:0.96985
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34340 validation-auc:0.96534 validation-aucpr:0.96979
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33562 validation-auc:0.96536 validation-aucpr:0.96982
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32751 validation-auc:0.96575 validation-aucpr:0.97020
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.32026 validation-auc:0.96570 validation-aucpr:0.97044
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31355 validation-auc:0.96578 validation-aucpr:0.97053
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30706 validation-auc:0.96605 validation-aucpr:0.97075
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30049 validation-auc:0.96622 validation-aucpr:0.97095
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29473 validation-auc:0.96671 validation-aucpr:0.97226
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28924 validation-auc:0.96678 validation-aucpr:0.97238
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28438 validation-auc:0.96677 validation-aucpr:0.97234
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27997 validation-auc:0.96665 validation-aucpr:0.97225
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27614 validation-auc:0.96680 validation-aucpr:0.97236
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27256 validation-auc:0.96680 validation-aucpr:0.97229
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26822 validation-auc:0.96689 validation-aucpr:0.97235
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26427 validation-auc:0.96699 validation-aucpr:0.97243
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26029 validation-auc:0.96722 validation-aucpr:0.97264
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25666 validation-auc:0.96740 validation-aucpr:0.97314
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25326 validation-auc:0.96755 validation-aucpr:0.97327
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25075 validation-auc:0.96779 validation-aucpr:0.97341
{'best_iteration': '38', 'best_score': '0.9734073712231963'}
Trial 15, Fold 1: Log loss = 0.2507485350769062, Average precision = 0.9734117928534539, ROC-AUC = 0.9677870621298805, Elapsed Time = 5.148564700000861 seconds
Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 15, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65842 validation-auc:0.95149 validation-aucpr:0.95086
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62647 validation-auc:0.95926 validation-aucpr:0.95947
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.59846 validation-auc:0.96257 validation-aucpr:0.96585
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57212 validation-auc:0.96383 validation-aucpr:0.96792
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.54773 validation-auc:0.96486 validation-aucpr:0.96906
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.52494 validation-auc:0.96559 validation-aucpr:0.96983
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.50660 validation-auc:0.96684 validation-aucpr:0.97087
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.48925 validation-auc:0.96658 validation-aucpr:0.97100
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47305 validation-auc:0.96670 validation-aucpr:0.97108
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45704 validation-auc:0.96662 validation-aucpr:0.97116
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44329 validation-auc:0.96684 validation-aucpr:0.97120
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.42896 validation-auc:0.96697 validation-aucpr:0.97131
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41478 validation-auc:0.96729 validation-aucpr:0.97162
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40140 validation-auc:0.96790 validation-aucpr:0.97209
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.38867 validation-auc:0.96862 validation-aucpr:0.97262
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.37852 validation-auc:0.96859 validation-aucpr:0.97257
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.36953 validation-auc:0.96876 validation-aucpr:0.97262
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.35897 validation-auc:0.96898 validation-aucpr:0.97284
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35111 validation-auc:0.96894 validation-aucpr:0.97274
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34180 validation-auc:0.96937 validation-aucpr:0.97309
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33334 validation-auc:0.96950 validation-aucpr:0.97318
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.32668 validation-auc:0.96942 validation-aucpr:0.97278
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.31899 validation-auc:0.96946 validation-aucpr:0.97287
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31209 validation-auc:0.96957 validation-aucpr:0.97299
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.30523 validation-auc:0.96952 validation-aucpr:0.97302
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.29872 validation-auc:0.96960 validation-aucpr:0.97310
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29299 validation-auc:0.96954 validation-aucpr:0.97299
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.28731 validation-auc:0.96943 validation-aucpr:0.97293
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28294 validation-auc:0.96945 validation-aucpr:0.97295
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.27739 validation-auc:0.96971 validation-aucpr:0.97320
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27383 validation-auc:0.96964 validation-aucpr:0.97312
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.26917 validation-auc:0.96979 validation-aucpr:0.97328
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.26477 validation-auc:0.96995 validation-aucpr:0.97342
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26067 validation-auc:0.97011 validation-aucpr:0.97350
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.25691 validation-auc:0.97002 validation-aucpr:0.97349
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25396 validation-auc:0.96996 validation-aucpr:0.97344
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25028 validation-auc:0.97007 validation-aucpr:0.97352
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.24758 validation-auc:0.97012 validation-aucpr:0.97351
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24442 validation-auc:0.97013 validation-aucpr:0.97353
{'best_iteration': '38', 'best_score': '0.9735321248399427'}
Trial 15, Fold 2: Log loss = 0.24442360141281588, Average precision = 0.9735330060498169, ROC-AUC = 0.9701298141605572, Elapsed Time = 5.283658000000287 seconds
Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 15, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65832 validation-auc:0.94735 validation-aucpr:0.94594
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62628 validation-auc:0.95822 validation-aucpr:0.95927
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.59758 validation-auc:0.96044 validation-aucpr:0.96377
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57100 validation-auc:0.96076 validation-aucpr:0.96423
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.54634 validation-auc:0.96319 validation-aucpr:0.96671
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.52340 validation-auc:0.96474 validation-aucpr:0.96794
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.50501 validation-auc:0.96504 validation-aucpr:0.96844
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.48776 validation-auc:0.96524 validation-aucpr:0.96854
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47153 validation-auc:0.96642 validation-aucpr:0.97066
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45753 validation-auc:0.96637 validation-aucpr:0.97066
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44218 validation-auc:0.96647 validation-aucpr:0.97062
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.42917 validation-auc:0.96658 validation-aucpr:0.97066
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41731 validation-auc:0.96651 validation-aucpr:0.97079
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40377 validation-auc:0.96742 validation-aucpr:0.97225
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39155 validation-auc:0.96740 validation-aucpr:0.97226
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.37977 validation-auc:0.96761 validation-aucpr:0.97247
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.36887 validation-auc:0.96791 validation-aucpr:0.97275
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36036 validation-auc:0.96783 validation-aucpr:0.97260
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35255 validation-auc:0.96780 validation-aucpr:0.97270
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34339 validation-auc:0.96796 validation-aucpr:0.97288
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33573 validation-auc:0.96816 validation-aucpr:0.97303
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.32893 validation-auc:0.96816 validation-aucpr:0.97295
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32092 validation-auc:0.96831 validation-aucpr:0.97308
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31510 validation-auc:0.96818 validation-aucpr:0.97299
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.30834 validation-auc:0.96797 validation-aucpr:0.97177
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30204 validation-auc:0.96786 validation-aucpr:0.97175
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29599 validation-auc:0.96784 validation-aucpr:0.97112
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29120 validation-auc:0.96772 validation-aucpr:0.97089
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28621 validation-auc:0.96756 validation-aucpr:0.97044
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28159 validation-auc:0.96804 validation-aucpr:0.97077
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27739 validation-auc:0.96810 validation-aucpr:0.97078
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27294 validation-auc:0.96838 validation-aucpr:0.97240
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.26865 validation-auc:0.96840 validation-aucpr:0.97250
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26559 validation-auc:0.96834 validation-aucpr:0.97245
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26225 validation-auc:0.96839 validation-aucpr:0.97248
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25853 validation-auc:0.96839 validation-aucpr:0.97246
[18:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25559 validation-auc:0.96832 validation-aucpr:0.97241
[18:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25227 validation-auc:0.96829 validation-aucpr:0.97240
[18:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24930 validation-auc:0.96855 validation-aucpr:0.97255
{'best_iteration': '22', 'best_score': '0.9730788157534537'}
Trial 15, Fold 3: Log loss = 0.24930300686625795, Average precision = 0.9727403636924389, ROC-AUC = 0.9685533162061426, Elapsed Time = 5.271522000000914 seconds
Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 15, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65897 validation-auc:0.94989 validation-aucpr:0.95155
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63117 validation-auc:0.95327 validation-aucpr:0.95725
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60142 validation-auc:0.95973 validation-aucpr:0.96495
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57778 validation-auc:0.96069 validation-aucpr:0.96648
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55610 validation-auc:0.96158 validation-aucpr:0.96718
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53242 validation-auc:0.96334 validation-aucpr:0.96905
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51115 validation-auc:0.96442 validation-aucpr:0.97006
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49378 validation-auc:0.96480 validation-aucpr:0.97024
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47537 validation-auc:0.96499 validation-aucpr:0.97057
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46020 validation-auc:0.96500 validation-aucpr:0.97046
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44416 validation-auc:0.96576 validation-aucpr:0.97118
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43254 validation-auc:0.96503 validation-aucpr:0.97053
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41816 validation-auc:0.96583 validation-aucpr:0.97121
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40543 validation-auc:0.96575 validation-aucpr:0.97129
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39301 validation-auc:0.96582 validation-aucpr:0.97145
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38135 validation-auc:0.96622 validation-aucpr:0.97176
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37215 validation-auc:0.96609 validation-aucpr:0.97171
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36297 validation-auc:0.96645 validation-aucpr:0.97197
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35502 validation-auc:0.96627 validation-aucpr:0.97178
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34777 validation-auc:0.96637 validation-aucpr:0.97183
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33934 validation-auc:0.96656 validation-aucpr:0.97198
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33112 validation-auc:0.96669 validation-aucpr:0.97211
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32464 validation-auc:0.96700 validation-aucpr:0.97231
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31879 validation-auc:0.96692 validation-aucpr:0.97226
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31187 validation-auc:0.96711 validation-aucpr:0.97238
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30665 validation-auc:0.96710 validation-aucpr:0.97238
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30039 validation-auc:0.96737 validation-aucpr:0.97256
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29538 validation-auc:0.96741 validation-aucpr:0.97257
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28964 validation-auc:0.96762 validation-aucpr:0.97277
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28405 validation-auc:0.96786 validation-aucpr:0.97299
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27887 validation-auc:0.96815 validation-aucpr:0.97320
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27356 validation-auc:0.96849 validation-aucpr:0.97349
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.26936 validation-auc:0.96871 validation-aucpr:0.97368
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26520 validation-auc:0.96894 validation-aucpr:0.97386
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26242 validation-auc:0.96878 validation-aucpr:0.97373
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25923 validation-auc:0.96891 validation-aucpr:0.97381
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25575 validation-auc:0.96926 validation-aucpr:0.97406
[18:31:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25193 validation-auc:0.96949 validation-aucpr:0.97423
[18:31:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24844 validation-auc:0.96965 validation-aucpr:0.97438
{'best_iteration': '38', 'best_score': '0.9743752728524171'}
Trial 15, Fold 4: Log loss = 0.24843866113024915, Average precision = 0.9743756236521505, ROC-AUC = 0.9696492950492499, Elapsed Time = 5.2661658999986685 seconds
Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 15, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65847 validation-auc:0.95169 validation-aucpr:0.95688
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62713 validation-auc:0.95678 validation-aucpr:0.96259
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.59869 validation-auc:0.95860 validation-aucpr:0.96373
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57244 validation-auc:0.95982 validation-aucpr:0.96521
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.54877 validation-auc:0.96048 validation-aucpr:0.96587
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.52637 validation-auc:0.96112 validation-aucpr:0.96637
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.50830 validation-auc:0.96140 validation-aucpr:0.96640
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49137 validation-auc:0.96139 validation-aucpr:0.96649
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47365 validation-auc:0.96215 validation-aucpr:0.96633
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45713 validation-auc:0.96227 validation-aucpr:0.96673
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44398 validation-auc:0.96289 validation-aucpr:0.96704
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43102 validation-auc:0.96309 validation-aucpr:0.96726
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41859 validation-auc:0.96285 validation-aucpr:0.96713
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40581 validation-auc:0.96322 validation-aucpr:0.96747
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39400 validation-auc:0.96304 validation-aucpr:0.96752
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38263 validation-auc:0.96334 validation-aucpr:0.96759
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37368 validation-auc:0.96337 validation-aucpr:0.96750
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36339 validation-auc:0.96368 validation-aucpr:0.96773
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35640 validation-auc:0.96333 validation-aucpr:0.96855
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34906 validation-auc:0.96349 validation-aucpr:0.96862
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34069 validation-auc:0.96379 validation-aucpr:0.96817
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33412 validation-auc:0.96386 validation-aucpr:0.96819
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32766 validation-auc:0.96402 validation-aucpr:0.96839
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.32099 validation-auc:0.96407 validation-aucpr:0.96847
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31375 validation-auc:0.96444 validation-aucpr:0.96873
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30850 validation-auc:0.96438 validation-aucpr:0.96865
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30351 validation-auc:0.96451 validation-aucpr:0.96873
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29911 validation-auc:0.96426 validation-aucpr:0.96856
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29369 validation-auc:0.96454 validation-aucpr:0.96886
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28841 validation-auc:0.96467 validation-aucpr:0.96901
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28476 validation-auc:0.96471 validation-aucpr:0.96910
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.28098 validation-auc:0.96472 validation-aucpr:0.96904
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27654 validation-auc:0.96476 validation-aucpr:0.96908
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.27246 validation-auc:0.96484 validation-aucpr:0.96914
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26870 validation-auc:0.96529 validation-aucpr:0.96950
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26591 validation-auc:0.96539 validation-aucpr:0.96955
[18:31:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.26305 validation-auc:0.96538 validation-aucpr:0.96951
[18:31:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25959 validation-auc:0.96554 validation-aucpr:0.96980
[18:31:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25668 validation-auc:0.96559 validation-aucpr:0.96999
{'best_iteration': '38', 'best_score': '0.969985383677907'}
Trial 15, Fold 5: Log loss = 0.25668126798836727, Average precision = 0.9701177753994549, ROC-AUC = 0.9655906211099343, Elapsed Time = 6.848654200000965 seconds
Optimization Progress: 16%|#6 | 16/100 [32:52<3:35:03, 153.61s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 16, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66463 validation-auc:0.95925 validation-aucpr:0.96434
[1] validation-logloss:0.64010 validation-auc:0.96240 validation-aucpr:0.96801
[2] validation-logloss:0.61513 validation-auc:0.96566 validation-aucpr:0.97118
[3] validation-logloss:0.59295 validation-auc:0.96626 validation-aucpr:0.97152
[4] validation-logloss:0.57390 validation-auc:0.96643 validation-aucpr:0.97140
[5] validation-logloss:0.55627 validation-auc:0.96664 validation-aucpr:0.97154
[6] validation-logloss:0.54065 validation-auc:0.96639 validation-aucpr:0.97129
[7] validation-logloss:0.52491 validation-auc:0.96629 validation-aucpr:0.97117
[8] validation-logloss:0.51049 validation-auc:0.96623 validation-aucpr:0.97103
[9] validation-logloss:0.49658 validation-auc:0.96627 validation-aucpr:0.97104
[10] validation-logloss:0.48341 validation-auc:0.96618 validation-aucpr:0.97090
[11] validation-logloss:0.47140 validation-auc:0.96601 validation-aucpr:0.97074
[12] validation-logloss:0.45757 validation-auc:0.96618 validation-aucpr:0.97104
[13] validation-logloss:0.44432 validation-auc:0.96663 validation-aucpr:0.97148
[14] validation-logloss:0.43193 validation-auc:0.96704 validation-aucpr:0.97187
[15] validation-logloss:0.42049 validation-auc:0.96731 validation-aucpr:0.97216
[16] validation-logloss:0.40985 validation-auc:0.96739 validation-aucpr:0.97221
[17] validation-logloss:0.39931 validation-auc:0.96771 validation-aucpr:0.97255
[18] validation-logloss:0.39106 validation-auc:0.96761 validation-aucpr:0.97246
[19] validation-logloss:0.38149 validation-auc:0.96782 validation-aucpr:0.97265
[20] validation-logloss:0.37406 validation-auc:0.96785 validation-aucpr:0.97269
[21] validation-logloss:0.36550 validation-auc:0.96811 validation-aucpr:0.97293
[22] validation-logloss:0.35742 validation-auc:0.96838 validation-aucpr:0.97314
[23] validation-logloss:0.35108 validation-auc:0.96834 validation-aucpr:0.97322
[24] validation-logloss:0.34373 validation-auc:0.96851 validation-aucpr:0.97339
[25] validation-logloss:0.33679 validation-auc:0.96875 validation-aucpr:0.97360
[26] validation-logloss:0.33135 validation-auc:0.96880 validation-aucpr:0.97366
[27] validation-logloss:0.32601 validation-auc:0.96882 validation-aucpr:0.97366
[28] validation-logloss:0.32013 validation-auc:0.96894 validation-aucpr:0.97375
[29] validation-logloss:0.31433 validation-auc:0.96902 validation-aucpr:0.97402
[30] validation-logloss:0.30993 validation-auc:0.96904 validation-aucpr:0.97402
[31] validation-logloss:0.30471 validation-auc:0.96920 validation-aucpr:0.97417
[32] validation-logloss:0.29986 validation-auc:0.96945 validation-aucpr:0.97437
[33] validation-logloss:0.29584 validation-auc:0.96948 validation-aucpr:0.97437
[34] validation-logloss:0.29097 validation-auc:0.96967 validation-aucpr:0.97451
[35] validation-logloss:0.28652 validation-auc:0.96988 validation-aucpr:0.97472
[36] validation-logloss:0.28229 validation-auc:0.97004 validation-aucpr:0.97488
[37] validation-logloss:0.27841 validation-auc:0.97008 validation-aucpr:0.97492
[38] validation-logloss:0.27517 validation-auc:0.97004 validation-aucpr:0.97489
[39] validation-logloss:0.27166 validation-auc:0.97003 validation-aucpr:0.97494
[40] validation-logloss:0.26821 validation-auc:0.97001 validation-aucpr:0.97493
[41] validation-logloss:0.26478 validation-auc:0.97009 validation-aucpr:0.97500
[42] validation-logloss:0.26168 validation-auc:0.97012 validation-aucpr:0.97503
[43] validation-logloss:0.25852 validation-auc:0.97033 validation-aucpr:0.97517
[44] validation-logloss:0.25582 validation-auc:0.97031 validation-aucpr:0.97521
[45] validation-logloss:0.25351 validation-auc:0.97036 validation-aucpr:0.97522
[46] validation-logloss:0.25072 validation-auc:0.97054 validation-aucpr:0.97533
[47] validation-logloss:0.24813 validation-auc:0.97061 validation-aucpr:0.97538
[48] validation-logloss:0.24560 validation-auc:0.97069 validation-aucpr:0.97552
[49] validation-logloss:0.24375 validation-auc:0.97077 validation-aucpr:0.97556
[50] validation-logloss:0.24169 validation-auc:0.97067 validation-aucpr:0.97547
[51] validation-logloss:0.23988 validation-auc:0.97073 validation-aucpr:0.97555
[52] validation-logloss:0.23763 validation-auc:0.97087 validation-aucpr:0.97565
[53] validation-logloss:0.23565 validation-auc:0.97099 validation-aucpr:0.97578
[54] validation-logloss:0.23405 validation-auc:0.97109 validation-aucpr:0.97584
[55] validation-logloss:0.23195 validation-auc:0.97126 validation-aucpr:0.97598
[56] validation-logloss:0.22980 validation-auc:0.97153 validation-aucpr:0.97620
{'best_iteration': '56', 'best_score': '0.9761988403450483'}
Trial 16, Fold 1: Log loss = 0.22979919630740303, Average precision = 0.9762028387089069, ROC-AUC = 0.9715299285929367, Elapsed Time = 9.99813069999982 seconds
Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 16, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66419 validation-auc:0.95790 validation-aucpr:0.96292
[1] validation-logloss:0.63789 validation-auc:0.96261 validation-aucpr:0.96685
[2] validation-logloss:0.61439 validation-auc:0.96572 validation-aucpr:0.96950
[3] validation-logloss:0.59173 validation-auc:0.96657 validation-aucpr:0.97030
[4] validation-logloss:0.57056 validation-auc:0.96751 validation-aucpr:0.97112
[5] validation-logloss:0.55304 validation-auc:0.96809 validation-aucpr:0.97158
[6] validation-logloss:0.53432 validation-auc:0.96892 validation-aucpr:0.97226
[7] validation-logloss:0.51646 validation-auc:0.96955 validation-aucpr:0.97297
[8] validation-logloss:0.50081 validation-auc:0.96990 validation-aucpr:0.97310
[9] validation-logloss:0.48717 validation-auc:0.96988 validation-aucpr:0.97308
[10] validation-logloss:0.47244 validation-auc:0.97001 validation-aucpr:0.97348
[11] validation-logloss:0.45849 validation-auc:0.97022 validation-aucpr:0.97385
[12] validation-logloss:0.44562 validation-auc:0.97023 validation-aucpr:0.97389
[13] validation-logloss:0.43322 validation-auc:0.97028 validation-aucpr:0.97402
[14] validation-logloss:0.42152 validation-auc:0.97040 validation-aucpr:0.97410
[15] validation-logloss:0.41179 validation-auc:0.97046 validation-aucpr:0.97411
[16] validation-logloss:0.40120 validation-auc:0.97069 validation-aucpr:0.97428
[17] validation-logloss:0.39138 validation-auc:0.97063 validation-aucpr:0.97421
[18] validation-logloss:0.38181 validation-auc:0.97084 validation-aucpr:0.97434
[19] validation-logloss:0.37394 validation-auc:0.97099 validation-aucpr:0.97436
[20] validation-logloss:0.36524 validation-auc:0.97122 validation-aucpr:0.97454
[21] validation-logloss:0.35732 validation-auc:0.97116 validation-aucpr:0.97447
[22] validation-logloss:0.34972 validation-auc:0.97118 validation-aucpr:0.97452
[23] validation-logloss:0.34235 validation-auc:0.97119 validation-aucpr:0.97442
[24] validation-logloss:0.33543 validation-auc:0.97119 validation-aucpr:0.97436
[25] validation-logloss:0.32968 validation-auc:0.97098 validation-aucpr:0.97420
[26] validation-logloss:0.32337 validation-auc:0.97095 validation-aucpr:0.97417
[27] validation-logloss:0.31815 validation-auc:0.97101 validation-aucpr:0.97422
[28] validation-logloss:0.31238 validation-auc:0.97104 validation-aucpr:0.97423
[29] validation-logloss:0.30744 validation-auc:0.97117 validation-aucpr:0.97430
[30] validation-logloss:0.30200 validation-auc:0.97137 validation-aucpr:0.97443
[31] validation-logloss:0.29760 validation-auc:0.97139 validation-aucpr:0.97446
[32] validation-logloss:0.29271 validation-auc:0.97146 validation-aucpr:0.97453
[33] validation-logloss:0.28795 validation-auc:0.97150 validation-aucpr:0.97458
[34] validation-logloss:0.28347 validation-auc:0.97161 validation-aucpr:0.97467
[35] validation-logloss:0.27992 validation-auc:0.97172 validation-aucpr:0.97472
[36] validation-logloss:0.27589 validation-auc:0.97160 validation-aucpr:0.97465
[37] validation-logloss:0.27179 validation-auc:0.97166 validation-aucpr:0.97471
[38] validation-logloss:0.26864 validation-auc:0.97177 validation-aucpr:0.97478
[39] validation-logloss:0.26511 validation-auc:0.97173 validation-aucpr:0.97475
[40] validation-logloss:0.26180 validation-auc:0.97169 validation-aucpr:0.97467
[41] validation-logloss:0.25870 validation-auc:0.97168 validation-aucpr:0.97464
[42] validation-logloss:0.25612 validation-auc:0.97170 validation-aucpr:0.97462
[43] validation-logloss:0.25370 validation-auc:0.97176 validation-aucpr:0.97465
[44] validation-logloss:0.25091 validation-auc:0.97166 validation-aucpr:0.97458
[45] validation-logloss:0.24821 validation-auc:0.97178 validation-aucpr:0.97467
[46] validation-logloss:0.24562 validation-auc:0.97183 validation-aucpr:0.97472
[47] validation-logloss:0.24340 validation-auc:0.97181 validation-aucpr:0.97451
[48] validation-logloss:0.24090 validation-auc:0.97195 validation-aucpr:0.97459
[49] validation-logloss:0.23853 validation-auc:0.97189 validation-aucpr:0.97448
[50] validation-logloss:0.23603 validation-auc:0.97200 validation-aucpr:0.97455
[51] validation-logloss:0.23419 validation-auc:0.97201 validation-aucpr:0.97456
[52] validation-logloss:0.23208 validation-auc:0.97208 validation-aucpr:0.97458
[53] validation-logloss:0.23013 validation-auc:0.97218 validation-aucpr:0.97495
[54] validation-logloss:0.22815 validation-auc:0.97217 validation-aucpr:0.97493
[55] validation-logloss:0.22616 validation-auc:0.97229 validation-aucpr:0.97501
[56] validation-logloss:0.22446 validation-auc:0.97222 validation-aucpr:0.97496
{'best_iteration': '55', 'best_score': '0.9750061463455643'}
Trial 16, Fold 2: Log loss = 0.22445777337154255, Average precision = 0.9749343967660432, ROC-AUC = 0.9722240094739177, Elapsed Time = 10.321142300001156 seconds
Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 16, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66447 validation-auc:0.95985 validation-aucpr:0.96127
[1] validation-logloss:0.63915 validation-auc:0.96178 validation-aucpr:0.96616
[2] validation-logloss:0.61443 validation-auc:0.96493 validation-aucpr:0.96916
[3] validation-logloss:0.59137 validation-auc:0.96618 validation-aucpr:0.97034
[4] validation-logloss:0.56991 validation-auc:0.96786 validation-aucpr:0.97140
[5] validation-logloss:0.55269 validation-auc:0.96819 validation-aucpr:0.97203
[6] validation-logloss:0.53402 validation-auc:0.96886 validation-aucpr:0.97213
[7] validation-logloss:0.51946 validation-auc:0.96807 validation-aucpr:0.97146
[8] validation-logloss:0.50278 validation-auc:0.96827 validation-aucpr:0.97156
[9] validation-logloss:0.48702 validation-auc:0.96851 validation-aucpr:0.97163
[10] validation-logloss:0.47267 validation-auc:0.96812 validation-aucpr:0.97122
[11] validation-logloss:0.46006 validation-auc:0.96846 validation-aucpr:0.97149
[12] validation-logloss:0.44725 validation-auc:0.96851 validation-aucpr:0.97154
[13] validation-logloss:0.43562 validation-auc:0.96812 validation-aucpr:0.97101
[14] validation-logloss:0.42499 validation-auc:0.96814 validation-aucpr:0.97125
[15] validation-logloss:0.41379 validation-auc:0.96825 validation-aucpr:0.97191
[16] validation-logloss:0.40429 validation-auc:0.96851 validation-aucpr:0.97209
[17] validation-logloss:0.39424 validation-auc:0.96858 validation-aucpr:0.97211
[18] validation-logloss:0.38442 validation-auc:0.96930 validation-aucpr:0.97270
[19] validation-logloss:0.37713 validation-auc:0.96914 validation-aucpr:0.97340
[20] validation-logloss:0.36878 validation-auc:0.96926 validation-aucpr:0.97347
[21] validation-logloss:0.36044 validation-auc:0.96933 validation-aucpr:0.97355
[22] validation-logloss:0.35362 validation-auc:0.96937 validation-aucpr:0.97299
[23] validation-logloss:0.34595 validation-auc:0.96949 validation-aucpr:0.97303
[24] validation-logloss:0.33970 validation-auc:0.96955 validation-aucpr:0.97307
[25] validation-logloss:0.33371 validation-auc:0.96962 validation-aucpr:0.97309
[26] validation-logloss:0.32707 validation-auc:0.96997 validation-aucpr:0.97437
[27] validation-logloss:0.32064 validation-auc:0.97014 validation-aucpr:0.97449
[28] validation-logloss:0.31453 validation-auc:0.97027 validation-aucpr:0.97456
[29] validation-logloss:0.30974 validation-auc:0.97025 validation-aucpr:0.97454
[30] validation-logloss:0.30411 validation-auc:0.97055 validation-aucpr:0.97480
[31] validation-logloss:0.29880 validation-auc:0.97075 validation-aucpr:0.97496
[32] validation-logloss:0.29386 validation-auc:0.97082 validation-aucpr:0.97503
[33] validation-logloss:0.28936 validation-auc:0.97073 validation-aucpr:0.97497
[34] validation-logloss:0.28503 validation-auc:0.97063 validation-aucpr:0.97488
[35] validation-logloss:0.28132 validation-auc:0.97075 validation-aucpr:0.97502
[36] validation-logloss:0.27758 validation-auc:0.97083 validation-aucpr:0.97508
[37] validation-logloss:0.27393 validation-auc:0.97087 validation-aucpr:0.97513
[38] validation-logloss:0.26992 validation-auc:0.97103 validation-aucpr:0.97529
[39] validation-logloss:0.26630 validation-auc:0.97099 validation-aucpr:0.97526
[40] validation-logloss:0.26290 validation-auc:0.97116 validation-aucpr:0.97535
[41] validation-logloss:0.25957 validation-auc:0.97125 validation-aucpr:0.97539
[42] validation-logloss:0.25693 validation-auc:0.97123 validation-aucpr:0.97533
[43] validation-logloss:0.25399 validation-auc:0.97126 validation-aucpr:0.97538
[44] validation-logloss:0.25100 validation-auc:0.97127 validation-aucpr:0.97540
[45] validation-logloss:0.24815 validation-auc:0.97127 validation-aucpr:0.97540
[46] validation-logloss:0.24530 validation-auc:0.97138 validation-aucpr:0.97551
[47] validation-logloss:0.24273 validation-auc:0.97148 validation-aucpr:0.97557
[48] validation-logloss:0.24018 validation-auc:0.97158 validation-aucpr:0.97571
[49] validation-logloss:0.23780 validation-auc:0.97166 validation-aucpr:0.97574
[50] validation-logloss:0.23546 validation-auc:0.97173 validation-aucpr:0.97578
[51] validation-logloss:0.23339 validation-auc:0.97169 validation-aucpr:0.97576
[52] validation-logloss:0.23175 validation-auc:0.97174 validation-aucpr:0.97576
[53] validation-logloss:0.22975 validation-auc:0.97176 validation-aucpr:0.97578
[54] validation-logloss:0.22805 validation-auc:0.97162 validation-aucpr:0.97565
[55] validation-logloss:0.22612 validation-auc:0.97168 validation-aucpr:0.97566
[56] validation-logloss:0.22442 validation-auc:0.97168 validation-aucpr:0.97564
{'best_iteration': '53', 'best_score': '0.975780860731931'}
Trial 16, Fold 3: Log loss = 0.2244200578116086, Average precision = 0.9756433442739769, ROC-AUC = 0.971681897747301, Elapsed Time = 10.35664270000052 seconds
Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 16, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66455 validation-auc:0.95339 validation-aucpr:0.95766
[1] validation-logloss:0.63835 validation-auc:0.96090 validation-aucpr:0.96545
[2] validation-logloss:0.61414 validation-auc:0.96464 validation-aucpr:0.97065
[3] validation-logloss:0.59135 validation-auc:0.96593 validation-aucpr:0.97133
[4] validation-logloss:0.56988 validation-auc:0.96776 validation-aucpr:0.97271
[5] validation-logloss:0.55220 validation-auc:0.96762 validation-aucpr:0.97266
[6] validation-logloss:0.53339 validation-auc:0.96837 validation-aucpr:0.97336
[7] validation-logloss:0.51833 validation-auc:0.96785 validation-aucpr:0.97304
[8] validation-logloss:0.50365 validation-auc:0.96721 validation-aucpr:0.97262
[9] validation-logloss:0.49009 validation-auc:0.96684 validation-aucpr:0.97219
[10] validation-logloss:0.47590 validation-auc:0.96720 validation-aucpr:0.97252
[11] validation-logloss:0.46173 validation-auc:0.96766 validation-aucpr:0.97290
[12] validation-logloss:0.44827 validation-auc:0.96819 validation-aucpr:0.97330
[13] validation-logloss:0.43581 validation-auc:0.96834 validation-aucpr:0.97343
[14] validation-logloss:0.42541 validation-auc:0.96822 validation-aucpr:0.97335
[15] validation-logloss:0.41455 validation-auc:0.96829 validation-aucpr:0.97344
[16] validation-logloss:0.40524 validation-auc:0.96836 validation-aucpr:0.97350
[17] validation-logloss:0.39523 validation-auc:0.96859 validation-aucpr:0.97363
[18] validation-logloss:0.38584 validation-auc:0.96867 validation-aucpr:0.97375
[19] validation-logloss:0.37822 validation-auc:0.96863 validation-aucpr:0.97370
[20] validation-logloss:0.37001 validation-auc:0.96881 validation-aucpr:0.97380
[21] validation-logloss:0.36181 validation-auc:0.96876 validation-aucpr:0.97377
[22] validation-logloss:0.35551 validation-auc:0.96847 validation-aucpr:0.97355
[23] validation-logloss:0.34919 validation-auc:0.96849 validation-aucpr:0.97354
[24] validation-logloss:0.34206 validation-auc:0.96845 validation-aucpr:0.97350
[25] validation-logloss:0.33537 validation-auc:0.96871 validation-aucpr:0.97373
[26] validation-logloss:0.32975 validation-auc:0.96874 validation-aucpr:0.97373
[27] validation-logloss:0.32358 validation-auc:0.96892 validation-aucpr:0.97389
[28] validation-logloss:0.31850 validation-auc:0.96893 validation-aucpr:0.97385
[29] validation-logloss:0.31382 validation-auc:0.96893 validation-aucpr:0.97384
[30] validation-logloss:0.30812 validation-auc:0.96924 validation-aucpr:0.97408
[31] validation-logloss:0.30291 validation-auc:0.96936 validation-aucpr:0.97418
[32] validation-logloss:0.29794 validation-auc:0.96949 validation-aucpr:0.97428
[33] validation-logloss:0.29327 validation-auc:0.96954 validation-aucpr:0.97432
[34] validation-logloss:0.28948 validation-auc:0.96951 validation-aucpr:0.97431
[35] validation-logloss:0.28507 validation-auc:0.96971 validation-aucpr:0.97449
[36] validation-logloss:0.28154 validation-auc:0.96975 validation-aucpr:0.97454
[37] validation-logloss:0.27824 validation-auc:0.96977 validation-aucpr:0.97453
[38] validation-logloss:0.27507 validation-auc:0.96974 validation-aucpr:0.97451
[39] validation-logloss:0.27112 validation-auc:0.96987 validation-aucpr:0.97463
[40] validation-logloss:0.26738 validation-auc:0.97009 validation-aucpr:0.97481
[41] validation-logloss:0.26399 validation-auc:0.97015 validation-aucpr:0.97483
[42] validation-logloss:0.26129 validation-auc:0.97012 validation-aucpr:0.97480
[43] validation-logloss:0.25790 validation-auc:0.97028 validation-aucpr:0.97494
[44] validation-logloss:0.25550 validation-auc:0.97027 validation-aucpr:0.97492
[45] validation-logloss:0.25343 validation-auc:0.97015 validation-aucpr:0.97481
[46] validation-logloss:0.25110 validation-auc:0.97027 validation-aucpr:0.97490
[47] validation-logloss:0.24838 validation-auc:0.97026 validation-aucpr:0.97487
[48] validation-logloss:0.24595 validation-auc:0.97024 validation-aucpr:0.97487
[49] validation-logloss:0.24405 validation-auc:0.97022 validation-aucpr:0.97485
[50] validation-logloss:0.24209 validation-auc:0.97026 validation-aucpr:0.97487
[51] validation-logloss:0.23972 validation-auc:0.97037 validation-aucpr:0.97499
[52] validation-logloss:0.23755 validation-auc:0.97040 validation-aucpr:0.97501
[53] validation-logloss:0.23605 validation-auc:0.97037 validation-aucpr:0.97497
[54] validation-logloss:0.23395 validation-auc:0.97042 validation-aucpr:0.97502
[55] validation-logloss:0.23193 validation-auc:0.97056 validation-aucpr:0.97513
[56] validation-logloss:0.23063 validation-auc:0.97041 validation-aucpr:0.97505
{'best_iteration': '55', 'best_score': '0.9751346047427522'}
Trial 16, Fold 4: Log loss = 0.23063088965856088, Average precision = 0.9750531428006062, ROC-AUC = 0.9704149141254818, Elapsed Time = 10.488736199999039 seconds
Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 16, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66464 validation-auc:0.95664 validation-aucpr:0.96172
[1] validation-logloss:0.63920 validation-auc:0.96086 validation-aucpr:0.96353
[2] validation-logloss:0.61741 validation-auc:0.96288 validation-aucpr:0.96780
[3] validation-logloss:0.59442 validation-auc:0.96538 validation-aucpr:0.96991
[4] validation-logloss:0.57322 validation-auc:0.96664 validation-aucpr:0.97098
[5] validation-logloss:0.55315 validation-auc:0.96683 validation-aucpr:0.97100
[6] validation-logloss:0.53423 validation-auc:0.96745 validation-aucpr:0.97135
[7] validation-logloss:0.51695 validation-auc:0.96775 validation-aucpr:0.97159
[8] validation-logloss:0.50077 validation-auc:0.96790 validation-aucpr:0.97196
[9] validation-logloss:0.48736 validation-auc:0.96769 validation-aucpr:0.97183
[10] validation-logloss:0.47464 validation-auc:0.96733 validation-aucpr:0.97158
[11] validation-logloss:0.46247 validation-auc:0.96770 validation-aucpr:0.97182
[12] validation-logloss:0.44934 validation-auc:0.96799 validation-aucpr:0.97209
[13] validation-logloss:0.43861 validation-auc:0.96775 validation-aucpr:0.97182
[14] validation-logloss:0.42687 validation-auc:0.96785 validation-aucpr:0.97194
[15] validation-logloss:0.41571 validation-auc:0.96822 validation-aucpr:0.97221
[16] validation-logloss:0.40617 validation-auc:0.96863 validation-aucpr:0.97259
[17] validation-logloss:0.39660 validation-auc:0.96855 validation-aucpr:0.97258
[18] validation-logloss:0.38839 validation-auc:0.96838 validation-aucpr:0.97236
[19] validation-logloss:0.38059 validation-auc:0.96827 validation-aucpr:0.97220
[20] validation-logloss:0.37184 validation-auc:0.96862 validation-aucpr:0.97248
[21] validation-logloss:0.36528 validation-auc:0.96836 validation-aucpr:0.97245
[22] validation-logloss:0.35720 validation-auc:0.96876 validation-aucpr:0.97278
[23] validation-logloss:0.35115 validation-auc:0.96860 validation-aucpr:0.97267
[24] validation-logloss:0.34421 validation-auc:0.96888 validation-aucpr:0.97288
[25] validation-logloss:0.33875 validation-auc:0.96877 validation-aucpr:0.97269
[26] validation-logloss:0.33201 validation-auc:0.96903 validation-aucpr:0.97289
[27] validation-logloss:0.32570 validation-auc:0.96919 validation-aucpr:0.97235
[28] validation-logloss:0.32022 validation-auc:0.96948 validation-aucpr:0.97256
[29] validation-logloss:0.31535 validation-auc:0.96959 validation-aucpr:0.97263
[30] validation-logloss:0.30983 validation-auc:0.96979 validation-aucpr:0.97347
[31] validation-logloss:0.30550 validation-auc:0.96967 validation-aucpr:0.97335
[32] validation-logloss:0.30025 validation-auc:0.96988 validation-aucpr:0.97362
[33] validation-logloss:0.29548 validation-auc:0.96998 validation-aucpr:0.97370
[34] validation-logloss:0.29102 validation-auc:0.97001 validation-aucpr:0.97372
[35] validation-logloss:0.28702 validation-auc:0.96997 validation-aucpr:0.97368
[36] validation-logloss:0.28293 validation-auc:0.96998 validation-aucpr:0.97373
[37] validation-logloss:0.27897 validation-auc:0.97017 validation-aucpr:0.97385
[38] validation-logloss:0.27514 validation-auc:0.97033 validation-aucpr:0.97399
[39] validation-logloss:0.27204 validation-auc:0.97021 validation-aucpr:0.97392
[40] validation-logloss:0.26864 validation-auc:0.97019 validation-aucpr:0.97389
[41] validation-logloss:0.26541 validation-auc:0.97032 validation-aucpr:0.97397
[42] validation-logloss:0.26282 validation-auc:0.97023 validation-aucpr:0.97386
[43] validation-logloss:0.26046 validation-auc:0.97017 validation-aucpr:0.97390
[44] validation-logloss:0.25802 validation-auc:0.97015 validation-aucpr:0.97380
[45] validation-logloss:0.25511 validation-auc:0.97030 validation-aucpr:0.97385
[46] validation-logloss:0.25307 validation-auc:0.97022 validation-aucpr:0.97370
[47] validation-logloss:0.25054 validation-auc:0.97020 validation-aucpr:0.97369
[48] validation-logloss:0.24860 validation-auc:0.97032 validation-aucpr:0.97376
[49] validation-logloss:0.24612 validation-auc:0.97043 validation-aucpr:0.97383
[50] validation-logloss:0.24360 validation-auc:0.97058 validation-aucpr:0.97392
[51] validation-logloss:0.24130 validation-auc:0.97067 validation-aucpr:0.97400
[52] validation-logloss:0.23920 validation-auc:0.97071 validation-aucpr:0.97388
[53] validation-logloss:0.23752 validation-auc:0.97072 validation-aucpr:0.97386
[54] validation-logloss:0.23620 validation-auc:0.97050 validation-aucpr:0.97369
[55] validation-logloss:0.23413 validation-auc:0.97077 validation-aucpr:0.97386
[56] validation-logloss:0.23209 validation-auc:0.97098 validation-aucpr:0.97403
{'best_iteration': '56', 'best_score': '0.9740272707349765'}
Trial 16, Fold 5: Log loss = 0.23209462109948828, Average precision = 0.9740309172331549, ROC-AUC = 0.970983482897646, Elapsed Time = 10.367234399998779 seconds
Optimization Progress: 17%|#7 | 17/100 [33:52<2:53:29, 125.41s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 17, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68070 validation-auc:0.94898 validation-aucpr:0.92672
[1] validation-logloss:0.67016 validation-auc:0.96222 validation-aucpr:0.96208
[2] validation-logloss:0.65893 validation-auc:0.96484 validation-aucpr:0.96820
[3] validation-logloss:0.64785 validation-auc:0.96631 validation-aucpr:0.96911
[4] validation-logloss:0.63708 validation-auc:0.96744 validation-aucpr:0.97010
[5] validation-logloss:0.62658 validation-auc:0.96754 validation-aucpr:0.96975
[6] validation-logloss:0.61637 validation-auc:0.96832 validation-aucpr:0.97309
[7] validation-logloss:0.60679 validation-auc:0.96827 validation-aucpr:0.97310
[8] validation-logloss:0.59797 validation-auc:0.96876 validation-aucpr:0.97353
[9] validation-logloss:0.58967 validation-auc:0.96903 validation-aucpr:0.97361
[10] validation-logloss:0.58167 validation-auc:0.96863 validation-aucpr:0.97336
[11] validation-logloss:0.57302 validation-auc:0.96858 validation-aucpr:0.97330
[12] validation-logloss:0.56516 validation-auc:0.96856 validation-aucpr:0.97329
[13] validation-logloss:0.55678 validation-auc:0.96853 validation-aucpr:0.97329
[14] validation-logloss:0.54872 validation-auc:0.96874 validation-aucpr:0.97356
[15] validation-logloss:0.54084 validation-auc:0.96906 validation-aucpr:0.97380
[16] validation-logloss:0.53407 validation-auc:0.96879 validation-aucpr:0.97383
[17] validation-logloss:0.52748 validation-auc:0.96864 validation-aucpr:0.97363
[18] validation-logloss:0.52008 validation-auc:0.96877 validation-aucpr:0.97371
[19] validation-logloss:0.51293 validation-auc:0.96881 validation-aucpr:0.97380
[20] validation-logloss:0.50591 validation-auc:0.96900 validation-aucpr:0.97396
[21] validation-logloss:0.49994 validation-auc:0.96878 validation-aucpr:0.97374
[22] validation-logloss:0.49338 validation-auc:0.96887 validation-aucpr:0.97380
[23] validation-logloss:0.48702 validation-auc:0.96894 validation-aucpr:0.97389
[24] validation-logloss:0.48086 validation-auc:0.96901 validation-aucpr:0.97392
[25] validation-logloss:0.47488 validation-auc:0.96927 validation-aucpr:0.97420
[26] validation-logloss:0.46956 validation-auc:0.96922 validation-aucpr:0.97409
[27] validation-logloss:0.46372 validation-auc:0.96940 validation-aucpr:0.97424
[28] validation-logloss:0.45803 validation-auc:0.96941 validation-aucpr:0.97423
[29] validation-logloss:0.45247 validation-auc:0.96961 validation-aucpr:0.97440
[30] validation-logloss:0.44764 validation-auc:0.96951 validation-aucpr:0.97427
[31] validation-logloss:0.44230 validation-auc:0.96968 validation-aucpr:0.97441
[32] validation-logloss:0.43704 validation-auc:0.96977 validation-aucpr:0.97450
[33] validation-logloss:0.43202 validation-auc:0.96985 validation-aucpr:0.97456
[34] validation-logloss:0.42703 validation-auc:0.96993 validation-aucpr:0.97464
[35] validation-logloss:0.42260 validation-auc:0.96977 validation-aucpr:0.97447
[36] validation-logloss:0.41841 validation-auc:0.96968 validation-aucpr:0.97443
[37] validation-logloss:0.41366 validation-auc:0.96981 validation-aucpr:0.97453
[38] validation-logloss:0.40921 validation-auc:0.96978 validation-aucpr:0.97454
[39] validation-logloss:0.40466 validation-auc:0.97000 validation-aucpr:0.97469
[40] validation-logloss:0.40024 validation-auc:0.97011 validation-aucpr:0.97478
[41] validation-logloss:0.39648 validation-auc:0.97007 validation-aucpr:0.97473
{'best_iteration': '40', 'best_score': '0.9747813685044032'}
Trial 17, Fold 1: Log loss = 0.39647990137081396, Average precision = 0.9747384831870218, ROC-AUC = 0.9700692413119016, Elapsed Time = 1.3540717999985645 seconds
Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 17, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68110 validation-auc:0.94313 validation-aucpr:0.91708
[1] validation-logloss:0.66937 validation-auc:0.96204 validation-aucpr:0.95203
[2] validation-logloss:0.65910 validation-auc:0.96507 validation-aucpr:0.96812
[3] validation-logloss:0.64823 validation-auc:0.96729 validation-aucpr:0.97087
[4] validation-logloss:0.63878 validation-auc:0.96777 validation-aucpr:0.97130
[5] validation-logloss:0.62843 validation-auc:0.96854 validation-aucpr:0.97194
[6] validation-logloss:0.61821 validation-auc:0.96897 validation-aucpr:0.97236
[7] validation-logloss:0.60838 validation-auc:0.96928 validation-aucpr:0.97272
[8] validation-logloss:0.59990 validation-auc:0.96885 validation-aucpr:0.97233
[9] validation-logloss:0.59051 validation-auc:0.96896 validation-aucpr:0.97245
[10] validation-logloss:0.58142 validation-auc:0.96913 validation-aucpr:0.97257
[11] validation-logloss:0.57258 validation-auc:0.96918 validation-aucpr:0.97277
[12] validation-logloss:0.56387 validation-auc:0.96969 validation-aucpr:0.97312
[13] validation-logloss:0.55546 validation-auc:0.96968 validation-aucpr:0.97313
[14] validation-logloss:0.54733 validation-auc:0.96969 validation-aucpr:0.97310
[15] validation-logloss:0.53945 validation-auc:0.96966 validation-aucpr:0.97309
[16] validation-logloss:0.53180 validation-auc:0.96947 validation-aucpr:0.97296
[17] validation-logloss:0.52440 validation-auc:0.96937 validation-aucpr:0.97287
[18] validation-logloss:0.51788 validation-auc:0.96949 validation-aucpr:0.97296
[19] validation-logloss:0.51148 validation-auc:0.96967 validation-aucpr:0.97322
[20] validation-logloss:0.50458 validation-auc:0.96966 validation-aucpr:0.97322
[21] validation-logloss:0.49870 validation-auc:0.96943 validation-aucpr:0.97305
[22] validation-logloss:0.49200 validation-auc:0.96967 validation-aucpr:0.97324
[23] validation-logloss:0.48550 validation-auc:0.96977 validation-aucpr:0.97331
[24] validation-logloss:0.47928 validation-auc:0.96974 validation-aucpr:0.97327
[25] validation-logloss:0.47337 validation-auc:0.96981 validation-aucpr:0.97331
[26] validation-logloss:0.46733 validation-auc:0.96997 validation-aucpr:0.97342
[27] validation-logloss:0.46157 validation-auc:0.96992 validation-aucpr:0.97337
[28] validation-logloss:0.45655 validation-auc:0.96992 validation-aucpr:0.97334
[29] validation-logloss:0.45179 validation-auc:0.96981 validation-aucpr:0.97318
[30] validation-logloss:0.44636 validation-auc:0.96979 validation-aucpr:0.97318
[31] validation-logloss:0.44101 validation-auc:0.96987 validation-aucpr:0.97325
[32] validation-logloss:0.43584 validation-auc:0.96994 validation-aucpr:0.97328
[33] validation-logloss:0.43143 validation-auc:0.96988 validation-aucpr:0.97324
[34] validation-logloss:0.42635 validation-auc:0.97007 validation-aucpr:0.97337
[35] validation-logloss:0.42213 validation-auc:0.97010 validation-aucpr:0.97337
[36] validation-logloss:0.41756 validation-auc:0.97010 validation-aucpr:0.97338
[37] validation-logloss:0.41344 validation-auc:0.97023 validation-aucpr:0.97350
[38] validation-logloss:0.40897 validation-auc:0.97027 validation-aucpr:0.97351
[39] validation-logloss:0.40460 validation-auc:0.97027 validation-aucpr:0.97352
[40] validation-logloss:0.40021 validation-auc:0.97025 validation-aucpr:0.97353
[41] validation-logloss:0.39626 validation-auc:0.97022 validation-aucpr:0.97344
{'best_iteration': '40', 'best_score': '0.9735317036055212'}
Trial 17, Fold 2: Log loss = 0.3962613455256332, Average precision = 0.9732858310430789, ROC-AUC = 0.9702157034071321, Elapsed Time = 1.8218703000002279 seconds
Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 17, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68088 validation-auc:0.94306 validation-aucpr:0.92744
[1] validation-logloss:0.66888 validation-auc:0.96486 validation-aucpr:0.96352
[2] validation-logloss:0.65766 validation-auc:0.96609 validation-aucpr:0.96783
[3] validation-logloss:0.64664 validation-auc:0.96827 validation-aucpr:0.97263
[4] validation-logloss:0.63599 validation-auc:0.96896 validation-aucpr:0.97321
[5] validation-logloss:0.62545 validation-auc:0.96974 validation-aucpr:0.97372
[6] validation-logloss:0.61527 validation-auc:0.97027 validation-aucpr:0.97416
[7] validation-logloss:0.60568 validation-auc:0.97077 validation-aucpr:0.97456
[8] validation-logloss:0.59627 validation-auc:0.97097 validation-aucpr:0.97504
[9] validation-logloss:0.58727 validation-auc:0.97070 validation-aucpr:0.97513
[10] validation-logloss:0.57901 validation-auc:0.97081 validation-aucpr:0.97512
[11] validation-logloss:0.57003 validation-auc:0.97109 validation-aucpr:0.97536
[12] validation-logloss:0.56155 validation-auc:0.97099 validation-aucpr:0.97527
[13] validation-logloss:0.55330 validation-auc:0.97100 validation-aucpr:0.97527
[14] validation-logloss:0.54592 validation-auc:0.97135 validation-aucpr:0.97552
[15] validation-logloss:0.53882 validation-auc:0.97127 validation-aucpr:0.97536
[16] validation-logloss:0.53199 validation-auc:0.97140 validation-aucpr:0.97535
[17] validation-logloss:0.52456 validation-auc:0.97143 validation-aucpr:0.97535
[18] validation-logloss:0.51724 validation-auc:0.97160 validation-aucpr:0.97546
[19] validation-logloss:0.51002 validation-auc:0.97166 validation-aucpr:0.97553
[20] validation-logloss:0.50296 validation-auc:0.97178 validation-aucpr:0.97564
[21] validation-logloss:0.49611 validation-auc:0.97192 validation-aucpr:0.97576
[22] validation-logloss:0.48946 validation-auc:0.97188 validation-aucpr:0.97570
[23] validation-logloss:0.48352 validation-auc:0.97213 validation-aucpr:0.97602
[24] validation-logloss:0.47729 validation-auc:0.97199 validation-aucpr:0.97592
[25] validation-logloss:0.47103 validation-auc:0.97206 validation-aucpr:0.97600
[26] validation-logloss:0.46580 validation-auc:0.97180 validation-aucpr:0.97570
[27] validation-logloss:0.46000 validation-auc:0.97189 validation-aucpr:0.97576
[28] validation-logloss:0.45497 validation-auc:0.97183 validation-aucpr:0.97574
[29] validation-logloss:0.44959 validation-auc:0.97174 validation-aucpr:0.97565
[30] validation-logloss:0.44409 validation-auc:0.97185 validation-aucpr:0.97574
[31] validation-logloss:0.43876 validation-auc:0.97192 validation-aucpr:0.97580
[32] validation-logloss:0.43422 validation-auc:0.97198 validation-aucpr:0.97591
[33] validation-logloss:0.42923 validation-auc:0.97186 validation-aucpr:0.97582
[34] validation-logloss:0.42490 validation-auc:0.97185 validation-aucpr:0.97577
[35] validation-logloss:0.42002 validation-auc:0.97185 validation-aucpr:0.97580
[36] validation-logloss:0.41534 validation-auc:0.97189 validation-aucpr:0.97585
[37] validation-logloss:0.41061 validation-auc:0.97190 validation-aucpr:0.97586
[38] validation-logloss:0.40599 validation-auc:0.97198 validation-aucpr:0.97594
[39] validation-logloss:0.40156 validation-auc:0.97198 validation-aucpr:0.97592
[40] validation-logloss:0.39710 validation-auc:0.97205 validation-aucpr:0.97598
[41] validation-logloss:0.39307 validation-auc:0.97211 validation-aucpr:0.97601
{'best_iteration': '23', 'best_score': '0.9760153538535357'}
Trial 17, Fold 3: Log loss = 0.3930701404115779, Average precision = 0.9760085592503406, ROC-AUC = 0.9721139399601276, Elapsed Time = 1.6774910000003729 seconds
Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 17, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68092 validation-auc:0.93976 validation-aucpr:0.92820
[1] validation-logloss:0.67073 validation-auc:0.95822 validation-aucpr:0.96040
[2] validation-logloss:0.65918 validation-auc:0.96465 validation-aucpr:0.96808
[3] validation-logloss:0.64802 validation-auc:0.96658 validation-aucpr:0.96915
[4] validation-logloss:0.63726 validation-auc:0.96750 validation-aucpr:0.97281
[5] validation-logloss:0.62668 validation-auc:0.96890 validation-aucpr:0.97381
[6] validation-logloss:0.61663 validation-auc:0.96905 validation-aucpr:0.97402
[7] validation-logloss:0.60789 validation-auc:0.96827 validation-aucpr:0.97340
[8] validation-logloss:0.59942 validation-auc:0.96815 validation-aucpr:0.97325
[9] validation-logloss:0.59114 validation-auc:0.96793 validation-aucpr:0.97318
[10] validation-logloss:0.58205 validation-auc:0.96853 validation-aucpr:0.97369
[11] validation-logloss:0.57315 validation-auc:0.96898 validation-aucpr:0.97402
[12] validation-logloss:0.56464 validation-auc:0.96914 validation-aucpr:0.97417
[13] validation-logloss:0.55623 validation-auc:0.96938 validation-aucpr:0.97436
[14] validation-logloss:0.54800 validation-auc:0.96940 validation-aucpr:0.97441
[15] validation-logloss:0.54018 validation-auc:0.96933 validation-aucpr:0.97439
[16] validation-logloss:0.53338 validation-auc:0.96898 validation-aucpr:0.97412
[17] validation-logloss:0.52569 validation-auc:0.96941 validation-aucpr:0.97445
[18] validation-logloss:0.51920 validation-auc:0.96929 validation-aucpr:0.97431
[19] validation-logloss:0.51193 validation-auc:0.96982 validation-aucpr:0.97471
[20] validation-logloss:0.50543 validation-auc:0.96971 validation-aucpr:0.97461
[21] validation-logloss:0.49858 validation-auc:0.96983 validation-aucpr:0.97468
[22] validation-logloss:0.49190 validation-auc:0.96999 validation-aucpr:0.97483
[23] validation-logloss:0.48616 validation-auc:0.96996 validation-aucpr:0.97480
[24] validation-logloss:0.47977 validation-auc:0.97019 validation-aucpr:0.97498
[25] validation-logloss:0.47382 validation-auc:0.97009 validation-aucpr:0.97493
[26] validation-logloss:0.46781 validation-auc:0.97027 validation-aucpr:0.97507
[27] validation-logloss:0.46200 validation-auc:0.97030 validation-aucpr:0.97507
[28] validation-logloss:0.45656 validation-auc:0.97029 validation-aucpr:0.97505
[29] validation-logloss:0.45101 validation-auc:0.97024 validation-aucpr:0.97501
[30] validation-logloss:0.44545 validation-auc:0.97033 validation-aucpr:0.97508
[31] validation-logloss:0.44058 validation-auc:0.97036 validation-aucpr:0.97509
[32] validation-logloss:0.43539 validation-auc:0.97038 validation-aucpr:0.97510
[33] validation-logloss:0.43028 validation-auc:0.97064 validation-aucpr:0.97533
[34] validation-logloss:0.42604 validation-auc:0.97053 validation-aucpr:0.97524
[35] validation-logloss:0.42182 validation-auc:0.97046 validation-aucpr:0.97517
[36] validation-logloss:0.41776 validation-auc:0.97021 validation-aucpr:0.97499
[37] validation-logloss:0.41362 validation-auc:0.97016 validation-aucpr:0.97495
[38] validation-logloss:0.40902 validation-auc:0.97026 validation-aucpr:0.97504
[39] validation-logloss:0.40513 validation-auc:0.97013 validation-aucpr:0.97494
[40] validation-logloss:0.40081 validation-auc:0.97021 validation-aucpr:0.97498
[41] validation-logloss:0.39658 validation-auc:0.97035 validation-aucpr:0.97509
{'best_iteration': '33', 'best_score': '0.9753345323573815'}
Trial 17, Fold 4: Log loss = 0.3965803346159523, Average precision = 0.9750878709584755, ROC-AUC = 0.9703468839340855, Elapsed Time = 1.805470499999501 seconds
Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 17, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68100 validation-auc:0.93722 validation-aucpr:0.91276
[1] validation-logloss:0.66927 validation-auc:0.96019 validation-aucpr:0.95802
[2] validation-logloss:0.65798 validation-auc:0.96497 validation-aucpr:0.96872
[3] validation-logloss:0.64696 validation-auc:0.96467 validation-aucpr:0.96643
[4] validation-logloss:0.63619 validation-auc:0.96601 validation-aucpr:0.96870
[5] validation-logloss:0.62584 validation-auc:0.96626 validation-aucpr:0.96794
[6] validation-logloss:0.61580 validation-auc:0.96677 validation-aucpr:0.97012
[7] validation-logloss:0.60706 validation-auc:0.96667 validation-aucpr:0.97029
[8] validation-logloss:0.59752 validation-auc:0.96678 validation-aucpr:0.97033
[9] validation-logloss:0.58826 validation-auc:0.96721 validation-aucpr:0.97089
[10] validation-logloss:0.58063 validation-auc:0.96742 validation-aucpr:0.97213
[11] validation-logloss:0.57225 validation-auc:0.96760 validation-aucpr:0.97233
[12] validation-logloss:0.56365 validation-auc:0.96795 validation-aucpr:0.97264
[13] validation-logloss:0.55542 validation-auc:0.96841 validation-aucpr:0.97295
[14] validation-logloss:0.54815 validation-auc:0.96841 validation-aucpr:0.97288
[15] validation-logloss:0.54032 validation-auc:0.96868 validation-aucpr:0.97296
[16] validation-logloss:0.53282 validation-auc:0.96860 validation-aucpr:0.97287
[17] validation-logloss:0.52628 validation-auc:0.96821 validation-aucpr:0.97256
[18] validation-logloss:0.51910 validation-auc:0.96824 validation-aucpr:0.97258
[19] validation-logloss:0.51287 validation-auc:0.96801 validation-aucpr:0.97236
[20] validation-logloss:0.50587 validation-auc:0.96827 validation-aucpr:0.97257
[21] validation-logloss:0.49936 validation-auc:0.96818 validation-aucpr:0.97250
[22] validation-logloss:0.49279 validation-auc:0.96820 validation-aucpr:0.97249
[23] validation-logloss:0.48645 validation-auc:0.96830 validation-aucpr:0.97257
[24] validation-logloss:0.48025 validation-auc:0.96848 validation-aucpr:0.97269
[25] validation-logloss:0.47529 validation-auc:0.96823 validation-aucpr:0.97267
[26] validation-logloss:0.47006 validation-auc:0.96811 validation-aucpr:0.97254
[27] validation-logloss:0.46480 validation-auc:0.96812 validation-aucpr:0.97252
[28] validation-logloss:0.45980 validation-auc:0.96807 validation-aucpr:0.97247
[29] validation-logloss:0.45426 validation-auc:0.96822 validation-aucpr:0.97259
[30] validation-logloss:0.44943 validation-auc:0.96834 validation-aucpr:0.97268
[31] validation-logloss:0.44406 validation-auc:0.96843 validation-aucpr:0.97275
[32] validation-logloss:0.43966 validation-auc:0.96825 validation-aucpr:0.97260
[33] validation-logloss:0.43455 validation-auc:0.96834 validation-aucpr:0.97267
[34] validation-logloss:0.42951 validation-auc:0.96852 validation-aucpr:0.97279
[35] validation-logloss:0.42534 validation-auc:0.96848 validation-aucpr:0.97272
[36] validation-logloss:0.42052 validation-auc:0.96860 validation-aucpr:0.97285
[37] validation-logloss:0.41637 validation-auc:0.96858 validation-aucpr:0.97283
[38] validation-logloss:0.41174 validation-auc:0.96879 validation-aucpr:0.97298
[39] validation-logloss:0.40778 validation-auc:0.96887 validation-aucpr:0.97306
[40] validation-logloss:0.40396 validation-auc:0.96886 validation-aucpr:0.97303
[41] validation-logloss:0.39983 validation-auc:0.96884 validation-aucpr:0.97300
{'best_iteration': '39', 'best_score': '0.9730560035632158'}
Trial 17, Fold 5: Log loss = 0.3998339284718829, Average precision = 0.9729822569312114, ROC-AUC = 0.9688389721866116, Elapsed Time = 1.7565878000004886 seconds
Optimization Progress: 18%|#8 | 18/100 [34:09<2:06:46, 92.76s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 18, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66415 validation-auc:0.90117 validation-aucpr:0.89825
[1] validation-logloss:0.63802 validation-auc:0.91492 validation-aucpr:0.91811
[2] validation-logloss:0.61375 validation-auc:0.92827 validation-aucpr:0.93063
[3] validation-logloss:0.58648 validation-auc:0.94237 validation-aucpr:0.94863
[4] validation-logloss:0.56778 validation-auc:0.94191 validation-aucpr:0.95001
[5] validation-logloss:0.54988 validation-auc:0.94233 validation-aucpr:0.95046
[6] validation-logloss:0.52795 validation-auc:0.94457 validation-aucpr:0.95350
[7] validation-logloss:0.50871 validation-auc:0.94546 validation-aucpr:0.95459
[8] validation-logloss:0.49224 validation-auc:0.94577 validation-aucpr:0.95474
[9] validation-logloss:0.47824 validation-auc:0.94599 validation-aucpr:0.95491
[10] validation-logloss:0.46174 validation-auc:0.94668 validation-aucpr:0.95575
[11] validation-logloss:0.44955 validation-auc:0.94784 validation-aucpr:0.95607
[12] validation-logloss:0.44034 validation-auc:0.94804 validation-aucpr:0.95650
[13] validation-logloss:0.42811 validation-auc:0.94834 validation-aucpr:0.95684
[14] validation-logloss:0.42058 validation-auc:0.94795 validation-aucpr:0.95659
[15] validation-logloss:0.41008 validation-auc:0.94862 validation-aucpr:0.95689
[16] validation-logloss:0.40055 validation-auc:0.95022 validation-aucpr:0.95758
[17] validation-logloss:0.39381 validation-auc:0.95082 validation-aucpr:0.95788
[18] validation-logloss:0.38767 validation-auc:0.95060 validation-aucpr:0.95790
[19] validation-logloss:0.37982 validation-auc:0.95092 validation-aucpr:0.95826
[20] validation-logloss:0.37431 validation-auc:0.95039 validation-aucpr:0.95791
[21] validation-logloss:0.36926 validation-auc:0.94992 validation-aucpr:0.95775
[22] validation-logloss:0.36471 validation-auc:0.94962 validation-aucpr:0.95792
[23] validation-logloss:0.36034 validation-auc:0.94948 validation-aucpr:0.95794
[24] validation-logloss:0.35334 validation-auc:0.95030 validation-aucpr:0.95890
[25] validation-logloss:0.34727 validation-auc:0.95183 validation-aucpr:0.95980
{'best_iteration': '25', 'best_score': '0.9597999490308322'}
Trial 18, Fold 1: Log loss = 0.34726644783133376, Average precision = 0.9578838551170721, ROC-AUC = 0.9518273415207698, Elapsed Time = 1.8354823999998189 seconds
Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 18, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66396 validation-auc:0.90215 validation-aucpr:0.89116
[1] validation-logloss:0.63839 validation-auc:0.91495 validation-aucpr:0.90589
[2] validation-logloss:0.60834 validation-auc:0.94015 validation-aucpr:0.93914
[3] validation-logloss:0.58667 validation-auc:0.93817 validation-aucpr:0.93790
[4] validation-logloss:0.56317 validation-auc:0.94234 validation-aucpr:0.94322
[5] validation-logloss:0.54371 validation-auc:0.94296 validation-aucpr:0.94273
[6] validation-logloss:0.52768 validation-auc:0.94360 validation-aucpr:0.94403
[7] validation-logloss:0.51333 validation-auc:0.94263 validation-aucpr:0.94271
[8] validation-logloss:0.49840 validation-auc:0.94278 validation-aucpr:0.94299
[9] validation-logloss:0.48582 validation-auc:0.94358 validation-aucpr:0.94625
[10] validation-logloss:0.47275 validation-auc:0.94369 validation-aucpr:0.94599
[11] validation-logloss:0.45827 validation-auc:0.94465 validation-aucpr:0.94708
[12] validation-logloss:0.44856 validation-auc:0.94487 validation-aucpr:0.94704
[13] validation-logloss:0.43952 validation-auc:0.94487 validation-aucpr:0.94714
[14] validation-logloss:0.42983 validation-auc:0.94437 validation-aucpr:0.94624
[15] validation-logloss:0.42206 validation-auc:0.94433 validation-aucpr:0.94602
[16] validation-logloss:0.41155 validation-auc:0.94514 validation-aucpr:0.94630
[17] validation-logloss:0.40252 validation-auc:0.94613 validation-aucpr:0.95024
[18] validation-logloss:0.39595 validation-auc:0.94658 validation-aucpr:0.95086
[19] validation-logloss:0.38673 validation-auc:0.94734 validation-aucpr:0.95204
[20] validation-logloss:0.37807 validation-auc:0.94778 validation-aucpr:0.95261
[21] validation-logloss:0.37049 validation-auc:0.94806 validation-aucpr:0.95308
[22] validation-logloss:0.36365 validation-auc:0.94825 validation-aucpr:0.95353
[23] validation-logloss:0.35933 validation-auc:0.94777 validation-aucpr:0.95301
[24] validation-logloss:0.35311 validation-auc:0.94848 validation-aucpr:0.95338
[25] validation-logloss:0.34717 validation-auc:0.94943 validation-aucpr:0.95398
{'best_iteration': '25', 'best_score': '0.9539815995939754'}
Trial 18, Fold 2: Log loss = 0.34717386262269745, Average precision = 0.9518878146326276, ROC-AUC = 0.9494315679704128, Elapsed Time = 2.105443700000251 seconds
Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 18, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66343 validation-auc:0.90742 validation-aucpr:0.90215
[1] validation-logloss:0.63726 validation-auc:0.92023 validation-aucpr:0.91689
[2] validation-logloss:0.61301 validation-auc:0.92445 validation-aucpr:0.92579
[3] validation-logloss:0.58562 validation-auc:0.94587 validation-aucpr:0.95187
[4] validation-logloss:0.56645 validation-auc:0.94688 validation-aucpr:0.95475
[5] validation-logloss:0.54810 validation-auc:0.94739 validation-aucpr:0.95548
[6] validation-logloss:0.52958 validation-auc:0.94960 validation-aucpr:0.95656
[7] validation-logloss:0.51453 validation-auc:0.95045 validation-aucpr:0.95695
[8] validation-logloss:0.49960 validation-auc:0.94924 validation-aucpr:0.95620
[9] validation-logloss:0.48648 validation-auc:0.94946 validation-aucpr:0.95648
[10] validation-logloss:0.47340 validation-auc:0.94926 validation-aucpr:0.95610
[11] validation-logloss:0.45838 validation-auc:0.95025 validation-aucpr:0.95725
[12] validation-logloss:0.44830 validation-auc:0.95108 validation-aucpr:0.95767
[13] validation-logloss:0.43866 validation-auc:0.95080 validation-aucpr:0.95763
[14] validation-logloss:0.42867 validation-auc:0.95312 validation-aucpr:0.95869
[15] validation-logloss:0.41645 validation-auc:0.95364 validation-aucpr:0.95942
[16] validation-logloss:0.40939 validation-auc:0.95233 validation-aucpr:0.95869
[17] validation-logloss:0.40296 validation-auc:0.95232 validation-aucpr:0.95871
[18] validation-logloss:0.39653 validation-auc:0.95205 validation-aucpr:0.95858
[19] validation-logloss:0.38636 validation-auc:0.95208 validation-aucpr:0.95869
[20] validation-logloss:0.37795 validation-auc:0.95269 validation-aucpr:0.95915
[21] validation-logloss:0.37281 validation-auc:0.95225 validation-aucpr:0.95900
[22] validation-logloss:0.36516 validation-auc:0.95242 validation-aucpr:0.95941
[23] validation-logloss:0.35774 validation-auc:0.95275 validation-aucpr:0.95993
[24] validation-logloss:0.35191 validation-auc:0.95430 validation-aucpr:0.96066
[25] validation-logloss:0.34656 validation-auc:0.95432 validation-aucpr:0.96064
{'best_iteration': '24', 'best_score': '0.9606556106886179'}
Trial 18, Fold 3: Log loss = 0.346561476570241, Average precision = 0.9575752170927161, ROC-AUC = 0.9543186464061794, Elapsed Time = 2.0811799000002793 seconds
Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 18, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66376 validation-auc:0.90244 validation-aucpr:0.89434
[1] validation-logloss:0.63805 validation-auc:0.91080 validation-aucpr:0.90586
[2] validation-logloss:0.60809 validation-auc:0.93520 validation-aucpr:0.94036
[3] validation-logloss:0.58689 validation-auc:0.93533 validation-aucpr:0.94122
[4] validation-logloss:0.56326 validation-auc:0.93942 validation-aucpr:0.94666
[5] validation-logloss:0.54330 validation-auc:0.94206 validation-aucpr:0.94668
[6] validation-logloss:0.52710 validation-auc:0.94376 validation-aucpr:0.94962
[7] validation-logloss:0.51238 validation-auc:0.94520 validation-aucpr:0.95045
[8] validation-logloss:0.49729 validation-auc:0.94402 validation-aucpr:0.94893
[9] validation-logloss:0.48228 validation-auc:0.94413 validation-aucpr:0.94771
[10] validation-logloss:0.46944 validation-auc:0.94460 validation-aucpr:0.94825
[11] validation-logloss:0.45466 validation-auc:0.94543 validation-aucpr:0.94882
[12] validation-logloss:0.44470 validation-auc:0.94546 validation-aucpr:0.94878
[13] validation-logloss:0.43541 validation-auc:0.94536 validation-aucpr:0.95016
[14] validation-logloss:0.42562 validation-auc:0.94677 validation-aucpr:0.95065
[15] validation-logloss:0.41795 validation-auc:0.94678 validation-aucpr:0.95062
[16] validation-logloss:0.40750 validation-auc:0.94818 validation-aucpr:0.95163
[17] validation-logloss:0.39838 validation-auc:0.95058 validation-aucpr:0.95601
[18] validation-logloss:0.39167 validation-auc:0.95084 validation-aucpr:0.95647
[19] validation-logloss:0.38258 validation-auc:0.95178 validation-aucpr:0.95771
[20] validation-logloss:0.37439 validation-auc:0.95193 validation-aucpr:0.95781
[21] validation-logloss:0.36659 validation-auc:0.95239 validation-aucpr:0.95863
[22] validation-logloss:0.35969 validation-auc:0.95268 validation-aucpr:0.95894
[23] validation-logloss:0.35511 validation-auc:0.95094 validation-aucpr:0.95747
[24] validation-logloss:0.34843 validation-auc:0.95201 validation-aucpr:0.95902
[25] validation-logloss:0.34473 validation-auc:0.95119 validation-aucpr:0.95824
{'best_iteration': '24', 'best_score': '0.9590165483824703'}
Trial 18, Fold 4: Log loss = 0.34472849653115334, Average precision = 0.9551434753025292, ROC-AUC = 0.9511935908257451, Elapsed Time = 2.024505199999112 seconds
Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 18, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66397 validation-auc:0.90168 validation-aucpr:0.89628
[1] validation-logloss:0.63881 validation-auc:0.90722 validation-aucpr:0.90642
[2] validation-logloss:0.60858 validation-auc:0.93393 validation-aucpr:0.94129
[3] validation-logloss:0.58734 validation-auc:0.93425 validation-aucpr:0.94134
[4] validation-logloss:0.56411 validation-auc:0.93753 validation-aucpr:0.94571
[5] validation-logloss:0.54450 validation-auc:0.94128 validation-aucpr:0.94725
[6] validation-logloss:0.52910 validation-auc:0.94241 validation-aucpr:0.94920
[7] validation-logloss:0.51425 validation-auc:0.94460 validation-aucpr:0.95063
[8] validation-logloss:0.49960 validation-auc:0.94427 validation-aucpr:0.95206
[9] validation-logloss:0.48696 validation-auc:0.94469 validation-aucpr:0.95235
[10] validation-logloss:0.47403 validation-auc:0.94451 validation-aucpr:0.95195
[11] validation-logloss:0.46377 validation-auc:0.94506 validation-aucpr:0.95277
[12] validation-logloss:0.45471 validation-auc:0.94534 validation-aucpr:0.95304
[13] validation-logloss:0.44440 validation-auc:0.94527 validation-aucpr:0.95292
[14] validation-logloss:0.43473 validation-auc:0.94494 validation-aucpr:0.95256
[15] validation-logloss:0.42731 validation-auc:0.94605 validation-aucpr:0.95298
[16] validation-logloss:0.41653 validation-auc:0.94699 validation-aucpr:0.95416
[17] validation-logloss:0.41046 validation-auc:0.94688 validation-aucpr:0.95400
[18] validation-logloss:0.40393 validation-auc:0.94721 validation-aucpr:0.95437
[19] validation-logloss:0.39421 validation-auc:0.94785 validation-aucpr:0.95517
[20] validation-logloss:0.38551 validation-auc:0.94795 validation-aucpr:0.95517
[21] validation-logloss:0.37731 validation-auc:0.94846 validation-aucpr:0.95576
[22] validation-logloss:0.37014 validation-auc:0.94856 validation-aucpr:0.95583
[23] validation-logloss:0.36571 validation-auc:0.94839 validation-aucpr:0.95603
[24] validation-logloss:0.35931 validation-auc:0.94862 validation-aucpr:0.95636
[25] validation-logloss:0.35366 validation-auc:0.94931 validation-aucpr:0.95701
{'best_iteration': '25', 'best_score': '0.9570113734803808'}
Trial 18, Fold 5: Log loss = 0.35366369208012155, Average precision = 0.9551741061597449, ROC-AUC = 0.9493108893967263, Elapsed Time = 2.0203742000012426 seconds
Optimization Progress: 19%|#9 | 19/100 [34:27<1:35:04, 70.43s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 19, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66687 validation-auc:0.95022 validation-aucpr:0.94356
[1] validation-logloss:0.64264 validation-auc:0.96143 validation-aucpr:0.96320
[2] validation-logloss:0.62001 validation-auc:0.96411 validation-aucpr:0.96722
[3] validation-logloss:0.60135 validation-auc:0.96370 validation-aucpr:0.96627
[4] validation-logloss:0.58193 validation-auc:0.96485 validation-aucpr:0.96795
[5] validation-logloss:0.56536 validation-auc:0.96431 validation-aucpr:0.97006
[6] validation-logloss:0.55004 validation-auc:0.96422 validation-aucpr:0.96988
[7] validation-logloss:0.53504 validation-auc:0.96411 validation-aucpr:0.96973
[8] validation-logloss:0.52199 validation-auc:0.96384 validation-aucpr:0.96953
[9] validation-logloss:0.50660 validation-auc:0.96426 validation-aucpr:0.96991
[10] validation-logloss:0.49394 validation-auc:0.96475 validation-aucpr:0.97020
[11] validation-logloss:0.48017 validation-auc:0.96500 validation-aucpr:0.97073
[12] validation-logloss:0.46709 validation-auc:0.96545 validation-aucpr:0.97107
{'best_iteration': '12', 'best_score': '0.9710723987838434'}
Trial 19, Fold 1: Log loss = 0.4670901924666118, Average precision = 0.9709924434484682, ROC-AUC = 0.9654467810661351, Elapsed Time = 0.4070884999982809 seconds
Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 19, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66707 validation-auc:0.95571 validation-aucpr:0.94803
[1] validation-logloss:0.64327 validation-auc:0.96257 validation-aucpr:0.96563
[2] validation-logloss:0.62058 validation-auc:0.96486 validation-aucpr:0.96956
[3] validation-logloss:0.59939 validation-auc:0.96540 validation-aucpr:0.97003
[4] validation-logloss:0.58172 validation-auc:0.96602 validation-aucpr:0.97049
[5] validation-logloss:0.56536 validation-auc:0.96716 validation-aucpr:0.97131
[6] validation-logloss:0.54724 validation-auc:0.96808 validation-aucpr:0.97207
[7] validation-logloss:0.53238 validation-auc:0.96789 validation-aucpr:0.97190
[8] validation-logloss:0.51629 validation-auc:0.96784 validation-aucpr:0.97193
[9] validation-logloss:0.50115 validation-auc:0.96809 validation-aucpr:0.97212
[10] validation-logloss:0.48679 validation-auc:0.96844 validation-aucpr:0.97242
[11] validation-logloss:0.47526 validation-auc:0.96839 validation-aucpr:0.97235
[12] validation-logloss:0.46397 validation-auc:0.96831 validation-aucpr:0.97227
{'best_iteration': '10', 'best_score': '0.9724188374268375'}
Trial 19, Fold 2: Log loss = 0.46397375509965866, Average precision = 0.9720977182252877, ROC-AUC = 0.9683108134561439, Elapsed Time = 0.4283958000014536 seconds
Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 19, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66689 validation-auc:0.95270 validation-aucpr:0.93948
[1] validation-logloss:0.64245 validation-auc:0.96317 validation-aucpr:0.96668
[2] validation-logloss:0.62016 validation-auc:0.96576 validation-aucpr:0.97077
[3] validation-logloss:0.60098 validation-auc:0.96729 validation-aucpr:0.97206
[4] validation-logloss:0.58192 validation-auc:0.96756 validation-aucpr:0.97243
[5] validation-logloss:0.56555 validation-auc:0.96766 validation-aucpr:0.97257
[6] validation-logloss:0.54955 validation-auc:0.96798 validation-aucpr:0.97310
[7] validation-logloss:0.53546 validation-auc:0.96799 validation-aucpr:0.97279
[8] validation-logloss:0.52131 validation-auc:0.96808 validation-aucpr:0.97284
[9] validation-logloss:0.50854 validation-auc:0.96782 validation-aucpr:0.97257
[10] validation-logloss:0.49354 validation-auc:0.96826 validation-aucpr:0.97303
[11] validation-logloss:0.47939 validation-auc:0.96866 validation-aucpr:0.97336
[12] validation-logloss:0.46681 validation-auc:0.96887 validation-aucpr:0.97360
{'best_iteration': '12', 'best_score': '0.9735955391456544'}
Trial 19, Fold 3: Log loss = 0.4668126306623334, Average precision = 0.9733190556635031, ROC-AUC = 0.9688660991177973, Elapsed Time = 0.5241953000004287 seconds
Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 19, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66686 validation-auc:0.95712 validation-aucpr:0.95982
[1] validation-logloss:0.64275 validation-auc:0.96260 validation-aucpr:0.96550
[2] validation-logloss:0.61980 validation-auc:0.96674 validation-aucpr:0.97187
[3] validation-logloss:0.60129 validation-auc:0.96496 validation-aucpr:0.97056
[4] validation-logloss:0.58343 validation-auc:0.96491 validation-aucpr:0.97047
[5] validation-logloss:0.56449 validation-auc:0.96523 validation-aucpr:0.97089
[6] validation-logloss:0.54837 validation-auc:0.96564 validation-aucpr:0.97117
[7] validation-logloss:0.53171 validation-auc:0.96572 validation-aucpr:0.97123
[8] validation-logloss:0.51810 validation-auc:0.96609 validation-aucpr:0.97154
[9] validation-logloss:0.50495 validation-auc:0.96592 validation-aucpr:0.97137
[10] validation-logloss:0.49034 validation-auc:0.96640 validation-aucpr:0.97185
[11] validation-logloss:0.47681 validation-auc:0.96642 validation-aucpr:0.97190
[12] validation-logloss:0.46528 validation-auc:0.96672 validation-aucpr:0.97205
{'best_iteration': '12', 'best_score': '0.9720525847713024'}
Trial 19, Fold 4: Log loss = 0.46527833454358125, Average precision = 0.9719904271170401, ROC-AUC = 0.9667181848203704, Elapsed Time = 0.5389599999998609 seconds
Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 19, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66701 validation-auc:0.95208 validation-aucpr:0.94348
[1] validation-logloss:0.64323 validation-auc:0.96138 validation-aucpr:0.96223
[2] validation-logloss:0.62083 validation-auc:0.96242 validation-aucpr:0.96299
[3] validation-logloss:0.60224 validation-auc:0.96229 validation-aucpr:0.96498
[4] validation-logloss:0.58241 validation-auc:0.96338 validation-aucpr:0.96563
[5] validation-logloss:0.56594 validation-auc:0.96345 validation-aucpr:0.96828
[6] validation-logloss:0.54808 validation-auc:0.96453 validation-aucpr:0.96897
[7] validation-logloss:0.53136 validation-auc:0.96520 validation-aucpr:0.96955
[8] validation-logloss:0.51735 validation-auc:0.96536 validation-aucpr:0.96962
[9] validation-logloss:0.50206 validation-auc:0.96621 validation-aucpr:0.97033
[10] validation-logloss:0.48779 validation-auc:0.96686 validation-aucpr:0.97107
[11] validation-logloss:0.47446 validation-auc:0.96715 validation-aucpr:0.97126
[12] validation-logloss:0.46176 validation-auc:0.96744 validation-aucpr:0.97144
{'best_iteration': '12', 'best_score': '0.9714434035775554'}
Trial 19, Fold 5: Log loss = 0.46175643038908776, Average precision = 0.9713472304358971, ROC-AUC = 0.9674423945153559, Elapsed Time = 0.4952616999999009 seconds
Optimization Progress: 20%|## | 20/100 [34:37<1:09:46, 52.33s/it]
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 20, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66078 validation-auc:0.93642 validation-aucpr:0.93490
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63086 validation-auc:0.94541 validation-aucpr:0.94612
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60533 validation-auc:0.94667 validation-aucpr:0.94864
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57494 validation-auc:0.96064 validation-aucpr:0.96468
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55402 validation-auc:0.95961 validation-aucpr:0.96464
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53391 validation-auc:0.95974 validation-aucpr:0.96486
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51407 validation-auc:0.96001 validation-aucpr:0.96500
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49586 validation-auc:0.96079 validation-aucpr:0.96561
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.48017 validation-auc:0.96084 validation-aucpr:0.96560
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46509 validation-auc:0.96108 validation-aucpr:0.96617
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.45107 validation-auc:0.96107 validation-aucpr:0.96614
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43329 validation-auc:0.96356 validation-aucpr:0.96881
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42068 validation-auc:0.96355 validation-aucpr:0.96875
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.41054 validation-auc:0.96313 validation-aucpr:0.96844
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.40002 validation-auc:0.96317 validation-aucpr:0.96835
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.39087 validation-auc:0.96302 validation-aucpr:0.96811
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.38129 validation-auc:0.96306 validation-aucpr:0.96809
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.37278 validation-auc:0.96305 validation-aucpr:0.96805
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.36113 validation-auc:0.96437 validation-aucpr:0.96946
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.35383 validation-auc:0.96429 validation-aucpr:0.96925
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34689 validation-auc:0.96440 validation-aucpr:0.96929
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.34052 validation-auc:0.96455 validation-aucpr:0.96938
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.33421 validation-auc:0.96457 validation-aucpr:0.96940
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.32819 validation-auc:0.96479 validation-aucpr:0.96961
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.32325 validation-auc:0.96472 validation-aucpr:0.96951
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.31785 validation-auc:0.96483 validation-aucpr:0.96957
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.31041 validation-auc:0.96546 validation-aucpr:0.97028
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.30611 validation-auc:0.96549 validation-aucpr:0.97029
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.30186 validation-auc:0.96545 validation-aucpr:0.97023
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.29810 validation-auc:0.96541 validation-aucpr:0.97024
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.29178 validation-auc:0.96594 validation-aucpr:0.97077
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.28543 validation-auc:0.96636 validation-aucpr:0.97122
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.28206 validation-auc:0.96644 validation-aucpr:0.97130
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.27896 validation-auc:0.96642 validation-aucpr:0.97130
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.27338 validation-auc:0.96681 validation-aucpr:0.97168
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.27077 validation-auc:0.96678 validation-aucpr:0.97166
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.26814 validation-auc:0.96688 validation-aucpr:0.97174
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.26585 validation-auc:0.96692 validation-aucpr:0.97175
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.26370 validation-auc:0.96694 validation-aucpr:0.97174
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.26158 validation-auc:0.96692 validation-aucpr:0.97173
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.25936 validation-auc:0.96701 validation-aucpr:0.97177
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.25734 validation-auc:0.96702 validation-aucpr:0.97172
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.25586 validation-auc:0.96699 validation-aucpr:0.97169
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.25194 validation-auc:0.96735 validation-aucpr:0.97211
[18:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25013 validation-auc:0.96746 validation-aucpr:0.97234
[18:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.24669 validation-auc:0.96770 validation-aucpr:0.97263
[18:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.24553 validation-auc:0.96762 validation-aucpr:0.97256
{'best_iteration': '45', 'best_score': '0.9726338681129421'}
Trial 20, Fold 1: Log loss = 0.24552598115549665, Average precision = 0.9725694283267494, ROC-AUC = 0.9676181335226051, Elapsed Time = 6.571905600001628 seconds
Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 20, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66057 validation-auc:0.93451 validation-aucpr:0.93073
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63085 validation-auc:0.94389 validation-aucpr:0.94285
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60271 validation-auc:0.95107 validation-aucpr:0.95059
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57773 validation-auc:0.95352 validation-aucpr:0.95378
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55508 validation-auc:0.95537 validation-aucpr:0.95697
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53442 validation-auc:0.95650 validation-aucpr:0.95855
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51557 validation-auc:0.95651 validation-aucpr:0.95845
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49883 validation-auc:0.95678 validation-aucpr:0.95940
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47874 validation-auc:0.96128 validation-aucpr:0.96481
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45925 validation-auc:0.96369 validation-aucpr:0.96760
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44154 validation-auc:0.96490 validation-aucpr:0.96880
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.42830 validation-auc:0.96492 validation-aucpr:0.96885
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41591 validation-auc:0.96504 validation-aucpr:0.96889
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40452 validation-auc:0.96509 validation-aucpr:0.96886
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39408 validation-auc:0.96521 validation-aucpr:0.96893
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38451 validation-auc:0.96535 validation-aucpr:0.96893
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37482 validation-auc:0.96550 validation-aucpr:0.96910
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36309 validation-auc:0.96622 validation-aucpr:0.96992
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35479 validation-auc:0.96626 validation-aucpr:0.96988
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34711 validation-auc:0.96620 validation-aucpr:0.96991
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33973 validation-auc:0.96637 validation-aucpr:0.97002
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33404 validation-auc:0.96625 validation-aucpr:0.96975
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32785 validation-auc:0.96626 validation-aucpr:0.96971
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31884 validation-auc:0.96690 validation-aucpr:0.97035
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31320 validation-auc:0.96702 validation-aucpr:0.97050
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30801 validation-auc:0.96707 validation-aucpr:0.97050
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30336 validation-auc:0.96715 validation-aucpr:0.97077
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29657 validation-auc:0.96759 validation-aucpr:0.97115
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29018 validation-auc:0.96785 validation-aucpr:0.97144
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28390 validation-auc:0.96818 validation-aucpr:0.97173
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27846 validation-auc:0.96848 validation-aucpr:0.97198
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27462 validation-auc:0.96870 validation-aucpr:0.97214
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27158 validation-auc:0.96864 validation-aucpr:0.97203
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26840 validation-auc:0.96888 validation-aucpr:0.97220
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26537 validation-auc:0.96899 validation-aucpr:0.97226
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26253 validation-auc:0.96903 validation-aucpr:0.97228
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.26018 validation-auc:0.96891 validation-aucpr:0.97220
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25560 validation-auc:0.96918 validation-aucpr:0.97246
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25108 validation-auc:0.96952 validation-aucpr:0.97278
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24915 validation-auc:0.96949 validation-aucpr:0.97273
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24581 validation-auc:0.96956 validation-aucpr:0.97279
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24229 validation-auc:0.96974 validation-aucpr:0.97296
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23889 validation-auc:0.96996 validation-aucpr:0.97312
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23603 validation-auc:0.97014 validation-aucpr:0.97337
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23436 validation-auc:0.97013 validation-aucpr:0.97335
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23255 validation-auc:0.97022 validation-aucpr:0.97340
[18:33:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23098 validation-auc:0.97024 validation-aucpr:0.97340
{'best_iteration': '45', 'best_score': '0.9734037572532945'}
Trial 20, Fold 2: Log loss = 0.2309817435022436, Average precision = 0.973403500713181, ROC-AUC = 0.9702374792143471, Elapsed Time = 8.763767099999313 seconds
Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 20, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66043 validation-auc:0.93767 validation-aucpr:0.93605
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63148 validation-auc:0.94698 validation-aucpr:0.94963
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.59849 validation-auc:0.96370 validation-aucpr:0.96786
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57263 validation-auc:0.96510 validation-aucpr:0.96889
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55136 validation-auc:0.96347 validation-aucpr:0.96780
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53029 validation-auc:0.96312 validation-aucpr:0.96742
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.50595 validation-auc:0.96597 validation-aucpr:0.97052
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.48783 validation-auc:0.96639 validation-aucpr:0.97070
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47152 validation-auc:0.96622 validation-aucpr:0.97036
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45396 validation-auc:0.96672 validation-aucpr:0.97116
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.43662 validation-auc:0.96727 validation-aucpr:0.97180
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.42007 validation-auc:0.96788 validation-aucpr:0.97239
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.40847 validation-auc:0.96765 validation-aucpr:0.97220
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.39838 validation-auc:0.96727 validation-aucpr:0.97187
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.38811 validation-auc:0.96719 validation-aucpr:0.97180
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.37846 validation-auc:0.96699 validation-aucpr:0.97162
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.36716 validation-auc:0.96707 validation-aucpr:0.97180
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.35929 validation-auc:0.96701 validation-aucpr:0.97173
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.34863 validation-auc:0.96734 validation-aucpr:0.97201
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34035 validation-auc:0.96759 validation-aucpr:0.97219
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33364 validation-auc:0.96764 validation-aucpr:0.97218
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.32737 validation-auc:0.96765 validation-aucpr:0.97230
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.31893 validation-auc:0.96801 validation-aucpr:0.97264
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31297 validation-auc:0.96805 validation-aucpr:0.97266
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.30825 validation-auc:0.96797 validation-aucpr:0.97262
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30167 validation-auc:0.96795 validation-aucpr:0.97262
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29690 validation-auc:0.96803 validation-aucpr:0.97268
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29274 validation-auc:0.96804 validation-aucpr:0.97270
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28832 validation-auc:0.96809 validation-aucpr:0.97272
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28454 validation-auc:0.96806 validation-aucpr:0.97268
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27905 validation-auc:0.96818 validation-aucpr:0.97282
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27555 validation-auc:0.96825 validation-aucpr:0.97283
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27025 validation-auc:0.96855 validation-aucpr:0.97309
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26758 validation-auc:0.96851 validation-aucpr:0.97301
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26318 validation-auc:0.96859 validation-aucpr:0.97307
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25998 validation-auc:0.96870 validation-aucpr:0.97314
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25737 validation-auc:0.96874 validation-aucpr:0.97318
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25359 validation-auc:0.96880 validation-aucpr:0.97327
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25041 validation-auc:0.96878 validation-aucpr:0.97328
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24807 validation-auc:0.96887 validation-aucpr:0.97334
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24425 validation-auc:0.96910 validation-aucpr:0.97353
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24156 validation-auc:0.96916 validation-aucpr:0.97360
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23941 validation-auc:0.96928 validation-aucpr:0.97367
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23749 validation-auc:0.96935 validation-aucpr:0.97370
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23571 validation-auc:0.96933 validation-aucpr:0.97367
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23298 validation-auc:0.96947 validation-aucpr:0.97382
[18:33:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23158 validation-auc:0.96949 validation-aucpr:0.97380
{'best_iteration': '45', 'best_score': '0.9738152691478276'}
Trial 20, Fold 3: Log loss = 0.23158353629015746, Average precision = 0.9738055826676788, ROC-AUC = 0.9694872256358891, Elapsed Time = 7.577756599999702 seconds
Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 20, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66059 validation-auc:0.93175 validation-aucpr:0.93407
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62531 validation-auc:0.95875 validation-aucpr:0.96446
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.59299 validation-auc:0.96314 validation-aucpr:0.96873
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.56817 validation-auc:0.96340 validation-aucpr:0.96892
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.54561 validation-auc:0.96322 validation-aucpr:0.96887
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.52424 validation-auc:0.96346 validation-aucpr:0.96901
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.50568 validation-auc:0.96356 validation-aucpr:0.96921
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.48917 validation-auc:0.96354 validation-aucpr:0.96910
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47320 validation-auc:0.96330 validation-aucpr:0.96888
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45895 validation-auc:0.96316 validation-aucpr:0.96866
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44456 validation-auc:0.96317 validation-aucpr:0.96864
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43174 validation-auc:0.96308 validation-aucpr:0.96849
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42149 validation-auc:0.96246 validation-aucpr:0.96796
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.41152 validation-auc:0.96204 validation-aucpr:0.96767
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.40075 validation-auc:0.96221 validation-aucpr:0.96784
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.39058 validation-auc:0.96221 validation-aucpr:0.96788
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.38161 validation-auc:0.96208 validation-aucpr:0.96775
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36959 validation-auc:0.96324 validation-aucpr:0.96900
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.36153 validation-auc:0.96324 validation-aucpr:0.96899
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.35090 validation-auc:0.96381 validation-aucpr:0.96958
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34386 validation-auc:0.96377 validation-aucpr:0.96956
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33707 validation-auc:0.96389 validation-aucpr:0.96965
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.33066 validation-auc:0.96409 validation-aucpr:0.96982
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.32469 validation-auc:0.96423 validation-aucpr:0.96990
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31629 validation-auc:0.96472 validation-aucpr:0.97041
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30867 validation-auc:0.96517 validation-aucpr:0.97084
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30353 validation-auc:0.96524 validation-aucpr:0.97085
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29901 validation-auc:0.96515 validation-aucpr:0.97078
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29504 validation-auc:0.96524 validation-aucpr:0.97082
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.29110 validation-auc:0.96534 validation-aucpr:0.97088
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28529 validation-auc:0.96578 validation-aucpr:0.97128
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.28177 validation-auc:0.96574 validation-aucpr:0.97123
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27887 validation-auc:0.96558 validation-aucpr:0.97111
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.27570 validation-auc:0.96552 validation-aucpr:0.97107
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.27340 validation-auc:0.96548 validation-aucpr:0.97102
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26824 validation-auc:0.96583 validation-aucpr:0.97134
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.26525 validation-auc:0.96590 validation-aucpr:0.97140
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.26079 validation-auc:0.96616 validation-aucpr:0.97165
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25639 validation-auc:0.96644 validation-aucpr:0.97192
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.25242 validation-auc:0.96668 validation-aucpr:0.97213
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.25020 validation-auc:0.96679 validation-aucpr:0.97220
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24687 validation-auc:0.96694 validation-aucpr:0.97234
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.24522 validation-auc:0.96700 validation-aucpr:0.97237
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.24170 validation-auc:0.96721 validation-aucpr:0.97256
[18:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23974 validation-auc:0.96736 validation-aucpr:0.97267
[18:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23700 validation-auc:0.96738 validation-aucpr:0.97271
[18:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23554 validation-auc:0.96747 validation-aucpr:0.97278
{'best_iteration': '46', 'best_score': '0.9727819744692342'}
Trial 20, Fold 4: Log loss = 0.23554353868065628, Average precision = 0.9727860480148764, ROC-AUC = 0.9674674110793984, Elapsed Time = 6.664979299999686 seconds
Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 20, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66165 validation-auc:0.92788 validation-aucpr:0.92757
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63181 validation-auc:0.94335 validation-aucpr:0.94601
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60371 validation-auc:0.95083 validation-aucpr:0.95442
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57927 validation-auc:0.95115 validation-aucpr:0.95461
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55855 validation-auc:0.95127 validation-aucpr:0.95499
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53778 validation-auc:0.95283 validation-aucpr:0.95735
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51329 validation-auc:0.95969 validation-aucpr:0.96498
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49186 validation-auc:0.96111 validation-aucpr:0.96654
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47589 validation-auc:0.96102 validation-aucpr:0.96644
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45758 validation-auc:0.96195 validation-aucpr:0.96740
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44409 validation-auc:0.96202 validation-aucpr:0.96750
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43087 validation-auc:0.96250 validation-aucpr:0.96791
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41892 validation-auc:0.96251 validation-aucpr:0.96787
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40452 validation-auc:0.96316 validation-aucpr:0.96852
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39440 validation-auc:0.96327 validation-aucpr:0.96863
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38193 validation-auc:0.96360 validation-aucpr:0.96896
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37133 validation-auc:0.96382 validation-aucpr:0.96920
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36336 validation-auc:0.96378 validation-aucpr:0.96918
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35554 validation-auc:0.96403 validation-aucpr:0.96933
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34756 validation-auc:0.96426 validation-aucpr:0.96949
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34072 validation-auc:0.96419 validation-aucpr:0.96946
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33458 validation-auc:0.96436 validation-aucpr:0.96956
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32850 validation-auc:0.96446 validation-aucpr:0.96964
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.32289 validation-auc:0.96463 validation-aucpr:0.96972
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31751 validation-auc:0.96462 validation-aucpr:0.96967
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.31247 validation-auc:0.96470 validation-aucpr:0.96967
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30759 validation-auc:0.96487 validation-aucpr:0.96979
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.30344 validation-auc:0.96486 validation-aucpr:0.96973
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29648 validation-auc:0.96529 validation-aucpr:0.97014
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.29227 validation-auc:0.96542 validation-aucpr:0.97023
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28858 validation-auc:0.96542 validation-aucpr:0.97022
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.28453 validation-auc:0.96559 validation-aucpr:0.97033
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.28165 validation-auc:0.96552 validation-aucpr:0.97025
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.27877 validation-auc:0.96554 validation-aucpr:0.97021
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.27603 validation-auc:0.96544 validation-aucpr:0.97016
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.27345 validation-auc:0.96547 validation-aucpr:0.97021
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.27088 validation-auc:0.96554 validation-aucpr:0.97023
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.26844 validation-auc:0.96558 validation-aucpr:0.97026
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.26578 validation-auc:0.96571 validation-aucpr:0.97035
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.26146 validation-auc:0.96602 validation-aucpr:0.97064
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.25919 validation-auc:0.96609 validation-aucpr:0.97069
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.25703 validation-auc:0.96620 validation-aucpr:0.97075
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.25508 validation-auc:0.96626 validation-aucpr:0.97081
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.25343 validation-auc:0.96620 validation-aucpr:0.97075
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25158 validation-auc:0.96636 validation-aucpr:0.97083
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.24978 validation-auc:0.96649 validation-aucpr:0.97099
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.24816 validation-auc:0.96653 validation-aucpr:0.97102
{'best_iteration': '46', 'best_score': '0.971019754688506'}
Trial 20, Fold 5: Log loss = 0.248159576737433, Average precision = 0.9710249427718667, ROC-AUC = 0.9665340096241384, Elapsed Time = 6.658546999999089 seconds
Optimization Progress: 21%|##1 | 21/100 [35:22<1:05:56, 50.08s/it]
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 21, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68447 validation-auc:0.93569 validation-aucpr:0.89227
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67583 validation-auc:0.96004 validation-aucpr:0.95021
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66753 validation-auc:0.96484 validation-aucpr:0.96664
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65918 validation-auc:0.96775 validation-aucpr:0.97129
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65118 validation-auc:0.96849 validation-aucpr:0.97242
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64333 validation-auc:0.96874 validation-aucpr:0.97260
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63576 validation-auc:0.96896 validation-aucpr:0.97379
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62881 validation-auc:0.96941 validation-aucpr:0.97434
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62216 validation-auc:0.96939 validation-aucpr:0.97411
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61564 validation-auc:0.96902 validation-aucpr:0.97375
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60868 validation-auc:0.96906 validation-aucpr:0.97392
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60235 validation-auc:0.96916 validation-aucpr:0.97391
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59550 validation-auc:0.96952 validation-aucpr:0.97423
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58884 validation-auc:0.96978 validation-aucpr:0.97450
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58253 validation-auc:0.96952 validation-aucpr:0.97434
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57612 validation-auc:0.96983 validation-aucpr:0.97456
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56985 validation-auc:0.96983 validation-aucpr:0.97459
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56425 validation-auc:0.96978 validation-aucpr:0.97452
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55814 validation-auc:0.96992 validation-aucpr:0.97464
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55218 validation-auc:0.97001 validation-aucpr:0.97472
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54643 validation-auc:0.97003 validation-aucpr:0.97392
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54079 validation-auc:0.96997 validation-aucpr:0.97389
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53520 validation-auc:0.97004 validation-aucpr:0.97397
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52974 validation-auc:0.97006 validation-aucpr:0.97399
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52433 validation-auc:0.97013 validation-aucpr:0.97403
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51911 validation-auc:0.97011 validation-aucpr:0.97404
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[26] validation-logloss:0.51389 validation-auc:0.97023 validation-aucpr:0.97413
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.50880 validation-auc:0.97035 validation-aucpr:0.97422
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.50380 validation-auc:0.97037 validation-aucpr:0.97424
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.49944 validation-auc:0.97027 validation-aucpr:0.97411
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.49464 validation-auc:0.97032 validation-aucpr:0.97412
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49041 validation-auc:0.97018 validation-aucpr:0.97397
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.48578 validation-auc:0.97031 validation-aucpr:0.97405
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48130 validation-auc:0.97036 validation-aucpr:0.97411
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.47681 validation-auc:0.97037 validation-aucpr:0.97411
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.47235 validation-auc:0.97051 validation-aucpr:0.97422
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.46809 validation-auc:0.97049 validation-aucpr:0.97422
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.46380 validation-auc:0.97063 validation-aucpr:0.97431
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45963 validation-auc:0.97066 validation-aucpr:0.97433
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.45590 validation-auc:0.97063 validation-aucpr:0.97429
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.45189 validation-auc:0.97077 validation-aucpr:0.97438
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.44832 validation-auc:0.97081 validation-aucpr:0.97437
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.44441 validation-auc:0.97084 validation-aucpr:0.97436
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.44054 validation-auc:0.97106 validation-aucpr:0.97451
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.43721 validation-auc:0.97099 validation-aucpr:0.97444
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.43346 validation-auc:0.97117 validation-aucpr:0.97454
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.42980 validation-auc:0.97119 validation-aucpr:0.97453
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.42626 validation-auc:0.97116 validation-aucpr:0.97444
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.42313 validation-auc:0.97103 validation-aucpr:0.97430
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.41966 validation-auc:0.97098 validation-aucpr:0.97417
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.41624 validation-auc:0.97106 validation-aucpr:0.97426
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.41332 validation-auc:0.97087 validation-aucpr:0.97407
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.41005 validation-auc:0.97081 validation-aucpr:0.97403
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.40678 validation-auc:0.97095 validation-aucpr:0.97419
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.40399 validation-auc:0.97110 validation-aucpr:0.97560
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.40091 validation-auc:0.97108 validation-aucpr:0.97558
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.39777 validation-auc:0.97117 validation-aucpr:0.97564
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.39504 validation-auc:0.97116 validation-aucpr:0.97563
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.39201 validation-auc:0.97124 validation-aucpr:0.97568
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.38909 validation-auc:0.97125 validation-aucpr:0.97569
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.38626 validation-auc:0.97125 validation-aucpr:0.97568
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.38344 validation-auc:0.97123 validation-aucpr:0.97567
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.38094 validation-auc:0.97122 validation-aucpr:0.97562
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.37848 validation-auc:0.97118 validation-aucpr:0.97559
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.37578 validation-auc:0.97120 validation-aucpr:0.97561
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.37306 validation-auc:0.97122 validation-aucpr:0.97562
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.37039 validation-auc:0.97122 validation-aucpr:0.97563
[18:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.36808 validation-auc:0.97121 validation-aucpr:0.97561
[18:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.36556 validation-auc:0.97126 validation-aucpr:0.97568
[18:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.36310 validation-auc:0.97125 validation-aucpr:0.97566
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.36093 validation-auc:0.97119 validation-aucpr:0.97561
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.35874 validation-auc:0.97115 validation-aucpr:0.97559
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.35659 validation-auc:0.97114 validation-aucpr:0.97557
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.35413 validation-auc:0.97124 validation-aucpr:0.97564
[18:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.35180 validation-auc:0.97129 validation-aucpr:0.97567
[18:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.34980 validation-auc:0.97123 validation-aucpr:0.97560
[18:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.34749 validation-auc:0.97127 validation-aucpr:0.97563
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.34521 validation-auc:0.97132 validation-aucpr:0.97568
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.34302 validation-auc:0.97137 validation-aucpr:0.97577
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.34084 validation-auc:0.97142 validation-aucpr:0.97581
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.33860 validation-auc:0.97153 validation-aucpr:0.97589
[18:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.33656 validation-auc:0.97153 validation-aucpr:0.97589
[18:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.33451 validation-auc:0.97151 validation-aucpr:0.97588
{'best_iteration': '81', 'best_score': '0.9758924405030649'}
Trial 21, Fold 1: Log loss = 0.3345103914095373, Average precision = 0.9758799882601042, ROC-AUC = 0.9715121544954426, Elapsed Time = 20.817922200001703 seconds
Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 21, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68394 validation-auc:0.94208 validation-aucpr:0.90150
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67606 validation-auc:0.96278 validation-aucpr:0.95869
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66762 validation-auc:0.96656 validation-aucpr:0.96660
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65946 validation-auc:0.96733 validation-aucpr:0.96997
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65137 validation-auc:0.96827 validation-aucpr:0.96890
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64416 validation-auc:0.96852 validation-aucpr:0.97179
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63640 validation-auc:0.96892 validation-aucpr:0.97223
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62881 validation-auc:0.96970 validation-aucpr:0.97305
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62141 validation-auc:0.96982 validation-aucpr:0.97320
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61492 validation-auc:0.96983 validation-aucpr:0.97316
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60849 validation-auc:0.96991 validation-aucpr:0.97323
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60151 validation-auc:0.97008 validation-aucpr:0.97344
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59474 validation-auc:0.97022 validation-aucpr:0.97362
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58863 validation-auc:0.97044 validation-aucpr:0.97388
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58269 validation-auc:0.97054 validation-aucpr:0.97384
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57633 validation-auc:0.97070 validation-aucpr:0.97401
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.57004 validation-auc:0.97084 validation-aucpr:0.97414
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56461 validation-auc:0.97048 validation-aucpr:0.97384
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55849 validation-auc:0.97070 validation-aucpr:0.97402
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55252 validation-auc:0.97074 validation-aucpr:0.97405
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54668 validation-auc:0.97077 validation-aucpr:0.97401
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54098 validation-auc:0.97078 validation-aucpr:0.97400
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53592 validation-auc:0.97083 validation-aucpr:0.97405
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.53042 validation-auc:0.97095 validation-aucpr:0.97416
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52549 validation-auc:0.97092 validation-aucpr:0.97417
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.52009 validation-auc:0.97104 validation-aucpr:0.97424
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.51538 validation-auc:0.97117 validation-aucpr:0.97431
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.51025 validation-auc:0.97115 validation-aucpr:0.97427
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.50525 validation-auc:0.97110 validation-aucpr:0.97424
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.50021 validation-auc:0.97111 validation-aucpr:0.97429
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.49532 validation-auc:0.97131 validation-aucpr:0.97445
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49062 validation-auc:0.97137 validation-aucpr:0.97449
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.48591 validation-auc:0.97155 validation-aucpr:0.97461
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48126 validation-auc:0.97171 validation-aucpr:0.97474
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.47681 validation-auc:0.97171 validation-aucpr:0.97468
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.47233 validation-auc:0.97175 validation-aucpr:0.97472
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.46848 validation-auc:0.97165 validation-aucpr:0.97463
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.46416 validation-auc:0.97188 validation-aucpr:0.97480
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45987 validation-auc:0.97187 validation-aucpr:0.97481
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.45577 validation-auc:0.97186 validation-aucpr:0.97484
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.45162 validation-auc:0.97200 validation-aucpr:0.97494
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.44806 validation-auc:0.97199 validation-aucpr:0.97496
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.44410 validation-auc:0.97212 validation-aucpr:0.97501
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.44033 validation-auc:0.97214 validation-aucpr:0.97498
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.43662 validation-auc:0.97213 validation-aucpr:0.97497
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.43288 validation-auc:0.97216 validation-aucpr:0.97500
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.42916 validation-auc:0.97227 validation-aucpr:0.97509
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.42550 validation-auc:0.97233 validation-aucpr:0.97514
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.42187 validation-auc:0.97243 validation-aucpr:0.97520
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.41841 validation-auc:0.97234 validation-aucpr:0.97514
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.41496 validation-auc:0.97239 validation-aucpr:0.97516
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.41202 validation-auc:0.97236 validation-aucpr:0.97513
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.40883 validation-auc:0.97234 validation-aucpr:0.97511
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.40555 validation-auc:0.97237 validation-aucpr:0.97512
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.40232 validation-auc:0.97238 validation-aucpr:0.97512
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.39918 validation-auc:0.97233 validation-aucpr:0.97509
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.39605 validation-auc:0.97240 validation-aucpr:0.97514
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.39331 validation-auc:0.97237 validation-aucpr:0.97512
[18:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.39032 validation-auc:0.97236 validation-aucpr:0.97510
[18:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.38765 validation-auc:0.97228 validation-aucpr:0.97509
[18:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.38461 validation-auc:0.97232 validation-aucpr:0.97512
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.38171 validation-auc:0.97238 validation-aucpr:0.97518
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.37916 validation-auc:0.97260 validation-aucpr:0.97529
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.37660 validation-auc:0.97261 validation-aucpr:0.97530
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.37384 validation-auc:0.97260 validation-aucpr:0.97530
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.37116 validation-auc:0.97254 validation-aucpr:0.97525
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.36843 validation-auc:0.97259 validation-aucpr:0.97529
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.36576 validation-auc:0.97265 validation-aucpr:0.97533
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.36308 validation-auc:0.97270 validation-aucpr:0.97538
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.36044 validation-auc:0.97271 validation-aucpr:0.97539
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.35792 validation-auc:0.97277 validation-aucpr:0.97540
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.35550 validation-auc:0.97279 validation-aucpr:0.97540
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.35310 validation-auc:0.97274 validation-aucpr:0.97536
[18:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.35071 validation-auc:0.97271 validation-aucpr:0.97533
[18:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.34860 validation-auc:0.97271 validation-aucpr:0.97533
[18:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.34624 validation-auc:0.97275 validation-aucpr:0.97537
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.34393 validation-auc:0.97272 validation-aucpr:0.97536
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.34165 validation-auc:0.97278 validation-aucpr:0.97538
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.33966 validation-auc:0.97277 validation-aucpr:0.97538
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.33742 validation-auc:0.97282 validation-aucpr:0.97542
[18:34:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.33518 validation-auc:0.97288 validation-aucpr:0.97547
[18:34:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.33304 validation-auc:0.97289 validation-aucpr:0.97549
[18:34:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.33097 validation-auc:0.97285 validation-aucpr:0.97546
{'best_iteration': '81', 'best_score': '0.9754875814965706'}
Trial 21, Fold 2: Log loss = 0.33096566923356896, Average precision = 0.9754165941955653, ROC-AUC = 0.9728503484584398, Elapsed Time = 20.76533600000039 seconds
Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 21, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68430 validation-auc:0.94102 validation-aucpr:0.90218
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67563 validation-auc:0.96315 validation-aucpr:0.95918
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66714 validation-auc:0.96681 validation-aucpr:0.97083
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65887 validation-auc:0.96832 validation-aucpr:0.97210
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65096 validation-auc:0.96840 validation-aucpr:0.97242
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64380 validation-auc:0.96784 validation-aucpr:0.97210
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63613 validation-auc:0.96834 validation-aucpr:0.97243
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62850 validation-auc:0.96869 validation-aucpr:0.97160
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62184 validation-auc:0.96856 validation-aucpr:0.97116
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61458 validation-auc:0.96856 validation-aucpr:0.97116
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60746 validation-auc:0.96889 validation-aucpr:0.97046
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60071 validation-auc:0.96879 validation-aucpr:0.97022
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59390 validation-auc:0.96895 validation-aucpr:0.97031
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58719 validation-auc:0.96929 validation-aucpr:0.97153
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58066 validation-auc:0.96955 validation-aucpr:0.97105
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57427 validation-auc:0.96955 validation-aucpr:0.97112
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56804 validation-auc:0.96953 validation-aucpr:0.97139
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56194 validation-auc:0.96952 validation-aucpr:0.97144
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55637 validation-auc:0.96974 validation-aucpr:0.97158
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55046 validation-auc:0.96977 validation-aucpr:0.97160
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54453 validation-auc:0.97014 validation-aucpr:0.97054
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.53874 validation-auc:0.97037 validation-aucpr:0.97073
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53317 validation-auc:0.97074 validation-aucpr:0.97212
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52771 validation-auc:0.97069 validation-aucpr:0.97211
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52231 validation-auc:0.97083 validation-aucpr:0.97221
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51742 validation-auc:0.97108 validation-aucpr:0.97324
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.51269 validation-auc:0.97112 validation-aucpr:0.97326
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.50772 validation-auc:0.97110 validation-aucpr:0.97326
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.50273 validation-auc:0.97116 validation-aucpr:0.97328
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.49823 validation-auc:0.97117 validation-aucpr:0.97329
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.49333 validation-auc:0.97126 validation-aucpr:0.97336
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.48849 validation-auc:0.97146 validation-aucpr:0.97351
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.48392 validation-auc:0.97127 validation-aucpr:0.97461
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.47930 validation-auc:0.97130 validation-aucpr:0.97463
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.47483 validation-auc:0.97155 validation-aucpr:0.97474
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.47039 validation-auc:0.97163 validation-aucpr:0.97480
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.46603 validation-auc:0.97178 validation-aucpr:0.97489
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.46183 validation-auc:0.97176 validation-aucpr:0.97488
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45805 validation-auc:0.97168 validation-aucpr:0.97487
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.45388 validation-auc:0.97169 validation-aucpr:0.97487
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.44983 validation-auc:0.97177 validation-aucpr:0.97493
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.44592 validation-auc:0.97171 validation-aucpr:0.97477
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.44212 validation-auc:0.97169 validation-aucpr:0.97474
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.43839 validation-auc:0.97181 validation-aucpr:0.97415
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.43465 validation-auc:0.97174 validation-aucpr:0.97411
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.43133 validation-auc:0.97174 validation-aucpr:0.97410
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.42770 validation-auc:0.97175 validation-aucpr:0.97409
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.42413 validation-auc:0.97181 validation-aucpr:0.97415
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.42054 validation-auc:0.97185 validation-aucpr:0.97418
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.41732 validation-auc:0.97190 validation-aucpr:0.97420
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.41397 validation-auc:0.97192 validation-aucpr:0.97420
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.41057 validation-auc:0.97202 validation-aucpr:0.97426
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.40757 validation-auc:0.97228 validation-aucpr:0.97606
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.40459 validation-auc:0.97231 validation-aucpr:0.97608
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.40134 validation-auc:0.97233 validation-aucpr:0.97610
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.39842 validation-auc:0.97240 validation-aucpr:0.97616
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.39537 validation-auc:0.97235 validation-aucpr:0.97613
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.39235 validation-auc:0.97234 validation-aucpr:0.97612
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.38932 validation-auc:0.97237 validation-aucpr:0.97615
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.38632 validation-auc:0.97242 validation-aucpr:0.97618
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.38338 validation-auc:0.97246 validation-aucpr:0.97621
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.38080 validation-auc:0.97244 validation-aucpr:0.97620
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.37805 validation-auc:0.97234 validation-aucpr:0.97611
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.37553 validation-auc:0.97243 validation-aucpr:0.97617
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.37273 validation-auc:0.97247 validation-aucpr:0.97620
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.36993 validation-auc:0.97255 validation-aucpr:0.97627
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.36762 validation-auc:0.97258 validation-aucpr:0.97636
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.36521 validation-auc:0.97261 validation-aucpr:0.97640
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.36268 validation-auc:0.97254 validation-aucpr:0.97636
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.36022 validation-auc:0.97257 validation-aucpr:0.97637
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.35800 validation-auc:0.97254 validation-aucpr:0.97633
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.35559 validation-auc:0.97249 validation-aucpr:0.97630
[18:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.35345 validation-auc:0.97248 validation-aucpr:0.97631
[18:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.35130 validation-auc:0.97249 validation-aucpr:0.97630
[18:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.34893 validation-auc:0.97254 validation-aucpr:0.97633
[18:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.34663 validation-auc:0.97254 validation-aucpr:0.97633
[18:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.34437 validation-auc:0.97258 validation-aucpr:0.97645
[18:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.34242 validation-auc:0.97250 validation-aucpr:0.97637
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.34014 validation-auc:0.97250 validation-aucpr:0.97637
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.33798 validation-auc:0.97248 validation-aucpr:0.97636
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.33602 validation-auc:0.97250 validation-aucpr:0.97638
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.33396 validation-auc:0.97248 validation-aucpr:0.97635
[18:35:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.33180 validation-auc:0.97250 validation-aucpr:0.97637
{'best_iteration': '76', 'best_score': '0.9764509129277438'}
Trial 21, Fold 3: Log loss = 0.33180345818438056, Average precision = 0.976378138082869, ROC-AUC = 0.9724998585441726, Elapsed Time = 20.840254299999287 seconds
Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 21, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68401 validation-auc:0.94302 validation-aucpr:0.91190
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67538 validation-auc:0.96397 validation-aucpr:0.97035
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66763 validation-auc:0.96476 validation-aucpr:0.96867
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65936 validation-auc:0.96725 validation-aucpr:0.97103
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65188 validation-auc:0.96816 validation-aucpr:0.97317
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64394 validation-auc:0.96913 validation-aucpr:0.97399
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63623 validation-auc:0.96976 validation-aucpr:0.97445
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62873 validation-auc:0.97004 validation-aucpr:0.97469
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62138 validation-auc:0.97019 validation-aucpr:0.97478
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61477 validation-auc:0.97024 validation-aucpr:0.97478
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60773 validation-auc:0.97016 validation-aucpr:0.97478
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60144 validation-auc:0.96995 validation-aucpr:0.97459
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59474 validation-auc:0.97002 validation-aucpr:0.97464
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58800 validation-auc:0.97028 validation-aucpr:0.97489
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58163 validation-auc:0.97026 validation-aucpr:0.97492
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57525 validation-auc:0.97031 validation-aucpr:0.97499
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56893 validation-auc:0.97055 validation-aucpr:0.97517
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56329 validation-auc:0.97047 validation-aucpr:0.97504
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55719 validation-auc:0.97069 validation-aucpr:0.97525
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55137 validation-auc:0.97063 validation-aucpr:0.97523
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54614 validation-auc:0.97055 validation-aucpr:0.97517
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54051 validation-auc:0.97048 validation-aucpr:0.97513
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53494 validation-auc:0.97065 validation-aucpr:0.97524
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52948 validation-auc:0.97067 validation-aucpr:0.97525
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52458 validation-auc:0.97065 validation-aucpr:0.97525
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51921 validation-auc:0.97093 validation-aucpr:0.97545
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.51449 validation-auc:0.97098 validation-aucpr:0.97547
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.50941 validation-auc:0.97097 validation-aucpr:0.97549
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.50491 validation-auc:0.97082 validation-aucpr:0.97536
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.50007 validation-auc:0.97078 validation-aucpr:0.97536
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.49537 validation-auc:0.97065 validation-aucpr:0.97526
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49069 validation-auc:0.97064 validation-aucpr:0.97527
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.48613 validation-auc:0.97056 validation-aucpr:0.97523
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48142 validation-auc:0.97080 validation-aucpr:0.97542
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.47684 validation-auc:0.97095 validation-aucpr:0.97554
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.47241 validation-auc:0.97100 validation-aucpr:0.97558
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.46807 validation-auc:0.97109 validation-aucpr:0.97565
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.46389 validation-auc:0.97108 validation-aucpr:0.97562
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.46010 validation-auc:0.97102 validation-aucpr:0.97557
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.45598 validation-auc:0.97109 validation-aucpr:0.97563
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.45198 validation-auc:0.97100 validation-aucpr:0.97559
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.44804 validation-auc:0.97092 validation-aucpr:0.97553
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.44415 validation-auc:0.97091 validation-aucpr:0.97552
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.44026 validation-auc:0.97101 validation-aucpr:0.97559
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.43692 validation-auc:0.97092 validation-aucpr:0.97553
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.43322 validation-auc:0.97094 validation-aucpr:0.97557
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.42954 validation-auc:0.97103 validation-aucpr:0.97564
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.42597 validation-auc:0.97112 validation-aucpr:0.97569
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.42274 validation-auc:0.97117 validation-aucpr:0.97572
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.41921 validation-auc:0.97116 validation-aucpr:0.97573
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.41577 validation-auc:0.97119 validation-aucpr:0.97578
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.41243 validation-auc:0.97125 validation-aucpr:0.97583
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.40941 validation-auc:0.97120 validation-aucpr:0.97578
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.40643 validation-auc:0.97129 validation-aucpr:0.97584
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.40325 validation-auc:0.97121 validation-aucpr:0.97580
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.40018 validation-auc:0.97122 validation-aucpr:0.97581
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.39746 validation-auc:0.97106 validation-aucpr:0.97570
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.39470 validation-auc:0.97108 validation-aucpr:0.97571
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.39168 validation-auc:0.97114 validation-aucpr:0.97574
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.38911 validation-auc:0.97105 validation-aucpr:0.97569
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.38625 validation-auc:0.97108 validation-aucpr:0.97572
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.38333 validation-auc:0.97109 validation-aucpr:0.97574
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.38052 validation-auc:0.97110 validation-aucpr:0.97576
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.37775 validation-auc:0.97111 validation-aucpr:0.97576
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.37492 validation-auc:0.97116 validation-aucpr:0.97579
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.37221 validation-auc:0.97123 validation-aucpr:0.97585
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.36953 validation-auc:0.97124 validation-aucpr:0.97585
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.36694 validation-auc:0.97127 validation-aucpr:0.97588
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.36434 validation-auc:0.97133 validation-aucpr:0.97593
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.36181 validation-auc:0.97140 validation-aucpr:0.97599
[18:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.35930 validation-auc:0.97140 validation-aucpr:0.97598
[18:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.35676 validation-auc:0.97146 validation-aucpr:0.97602
[18:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.35438 validation-auc:0.97140 validation-aucpr:0.97598
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.35235 validation-auc:0.97130 validation-aucpr:0.97591
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.35002 validation-auc:0.97129 validation-aucpr:0.97592
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.34775 validation-auc:0.97130 validation-aucpr:0.97592
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.34539 validation-auc:0.97141 validation-aucpr:0.97601
[18:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.34339 validation-auc:0.97138 validation-aucpr:0.97598
[18:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.34112 validation-auc:0.97142 validation-aucpr:0.97602
[18:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.33922 validation-auc:0.97139 validation-aucpr:0.97600
[18:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.33704 validation-auc:0.97142 validation-aucpr:0.97604
[18:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.33512 validation-auc:0.97138 validation-aucpr:0.97600
[18:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.33327 validation-auc:0.97135 validation-aucpr:0.97596
{'best_iteration': '80', 'best_score': '0.976037216196178'}
Trial 21, Fold 4: Log loss = 0.33326755809500186, Average precision = 0.9759524422770561, ROC-AUC = 0.9713480379884164, Elapsed Time = 20.94706589999987 seconds
Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 21, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68434 validation-auc:0.93097 validation-aucpr:0.87197
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67656 validation-auc:0.95917 validation-aucpr:0.95191
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66816 validation-auc:0.96334 validation-aucpr:0.96412
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66002 validation-auc:0.96502 validation-aucpr:0.97028
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65268 validation-auc:0.96527 validation-aucpr:0.97029
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64495 validation-auc:0.96578 validation-aucpr:0.97074
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63785 validation-auc:0.96613 validation-aucpr:0.97096
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63045 validation-auc:0.96691 validation-aucpr:0.97153
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62302 validation-auc:0.96766 validation-aucpr:0.97203
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61574 validation-auc:0.96799 validation-aucpr:0.97229
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60869 validation-auc:0.96824 validation-aucpr:0.97244
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60172 validation-auc:0.96851 validation-aucpr:0.97266
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.59497 validation-auc:0.96852 validation-aucpr:0.97276
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58847 validation-auc:0.96822 validation-aucpr:0.97264
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.58202 validation-auc:0.96847 validation-aucpr:0.97283
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.57573 validation-auc:0.96874 validation-aucpr:0.97309
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56960 validation-auc:0.96891 validation-aucpr:0.97318
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.56358 validation-auc:0.96899 validation-aucpr:0.97328
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55769 validation-auc:0.96880 validation-aucpr:0.97310
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.55193 validation-auc:0.96892 validation-aucpr:0.97317
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.54612 validation-auc:0.96895 validation-aucpr:0.97320
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.54043 validation-auc:0.96923 validation-aucpr:0.97339
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.53557 validation-auc:0.96918 validation-aucpr:0.97326
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.53008 validation-auc:0.96932 validation-aucpr:0.97337
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.52523 validation-auc:0.96923 validation-aucpr:0.97331
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.52036 validation-auc:0.96928 validation-aucpr:0.97332
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.51517 validation-auc:0.96940 validation-aucpr:0.97344
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.51029 validation-auc:0.96944 validation-aucpr:0.97350
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.50529 validation-auc:0.96955 validation-aucpr:0.97359
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.50040 validation-auc:0.96962 validation-aucpr:0.97358
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.49574 validation-auc:0.96958 validation-aucpr:0.97360
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.49100 validation-auc:0.96961 validation-aucpr:0.97361
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.48692 validation-auc:0.96962 validation-aucpr:0.97364
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.48234 validation-auc:0.96970 validation-aucpr:0.97370
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.47788 validation-auc:0.96973 validation-aucpr:0.97374
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.47356 validation-auc:0.96974 validation-aucpr:0.97368
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.46928 validation-auc:0.96973 validation-aucpr:0.97362
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.46503 validation-auc:0.96981 validation-aucpr:0.97373
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.46093 validation-auc:0.96991 validation-aucpr:0.97376
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.45739 validation-auc:0.96983 validation-aucpr:0.97370
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.45344 validation-auc:0.96972 validation-aucpr:0.97366
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.44946 validation-auc:0.96980 validation-aucpr:0.97373
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.44564 validation-auc:0.96980 validation-aucpr:0.97374
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.44177 validation-auc:0.96992 validation-aucpr:0.97383
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.43836 validation-auc:0.96992 validation-aucpr:0.97382
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.43469 validation-auc:0.96993 validation-aucpr:0.97383
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.43142 validation-auc:0.96994 validation-aucpr:0.97383
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.42792 validation-auc:0.97010 validation-aucpr:0.97388
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.42446 validation-auc:0.97005 validation-aucpr:0.97386
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.42105 validation-auc:0.97003 validation-aucpr:0.97386
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.41782 validation-auc:0.96991 validation-aucpr:0.97378
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.41486 validation-auc:0.96999 validation-aucpr:0.97382
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.41170 validation-auc:0.96997 validation-aucpr:0.97380
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.40846 validation-auc:0.97005 validation-aucpr:0.97384
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.40520 validation-auc:0.97017 validation-aucpr:0.97396
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.40202 validation-auc:0.97030 validation-aucpr:0.97406
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.39904 validation-auc:0.97031 validation-aucpr:0.97406
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.39636 validation-auc:0.97025 validation-aucpr:0.97398
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.39333 validation-auc:0.97029 validation-aucpr:0.97401
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.39036 validation-auc:0.97039 validation-aucpr:0.97409
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.38775 validation-auc:0.97028 validation-aucpr:0.97274
[18:35:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.38493 validation-auc:0.97023 validation-aucpr:0.97274
[18:35:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.38216 validation-auc:0.97027 validation-aucpr:0.97277
[18:35:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.37940 validation-auc:0.97025 validation-aucpr:0.97276
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.37695 validation-auc:0.97023 validation-aucpr:0.97266
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.37424 validation-auc:0.97030 validation-aucpr:0.97267
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.37192 validation-auc:0.97034 validation-aucpr:0.97270
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.36929 validation-auc:0.97043 validation-aucpr:0.97274
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.36670 validation-auc:0.97048 validation-aucpr:0.97280
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.36455 validation-auc:0.97044 validation-aucpr:0.97296
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.36233 validation-auc:0.97038 validation-aucpr:0.97290
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.35991 validation-auc:0.97044 validation-aucpr:0.97295
[18:36:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.35777 validation-auc:0.97038 validation-aucpr:0.97290
[18:36:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.35537 validation-auc:0.97045 validation-aucpr:0.97295
[18:36:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.35309 validation-auc:0.97051 validation-aucpr:0.97304
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.35070 validation-auc:0.97061 validation-aucpr:0.97315
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.34845 validation-auc:0.97058 validation-aucpr:0.97313
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.34644 validation-auc:0.97058 validation-aucpr:0.97312
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.34419 validation-auc:0.97066 validation-aucpr:0.97317
[18:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.34202 validation-auc:0.97070 validation-aucpr:0.97318
[18:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.34013 validation-auc:0.97066 validation-aucpr:0.97295
[18:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.33804 validation-auc:0.97068 validation-aucpr:0.97309
[18:36:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.33600 validation-auc:0.97074 validation-aucpr:0.97312
{'best_iteration': '59', 'best_score': '0.9740903400979094'}
Trial 21, Fold 5: Log loss = 0.33599993079141377, Average precision = 0.974024744362116, ROC-AUC = 0.9707432881853483, Elapsed Time = 22.729992100001255 seconds
Optimization Progress: 22%|##2 | 22/100 [37:17<1:30:15, 69.43s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 22, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66534 validation-auc:0.82859 validation-aucpr:0.81740
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63069 validation-auc:0.84888 validation-aucpr:0.82950
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61584 validation-auc:0.86316 validation-aucpr:0.85073
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61219 validation-auc:0.85958 validation-aucpr:0.85603
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.61037 validation-auc:0.85655 validation-aucpr:0.84991
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60284 validation-auc:0.85753 validation-aucpr:0.83697
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59989 validation-auc:0.84902 validation-aucpr:0.83168
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58183 validation-auc:0.85358 validation-aucpr:0.84506
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58032 validation-auc:0.84760 validation-aucpr:0.83470
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.55682 validation-auc:0.86457 validation-aucpr:0.85791
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.53023 validation-auc:0.90063 validation-aucpr:0.90279
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.51509 validation-auc:0.91001 validation-aucpr:0.91155
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.51445 validation-auc:0.90667 validation-aucpr:0.90656
{'best_iteration': '11', 'best_score': '0.9115515191694321'}
Trial 22, Fold 1: Log loss = 0.5144471949959329, Average precision = 0.9039888838431559, ROC-AUC = 0.9066697076803218, Elapsed Time = 0.4393142999997508 seconds
Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 22, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66534 validation-auc:0.82738 validation-aucpr:0.82150
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65950 validation-auc:0.82413 validation-aucpr:0.82233
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64338 validation-auc:0.82202 validation-aucpr:0.78106
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64025 validation-auc:0.80404 validation-aucpr:0.75031
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.61070 validation-auc:0.84332 validation-aucpr:0.79836
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60712 validation-auc:0.83594 validation-aucpr:0.79079
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60595 validation-auc:0.82719 validation-aucpr:0.77069
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.60434 validation-auc:0.82479 validation-aucpr:0.76080
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60269 validation-auc:0.82752 validation-aucpr:0.76604
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.60190 validation-auc:0.82646 validation-aucpr:0.76179
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.59018 validation-auc:0.83292 validation-aucpr:0.76908
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55934 validation-auc:0.86031 validation-aucpr:0.82476
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.55321 validation-auc:0.85851 validation-aucpr:0.82720
{'best_iteration': '12', 'best_score': '0.82720031891643'}
Trial 22, Fold 2: Log loss = 0.553212866445822, Average precision = 0.8235345231859007, ROC-AUC = 0.858507178125496, Elapsed Time = 0.4681982000001881 seconds
Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 22, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66482 validation-auc:0.82983 validation-aucpr:0.81626
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66482 validation-auc:0.82983 validation-aucpr:0.81626
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65871 validation-auc:0.81927 validation-aucpr:0.80524
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65577 validation-auc:0.79766 validation-aucpr:0.73470
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65339 validation-auc:0.79757 validation-aucpr:0.73619
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64140 validation-auc:0.79833 validation-aucpr:0.73805
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64140 validation-auc:0.79833 validation-aucpr:0.73805
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63474 validation-auc:0.80063 validation-aucpr:0.75471
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62018 validation-auc:0.81373 validation-aucpr:0.76437
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.60200 validation-auc:0.84022 validation-aucpr:0.79799
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.57422 validation-auc:0.86227 validation-aucpr:0.83985
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55015 validation-auc:0.87818 validation-aucpr:0.87099
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54954 validation-auc:0.87116 validation-aucpr:0.85570
{'best_iteration': '11', 'best_score': '0.8709905104598027'}
Trial 22, Fold 3: Log loss = 0.5495449233834574, Average precision = 0.8494831472255703, ROC-AUC = 0.8711569912887287, Elapsed Time = 0.5590210000009392 seconds
Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 22, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66513 validation-auc:0.82575 validation-aucpr:0.80961
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65613 validation-auc:0.81019 validation-aucpr:0.79149
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.62651 validation-auc:0.85436 validation-aucpr:0.85019
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61667 validation-auc:0.85176 validation-aucpr:0.84246
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60990 validation-auc:0.84522 validation-aucpr:0.83211
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.58277 validation-auc:0.86419 validation-aucpr:0.85499
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.58013 validation-auc:0.86915 validation-aucpr:0.86664
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.57805 validation-auc:0.85937 validation-aucpr:0.85414
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.55521 validation-auc:0.87155 validation-aucpr:0.86867
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.55454 validation-auc:0.86622 validation-aucpr:0.86019
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55367 validation-auc:0.86482 validation-aucpr:0.85700
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55322 validation-auc:0.85839 validation-aucpr:0.84371
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.55183 validation-auc:0.85330 validation-aucpr:0.83146
{'best_iteration': '8', 'best_score': '0.8686678124859214'}
Trial 22, Fold 4: Log loss = 0.5518270399325328, Average precision = 0.8193038375097094, ROC-AUC = 0.8533011259182958, Elapsed Time = 0.5348350000003848 seconds
Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 22, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66578 validation-auc:0.81850 validation-aucpr:0.82146
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65403 validation-auc:0.81988 validation-aucpr:0.82139
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64183 validation-auc:0.85871 validation-aucpr:0.85673
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63956 validation-auc:0.84010 validation-aucpr:0.80902
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62942 validation-auc:0.82334 validation-aucpr:0.78522
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62667 validation-auc:0.81153 validation-aucpr:0.74412
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.61833 validation-auc:0.80627 validation-aucpr:0.74120
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59032 validation-auc:0.84637 validation-aucpr:0.83656
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.56555 validation-auc:0.86916 validation-aucpr:0.87463
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.54567 validation-auc:0.87825 validation-aucpr:0.88727
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.52925 validation-auc:0.88512 validation-aucpr:0.89739
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.51573 validation-auc:0.90352 validation-aucpr:0.92329
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.51248 validation-auc:0.90250 validation-aucpr:0.92071
{'best_iteration': '11', 'best_score': '0.9232917725587191'}
Trial 22, Fold 5: Log loss = 0.5124814976954526, Average precision = 0.9202401278312367, ROC-AUC = 0.902498931683481, Elapsed Time = 0.5430703000001813 seconds
Optimization Progress: 23%|##3 | 23/100 [37:27<1:06:26, 51.77s/it]
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 23, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65142 validation-auc:0.94745 validation-aucpr:0.95243
[1] validation-logloss:0.62137 validation-auc:0.94927 validation-aucpr:0.95089
[2] validation-logloss:0.59166 validation-auc:0.95627 validation-aucpr:0.95819
[3] validation-logloss:0.56790 validation-auc:0.95636 validation-aucpr:0.96088
[4] validation-logloss:0.54506 validation-auc:0.95734 validation-aucpr:0.96296
[5] validation-logloss:0.52566 validation-auc:0.95732 validation-aucpr:0.96323
[6] validation-logloss:0.50700 validation-auc:0.95756 validation-aucpr:0.96351
[7] validation-logloss:0.49095 validation-auc:0.95730 validation-aucpr:0.96324
[8] validation-logloss:0.47611 validation-auc:0.95725 validation-aucpr:0.96301
[9] validation-logloss:0.46227 validation-auc:0.95769 validation-aucpr:0.96355
[10] validation-logloss:0.44962 validation-auc:0.95778 validation-aucpr:0.96382
[11] validation-logloss:0.43869 validation-auc:0.95727 validation-aucpr:0.96319
[12] validation-logloss:0.42806 validation-auc:0.95693 validation-aucpr:0.96267
[13] validation-logloss:0.41717 validation-auc:0.95729 validation-aucpr:0.96305
[14] validation-logloss:0.40813 validation-auc:0.95732 validation-aucpr:0.96313
[15] validation-logloss:0.39866 validation-auc:0.95750 validation-aucpr:0.96324
[16] validation-logloss:0.39102 validation-auc:0.95731 validation-aucpr:0.96300
[17] validation-logloss:0.38266 validation-auc:0.95769 validation-aucpr:0.96357
[18] validation-logloss:0.37561 validation-auc:0.95784 validation-aucpr:0.96363
[19] validation-logloss:0.36781 validation-auc:0.95813 validation-aucpr:0.96379
[20] validation-logloss:0.35548 validation-auc:0.95962 validation-aucpr:0.96542
[21] validation-logloss:0.34964 validation-auc:0.95965 validation-aucpr:0.96545
[22] validation-logloss:0.34032 validation-auc:0.96010 validation-aucpr:0.96593
[23] validation-logloss:0.33570 validation-auc:0.96010 validation-aucpr:0.96595
[24] validation-logloss:0.33073 validation-auc:0.96006 validation-aucpr:0.96597
[25] validation-logloss:0.32643 validation-auc:0.96002 validation-aucpr:0.96592
[26] validation-logloss:0.32250 validation-auc:0.96016 validation-aucpr:0.96612
[27] validation-logloss:0.31781 validation-auc:0.96024 validation-aucpr:0.96620
[28] validation-logloss:0.31404 validation-auc:0.96027 validation-aucpr:0.96624
[29] validation-logloss:0.31084 validation-auc:0.96014 validation-aucpr:0.96610
[30] validation-logloss:0.30620 validation-auc:0.96034 validation-aucpr:0.96620
[31] validation-logloss:0.30307 validation-auc:0.96037 validation-aucpr:0.96618
[32] validation-logloss:0.30012 validation-auc:0.96037 validation-aucpr:0.96621
[33] validation-logloss:0.29406 validation-auc:0.96078 validation-aucpr:0.96672
[34] validation-logloss:0.29074 validation-auc:0.96098 validation-aucpr:0.96685
[35] validation-logloss:0.28843 validation-auc:0.96086 validation-aucpr:0.96678
[36] validation-logloss:0.28256 validation-auc:0.96131 validation-aucpr:0.96724
[37] validation-logloss:0.28047 validation-auc:0.96140 validation-aucpr:0.96734
[38] validation-logloss:0.27881 validation-auc:0.96137 validation-aucpr:0.96733
[39] validation-logloss:0.27380 validation-auc:0.96173 validation-aucpr:0.96775
[40] validation-logloss:0.27198 validation-auc:0.96188 validation-aucpr:0.96784
[41] validation-logloss:0.26990 validation-auc:0.96197 validation-aucpr:0.96797
[42] validation-logloss:0.26509 validation-auc:0.96240 validation-aucpr:0.96838
[43] validation-logloss:0.26108 validation-auc:0.96266 validation-aucpr:0.96871
[44] validation-logloss:0.25987 validation-auc:0.96274 validation-aucpr:0.96882
[45] validation-logloss:0.25749 validation-auc:0.96296 validation-aucpr:0.96896
[46] validation-logloss:0.25402 validation-auc:0.96326 validation-aucpr:0.96924
[47] validation-logloss:0.25303 validation-auc:0.96322 validation-aucpr:0.96920
[48] validation-logloss:0.24918 validation-auc:0.96356 validation-aucpr:0.96953
[49] validation-logloss:0.24767 validation-auc:0.96370 validation-aucpr:0.96968
{'best_iteration': '49', 'best_score': '0.9696847725863038'}
Trial 23, Fold 1: Log loss = 0.24766681338758925, Average precision = 0.9696768625925263, ROC-AUC = 0.9637044714252259, Elapsed Time = 0.7275594000002457 seconds
Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 23, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65076 validation-auc:0.95547 validation-aucpr:0.95878
[1] validation-logloss:0.62079 validation-auc:0.95520 validation-aucpr:0.95925
[2] validation-logloss:0.59094 validation-auc:0.96035 validation-aucpr:0.96300
[3] validation-logloss:0.56574 validation-auc:0.95943 validation-aucpr:0.96253
[4] validation-logloss:0.54324 validation-auc:0.95905 validation-aucpr:0.96279
[5] validation-logloss:0.52416 validation-auc:0.95799 validation-aucpr:0.96168
[6] validation-logloss:0.50594 validation-auc:0.95773 validation-aucpr:0.96156
[7] validation-logloss:0.48921 validation-auc:0.95809 validation-aucpr:0.96176
[8] validation-logloss:0.47411 validation-auc:0.95814 validation-aucpr:0.96165
[9] validation-logloss:0.46001 validation-auc:0.95822 validation-aucpr:0.96180
[10] validation-logloss:0.44764 validation-auc:0.95859 validation-aucpr:0.96215
[11] validation-logloss:0.43637 validation-auc:0.95820 validation-aucpr:0.96164
[12] validation-logloss:0.42511 validation-auc:0.95776 validation-aucpr:0.96100
[13] validation-logloss:0.41542 validation-auc:0.95809 validation-aucpr:0.96139
[14] validation-logloss:0.40547 validation-auc:0.95825 validation-aucpr:0.96143
[15] validation-logloss:0.39649 validation-auc:0.95792 validation-aucpr:0.96112
[16] validation-logloss:0.38883 validation-auc:0.95767 validation-aucpr:0.96081
[17] validation-logloss:0.38098 validation-auc:0.95788 validation-aucpr:0.96105
[18] validation-logloss:0.37365 validation-auc:0.95801 validation-aucpr:0.96114
[19] validation-logloss:0.36631 validation-auc:0.95787 validation-aucpr:0.96084
[20] validation-logloss:0.35361 validation-auc:0.96002 validation-aucpr:0.96324
[21] validation-logloss:0.34797 validation-auc:0.96005 validation-aucpr:0.96396
[22] validation-logloss:0.33817 validation-auc:0.96124 validation-aucpr:0.96533
[23] validation-logloss:0.33317 validation-auc:0.96124 validation-aucpr:0.96524
[24] validation-logloss:0.32859 validation-auc:0.96103 validation-aucpr:0.96492
[25] validation-logloss:0.32413 validation-auc:0.96115 validation-aucpr:0.96500
[26] validation-logloss:0.32020 validation-auc:0.96120 validation-aucpr:0.96508
[27] validation-logloss:0.31554 validation-auc:0.96137 validation-aucpr:0.96520
[28] validation-logloss:0.31202 validation-auc:0.96132 validation-aucpr:0.96507
[29] validation-logloss:0.30877 validation-auc:0.96123 validation-aucpr:0.96495
[30] validation-logloss:0.30487 validation-auc:0.96133 validation-aucpr:0.96507
[31] validation-logloss:0.30191 validation-auc:0.96120 validation-aucpr:0.96481
[32] validation-logloss:0.29842 validation-auc:0.96149 validation-aucpr:0.96495
[33] validation-logloss:0.29569 validation-auc:0.96156 validation-aucpr:0.96495
[34] validation-logloss:0.29351 validation-auc:0.96147 validation-aucpr:0.96481
[35] validation-logloss:0.29049 validation-auc:0.96158 validation-aucpr:0.96485
[36] validation-logloss:0.28788 validation-auc:0.96178 validation-aucpr:0.96504
[37] validation-logloss:0.28599 validation-auc:0.96173 validation-aucpr:0.96522
[38] validation-logloss:0.28160 validation-auc:0.96221 validation-aucpr:0.96574
[39] validation-logloss:0.27990 validation-auc:0.96224 validation-aucpr:0.96577
[40] validation-logloss:0.27426 validation-auc:0.96297 validation-aucpr:0.96660
[41] validation-logloss:0.27208 validation-auc:0.96303 validation-aucpr:0.96658
[42] validation-logloss:0.27032 validation-auc:0.96320 validation-aucpr:0.96666
[43] validation-logloss:0.26775 validation-auc:0.96356 validation-aucpr:0.96697
[44] validation-logloss:0.26599 validation-auc:0.96380 validation-aucpr:0.96717
[45] validation-logloss:0.26078 validation-auc:0.96424 validation-aucpr:0.96769
[46] validation-logloss:0.25623 validation-auc:0.96467 validation-aucpr:0.96822
[47] validation-logloss:0.25439 validation-auc:0.96492 validation-aucpr:0.96840
[48] validation-logloss:0.25253 validation-auc:0.96509 validation-aucpr:0.96856
[49] validation-logloss:0.25123 validation-auc:0.96519 validation-aucpr:0.96855
{'best_iteration': '48', 'best_score': '0.9685554995351819'}
Trial 23, Fold 2: Log loss = 0.2512295796579628, Average precision = 0.9685552808996968, ROC-AUC = 0.9651874646617346, Elapsed Time = 0.9321792999999161 seconds
Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 23, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65094 validation-auc:0.95011 validation-aucpr:0.95493
[1] validation-logloss:0.62074 validation-auc:0.95393 validation-aucpr:0.95924
[2] validation-logloss:0.59084 validation-auc:0.95915 validation-aucpr:0.96202
[3] validation-logloss:0.56659 validation-auc:0.95978 validation-aucpr:0.96225
[4] validation-logloss:0.54377 validation-auc:0.96088 validation-aucpr:0.96407
[5] validation-logloss:0.52402 validation-auc:0.96068 validation-aucpr:0.96381
[6] validation-logloss:0.50510 validation-auc:0.96063 validation-aucpr:0.96433
[7] validation-logloss:0.48890 validation-auc:0.96084 validation-aucpr:0.96447
[8] validation-logloss:0.47362 validation-auc:0.96101 validation-aucpr:0.96516
[9] validation-logloss:0.45971 validation-auc:0.96097 validation-aucpr:0.96512
[10] validation-logloss:0.44767 validation-auc:0.96091 validation-aucpr:0.96561
[11] validation-logloss:0.43616 validation-auc:0.96086 validation-aucpr:0.96555
[12] validation-logloss:0.42504 validation-auc:0.96080 validation-aucpr:0.96553
[13] validation-logloss:0.41525 validation-auc:0.96058 validation-aucpr:0.96536
[14] validation-logloss:0.40553 validation-auc:0.96068 validation-aucpr:0.96557
[15] validation-logloss:0.39607 validation-auc:0.96051 validation-aucpr:0.96532
[16] validation-logloss:0.38830 validation-auc:0.96043 validation-aucpr:0.96513
[17] validation-logloss:0.38025 validation-auc:0.96041 validation-aucpr:0.96517
[18] validation-logloss:0.37246 validation-auc:0.96107 validation-aucpr:0.96578
[19] validation-logloss:0.36432 validation-auc:0.96102 validation-aucpr:0.96567
[20] validation-logloss:0.35163 validation-auc:0.96277 validation-aucpr:0.96762
[21] validation-logloss:0.34558 validation-auc:0.96282 validation-aucpr:0.96765
[22] validation-logloss:0.34035 validation-auc:0.96282 validation-aucpr:0.96757
[23] validation-logloss:0.33545 validation-auc:0.96275 validation-aucpr:0.96748
[24] validation-logloss:0.33083 validation-auc:0.96262 validation-aucpr:0.96742
[25] validation-logloss:0.32681 validation-auc:0.96253 validation-aucpr:0.96728
[26] validation-logloss:0.32234 validation-auc:0.96271 validation-aucpr:0.96742
[27] validation-logloss:0.31705 validation-auc:0.96295 validation-aucpr:0.96766
[28] validation-logloss:0.30917 validation-auc:0.96358 validation-aucpr:0.96839
[29] validation-logloss:0.30557 validation-auc:0.96361 validation-aucpr:0.96839
[30] validation-logloss:0.30173 validation-auc:0.96373 validation-aucpr:0.96850
[31] validation-logloss:0.29417 validation-auc:0.96419 validation-aucpr:0.96906
[32] validation-logloss:0.29089 validation-auc:0.96424 validation-aucpr:0.96905
[33] validation-logloss:0.28750 validation-auc:0.96454 validation-aucpr:0.96928
[34] validation-logloss:0.28470 validation-auc:0.96464 validation-aucpr:0.96931
[35] validation-logloss:0.28159 validation-auc:0.96483 validation-aucpr:0.96945
[36] validation-logloss:0.27894 validation-auc:0.96503 validation-aucpr:0.96960
[37] validation-logloss:0.27702 validation-auc:0.96496 validation-aucpr:0.96950
[38] validation-logloss:0.27312 validation-auc:0.96528 validation-aucpr:0.96985
[39] validation-logloss:0.27116 validation-auc:0.96537 validation-aucpr:0.96993
[40] validation-logloss:0.26910 validation-auc:0.96543 validation-aucpr:0.96994
[41] validation-logloss:0.26757 validation-auc:0.96546 validation-aucpr:0.96990
[42] validation-logloss:0.26571 validation-auc:0.96558 validation-aucpr:0.96999
[43] validation-logloss:0.26388 validation-auc:0.96563 validation-aucpr:0.97009
[44] validation-logloss:0.26222 validation-auc:0.96565 validation-aucpr:0.97012
[45] validation-logloss:0.26045 validation-auc:0.96573 validation-aucpr:0.97019
[46] validation-logloss:0.25509 validation-auc:0.96613 validation-aucpr:0.97062
[47] validation-logloss:0.25374 validation-auc:0.96619 validation-aucpr:0.97069
[48] validation-logloss:0.24965 validation-auc:0.96642 validation-aucpr:0.97098
[49] validation-logloss:0.24819 validation-auc:0.96651 validation-aucpr:0.97104
{'best_iteration': '49', 'best_score': '0.9710446138361724'}
Trial 23, Fold 3: Log loss = 0.2481948596089837, Average precision = 0.9710479627303537, ROC-AUC = 0.9665101071693425, Elapsed Time = 0.9626014999994368 seconds
Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 23, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65127 validation-auc:0.95231 validation-aucpr:0.95618
[1] validation-logloss:0.62100 validation-auc:0.95361 validation-aucpr:0.95967
[2] validation-logloss:0.59108 validation-auc:0.95902 validation-aucpr:0.96362
[3] validation-logloss:0.56568 validation-auc:0.95876 validation-aucpr:0.96329
[4] validation-logloss:0.54216 validation-auc:0.95913 validation-aucpr:0.96411
[5] validation-logloss:0.52275 validation-auc:0.95806 validation-aucpr:0.96311
[6] validation-logloss:0.50436 validation-auc:0.95771 validation-aucpr:0.96312
[7] validation-logloss:0.48816 validation-auc:0.95754 validation-aucpr:0.96301
[8] validation-logloss:0.46874 validation-auc:0.95865 validation-aucpr:0.96445
[9] validation-logloss:0.45442 validation-auc:0.95822 validation-aucpr:0.96400
[10] validation-logloss:0.44039 validation-auc:0.95868 validation-aucpr:0.96439
[11] validation-logloss:0.42853 validation-auc:0.95842 validation-aucpr:0.96421
[12] validation-logloss:0.41335 validation-auc:0.95939 validation-aucpr:0.96542
[13] validation-logloss:0.40389 validation-auc:0.95910 validation-aucpr:0.96502
[14] validation-logloss:0.39455 validation-auc:0.95928 validation-aucpr:0.96531
[15] validation-logloss:0.38641 validation-auc:0.95885 validation-aucpr:0.96474
[16] validation-logloss:0.37758 validation-auc:0.95947 validation-aucpr:0.96527
[17] validation-logloss:0.37064 validation-auc:0.95915 validation-aucpr:0.96510
[18] validation-logloss:0.36283 validation-auc:0.95939 validation-aucpr:0.96515
[19] validation-logloss:0.35623 validation-auc:0.95959 validation-aucpr:0.96535
[20] validation-logloss:0.34765 validation-auc:0.95990 validation-aucpr:0.96570
[21] validation-logloss:0.33788 validation-auc:0.96041 validation-aucpr:0.96618
[22] validation-logloss:0.33286 validation-auc:0.96047 validation-aucpr:0.96629
[23] validation-logloss:0.32806 validation-auc:0.96056 validation-aucpr:0.96638
[24] validation-logloss:0.32353 validation-auc:0.96055 validation-aucpr:0.96643
[25] validation-logloss:0.31975 validation-auc:0.96031 validation-aucpr:0.96618
[26] validation-logloss:0.31514 validation-auc:0.96067 validation-aucpr:0.96655
[27] validation-logloss:0.31032 validation-auc:0.96126 validation-aucpr:0.96711
[28] validation-logloss:0.30632 validation-auc:0.96135 validation-aucpr:0.96723
[29] validation-logloss:0.30231 validation-auc:0.96148 validation-aucpr:0.96732
[30] validation-logloss:0.29866 validation-auc:0.96169 validation-aucpr:0.96746
[31] validation-logloss:0.29123 validation-auc:0.96245 validation-aucpr:0.96823
[32] validation-logloss:0.28793 validation-auc:0.96271 validation-aucpr:0.96844
[33] validation-logloss:0.28510 validation-auc:0.96289 validation-aucpr:0.96852
[34] validation-logloss:0.28273 validation-auc:0.96278 validation-aucpr:0.96849
[35] validation-logloss:0.27988 validation-auc:0.96291 validation-aucpr:0.96864
[36] validation-logloss:0.27747 validation-auc:0.96300 validation-aucpr:0.96868
[37] validation-logloss:0.27322 validation-auc:0.96308 validation-aucpr:0.96878
[38] validation-logloss:0.27006 validation-auc:0.96338 validation-aucpr:0.96904
[39] validation-logloss:0.26814 validation-auc:0.96341 validation-aucpr:0.96907
[40] validation-logloss:0.26349 validation-auc:0.96377 validation-aucpr:0.96947
[41] validation-logloss:0.26134 validation-auc:0.96377 validation-aucpr:0.96947
[42] validation-logloss:0.25983 validation-auc:0.96387 validation-aucpr:0.96953
[43] validation-logloss:0.25777 validation-auc:0.96407 validation-aucpr:0.96974
[44] validation-logloss:0.25627 validation-auc:0.96414 validation-aucpr:0.96980
[45] validation-logloss:0.25499 validation-auc:0.96418 validation-aucpr:0.96982
[46] validation-logloss:0.25342 validation-auc:0.96421 validation-aucpr:0.96983
[47] validation-logloss:0.25134 validation-auc:0.96452 validation-aucpr:0.97008
[48] validation-logloss:0.24962 validation-auc:0.96460 validation-aucpr:0.97014
[49] validation-logloss:0.24831 validation-auc:0.96464 validation-aucpr:0.97017
{'best_iteration': '49', 'best_score': '0.9701724688367994'}
Trial 23, Fold 4: Log loss = 0.24831006230194794, Average precision = 0.9701685420356017, ROC-AUC = 0.9646393520604877, Elapsed Time = 0.9430483999985881 seconds
Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 23, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65341 validation-auc:0.94036 validation-aucpr:0.94665
[1] validation-logloss:0.62349 validation-auc:0.94719 validation-aucpr:0.95075
[2] validation-logloss:0.59392 validation-auc:0.95151 validation-aucpr:0.95509
[3] validation-logloss:0.56876 validation-auc:0.95169 validation-aucpr:0.95491
[4] validation-logloss:0.54627 validation-auc:0.95179 validation-aucpr:0.95553
[5] validation-logloss:0.52703 validation-auc:0.95119 validation-aucpr:0.95475
[6] validation-logloss:0.50902 validation-auc:0.95145 validation-aucpr:0.95485
[7] validation-logloss:0.49313 validation-auc:0.95188 validation-aucpr:0.95539
[8] validation-logloss:0.47810 validation-auc:0.95206 validation-aucpr:0.95544
[9] validation-logloss:0.46450 validation-auc:0.95245 validation-aucpr:0.95590
[10] validation-logloss:0.45314 validation-auc:0.95285 validation-aucpr:0.95732
[11] validation-logloss:0.44223 validation-auc:0.95201 validation-aucpr:0.95620
[12] validation-logloss:0.43136 validation-auc:0.95245 validation-aucpr:0.95666
[13] validation-logloss:0.42132 validation-auc:0.95300 validation-aucpr:0.95781
[14] validation-logloss:0.41218 validation-auc:0.95364 validation-aucpr:0.95852
[15] validation-logloss:0.40326 validation-auc:0.95349 validation-aucpr:0.95822
[16] validation-logloss:0.39550 validation-auc:0.95354 validation-aucpr:0.95821
[17] validation-logloss:0.38717 validation-auc:0.95365 validation-aucpr:0.95878
[18] validation-logloss:0.37973 validation-auc:0.95397 validation-aucpr:0.95907
[19] validation-logloss:0.37334 validation-auc:0.95384 validation-aucpr:0.95888
[20] validation-logloss:0.36369 validation-auc:0.95533 validation-aucpr:0.96088
[21] validation-logloss:0.35262 validation-auc:0.95690 validation-aucpr:0.96268
[22] validation-logloss:0.34747 validation-auc:0.95699 validation-aucpr:0.96275
[23] validation-logloss:0.34284 validation-auc:0.95703 validation-aucpr:0.96275
[24] validation-logloss:0.33856 validation-auc:0.95686 validation-aucpr:0.96256
[25] validation-logloss:0.33481 validation-auc:0.95681 validation-aucpr:0.96247
[26] validation-logloss:0.33103 validation-auc:0.95679 validation-aucpr:0.96240
[27] validation-logloss:0.32584 validation-auc:0.95730 validation-aucpr:0.96285
[28] validation-logloss:0.32212 validation-auc:0.95731 validation-aucpr:0.96282
[29] validation-logloss:0.31790 validation-auc:0.95754 validation-aucpr:0.96303
[30] validation-logloss:0.31422 validation-auc:0.95769 validation-aucpr:0.96313
[31] validation-logloss:0.30608 validation-auc:0.95884 validation-aucpr:0.96436
[32] validation-logloss:0.30309 validation-auc:0.95884 validation-aucpr:0.96434
[33] validation-logloss:0.30032 validation-auc:0.95909 validation-aucpr:0.96449
[34] validation-logloss:0.29777 validation-auc:0.95917 validation-aucpr:0.96453
[35] validation-logloss:0.29496 validation-auc:0.95929 validation-aucpr:0.96457
[36] validation-logloss:0.29258 validation-auc:0.95940 validation-aucpr:0.96467
[37] validation-logloss:0.28761 validation-auc:0.95986 validation-aucpr:0.96524
[38] validation-logloss:0.28418 validation-auc:0.96020 validation-aucpr:0.96553
[39] validation-logloss:0.28254 validation-auc:0.96024 validation-aucpr:0.96552
[40] validation-logloss:0.27701 validation-auc:0.96075 validation-aucpr:0.96604
[41] validation-logloss:0.27492 validation-auc:0.96083 validation-aucpr:0.96608
[42] validation-logloss:0.27313 validation-auc:0.96102 validation-aucpr:0.96621
[43] validation-logloss:0.27094 validation-auc:0.96121 validation-aucpr:0.96640
[44] validation-logloss:0.26934 validation-auc:0.96140 validation-aucpr:0.96654
[45] validation-logloss:0.26473 validation-auc:0.96188 validation-aucpr:0.96703
[46] validation-logloss:0.26296 validation-auc:0.96204 validation-aucpr:0.96714
[47] validation-logloss:0.26193 validation-auc:0.96202 validation-aucpr:0.96708
[48] validation-logloss:0.25814 validation-auc:0.96221 validation-aucpr:0.96730
[49] validation-logloss:0.25625 validation-auc:0.96249 validation-aucpr:0.96753
{'best_iteration': '49', 'best_score': '0.9675298542241902'}
Trial 23, Fold 5: Log loss = 0.2562486586560138, Average precision = 0.9675302529511657, ROC-AUC = 0.9624879326681902, Elapsed Time = 0.9584814000008919 seconds
Optimization Progress: 24%|##4 | 24/100 [37:40<50:37, 39.97s/it]
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 24, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67754 validation-auc:0.94648 validation-aucpr:0.95313
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66446 validation-auc:0.95291 validation-aucpr:0.94587
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65146 validation-auc:0.95806 validation-aucpr:0.95520
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63909 validation-auc:0.96182 validation-aucpr:0.96495
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62699 validation-auc:0.96247 validation-aucpr:0.96495
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61423 validation-auc:0.96446 validation-aucpr:0.96677
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60175 validation-auc:0.96548 validation-aucpr:0.96685
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58963 validation-auc:0.96624 validation-aucpr:0.96807
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57821 validation-auc:0.96716 validation-aucpr:0.97022
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56892 validation-auc:0.96625 validation-aucpr:0.96933
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55941 validation-auc:0.96601 validation-aucpr:0.96871
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55067 validation-auc:0.96578 validation-aucpr:0.96819
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54168 validation-auc:0.96579 validation-aucpr:0.96820
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53233 validation-auc:0.96591 validation-aucpr:0.96825
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52405 validation-auc:0.96611 validation-aucpr:0.96853
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51486 validation-auc:0.96676 validation-aucpr:0.97094
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50584 validation-auc:0.96697 validation-aucpr:0.97092
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49724 validation-auc:0.96733 validation-aucpr:0.97127
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48909 validation-auc:0.96750 validation-aucpr:0.97149
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48088 validation-auc:0.96805 validation-aucpr:0.97265
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47322 validation-auc:0.96806 validation-aucpr:0.97261
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46678 validation-auc:0.96827 validation-aucpr:0.97284
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45953 validation-auc:0.96831 validation-aucpr:0.97290
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45338 validation-auc:0.96823 validation-aucpr:0.97294
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44648 validation-auc:0.96838 validation-aucpr:0.97315
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44021 validation-auc:0.96859 validation-aucpr:0.97336
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.43359 validation-auc:0.96883 validation-aucpr:0.97356
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42834 validation-auc:0.96874 validation-aucpr:0.97337
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.42314 validation-auc:0.96859 validation-aucpr:0.97323
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41808 validation-auc:0.96843 validation-aucpr:0.97308
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41306 validation-auc:0.96843 validation-aucpr:0.97304
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40727 validation-auc:0.96855 validation-aucpr:0.97313
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40193 validation-auc:0.96857 validation-aucpr:0.97325
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39664 validation-auc:0.96855 validation-aucpr:0.97326
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.39140 validation-auc:0.96859 validation-aucpr:0.97331
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38723 validation-auc:0.96852 validation-aucpr:0.97318
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.38311 validation-auc:0.96843 validation-aucpr:0.97305
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37909 validation-auc:0.96848 validation-aucpr:0.97308
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37509 validation-auc:0.96848 validation-aucpr:0.97306
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.37125 validation-auc:0.96835 validation-aucpr:0.97298
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.36685 validation-auc:0.96846 validation-aucpr:0.97302
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.36304 validation-auc:0.96848 validation-aucpr:0.97304
[18:36:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35967 validation-auc:0.96834 validation-aucpr:0.97288
[18:36:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.35625 validation-auc:0.96830 validation-aucpr:0.97281
{'best_iteration': '26', 'best_score': '0.9735624594295109'}
Trial 24, Fold 1: Log loss = 0.3562467255542872, Average precision = 0.9728185954572157, ROC-AUC = 0.9682985528598671, Elapsed Time = 4.798161999999138 seconds
Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 24, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67795 validation-auc:0.95003 validation-aucpr:0.95522
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66306 validation-auc:0.95893 validation-aucpr:0.96415
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64936 validation-auc:0.96179 validation-aucpr:0.96638
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63579 validation-auc:0.96512 validation-aucpr:0.96811
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62415 validation-auc:0.96673 validation-aucpr:0.97068
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61271 validation-auc:0.96744 validation-aucpr:0.97100
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60174 validation-auc:0.96816 validation-aucpr:0.97156
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59079 validation-auc:0.96877 validation-aucpr:0.97198
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57919 validation-auc:0.96929 validation-aucpr:0.97251
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56807 validation-auc:0.96969 validation-aucpr:0.97285
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55900 validation-auc:0.96921 validation-aucpr:0.97237
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54858 validation-auc:0.96939 validation-aucpr:0.97284
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53997 validation-auc:0.96886 validation-aucpr:0.97237
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53013 validation-auc:0.96942 validation-aucpr:0.97277
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52183 validation-auc:0.96954 validation-aucpr:0.97283
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51258 validation-auc:0.96997 validation-aucpr:0.97327
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50491 validation-auc:0.96963 validation-aucpr:0.97299
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49735 validation-auc:0.96973 validation-aucpr:0.97301
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48889 validation-auc:0.97011 validation-aucpr:0.97329
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48145 validation-auc:0.97019 validation-aucpr:0.97343
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47475 validation-auc:0.97019 validation-aucpr:0.97342
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46701 validation-auc:0.97026 validation-aucpr:0.97344
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.46030 validation-auc:0.97013 validation-aucpr:0.97329
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45406 validation-auc:0.97007 validation-aucpr:0.97314
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44778 validation-auc:0.97021 validation-aucpr:0.97322
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44202 validation-auc:0.97008 validation-aucpr:0.97314
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.43536 validation-auc:0.97028 validation-aucpr:0.97352
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42986 validation-auc:0.97028 validation-aucpr:0.97349
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.42455 validation-auc:0.97021 validation-aucpr:0.97343
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41942 validation-auc:0.97009 validation-aucpr:0.97327
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41425 validation-auc:0.97005 validation-aucpr:0.97326
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40943 validation-auc:0.97011 validation-aucpr:0.97340
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40441 validation-auc:0.97025 validation-aucpr:0.97342
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39989 validation-auc:0.97015 validation-aucpr:0.97330
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.39579 validation-auc:0.96989 validation-aucpr:0.97309
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.39141 validation-auc:0.96986 validation-aucpr:0.97305
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.38702 validation-auc:0.96988 validation-aucpr:0.97308
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.38202 validation-auc:0.97007 validation-aucpr:0.97317
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37813 validation-auc:0.96996 validation-aucpr:0.97311
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.37420 validation-auc:0.96994 validation-aucpr:0.97310
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.36930 validation-auc:0.97010 validation-aucpr:0.97325
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.36546 validation-auc:0.97011 validation-aucpr:0.97321
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.36082 validation-auc:0.97021 validation-aucpr:0.97330
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.35630 validation-auc:0.97040 validation-aucpr:0.97347
{'best_iteration': '26', 'best_score': '0.9735159251236497'}
Trial 24, Fold 2: Log loss = 0.3563022562597658, Average precision = 0.9734075077131078, ROC-AUC = 0.9704006080837259, Elapsed Time = 4.943635200001154 seconds
Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 24, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67755 validation-auc:0.95357 validation-aucpr:0.95829
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66279 validation-auc:0.95870 validation-aucpr:0.95148
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65024 validation-auc:0.96146 validation-aucpr:0.96566
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63640 validation-auc:0.96460 validation-aucpr:0.96668
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62300 validation-auc:0.96692 validation-aucpr:0.97016
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61010 validation-auc:0.96815 validation-aucpr:0.97122
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59754 validation-auc:0.96919 validation-aucpr:0.97155
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58724 validation-auc:0.96904 validation-aucpr:0.97005
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57719 validation-auc:0.96887 validation-aucpr:0.97008
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56767 validation-auc:0.96880 validation-aucpr:0.97016
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55707 validation-auc:0.96861 validation-aucpr:0.96992
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54784 validation-auc:0.96922 validation-aucpr:0.97178
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53779 validation-auc:0.96940 validation-aucpr:0.97197
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52927 validation-auc:0.96929 validation-aucpr:0.97188
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52018 validation-auc:0.96919 validation-aucpr:0.97178
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51237 validation-auc:0.96890 validation-aucpr:0.97051
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50398 validation-auc:0.96914 validation-aucpr:0.97070
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49556 validation-auc:0.96931 validation-aucpr:0.97286
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48843 validation-auc:0.96912 validation-aucpr:0.97268
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48123 validation-auc:0.96913 validation-aucpr:0.97277
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47312 validation-auc:0.96936 validation-aucpr:0.97300
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46636 validation-auc:0.96941 validation-aucpr:0.97295
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45988 validation-auc:0.96946 validation-aucpr:0.97285
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45264 validation-auc:0.96969 validation-aucpr:0.97298
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44636 validation-auc:0.96988 validation-aucpr:0.97317
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44054 validation-auc:0.96984 validation-aucpr:0.97388
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.43450 validation-auc:0.96997 validation-aucpr:0.97396
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42909 validation-auc:0.96992 validation-aucpr:0.97319
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.42290 validation-auc:0.96994 validation-aucpr:0.97301
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41751 validation-auc:0.97006 validation-aucpr:0.97400
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41235 validation-auc:0.97015 validation-aucpr:0.97403
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40740 validation-auc:0.97010 validation-aucpr:0.97387
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40163 validation-auc:0.97019 validation-aucpr:0.97399
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39602 validation-auc:0.97028 validation-aucpr:0.97405
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.39162 validation-auc:0.97026 validation-aucpr:0.97399
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38718 validation-auc:0.97029 validation-aucpr:0.97348
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.38219 validation-auc:0.97034 validation-aucpr:0.97349
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37710 validation-auc:0.97052 validation-aucpr:0.97364
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37232 validation-auc:0.97062 validation-aucpr:0.97377
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36849 validation-auc:0.97060 validation-aucpr:0.97372
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.36458 validation-auc:0.97061 validation-aucpr:0.97373
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.36018 validation-auc:0.97062 validation-aucpr:0.97376
[18:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35658 validation-auc:0.97057 validation-aucpr:0.97367
[18:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.35295 validation-auc:0.97065 validation-aucpr:0.97374
{'best_iteration': '33', 'best_score': '0.9740539892541278'}
Trial 24, Fold 3: Log loss = 0.3529549688708266, Average precision = 0.9737998160380797, ROC-AUC = 0.9706531828163073, Elapsed Time = 5.05997539999953 seconds
Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 24, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67756 validation-auc:0.94669 validation-aucpr:0.95494
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66441 validation-auc:0.95335 validation-aucpr:0.95834
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65023 validation-auc:0.95958 validation-aucpr:0.96440
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63669 validation-auc:0.96288 validation-aucpr:0.96940
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62349 validation-auc:0.96425 validation-aucpr:0.97044
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61083 validation-auc:0.96541 validation-aucpr:0.97136
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59887 validation-auc:0.96571 validation-aucpr:0.97077
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58734 validation-auc:0.96569 validation-aucpr:0.96967
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57621 validation-auc:0.96647 validation-aucpr:0.97228
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56590 validation-auc:0.96718 validation-aucpr:0.97268
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55677 validation-auc:0.96684 validation-aucpr:0.97236
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54668 validation-auc:0.96687 validation-aucpr:0.97244
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53704 validation-auc:0.96701 validation-aucpr:0.97253
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52735 validation-auc:0.96733 validation-aucpr:0.97280
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.51940 validation-auc:0.96715 validation-aucpr:0.97261
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51013 validation-auc:0.96780 validation-aucpr:0.97311
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50230 validation-auc:0.96802 validation-aucpr:0.97324
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49392 validation-auc:0.96819 validation-aucpr:0.97338
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48580 validation-auc:0.96831 validation-aucpr:0.97351
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47778 validation-auc:0.96863 validation-aucpr:0.97379
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47021 validation-auc:0.96845 validation-aucpr:0.97370
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46338 validation-auc:0.96858 validation-aucpr:0.97382
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45681 validation-auc:0.96876 validation-aucpr:0.97393
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45086 validation-auc:0.96878 validation-aucpr:0.97392
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44483 validation-auc:0.96885 validation-aucpr:0.97394
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.43821 validation-auc:0.96900 validation-aucpr:0.97407
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.43285 validation-auc:0.96875 validation-aucpr:0.97388
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42632 validation-auc:0.96907 validation-aucpr:0.97414
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.41998 validation-auc:0.96926 validation-aucpr:0.97433
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41465 validation-auc:0.96925 validation-aucpr:0.97431
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.40952 validation-auc:0.96923 validation-aucpr:0.97429
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40380 validation-auc:0.96929 validation-aucpr:0.97438
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.39826 validation-auc:0.96935 validation-aucpr:0.97446
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39361 validation-auc:0.96944 validation-aucpr:0.97452
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.38847 validation-auc:0.96949 validation-aucpr:0.97457
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38346 validation-auc:0.96936 validation-aucpr:0.97448
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.37917 validation-auc:0.96933 validation-aucpr:0.97443
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37502 validation-auc:0.96928 validation-aucpr:0.97437
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37039 validation-auc:0.96931 validation-aucpr:0.97439
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36575 validation-auc:0.96944 validation-aucpr:0.97449
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.36123 validation-auc:0.96952 validation-aucpr:0.97457
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.35742 validation-auc:0.96958 validation-aucpr:0.97458
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35381 validation-auc:0.96965 validation-aucpr:0.97463
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.34956 validation-auc:0.96986 validation-aucpr:0.97478
{'best_iteration': '43', 'best_score': '0.9747828782362944'}
Trial 24, Fold 4: Log loss = 0.3495584092387853, Average precision = 0.9747869080590805, ROC-AUC = 0.9698566641868804, Elapsed Time = 5.095767300001171 seconds
Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 24, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:36:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67775 validation-auc:0.94775 validation-aucpr:0.94986
[18:36:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66501 validation-auc:0.95325 validation-aucpr:0.95732
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65082 validation-auc:0.95775 validation-aucpr:0.96329
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63701 validation-auc:0.96022 validation-aucpr:0.96463
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62379 validation-auc:0.96338 validation-aucpr:0.96490
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61170 validation-auc:0.96486 validation-aucpr:0.96984
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60074 validation-auc:0.96507 validation-aucpr:0.96969
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58885 validation-auc:0.96609 validation-aucpr:0.97045
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57880 validation-auc:0.96583 validation-aucpr:0.97030
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56903 validation-auc:0.96595 validation-aucpr:0.96921
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55854 validation-auc:0.96601 validation-aucpr:0.96933
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54847 validation-auc:0.96635 validation-aucpr:0.96978
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53900 validation-auc:0.96689 validation-aucpr:0.97000
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52926 validation-auc:0.96753 validation-aucpr:0.97040
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.51993 validation-auc:0.96773 validation-aucpr:0.97061
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51115 validation-auc:0.96794 validation-aucpr:0.97066
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50249 validation-auc:0.96813 validation-aucpr:0.97088
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49523 validation-auc:0.96791 validation-aucpr:0.97068
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48731 validation-auc:0.96792 validation-aucpr:0.97040
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47950 validation-auc:0.96803 validation-aucpr:0.97050
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47286 validation-auc:0.96786 validation-aucpr:0.97029
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46656 validation-auc:0.96778 validation-aucpr:0.97237
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.46020 validation-auc:0.96780 validation-aucpr:0.97238
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45411 validation-auc:0.96763 validation-aucpr:0.97172
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44855 validation-auc:0.96737 validation-aucpr:0.97138
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44177 validation-auc:0.96758 validation-aucpr:0.97156
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.43605 validation-auc:0.96754 validation-aucpr:0.97149
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42990 validation-auc:0.96774 validation-aucpr:0.97165
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.42369 validation-auc:0.96795 validation-aucpr:0.97186
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41855 validation-auc:0.96788 validation-aucpr:0.97178
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41256 validation-auc:0.96815 validation-aucpr:0.97200
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40683 validation-auc:0.96837 validation-aucpr:0.97216
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40206 validation-auc:0.96829 validation-aucpr:0.97135
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39745 validation-auc:0.96819 validation-aucpr:0.97127
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.39293 validation-auc:0.96818 validation-aucpr:0.97126
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38786 validation-auc:0.96840 validation-aucpr:0.97142
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.38283 validation-auc:0.96864 validation-aucpr:0.97164
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37795 validation-auc:0.96886 validation-aucpr:0.97183
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37389 validation-auc:0.96879 validation-aucpr:0.97177
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36923 validation-auc:0.96898 validation-aucpr:0.97186
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.36563 validation-auc:0.96889 validation-aucpr:0.97170
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.36143 validation-auc:0.96905 validation-aucpr:0.97340
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35713 validation-auc:0.96925 validation-aucpr:0.97368
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.35370 validation-auc:0.96913 validation-aucpr:0.97357
{'best_iteration': '42', 'best_score': '0.973677385463933'}
Trial 24, Fold 5: Log loss = 0.3536975849183317, Average precision = 0.9735737443153737, ROC-AUC = 0.969126804525946, Elapsed Time = 5.088518399999884 seconds
Optimization Progress: 25%|##5 | 25/100 [38:13<47:30, 38.01s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 25, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67136 validation-auc:0.92645 validation-aucpr:0.92797
[1] validation-logloss:0.65045 validation-auc:0.93927 validation-aucpr:0.94577
[2] validation-logloss:0.62847 validation-auc:0.95043 validation-aucpr:0.95863
[3] validation-logloss:0.60879 validation-auc:0.95524 validation-aucpr:0.96105
[4] validation-logloss:0.59011 validation-auc:0.95747 validation-aucpr:0.96265
[5] validation-logloss:0.57531 validation-auc:0.95568 validation-aucpr:0.96092
[6] validation-logloss:0.55744 validation-auc:0.95778 validation-aucpr:0.96344
[7] validation-logloss:0.54321 validation-auc:0.95790 validation-aucpr:0.96430
[8] validation-logloss:0.53138 validation-auc:0.95740 validation-aucpr:0.96381
[9] validation-logloss:0.51916 validation-auc:0.95736 validation-aucpr:0.96369
[10] validation-logloss:0.50745 validation-auc:0.95717 validation-aucpr:0.96353
[11] validation-logloss:0.49565 validation-auc:0.95745 validation-aucpr:0.96393
[12] validation-logloss:0.48441 validation-auc:0.95766 validation-aucpr:0.96421
[13] validation-logloss:0.47120 validation-auc:0.95912 validation-aucpr:0.96579
[14] validation-logloss:0.46180 validation-auc:0.95924 validation-aucpr:0.96585
[15] validation-logloss:0.45253 validation-auc:0.95952 validation-aucpr:0.96603
[16] validation-logloss:0.44349 validation-auc:0.95957 validation-aucpr:0.96604
[17] validation-logloss:0.43490 validation-auc:0.95954 validation-aucpr:0.96600
[18] validation-logloss:0.42815 validation-auc:0.95919 validation-aucpr:0.96572
[19] validation-logloss:0.41788 validation-auc:0.96025 validation-aucpr:0.96671
[20] validation-logloss:0.41213 validation-auc:0.95990 validation-aucpr:0.96630
[21] validation-logloss:0.40480 validation-auc:0.96029 validation-aucpr:0.96658
[22] validation-logloss:0.39603 validation-auc:0.96066 validation-aucpr:0.96701
[23] validation-logloss:0.38666 validation-auc:0.96145 validation-aucpr:0.96774
[24] validation-logloss:0.38092 validation-auc:0.96136 validation-aucpr:0.96762
[25] validation-logloss:0.37635 validation-auc:0.96106 validation-aucpr:0.96740
[26] validation-logloss:0.37070 validation-auc:0.96120 validation-aucpr:0.96743
[27] validation-logloss:0.36546 validation-auc:0.96135 validation-aucpr:0.96751
[28] validation-logloss:0.35802 validation-auc:0.96189 validation-aucpr:0.96804
[29] validation-logloss:0.35351 validation-auc:0.96196 validation-aucpr:0.96805
[30] validation-logloss:0.34984 validation-auc:0.96183 validation-aucpr:0.96795
[31] validation-logloss:0.34655 validation-auc:0.96146 validation-aucpr:0.96765
[32] validation-logloss:0.34241 validation-auc:0.96152 validation-aucpr:0.96768
[33] validation-logloss:0.33647 validation-auc:0.96179 validation-aucpr:0.96796
[34] validation-logloss:0.33313 validation-auc:0.96163 validation-aucpr:0.96780
[35] validation-logloss:0.32905 validation-auc:0.96190 validation-aucpr:0.96800
[36] validation-logloss:0.32565 validation-auc:0.96200 validation-aucpr:0.96806
[37] validation-logloss:0.32215 validation-auc:0.96225 validation-aucpr:0.96826
[38] validation-logloss:0.31689 validation-auc:0.96252 validation-aucpr:0.96858
[39] validation-logloss:0.31322 validation-auc:0.96275 validation-aucpr:0.96874
[40] validation-logloss:0.31011 validation-auc:0.96282 validation-aucpr:0.96879
[41] validation-logloss:0.30612 validation-auc:0.96292 validation-aucpr:0.96890
[42] validation-logloss:0.30268 validation-auc:0.96329 validation-aucpr:0.96918
[43] validation-logloss:0.29816 validation-auc:0.96382 validation-aucpr:0.96962
[44] validation-logloss:0.29539 validation-auc:0.96402 validation-aucpr:0.96977
[45] validation-logloss:0.29267 validation-auc:0.96414 validation-aucpr:0.96985
[46] validation-logloss:0.28872 validation-auc:0.96439 validation-aucpr:0.97012
[47] validation-logloss:0.28674 validation-auc:0.96434 validation-aucpr:0.97007
[48] validation-logloss:0.28442 validation-auc:0.96440 validation-aucpr:0.97011
[49] validation-logloss:0.28251 validation-auc:0.96447 validation-aucpr:0.97017
[50] validation-logloss:0.27902 validation-auc:0.96474 validation-aucpr:0.97045
[51] validation-logloss:0.27705 validation-auc:0.96482 validation-aucpr:0.97051
[52] validation-logloss:0.27542 validation-auc:0.96481 validation-aucpr:0.97048
[53] validation-logloss:0.27344 validation-auc:0.96483 validation-aucpr:0.97048
[54] validation-logloss:0.27018 validation-auc:0.96504 validation-aucpr:0.97068
[55] validation-logloss:0.26907 validation-auc:0.96497 validation-aucpr:0.97063
{'best_iteration': '54', 'best_score': '0.9706754928377431'}
Trial 25, Fold 1: Log loss = 0.2690666545589348, Average precision = 0.9706323993273999, ROC-AUC = 0.9649702410824813, Elapsed Time = 1.1947266000006493 seconds
Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 25, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67061 validation-auc:0.93136 validation-aucpr:0.92698
[1] validation-logloss:0.65091 validation-auc:0.93686 validation-aucpr:0.93478
[2] validation-logloss:0.63111 validation-auc:0.94713 validation-aucpr:0.94825
[3] validation-logloss:0.61210 validation-auc:0.95005 validation-aucpr:0.95175
[4] validation-logloss:0.59208 validation-auc:0.95885 validation-aucpr:0.96273
[5] validation-logloss:0.57508 validation-auc:0.95946 validation-aucpr:0.96335
[6] validation-logloss:0.56160 validation-auc:0.95778 validation-aucpr:0.96170
[7] validation-logloss:0.54381 validation-auc:0.96128 validation-aucpr:0.96557
[8] validation-logloss:0.52942 validation-auc:0.96143 validation-aucpr:0.96536
[9] validation-logloss:0.51655 validation-auc:0.96130 validation-aucpr:0.96531
[10] validation-logloss:0.50556 validation-auc:0.96053 validation-aucpr:0.96458
[11] validation-logloss:0.49411 validation-auc:0.96059 validation-aucpr:0.96455
[12] validation-logloss:0.48347 validation-auc:0.96034 validation-aucpr:0.96424
[13] validation-logloss:0.47336 validation-auc:0.96002 validation-aucpr:0.96377
[14] validation-logloss:0.46312 validation-auc:0.96023 validation-aucpr:0.96376
[15] validation-logloss:0.45392 validation-auc:0.95998 validation-aucpr:0.96359
[16] validation-logloss:0.44160 validation-auc:0.96168 validation-aucpr:0.96537
[17] validation-logloss:0.43318 validation-auc:0.96156 validation-aucpr:0.96514
[18] validation-logloss:0.42520 validation-auc:0.96141 validation-aucpr:0.96508
[19] validation-logloss:0.41774 validation-auc:0.96124 validation-aucpr:0.96498
[20] validation-logloss:0.41024 validation-auc:0.96158 validation-aucpr:0.96511
[21] validation-logloss:0.40411 validation-auc:0.96119 validation-aucpr:0.96459
[22] validation-logloss:0.39732 validation-auc:0.96121 validation-aucpr:0.96456
[23] validation-logloss:0.39068 validation-auc:0.96113 validation-aucpr:0.96447
[24] validation-logloss:0.38439 validation-auc:0.96126 validation-aucpr:0.96462
[25] validation-logloss:0.37872 validation-auc:0.96112 validation-aucpr:0.96450
[26] validation-logloss:0.37376 validation-auc:0.96101 validation-aucpr:0.96437
[27] validation-logloss:0.36871 validation-auc:0.96090 validation-aucpr:0.96400
[28] validation-logloss:0.36333 validation-auc:0.96102 validation-aucpr:0.96405
[29] validation-logloss:0.35622 validation-auc:0.96191 validation-aucpr:0.96506
[30] validation-logloss:0.35142 validation-auc:0.96203 validation-aucpr:0.96515
[31] validation-logloss:0.34752 validation-auc:0.96186 validation-aucpr:0.96499
[32] validation-logloss:0.34069 validation-auc:0.96271 validation-aucpr:0.96592
[33] validation-logloss:0.33605 validation-auc:0.96297 validation-aucpr:0.96608
[34] validation-logloss:0.32954 validation-auc:0.96360 validation-aucpr:0.96679
[35] validation-logloss:0.32570 validation-auc:0.96368 validation-aucpr:0.96686
[36] validation-logloss:0.32188 validation-auc:0.96382 validation-aucpr:0.96696
[37] validation-logloss:0.31872 validation-auc:0.96378 validation-aucpr:0.96688
[38] validation-logloss:0.31593 validation-auc:0.96377 validation-aucpr:0.96681
[39] validation-logloss:0.31042 validation-auc:0.96424 validation-aucpr:0.96734
[40] validation-logloss:0.30741 validation-auc:0.96427 validation-aucpr:0.96769
[41] validation-logloss:0.30497 validation-auc:0.96420 validation-aucpr:0.96758
[42] validation-logloss:0.30223 validation-auc:0.96427 validation-aucpr:0.96762
[43] validation-logloss:0.29758 validation-auc:0.96469 validation-aucpr:0.96810
[44] validation-logloss:0.29477 validation-auc:0.96480 validation-aucpr:0.96814
[45] validation-logloss:0.29080 validation-auc:0.96493 validation-aucpr:0.96825
[46] validation-logloss:0.28693 validation-auc:0.96519 validation-aucpr:0.96852
[47] validation-logloss:0.28472 validation-auc:0.96517 validation-aucpr:0.96851
[48] validation-logloss:0.28120 validation-auc:0.96550 validation-aucpr:0.96883
[49] validation-logloss:0.27714 validation-auc:0.96588 validation-aucpr:0.96919
[50] validation-logloss:0.27505 validation-auc:0.96593 validation-aucpr:0.96919
[51] validation-logloss:0.27112 validation-auc:0.96628 validation-aucpr:0.96950
[52] validation-logloss:0.26929 validation-auc:0.96635 validation-aucpr:0.96956
[53] validation-logloss:0.26728 validation-auc:0.96646 validation-aucpr:0.96964
[54] validation-logloss:0.26555 validation-auc:0.96646 validation-aucpr:0.96961
[55] validation-logloss:0.26345 validation-auc:0.96671 validation-aucpr:0.96979
{'best_iteration': '55', 'best_score': '0.9697896161344084'}
Trial 25, Fold 2: Log loss = 0.2634486405045629, Average precision = 0.9697925827319714, ROC-AUC = 0.9667127575273731, Elapsed Time = 1.5920014000002993 seconds
Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 25, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67096 validation-auc:0.92747 validation-aucpr:0.92769
[1] validation-logloss:0.64721 validation-auc:0.95369 validation-aucpr:0.95988
[2] validation-logloss:0.62642 validation-auc:0.95662 validation-aucpr:0.96262
[3] validation-logloss:0.60830 validation-auc:0.96139 validation-aucpr:0.96630
[4] validation-logloss:0.58983 validation-auc:0.96198 validation-aucpr:0.96652
[5] validation-logloss:0.57351 validation-auc:0.96141 validation-aucpr:0.96608
[6] validation-logloss:0.55846 validation-auc:0.96103 validation-aucpr:0.96570
[7] validation-logloss:0.54134 validation-auc:0.96358 validation-aucpr:0.96832
[8] validation-logloss:0.52711 validation-auc:0.96378 validation-aucpr:0.96846
[9] validation-logloss:0.51435 validation-auc:0.96332 validation-aucpr:0.96793
[10] validation-logloss:0.50233 validation-auc:0.96290 validation-aucpr:0.96740
[11] validation-logloss:0.49082 validation-auc:0.96263 validation-aucpr:0.96711
[12] validation-logloss:0.47988 validation-auc:0.96267 validation-aucpr:0.96708
[13] validation-logloss:0.46958 validation-auc:0.96244 validation-aucpr:0.96687
[14] validation-logloss:0.45934 validation-auc:0.96216 validation-aucpr:0.96717
[15] validation-logloss:0.44964 validation-auc:0.96201 validation-aucpr:0.96697
[16] validation-logloss:0.43784 validation-auc:0.96338 validation-aucpr:0.96841
[17] validation-logloss:0.42886 validation-auc:0.96393 validation-aucpr:0.96876
[18] validation-logloss:0.42107 validation-auc:0.96422 validation-aucpr:0.96890
[19] validation-logloss:0.41281 validation-auc:0.96424 validation-aucpr:0.96888
[20] validation-logloss:0.40561 validation-auc:0.96420 validation-aucpr:0.96878
[21] validation-logloss:0.39898 validation-auc:0.96382 validation-aucpr:0.96849
[22] validation-logloss:0.39189 validation-auc:0.96414 validation-aucpr:0.96870
[23] validation-logloss:0.38533 validation-auc:0.96435 validation-aucpr:0.96885
[24] validation-logloss:0.37768 validation-auc:0.96487 validation-aucpr:0.96951
[25] validation-logloss:0.37250 validation-auc:0.96474 validation-aucpr:0.96954
[26] validation-logloss:0.36790 validation-auc:0.96441 validation-aucpr:0.96929
[27] validation-logloss:0.36108 validation-auc:0.96471 validation-aucpr:0.96965
[28] validation-logloss:0.35680 validation-auc:0.96436 validation-aucpr:0.96934
[29] validation-logloss:0.34959 validation-auc:0.96493 validation-aucpr:0.96989
[30] validation-logloss:0.34517 validation-auc:0.96481 validation-aucpr:0.96977
[31] validation-logloss:0.34129 validation-auc:0.96485 validation-aucpr:0.96984
[32] validation-logloss:0.33800 validation-auc:0.96458 validation-aucpr:0.96962
[33] validation-logloss:0.33220 validation-auc:0.96485 validation-aucpr:0.96994
[34] validation-logloss:0.32840 validation-auc:0.96484 validation-aucpr:0.96992
[35] validation-logloss:0.32387 validation-auc:0.96506 validation-aucpr:0.97009
[36] validation-logloss:0.32003 validation-auc:0.96517 validation-aucpr:0.97012
[37] validation-logloss:0.31686 validation-auc:0.96522 validation-aucpr:0.97014
[38] validation-logloss:0.31350 validation-auc:0.96523 validation-aucpr:0.97016
[39] validation-logloss:0.31083 validation-auc:0.96506 validation-aucpr:0.96999
[40] validation-logloss:0.30749 validation-auc:0.96522 validation-aucpr:0.97009
[41] validation-logloss:0.30229 validation-auc:0.96560 validation-aucpr:0.97045
[42] validation-logloss:0.29765 validation-auc:0.96576 validation-aucpr:0.97062
[43] validation-logloss:0.29495 validation-auc:0.96580 validation-aucpr:0.97063
[44] validation-logloss:0.29224 validation-auc:0.96583 validation-aucpr:0.97072
[45] validation-logloss:0.28999 validation-auc:0.96581 validation-aucpr:0.97069
[46] validation-logloss:0.28784 validation-auc:0.96577 validation-aucpr:0.97067
[47] validation-logloss:0.28551 validation-auc:0.96595 validation-aucpr:0.97078
[48] validation-logloss:0.28290 validation-auc:0.96608 validation-aucpr:0.97087
[49] validation-logloss:0.28053 validation-auc:0.96615 validation-aucpr:0.97090
[50] validation-logloss:0.27852 validation-auc:0.96617 validation-aucpr:0.97093
[51] validation-logloss:0.27661 validation-auc:0.96618 validation-aucpr:0.97092
[52] validation-logloss:0.27287 validation-auc:0.96649 validation-aucpr:0.97123
[53] validation-logloss:0.27084 validation-auc:0.96663 validation-aucpr:0.97132
[54] validation-logloss:0.26952 validation-auc:0.96652 validation-aucpr:0.97122
[55] validation-logloss:0.26764 validation-auc:0.96661 validation-aucpr:0.97131
{'best_iteration': '53', 'best_score': '0.9713172950178219'}
Trial 25, Fold 3: Log loss = 0.267635464730996, Average precision = 0.9713168943918258, ROC-AUC = 0.966612060704414, Elapsed Time = 1.624384500000815 seconds
Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 25, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67115 validation-auc:0.91979 validation-aucpr:0.91803
[1] validation-logloss:0.64973 validation-auc:0.94236 validation-aucpr:0.94599
[2] validation-logloss:0.62750 validation-auc:0.95848 validation-aucpr:0.96492
[3] validation-logloss:0.60959 validation-auc:0.95865 validation-aucpr:0.96487
[4] validation-logloss:0.59150 validation-auc:0.95936 validation-aucpr:0.96547
[5] validation-logloss:0.57424 validation-auc:0.96145 validation-aucpr:0.96776
[6] validation-logloss:0.55902 validation-auc:0.96092 validation-aucpr:0.96714
[7] validation-logloss:0.54229 validation-auc:0.96169 validation-aucpr:0.96796
[8] validation-logloss:0.52964 validation-auc:0.96073 validation-aucpr:0.96701
[9] validation-logloss:0.51694 validation-auc:0.96048 validation-aucpr:0.96675
[10] validation-logloss:0.50214 validation-auc:0.96103 validation-aucpr:0.96738
[11] validation-logloss:0.48849 validation-auc:0.96105 validation-aucpr:0.96757
[12] validation-logloss:0.47751 validation-auc:0.96054 validation-aucpr:0.96709
[13] validation-logloss:0.46514 validation-auc:0.96040 validation-aucpr:0.96720
[14] validation-logloss:0.45366 validation-auc:0.96050 validation-aucpr:0.96739
[15] validation-logloss:0.44469 validation-auc:0.96029 validation-aucpr:0.96710
[16] validation-logloss:0.43588 validation-auc:0.96056 validation-aucpr:0.96721
[17] validation-logloss:0.42686 validation-auc:0.96145 validation-aucpr:0.96782
[18] validation-logloss:0.41644 validation-auc:0.96199 validation-aucpr:0.96829
[19] validation-logloss:0.40871 validation-auc:0.96197 validation-aucpr:0.96831
[20] validation-logloss:0.39951 validation-auc:0.96221 validation-aucpr:0.96857
[21] validation-logloss:0.39327 validation-auc:0.96203 validation-aucpr:0.96836
[22] validation-logloss:0.38503 validation-auc:0.96215 validation-aucpr:0.96855
[23] validation-logloss:0.37917 validation-auc:0.96189 validation-aucpr:0.96831
[24] validation-logloss:0.37403 validation-auc:0.96182 validation-aucpr:0.96832
[25] validation-logloss:0.36834 validation-auc:0.96178 validation-aucpr:0.96824
[26] validation-logloss:0.36365 validation-auc:0.96169 validation-aucpr:0.96808
[27] validation-logloss:0.35854 validation-auc:0.96175 validation-aucpr:0.96809
[28] validation-logloss:0.35411 validation-auc:0.96161 validation-aucpr:0.96802
[29] validation-logloss:0.34907 validation-auc:0.96160 validation-aucpr:0.96804
[30] validation-logloss:0.34378 validation-auc:0.96202 validation-aucpr:0.96837
[31] validation-logloss:0.34033 validation-auc:0.96183 validation-aucpr:0.96821
[32] validation-logloss:0.33436 validation-auc:0.96221 validation-aucpr:0.96856
[33] validation-logloss:0.32862 validation-auc:0.96257 validation-aucpr:0.96887
[34] validation-logloss:0.32490 validation-auc:0.96244 validation-aucpr:0.96875
[35] validation-logloss:0.31972 validation-auc:0.96266 validation-aucpr:0.96895
[36] validation-logloss:0.31625 validation-auc:0.96255 validation-aucpr:0.96888
[37] validation-logloss:0.31301 validation-auc:0.96248 validation-aucpr:0.96885
[38] validation-logloss:0.30935 validation-auc:0.96266 validation-aucpr:0.96901
[39] validation-logloss:0.30444 validation-auc:0.96302 validation-aucpr:0.96932
[40] validation-logloss:0.30201 validation-auc:0.96302 validation-aucpr:0.96930
[41] validation-logloss:0.29797 validation-auc:0.96305 validation-aucpr:0.96935
[42] validation-logloss:0.29481 validation-auc:0.96324 validation-aucpr:0.96947
[43] validation-logloss:0.29174 validation-auc:0.96337 validation-aucpr:0.96953
[44] validation-logloss:0.28847 validation-auc:0.96369 validation-aucpr:0.96977
[45] validation-logloss:0.28598 validation-auc:0.96377 validation-aucpr:0.96979
[46] validation-logloss:0.28323 validation-auc:0.96394 validation-aucpr:0.96997
[47] validation-logloss:0.28073 validation-auc:0.96399 validation-aucpr:0.96999
[48] validation-logloss:0.27912 validation-auc:0.96382 validation-aucpr:0.96987
[49] validation-logloss:0.27658 validation-auc:0.96396 validation-aucpr:0.96997
[50] validation-logloss:0.27350 validation-auc:0.96415 validation-aucpr:0.97018
[51] validation-logloss:0.27128 validation-auc:0.96431 validation-aucpr:0.97030
[52] validation-logloss:0.26785 validation-auc:0.96457 validation-aucpr:0.97053
[53] validation-logloss:0.26509 validation-auc:0.96475 validation-aucpr:0.97072
[54] validation-logloss:0.26379 validation-auc:0.96470 validation-aucpr:0.97068
[55] validation-logloss:0.26189 validation-auc:0.96481 validation-aucpr:0.97079
{'best_iteration': '55', 'best_score': '0.9707935271000794'}
Trial 25, Fold 4: Log loss = 0.2618905262310492, Average precision = 0.9707982250800647, ROC-AUC = 0.9648084216161052, Elapsed Time = 1.4710076999999728 seconds
Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 25, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67141 validation-auc:0.92058 validation-aucpr:0.91798
[1] validation-logloss:0.65048 validation-auc:0.93603 validation-aucpr:0.94189
[2] validation-logloss:0.63149 validation-auc:0.93869 validation-aucpr:0.94458
[3] validation-logloss:0.61234 validation-auc:0.94543 validation-aucpr:0.95018
[4] validation-logloss:0.59490 validation-auc:0.94715 validation-aucpr:0.95168
[5] validation-logloss:0.57953 validation-auc:0.94706 validation-aucpr:0.95158
[6] validation-logloss:0.56517 validation-auc:0.94737 validation-aucpr:0.95219
[7] validation-logloss:0.55121 validation-auc:0.94698 validation-aucpr:0.95191
[8] validation-logloss:0.53763 validation-auc:0.94742 validation-aucpr:0.95207
[9] validation-logloss:0.52568 validation-auc:0.94794 validation-aucpr:0.95275
[10] validation-logloss:0.51307 validation-auc:0.94890 validation-aucpr:0.95359
[11] validation-logloss:0.49836 validation-auc:0.95373 validation-aucpr:0.95902
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[27] validation-logloss:0.37176 validation-auc:0.95869 validation-aucpr:0.96393
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[30] validation-logloss:0.35060 validation-auc:0.96051 validation-aucpr:0.96594
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[32] validation-logloss:0.34298 validation-auc:0.96039 validation-aucpr:0.96584
[33] validation-logloss:0.33881 validation-auc:0.96044 validation-aucpr:0.96587
[34] validation-logloss:0.33522 validation-auc:0.96040 validation-aucpr:0.96587
[35] validation-logloss:0.33207 validation-auc:0.96034 validation-aucpr:0.96579
[36] validation-logloss:0.32875 validation-auc:0.96039 validation-aucpr:0.96580
[37] validation-logloss:0.32504 validation-auc:0.96064 validation-aucpr:0.96604
[38] validation-logloss:0.32146 validation-auc:0.96109 validation-aucpr:0.96638
[39] validation-logloss:0.31822 validation-auc:0.96111 validation-aucpr:0.96641
[40] validation-logloss:0.31327 validation-auc:0.96150 validation-aucpr:0.96678
[41] validation-logloss:0.30825 validation-auc:0.96191 validation-aucpr:0.96721
[42] validation-logloss:0.30370 validation-auc:0.96225 validation-aucpr:0.96760
[43] validation-logloss:0.30148 validation-auc:0.96214 validation-aucpr:0.96748
[44] validation-logloss:0.29898 validation-auc:0.96219 validation-aucpr:0.96748
[45] validation-logloss:0.29511 validation-auc:0.96242 validation-aucpr:0.96770
[46] validation-logloss:0.29080 validation-auc:0.96270 validation-aucpr:0.96800
[47] validation-logloss:0.28917 validation-auc:0.96262 validation-aucpr:0.96791
[48] validation-logloss:0.28535 validation-auc:0.96288 validation-aucpr:0.96819
[49] validation-logloss:0.28317 validation-auc:0.96302 validation-aucpr:0.96834
[50] validation-logloss:0.28201 validation-auc:0.96284 validation-aucpr:0.96822
[51] validation-logloss:0.28024 validation-auc:0.96289 validation-aucpr:0.96825
[52] validation-logloss:0.27846 validation-auc:0.96286 validation-aucpr:0.96822
[53] validation-logloss:0.27703 validation-auc:0.96293 validation-aucpr:0.96831
[54] validation-logloss:0.27502 validation-auc:0.96304 validation-aucpr:0.96838
[55] validation-logloss:0.27291 validation-auc:0.96330 validation-aucpr:0.96859
{'best_iteration': '55', 'best_score': '0.968587545474199'}
Trial 25, Fold 5: Log loss = 0.2729147602555443, Average precision = 0.9685929768466469, ROC-AUC = 0.9633032718354607, Elapsed Time = 1.555640200000198 seconds
Optimization Progress: 26%|##6 | 26/100 [38:28<38:27, 31.18s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 26, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:37:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68542 validation-auc:0.95577 validation-aucpr:0.96213
[18:37:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67867 validation-auc:0.96068 validation-aucpr:0.96692
[18:37:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67120 validation-auc:0.96338 validation-aucpr:0.96919
[18:37:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66384 validation-auc:0.96580 validation-aucpr:0.97120
[18:37:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65661 validation-auc:0.96626 validation-aucpr:0.97158
[18:37:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64959 validation-auc:0.96711 validation-aucpr:0.97239
[18:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64273 validation-auc:0.96780 validation-aucpr:0.97295
[18:37:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63604 validation-auc:0.96813 validation-aucpr:0.97354
[18:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62933 validation-auc:0.96837 validation-aucpr:0.97372
[18:37:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62285 validation-auc:0.96863 validation-aucpr:0.97386
[18:37:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61637 validation-auc:0.96883 validation-aucpr:0.97397
[18:37:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61024 validation-auc:0.96871 validation-aucpr:0.97390
[18:37:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60476 validation-auc:0.96863 validation-aucpr:0.97377
[18:37:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59939 validation-auc:0.96873 validation-aucpr:0.97378
[18:37:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59348 validation-auc:0.96873 validation-aucpr:0.97380
[18:37:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58817 validation-auc:0.96866 validation-aucpr:0.97373
[18:37:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58251 validation-auc:0.96856 validation-aucpr:0.97369
[18:37:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57693 validation-auc:0.96862 validation-aucpr:0.97373
[18:37:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57188 validation-auc:0.96873 validation-aucpr:0.97378
[18:37:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56651 validation-auc:0.96867 validation-aucpr:0.97374
[18:37:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56116 validation-auc:0.96893 validation-aucpr:0.97395
[18:37:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55587 validation-auc:0.96895 validation-aucpr:0.97398
[18:37:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55064 validation-auc:0.96917 validation-aucpr:0.97416
[18:37:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54581 validation-auc:0.96929 validation-aucpr:0.97421
[18:37:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54080 validation-auc:0.96946 validation-aucpr:0.97436
[18:37:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53591 validation-auc:0.96953 validation-aucpr:0.97440
[18:37:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53108 validation-auc:0.96968 validation-aucpr:0.97451
[18:37:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52627 validation-auc:0.96982 validation-aucpr:0.97475
[18:38:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52151 validation-auc:0.97000 validation-aucpr:0.97491
[18:38:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51692 validation-auc:0.97016 validation-aucpr:0.97500
[18:38:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51234 validation-auc:0.97020 validation-aucpr:0.97510
[18:38:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50786 validation-auc:0.97023 validation-aucpr:0.97514
[18:38:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50350 validation-auc:0.97024 validation-aucpr:0.97514
[18:38:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49918 validation-auc:0.97031 validation-aucpr:0.97518
[18:38:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49495 validation-auc:0.97030 validation-aucpr:0.97520
[18:38:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49079 validation-auc:0.97036 validation-aucpr:0.97530
[18:38:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.48670 validation-auc:0.97038 validation-aucpr:0.97533
[18:38:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.48266 validation-auc:0.97049 validation-aucpr:0.97546
[18:38:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47908 validation-auc:0.97044 validation-aucpr:0.97542
[18:38:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.47512 validation-auc:0.97053 validation-aucpr:0.97548
[18:38:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47126 validation-auc:0.97055 validation-aucpr:0.97550
[18:38:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46742 validation-auc:0.97074 validation-aucpr:0.97563
[18:38:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.46389 validation-auc:0.97079 validation-aucpr:0.97565
[18:38:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46026 validation-auc:0.97071 validation-aucpr:0.97560
[18:38:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.45700 validation-auc:0.97078 validation-aucpr:0.97562
[18:38:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.45345 validation-auc:0.97081 validation-aucpr:0.97564
[18:38:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.44989 validation-auc:0.97086 validation-aucpr:0.97569
[18:38:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.44645 validation-auc:0.97091 validation-aucpr:0.97572
[18:38:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.44304 validation-auc:0.97100 validation-aucpr:0.97579
[18:38:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.43971 validation-auc:0.97098 validation-aucpr:0.97577
[18:38:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.43650 validation-auc:0.97090 validation-aucpr:0.97571
[18:38:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.43330 validation-auc:0.97078 validation-aucpr:0.97562
[18:38:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.43045 validation-auc:0.97074 validation-aucpr:0.97558
[18:38:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.42735 validation-auc:0.97078 validation-aucpr:0.97562
[18:38:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.42425 validation-auc:0.97079 validation-aucpr:0.97564
[18:38:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.42154 validation-auc:0.97069 validation-aucpr:0.97556
[18:38:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.41848 validation-auc:0.97075 validation-aucpr:0.97560
[18:38:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.41589 validation-auc:0.97070 validation-aucpr:0.97554
[18:38:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.41295 validation-auc:0.97078 validation-aucpr:0.97560
[18:38:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.41013 validation-auc:0.97081 validation-aucpr:0.97562
[18:38:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.40731 validation-auc:0.97080 validation-aucpr:0.97561
[18:38:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.40452 validation-auc:0.97075 validation-aucpr:0.97558
[18:38:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.40178 validation-auc:0.97081 validation-aucpr:0.97561
[18:38:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.39898 validation-auc:0.97087 validation-aucpr:0.97565
[18:38:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.39632 validation-auc:0.97086 validation-aucpr:0.97564
[18:38:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.39370 validation-auc:0.97080 validation-aucpr:0.97561
[18:38:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.39130 validation-auc:0.97077 validation-aucpr:0.97558
[18:38:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.38866 validation-auc:0.97083 validation-aucpr:0.97563
[18:38:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.38611 validation-auc:0.97099 validation-aucpr:0.97579
[18:38:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.38363 validation-auc:0.97100 validation-aucpr:0.97580
[18:38:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.38118 validation-auc:0.97102 validation-aucpr:0.97582
[18:39:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.37876 validation-auc:0.97108 validation-aucpr:0.97582
[18:39:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.37638 validation-auc:0.97119 validation-aucpr:0.97590
[18:39:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.37400 validation-auc:0.97125 validation-aucpr:0.97593
[18:39:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.37194 validation-auc:0.97117 validation-aucpr:0.97585
[18:39:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.36966 validation-auc:0.97121 validation-aucpr:0.97587
[18:39:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.36740 validation-auc:0.97124 validation-aucpr:0.97589
[18:39:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.36525 validation-auc:0.97126 validation-aucpr:0.97590
[18:39:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.36308 validation-auc:0.97137 validation-aucpr:0.97594
{'best_iteration': '78', 'best_score': '0.9759377173694493'}
Trial 26, Fold 1: Log loss = 0.3630798792810747, Average precision = 0.9759361280515932, ROC-AUC = 0.9713674972397873, Elapsed Time = 108.3564650999997 seconds
Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 26, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:39:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68565 validation-auc:0.95899 validation-aucpr:0.96175
[18:39:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67799 validation-auc:0.96605 validation-aucpr:0.96878
[18:39:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67063 validation-auc:0.96761 validation-aucpr:0.97093
[18:39:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66335 validation-auc:0.96813 validation-aucpr:0.97149
[18:39:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65622 validation-auc:0.96847 validation-aucpr:0.97178
[18:39:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64919 validation-auc:0.96838 validation-aucpr:0.97186
[18:39:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64298 validation-auc:0.96890 validation-aucpr:0.97236
[18:39:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63628 validation-auc:0.96883 validation-aucpr:0.97227
[18:39:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62956 validation-auc:0.96938 validation-aucpr:0.97272
[18:39:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62302 validation-auc:0.96977 validation-aucpr:0.97307
[18:39:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61658 validation-auc:0.96992 validation-aucpr:0.97313
[18:39:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61043 validation-auc:0.96967 validation-aucpr:0.97291
[18:39:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60430 validation-auc:0.96955 validation-aucpr:0.97286
[18:39:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59878 validation-auc:0.96985 validation-aucpr:0.97309
[18:39:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59294 validation-auc:0.97001 validation-aucpr:0.97322
[18:39:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58713 validation-auc:0.97023 validation-aucpr:0.97346
[18:39:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58188 validation-auc:0.97024 validation-aucpr:0.97348
[18:39:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57628 validation-auc:0.97036 validation-aucpr:0.97357
[18:39:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57078 validation-auc:0.97024 validation-aucpr:0.97347
[18:39:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56535 validation-auc:0.97032 validation-aucpr:0.97353
[18:39:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56005 validation-auc:0.97046 validation-aucpr:0.97359
[18:39:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55473 validation-auc:0.97073 validation-aucpr:0.97378
[18:39:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54972 validation-auc:0.97071 validation-aucpr:0.97379
[18:39:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54465 validation-auc:0.97079 validation-aucpr:0.97383
[18:39:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53975 validation-auc:0.97067 validation-aucpr:0.97375
[18:39:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53487 validation-auc:0.97086 validation-aucpr:0.97391
[18:39:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52995 validation-auc:0.97095 validation-aucpr:0.97398
[18:39:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52513 validation-auc:0.97103 validation-aucpr:0.97406
[18:39:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52056 validation-auc:0.97107 validation-aucpr:0.97410
[18:39:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51591 validation-auc:0.97119 validation-aucpr:0.97418
[18:39:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51190 validation-auc:0.97112 validation-aucpr:0.97412
[18:39:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50740 validation-auc:0.97136 validation-aucpr:0.97430
[18:39:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50296 validation-auc:0.97148 validation-aucpr:0.97438
[18:39:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49867 validation-auc:0.97148 validation-aucpr:0.97439
[18:39:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49439 validation-auc:0.97167 validation-aucpr:0.97461
[18:39:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49023 validation-auc:0.97165 validation-aucpr:0.97458
[18:40:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.48605 validation-auc:0.97172 validation-aucpr:0.97463
[18:40:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.48204 validation-auc:0.97177 validation-aucpr:0.97471
[18:40:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47833 validation-auc:0.97185 validation-aucpr:0.97478
[18:40:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.47440 validation-auc:0.97202 validation-aucpr:0.97491
[18:40:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47053 validation-auc:0.97202 validation-aucpr:0.97492
[18:40:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46671 validation-auc:0.97199 validation-aucpr:0.97489
[18:40:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.46314 validation-auc:0.97191 validation-aucpr:0.97483
[18:40:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45947 validation-auc:0.97188 validation-aucpr:0.97480
[18:40:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.45579 validation-auc:0.97189 validation-aucpr:0.97478
[18:40:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.45221 validation-auc:0.97188 validation-aucpr:0.97476
[18:40:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.44871 validation-auc:0.97182 validation-aucpr:0.97470
[18:40:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.44527 validation-auc:0.97177 validation-aucpr:0.97466
[18:40:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.44188 validation-auc:0.97171 validation-aucpr:0.97461
[18:40:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.43856 validation-auc:0.97177 validation-aucpr:0.97465
[18:40:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.43520 validation-auc:0.97184 validation-aucpr:0.97468
[18:40:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.43198 validation-auc:0.97180 validation-aucpr:0.97467
[18:40:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.42871 validation-auc:0.97185 validation-aucpr:0.97470
[18:40:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.42569 validation-auc:0.97195 validation-aucpr:0.97476
[18:40:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.42256 validation-auc:0.97192 validation-aucpr:0.97474
[18:40:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.41984 validation-auc:0.97196 validation-aucpr:0.97478
[18:40:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.41678 validation-auc:0.97201 validation-aucpr:0.97484
[18:40:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.41372 validation-auc:0.97205 validation-aucpr:0.97486
[18:40:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.41072 validation-auc:0.97204 validation-aucpr:0.97484
[18:40:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.40776 validation-auc:0.97211 validation-aucpr:0.97491
[18:40:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.40493 validation-auc:0.97212 validation-aucpr:0.97488
[18:40:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.40216 validation-auc:0.97206 validation-aucpr:0.97485
[18:40:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.39939 validation-auc:0.97210 validation-aucpr:0.97488
[18:40:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.39663 validation-auc:0.97214 validation-aucpr:0.97489
[18:40:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.39414 validation-auc:0.97212 validation-aucpr:0.97486
[18:40:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.39150 validation-auc:0.97218 validation-aucpr:0.97489
[18:40:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.38886 validation-auc:0.97220 validation-aucpr:0.97490
[18:40:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.38616 validation-auc:0.97234 validation-aucpr:0.97500
[18:40:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.38368 validation-auc:0.97228 validation-aucpr:0.97495
[18:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.38117 validation-auc:0.97227 validation-aucpr:0.97498
[18:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.37881 validation-auc:0.97220 validation-aucpr:0.97493
[18:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.37636 validation-auc:0.97219 validation-aucpr:0.97492
[18:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.37391 validation-auc:0.97222 validation-aucpr:0.97495
[18:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.37175 validation-auc:0.97224 validation-aucpr:0.97493
[18:40:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.36945 validation-auc:0.97225 validation-aucpr:0.97494
[18:40:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.36742 validation-auc:0.97218 validation-aucpr:0.97486
[18:40:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.36514 validation-auc:0.97216 validation-aucpr:0.97485
[18:40:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.36302 validation-auc:0.97213 validation-aucpr:0.97481
[18:40:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.36082 validation-auc:0.97213 validation-aucpr:0.97482
{'best_iteration': '67', 'best_score': '0.975003136856076'}
Trial 26, Fold 2: Log loss = 0.3608162098978818, Average precision = 0.9747282694988629, ROC-AUC = 0.97213250555928, Elapsed Time = 107.27267639999991 seconds
Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 26, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:41:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68550 validation-auc:0.95309 validation-aucpr:0.95686
[18:41:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67793 validation-auc:0.96434 validation-aucpr:0.96972
[18:41:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67041 validation-auc:0.96710 validation-aucpr:0.97192
[18:41:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66312 validation-auc:0.96727 validation-aucpr:0.97218
[18:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65591 validation-auc:0.96767 validation-aucpr:0.97246
[18:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64882 validation-auc:0.96821 validation-aucpr:0.97276
[18:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64191 validation-auc:0.96833 validation-aucpr:0.97306
[18:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63514 validation-auc:0.96850 validation-aucpr:0.97316
[18:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62849 validation-auc:0.96875 validation-aucpr:0.97341
[18:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62193 validation-auc:0.96921 validation-aucpr:0.97375
[18:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61557 validation-auc:0.96961 validation-aucpr:0.97400
[18:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.60924 validation-auc:0.96968 validation-aucpr:0.97411
[18:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60306 validation-auc:0.97005 validation-aucpr:0.97422
[18:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59755 validation-auc:0.97063 validation-aucpr:0.97486
[18:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59155 validation-auc:0.97064 validation-aucpr:0.97486
[18:41:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58571 validation-auc:0.97075 validation-aucpr:0.97496
[18:41:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58045 validation-auc:0.97084 validation-aucpr:0.97500
[18:41:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57488 validation-auc:0.97090 validation-aucpr:0.97506
[18:41:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.56931 validation-auc:0.97097 validation-aucpr:0.97510
[18:41:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56431 validation-auc:0.97124 validation-aucpr:0.97530
[18:41:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.55881 validation-auc:0.97153 validation-aucpr:0.97553
[18:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55353 validation-auc:0.97168 validation-aucpr:0.97566
[18:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54825 validation-auc:0.97175 validation-aucpr:0.97574
[18:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54318 validation-auc:0.97173 validation-aucpr:0.97571
[18:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53871 validation-auc:0.97168 validation-aucpr:0.97567
[18:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53371 validation-auc:0.97157 validation-aucpr:0.97561
[18:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52889 validation-auc:0.97138 validation-aucpr:0.97550
[18:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52414 validation-auc:0.97131 validation-aucpr:0.97545
[18:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.51943 validation-auc:0.97140 validation-aucpr:0.97551
[18:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51524 validation-auc:0.97146 validation-aucpr:0.97563
[18:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51064 validation-auc:0.97149 validation-aucpr:0.97565
[18:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50617 validation-auc:0.97147 validation-aucpr:0.97564
[18:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50169 validation-auc:0.97157 validation-aucpr:0.97570
[18:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49756 validation-auc:0.97149 validation-aucpr:0.97562
[18:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49343 validation-auc:0.97148 validation-aucpr:0.97562
[18:41:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.48979 validation-auc:0.97148 validation-aucpr:0.97561
[18:41:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.48575 validation-auc:0.97153 validation-aucpr:0.97565
[18:41:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.48177 validation-auc:0.97149 validation-aucpr:0.97563
[18:41:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47778 validation-auc:0.97159 validation-aucpr:0.97570
[18:41:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.47390 validation-auc:0.97145 validation-aucpr:0.97560
[18:41:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47002 validation-auc:0.97147 validation-aucpr:0.97563
[18:41:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46649 validation-auc:0.97152 validation-aucpr:0.97569
[18:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.46281 validation-auc:0.97145 validation-aucpr:0.97562
[18:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45941 validation-auc:0.97148 validation-aucpr:0.97564
[18:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.45611 validation-auc:0.97149 validation-aucpr:0.97559
[18:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.45262 validation-auc:0.97146 validation-aucpr:0.97557
[18:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.44905 validation-auc:0.97147 validation-aucpr:0.97559
[18:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.44559 validation-auc:0.97152 validation-aucpr:0.97562
[18:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.44253 validation-auc:0.97149 validation-aucpr:0.97561
[18:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.43921 validation-auc:0.97147 validation-aucpr:0.97560
[18:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.43588 validation-auc:0.97149 validation-aucpr:0.97562
[18:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.43259 validation-auc:0.97151 validation-aucpr:0.97564
[18:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.42979 validation-auc:0.97151 validation-aucpr:0.97566
[18:42:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.42665 validation-auc:0.97152 validation-aucpr:0.97565
[18:42:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.42356 validation-auc:0.97155 validation-aucpr:0.97568
[18:42:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.42044 validation-auc:0.97162 validation-aucpr:0.97573
[18:42:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.41741 validation-auc:0.97163 validation-aucpr:0.97575
[18:42:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.41439 validation-auc:0.97172 validation-aucpr:0.97581
[18:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.41144 validation-auc:0.97183 validation-aucpr:0.97591
[18:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.40850 validation-auc:0.97188 validation-aucpr:0.97595
[18:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.40560 validation-auc:0.97188 validation-aucpr:0.97595
[18:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.40292 validation-auc:0.97187 validation-aucpr:0.97596
[18:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.40015 validation-auc:0.97190 validation-aucpr:0.97596
[18:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.39739 validation-auc:0.97198 validation-aucpr:0.97603
[18:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.39464 validation-auc:0.97197 validation-aucpr:0.97602
[18:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.39198 validation-auc:0.97194 validation-aucpr:0.97601
[18:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.38960 validation-auc:0.97196 validation-aucpr:0.97597
[18:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.38708 validation-auc:0.97191 validation-aucpr:0.97596
[18:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.38457 validation-auc:0.97186 validation-aucpr:0.97596
[18:42:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.38206 validation-auc:0.97188 validation-aucpr:0.97598
[18:42:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.37962 validation-auc:0.97189 validation-aucpr:0.97600
[18:42:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.37739 validation-auc:0.97189 validation-aucpr:0.97599
[18:42:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.37494 validation-auc:0.97191 validation-aucpr:0.97600
[18:42:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.37256 validation-auc:0.97186 validation-aucpr:0.97597
[18:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.37027 validation-auc:0.97188 validation-aucpr:0.97598
[18:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.36795 validation-auc:0.97186 validation-aucpr:0.97596
[18:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.36573 validation-auc:0.97190 validation-aucpr:0.97599
[18:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.36345 validation-auc:0.97188 validation-aucpr:0.97598
[18:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.36119 validation-auc:0.97194 validation-aucpr:0.97609
{'best_iteration': '78', 'best_score': '0.9760926971288632'}
Trial 26, Fold 3: Log loss = 0.3611888171930082, Average precision = 0.9760961494961098, ROC-AUC = 0.9719414089963498, Elapsed Time = 108.23821640000097 seconds
Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 26, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68536 validation-auc:0.95745 validation-aucpr:0.95606
[18:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67782 validation-auc:0.96335 validation-aucpr:0.96994
[18:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67038 validation-auc:0.96625 validation-aucpr:0.97207
[18:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66315 validation-auc:0.96726 validation-aucpr:0.97260
[18:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65595 validation-auc:0.96774 validation-aucpr:0.97297
[18:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64891 validation-auc:0.96819 validation-aucpr:0.97336
[18:42:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64203 validation-auc:0.96809 validation-aucpr:0.97333
[18:42:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63593 validation-auc:0.96831 validation-aucpr:0.97367
[18:43:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62940 validation-auc:0.96854 validation-aucpr:0.97389
[18:43:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62296 validation-auc:0.96900 validation-aucpr:0.97411
[18:43:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61661 validation-auc:0.96910 validation-aucpr:0.97427
[18:43:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61030 validation-auc:0.96943 validation-aucpr:0.97451
[18:43:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60414 validation-auc:0.96957 validation-aucpr:0.97456
[18:43:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59819 validation-auc:0.96934 validation-aucpr:0.97445
[18:43:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59229 validation-auc:0.96928 validation-aucpr:0.97439
[18:43:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58695 validation-auc:0.96913 validation-aucpr:0.97426
[18:43:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58134 validation-auc:0.96899 validation-aucpr:0.97415
[18:43:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57585 validation-auc:0.96900 validation-aucpr:0.97418
[18:43:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57028 validation-auc:0.96931 validation-aucpr:0.97441
[18:43:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56480 validation-auc:0.96962 validation-aucpr:0.97464
[18:43:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.55947 validation-auc:0.96972 validation-aucpr:0.97474
[18:43:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55442 validation-auc:0.96973 validation-aucpr:0.97474
[18:43:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54934 validation-auc:0.96977 validation-aucpr:0.97478
[18:43:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54420 validation-auc:0.97004 validation-aucpr:0.97494
[18:43:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53924 validation-auc:0.96999 validation-aucpr:0.97493
[18:43:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53436 validation-auc:0.97005 validation-aucpr:0.97496
[18:43:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52954 validation-auc:0.97005 validation-aucpr:0.97498
[18:43:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52479 validation-auc:0.97022 validation-aucpr:0.97507
[18:43:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52029 validation-auc:0.97014 validation-aucpr:0.97503
[18:43:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51609 validation-auc:0.97022 validation-aucpr:0.97510
[18:43:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51163 validation-auc:0.97027 validation-aucpr:0.97513
[18:43:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50728 validation-auc:0.97026 validation-aucpr:0.97513
[18:43:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50290 validation-auc:0.97021 validation-aucpr:0.97510
[18:43:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49872 validation-auc:0.97032 validation-aucpr:0.97516
[18:43:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49453 validation-auc:0.97036 validation-aucpr:0.97518
[18:43:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49031 validation-auc:0.97050 validation-aucpr:0.97529
[18:43:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.48618 validation-auc:0.97067 validation-aucpr:0.97543
[18:43:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.48219 validation-auc:0.97068 validation-aucpr:0.97542
[18:43:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47821 validation-auc:0.97066 validation-aucpr:0.97542
[18:43:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.47442 validation-auc:0.97054 validation-aucpr:0.97531
[18:43:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47059 validation-auc:0.97067 validation-aucpr:0.97539
[18:43:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46678 validation-auc:0.97077 validation-aucpr:0.97546
[18:43:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.46341 validation-auc:0.97077 validation-aucpr:0.97547
[18:43:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.45974 validation-auc:0.97080 validation-aucpr:0.97550
[18:43:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.45612 validation-auc:0.97087 validation-aucpr:0.97552
[18:43:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.45254 validation-auc:0.97090 validation-aucpr:0.97556
[18:43:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.44907 validation-auc:0.97091 validation-aucpr:0.97556
[18:43:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.44595 validation-auc:0.97088 validation-aucpr:0.97555
[18:43:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.44254 validation-auc:0.97089 validation-aucpr:0.97555
[18:43:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.43918 validation-auc:0.97096 validation-aucpr:0.97561
[18:43:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.43591 validation-auc:0.97093 validation-aucpr:0.97559
[18:43:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.43276 validation-auc:0.97086 validation-aucpr:0.97552
[18:43:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.42987 validation-auc:0.97084 validation-aucpr:0.97552
[18:43:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.42669 validation-auc:0.97088 validation-aucpr:0.97556
[18:44:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.42356 validation-auc:0.97083 validation-aucpr:0.97554
[18:44:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.42076 validation-auc:0.97093 validation-aucpr:0.97562
[18:44:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.41774 validation-auc:0.97097 validation-aucpr:0.97565
[18:44:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.41474 validation-auc:0.97106 validation-aucpr:0.97570
[18:44:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.41179 validation-auc:0.97116 validation-aucpr:0.97576
[18:44:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.40919 validation-auc:0.97110 validation-aucpr:0.97573
[18:44:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.40635 validation-auc:0.97112 validation-aucpr:0.97575
[18:44:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.40354 validation-auc:0.97111 validation-aucpr:0.97575
[18:44:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.40077 validation-auc:0.97113 validation-aucpr:0.97575
[18:44:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.39801 validation-auc:0.97116 validation-aucpr:0.97577
[18:44:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.39527 validation-auc:0.97128 validation-aucpr:0.97585
[18:44:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.39259 validation-auc:0.97132 validation-aucpr:0.97587
[18:44:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.38997 validation-auc:0.97133 validation-aucpr:0.97587
[18:44:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.38740 validation-auc:0.97134 validation-aucpr:0.97587
[18:44:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.38504 validation-auc:0.97136 validation-aucpr:0.97589
[18:44:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.38248 validation-auc:0.97144 validation-aucpr:0.97595
[18:44:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.38005 validation-auc:0.97137 validation-aucpr:0.97589
[18:44:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.37764 validation-auc:0.97133 validation-aucpr:0.97587
[18:44:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.37520 validation-auc:0.97137 validation-aucpr:0.97589
[18:44:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.37287 validation-auc:0.97136 validation-aucpr:0.97588
[18:44:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.37051 validation-auc:0.97134 validation-aucpr:0.97587
[18:44:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.36822 validation-auc:0.97135 validation-aucpr:0.97587
[18:44:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.36598 validation-auc:0.97128 validation-aucpr:0.97582
[18:44:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.36374 validation-auc:0.97130 validation-aucpr:0.97582
[18:44:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.36154 validation-auc:0.97130 validation-aucpr:0.97581
{'best_iteration': '69', 'best_score': '0.975951096302995'}
Trial 26, Fold 4: Log loss = 0.3615393712953612, Average precision = 0.9757995200171711, ROC-AUC = 0.9712955064605582, Elapsed Time = 106.7030864999997 seconds
Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 26, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:44:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68543 validation-auc:0.95258 validation-aucpr:0.95746
[18:44:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67787 validation-auc:0.96072 validation-aucpr:0.96581
[18:44:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67039 validation-auc:0.96449 validation-aucpr:0.96857
[18:44:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66323 validation-auc:0.96455 validation-aucpr:0.96869
[18:44:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65615 validation-auc:0.96421 validation-aucpr:0.96853
[18:44:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64925 validation-auc:0.96447 validation-aucpr:0.96876
[18:44:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64256 validation-auc:0.96455 validation-aucpr:0.96888
[18:44:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63584 validation-auc:0.96545 validation-aucpr:0.96957
[18:44:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.62918 validation-auc:0.96607 validation-aucpr:0.97059
[18:44:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62271 validation-auc:0.96637 validation-aucpr:0.97081
[18:44:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61636 validation-auc:0.96662 validation-aucpr:0.97096
[18:44:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61018 validation-auc:0.96715 validation-aucpr:0.97132
[18:44:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60411 validation-auc:0.96724 validation-aucpr:0.97144
[18:44:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.59812 validation-auc:0.96736 validation-aucpr:0.97155
[18:44:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59224 validation-auc:0.96766 validation-aucpr:0.97171
[18:44:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.58642 validation-auc:0.96799 validation-aucpr:0.97197
[18:44:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58076 validation-auc:0.96798 validation-aucpr:0.97198
[18:44:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57584 validation-auc:0.96763 validation-aucpr:0.97198
[18:44:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57035 validation-auc:0.96790 validation-aucpr:0.97225
[18:45:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56497 validation-auc:0.96780 validation-aucpr:0.97217
[18:45:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.55974 validation-auc:0.96792 validation-aucpr:0.97227
[18:45:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55452 validation-auc:0.96822 validation-aucpr:0.97251
[18:45:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.54943 validation-auc:0.96838 validation-aucpr:0.97259
[18:45:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54438 validation-auc:0.96831 validation-aucpr:0.97259
[18:45:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.53941 validation-auc:0.96838 validation-aucpr:0.97258
[18:45:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53458 validation-auc:0.96833 validation-aucpr:0.97255
[18:45:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.52980 validation-auc:0.96848 validation-aucpr:0.97264
[18:45:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.52507 validation-auc:0.96876 validation-aucpr:0.97286
[18:45:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52047 validation-auc:0.96887 validation-aucpr:0.97292
[18:45:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.51591 validation-auc:0.96893 validation-aucpr:0.97294
[18:45:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51145 validation-auc:0.96893 validation-aucpr:0.97294
[18:45:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.50705 validation-auc:0.96899 validation-aucpr:0.97302
[18:45:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50263 validation-auc:0.96919 validation-aucpr:0.97315
[18:45:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.49843 validation-auc:0.96911 validation-aucpr:0.97316
[18:45:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49427 validation-auc:0.96909 validation-aucpr:0.97314
[18:45:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49015 validation-auc:0.96903 validation-aucpr:0.97310
[18:45:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.48611 validation-auc:0.96889 validation-aucpr:0.97302
[18:45:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.48250 validation-auc:0.96891 validation-aucpr:0.97306
[18:45:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.47857 validation-auc:0.96905 validation-aucpr:0.97316
[18:45:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.47471 validation-auc:0.96915 validation-aucpr:0.97322
[18:45:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47101 validation-auc:0.96915 validation-aucpr:0.97320
[18:45:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.46727 validation-auc:0.96910 validation-aucpr:0.97318
[18:45:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.46361 validation-auc:0.96910 validation-aucpr:0.97319
[18:45:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46007 validation-auc:0.96901 validation-aucpr:0.97309
[18:45:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.45655 validation-auc:0.96902 validation-aucpr:0.97310
[18:45:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.45303 validation-auc:0.96903 validation-aucpr:0.97311
[18:45:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.44954 validation-auc:0.96921 validation-aucpr:0.97321
[18:45:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.44622 validation-auc:0.96946 validation-aucpr:0.97331
[18:45:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.44288 validation-auc:0.96947 validation-aucpr:0.97333
[18:45:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.43968 validation-auc:0.96942 validation-aucpr:0.97329
[18:45:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.43647 validation-auc:0.96934 validation-aucpr:0.97323
[18:45:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.43355 validation-auc:0.96944 validation-aucpr:0.97334
[18:45:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.43046 validation-auc:0.96952 validation-aucpr:0.97338
[18:45:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.42742 validation-auc:0.96945 validation-aucpr:0.97332
[18:45:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.42434 validation-auc:0.96938 validation-aucpr:0.97328
[18:45:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.42145 validation-auc:0.96935 validation-aucpr:0.97330
[18:45:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.41844 validation-auc:0.96941 validation-aucpr:0.97335
[18:45:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.41583 validation-auc:0.96940 validation-aucpr:0.97334
[18:45:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.41295 validation-auc:0.96936 validation-aucpr:0.97322
[18:45:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.41020 validation-auc:0.96929 validation-aucpr:0.97317
[18:45:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.40739 validation-auc:0.96935 validation-aucpr:0.97322
[18:45:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.40456 validation-auc:0.96946 validation-aucpr:0.97329
[18:46:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.40182 validation-auc:0.96947 validation-aucpr:0.97329
[18:46:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.39915 validation-auc:0.96950 validation-aucpr:0.97332
[18:46:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.39648 validation-auc:0.96963 validation-aucpr:0.97340
[18:46:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.39391 validation-auc:0.96963 validation-aucpr:0.97345
[18:46:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.39134 validation-auc:0.96965 validation-aucpr:0.97345
[18:46:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.38885 validation-auc:0.96965 validation-aucpr:0.97345
[18:46:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.38633 validation-auc:0.96970 validation-aucpr:0.97349
[18:46:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.38384 validation-auc:0.96980 validation-aucpr:0.97357
[18:46:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.38163 validation-auc:0.96983 validation-aucpr:0.97357
[18:46:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.37923 validation-auc:0.96986 validation-aucpr:0.97359
[18:46:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.37683 validation-auc:0.96994 validation-aucpr:0.97365
[18:46:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.37483 validation-auc:0.96993 validation-aucpr:0.97369
[18:46:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.37254 validation-auc:0.96997 validation-aucpr:0.97371
[18:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.37030 validation-auc:0.96994 validation-aucpr:0.97369
[18:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.36812 validation-auc:0.96992 validation-aucpr:0.97367
[18:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.36581 validation-auc:0.97007 validation-aucpr:0.97379
[18:46:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.36361 validation-auc:0.97009 validation-aucpr:0.97379
{'best_iteration': '78', 'best_score': '0.9737867965658782'}
Trial 26, Fold 5: Log loss = 0.36361108693323485, Average precision = 0.9737929935568028, ROC-AUC = 0.9700917451647066, Elapsed Time = 107.7331622999991 seconds
Optimization Progress: 27%|##7 | 27/100 [47:35<3:45:59, 185.75s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 27, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67710 validation-auc:0.81462 validation-aucpr:0.74736
[1] validation-logloss:0.66225 validation-auc:0.89137 validation-aucpr:0.83793
[2] validation-logloss:0.64776 validation-auc:0.93149 validation-aucpr:0.91845
[3] validation-logloss:0.63378 validation-auc:0.94311 validation-aucpr:0.93988
[4] validation-logloss:0.62062 validation-auc:0.94563 validation-aucpr:0.94713
[5] validation-logloss:0.60765 validation-auc:0.95090 validation-aucpr:0.95911
[6] validation-logloss:0.59496 validation-auc:0.95299 validation-aucpr:0.96198
[7] validation-logloss:0.58297 validation-auc:0.95449 validation-aucpr:0.96269
[8] validation-logloss:0.57165 validation-auc:0.95677 validation-aucpr:0.96310
[9] validation-logloss:0.56193 validation-auc:0.95746 validation-aucpr:0.96273
[10] validation-logloss:0.55161 validation-auc:0.95838 validation-aucpr:0.96322
[11] validation-logloss:0.54112 validation-auc:0.95946 validation-aucpr:0.96455
[12] validation-logloss:0.53139 validation-auc:0.95968 validation-aucpr:0.96460
[13] validation-logloss:0.52203 validation-auc:0.95959 validation-aucpr:0.96460
[14] validation-logloss:0.51310 validation-auc:0.95997 validation-aucpr:0.96617
[15] validation-logloss:0.50506 validation-auc:0.96112 validation-aucpr:0.96647
[16] validation-logloss:0.49662 validation-auc:0.96170 validation-aucpr:0.96664
[17] validation-logloss:0.48829 validation-auc:0.96233 validation-aucpr:0.96854
[18] validation-logloss:0.48007 validation-auc:0.96307 validation-aucpr:0.96891
[19] validation-logloss:0.47225 validation-auc:0.96365 validation-aucpr:0.96924
[20] validation-logloss:0.46451 validation-auc:0.96405 validation-aucpr:0.96949
[21] validation-logloss:0.45703 validation-auc:0.96409 validation-aucpr:0.96953
[22] validation-logloss:0.45006 validation-auc:0.96443 validation-aucpr:0.96987
[23] validation-logloss:0.44294 validation-auc:0.96507 validation-aucpr:0.97022
[24] validation-logloss:0.43665 validation-auc:0.96504 validation-aucpr:0.97031
[25] validation-logloss:0.43005 validation-auc:0.96519 validation-aucpr:0.97046
[26] validation-logloss:0.42423 validation-auc:0.96519 validation-aucpr:0.97050
[27] validation-logloss:0.41800 validation-auc:0.96516 validation-aucpr:0.97058
[28] validation-logloss:0.41203 validation-auc:0.96521 validation-aucpr:0.97053
[29] validation-logloss:0.40632 validation-auc:0.96535 validation-aucpr:0.97093
[30] validation-logloss:0.40140 validation-auc:0.96548 validation-aucpr:0.97140
[31] validation-logloss:0.39606 validation-auc:0.96556 validation-aucpr:0.97154
[32] validation-logloss:0.39086 validation-auc:0.96572 validation-aucpr:0.97159
[33] validation-logloss:0.38553 validation-auc:0.96590 validation-aucpr:0.97172
[34] validation-logloss:0.38032 validation-auc:0.96626 validation-aucpr:0.97195
[35] validation-logloss:0.37585 validation-auc:0.96632 validation-aucpr:0.97193
[36] validation-logloss:0.37079 validation-auc:0.96668 validation-aucpr:0.97225
[37] validation-logloss:0.36636 validation-auc:0.96657 validation-aucpr:0.97215
[38] validation-logloss:0.36203 validation-auc:0.96658 validation-aucpr:0.97219
[39] validation-logloss:0.35778 validation-auc:0.96654 validation-aucpr:0.97216
[40] validation-logloss:0.35345 validation-auc:0.96657 validation-aucpr:0.97225
[41] validation-logloss:0.34948 validation-auc:0.96656 validation-aucpr:0.97228
[42] validation-logloss:0.34556 validation-auc:0.96673 validation-aucpr:0.97238
[43] validation-logloss:0.34156 validation-auc:0.96685 validation-aucpr:0.97245
[44] validation-logloss:0.33776 validation-auc:0.96694 validation-aucpr:0.97253
[45] validation-logloss:0.33408 validation-auc:0.96711 validation-aucpr:0.97261
[46] validation-logloss:0.33041 validation-auc:0.96730 validation-aucpr:0.97277
[47] validation-logloss:0.32697 validation-auc:0.96738 validation-aucpr:0.97283
{'best_iteration': '47', 'best_score': '0.9728271886681858'}
Trial 27, Fold 1: Log loss = 0.32696823726928415, Average precision = 0.9729045792862018, ROC-AUC = 0.9673756440496504, Elapsed Time = 14.147009500000422 seconds
Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 27, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67726 validation-auc:0.80978 validation-aucpr:0.71893
[1] validation-logloss:0.66200 validation-auc:0.91014 validation-aucpr:0.84861
[2] validation-logloss:0.64775 validation-auc:0.93518 validation-aucpr:0.90438
[3] validation-logloss:0.63508 validation-auc:0.94685 validation-aucpr:0.92445
[4] validation-logloss:0.62172 validation-auc:0.95331 validation-aucpr:0.94448
[5] validation-logloss:0.60906 validation-auc:0.95586 validation-aucpr:0.95279
[6] validation-logloss:0.59662 validation-auc:0.95748 validation-aucpr:0.95485
[7] validation-logloss:0.58487 validation-auc:0.95926 validation-aucpr:0.95898
[8] validation-logloss:0.57492 validation-auc:0.96014 validation-aucpr:0.95980
[9] validation-logloss:0.56374 validation-auc:0.96172 validation-aucpr:0.96498
[10] validation-logloss:0.55299 validation-auc:0.96246 validation-aucpr:0.96516
[11] validation-logloss:0.54246 validation-auc:0.96412 validation-aucpr:0.96612
[12] validation-logloss:0.53330 validation-auc:0.96480 validation-aucpr:0.96605
[13] validation-logloss:0.52388 validation-auc:0.96465 validation-aucpr:0.96558
[14] validation-logloss:0.51450 validation-auc:0.96550 validation-aucpr:0.96785
[15] validation-logloss:0.50569 validation-auc:0.96554 validation-aucpr:0.96792
[16] validation-logloss:0.49715 validation-auc:0.96630 validation-aucpr:0.97021
[17] validation-logloss:0.48826 validation-auc:0.96708 validation-aucpr:0.97074
[18] validation-logloss:0.47989 validation-auc:0.96695 validation-aucpr:0.97086
[19] validation-logloss:0.47190 validation-auc:0.96713 validation-aucpr:0.97106
[20] validation-logloss:0.46442 validation-auc:0.96726 validation-aucpr:0.97105
[21] validation-logloss:0.45706 validation-auc:0.96753 validation-aucpr:0.97114
[22] validation-logloss:0.44998 validation-auc:0.96764 validation-aucpr:0.97132
[23] validation-logloss:0.44282 validation-auc:0.96802 validation-aucpr:0.97113
[24] validation-logloss:0.43649 validation-auc:0.96814 validation-aucpr:0.97107
[25] validation-logloss:0.43020 validation-auc:0.96796 validation-aucpr:0.97091
[26] validation-logloss:0.42387 validation-auc:0.96802 validation-aucpr:0.97103
[27] validation-logloss:0.41766 validation-auc:0.96795 validation-aucpr:0.97100
[28] validation-logloss:0.41159 validation-auc:0.96798 validation-aucpr:0.97106
[29] validation-logloss:0.40563 validation-auc:0.96810 validation-aucpr:0.97115
[30] validation-logloss:0.39992 validation-auc:0.96815 validation-aucpr:0.97115
[31] validation-logloss:0.39418 validation-auc:0.96835 validation-aucpr:0.97128
[32] validation-logloss:0.38869 validation-auc:0.96843 validation-aucpr:0.97141
[33] validation-logloss:0.38334 validation-auc:0.96862 validation-aucpr:0.97151
[34] validation-logloss:0.37817 validation-auc:0.96869 validation-aucpr:0.97163
[35] validation-logloss:0.37329 validation-auc:0.96860 validation-aucpr:0.97176
[36] validation-logloss:0.36878 validation-auc:0.96867 validation-aucpr:0.97194
[37] validation-logloss:0.36402 validation-auc:0.96870 validation-aucpr:0.97254
[38] validation-logloss:0.35944 validation-auc:0.96894 validation-aucpr:0.97267
[39] validation-logloss:0.35510 validation-auc:0.96898 validation-aucpr:0.97272
[40] validation-logloss:0.35111 validation-auc:0.96912 validation-aucpr:0.97285
[41] validation-logloss:0.34704 validation-auc:0.96928 validation-aucpr:0.97290
[42] validation-logloss:0.34361 validation-auc:0.96923 validation-aucpr:0.97276
[43] validation-logloss:0.33974 validation-auc:0.96917 validation-aucpr:0.97271
[44] validation-logloss:0.33586 validation-auc:0.96932 validation-aucpr:0.97283
[45] validation-logloss:0.33198 validation-auc:0.96959 validation-aucpr:0.97309
[46] validation-logloss:0.32831 validation-auc:0.96960 validation-aucpr:0.97319
[47] validation-logloss:0.32496 validation-auc:0.96970 validation-aucpr:0.97324
{'best_iteration': '47', 'best_score': '0.9732447278228306'}
Trial 27, Fold 2: Log loss = 0.3249584929680322, Average precision = 0.9733388508949556, ROC-AUC = 0.9697001403060024, Elapsed Time = 13.547955099998944 seconds
Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 27, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67718 validation-auc:0.87853 validation-aucpr:0.90062
[1] validation-logloss:0.66183 validation-auc:0.91449 validation-aucpr:0.86609
[2] validation-logloss:0.64756 validation-auc:0.94079 validation-aucpr:0.92209
[3] validation-logloss:0.63387 validation-auc:0.94707 validation-aucpr:0.93535
[4] validation-logloss:0.62057 validation-auc:0.95179 validation-aucpr:0.94525
[5] validation-logloss:0.60746 validation-auc:0.95617 validation-aucpr:0.95559
[6] validation-logloss:0.59525 validation-auc:0.95750 validation-aucpr:0.96035
[7] validation-logloss:0.58461 validation-auc:0.95938 validation-aucpr:0.96129
[8] validation-logloss:0.57274 validation-auc:0.96218 validation-aucpr:0.96318
[9] validation-logloss:0.56155 validation-auc:0.96345 validation-aucpr:0.96621
[10] validation-logloss:0.55080 validation-auc:0.96390 validation-aucpr:0.96731
[11] validation-logloss:0.54138 validation-auc:0.96404 validation-aucpr:0.96578
[12] validation-logloss:0.53124 validation-auc:0.96421 validation-aucpr:0.96611
[13] validation-logloss:0.52160 validation-auc:0.96478 validation-aucpr:0.96638
[14] validation-logloss:0.51210 validation-auc:0.96517 validation-aucpr:0.96665
[15] validation-logloss:0.50331 validation-auc:0.96502 validation-aucpr:0.96572
[16] validation-logloss:0.49436 validation-auc:0.96576 validation-aucpr:0.96754
[17] validation-logloss:0.48630 validation-auc:0.96633 validation-aucpr:0.96846
[18] validation-logloss:0.47781 validation-auc:0.96683 validation-aucpr:0.96874
[19] validation-logloss:0.47007 validation-auc:0.96665 validation-aucpr:0.96851
[20] validation-logloss:0.46244 validation-auc:0.96713 validation-aucpr:0.96862
[21] validation-logloss:0.45500 validation-auc:0.96730 validation-aucpr:0.96869
[22] validation-logloss:0.44776 validation-auc:0.96763 validation-aucpr:0.96879
[23] validation-logloss:0.44062 validation-auc:0.96761 validation-aucpr:0.96838
[24] validation-logloss:0.43370 validation-auc:0.96753 validation-aucpr:0.96834
[25] validation-logloss:0.42684 validation-auc:0.96787 validation-aucpr:0.96810
[26] validation-logloss:0.42062 validation-auc:0.96746 validation-aucpr:0.96747
[27] validation-logloss:0.41452 validation-auc:0.96760 validation-aucpr:0.96795
[28] validation-logloss:0.40846 validation-auc:0.96793 validation-aucpr:0.96826
[29] validation-logloss:0.40279 validation-auc:0.96821 validation-aucpr:0.96937
[30] validation-logloss:0.39739 validation-auc:0.96808 validation-aucpr:0.96911
[31] validation-logloss:0.39189 validation-auc:0.96822 validation-aucpr:0.96921
[32] validation-logloss:0.38682 validation-auc:0.96814 validation-aucpr:0.96926
[33] validation-logloss:0.38192 validation-auc:0.96790 validation-aucpr:0.96913
[34] validation-logloss:0.37662 validation-auc:0.96807 validation-aucpr:0.96910
[35] validation-logloss:0.37144 validation-auc:0.96820 validation-aucpr:0.96921
[36] validation-logloss:0.36705 validation-auc:0.96808 validation-aucpr:0.96938
[37] validation-logloss:0.36291 validation-auc:0.96775 validation-aucpr:0.96821
[38] validation-logloss:0.35819 validation-auc:0.96787 validation-aucpr:0.96800
[39] validation-logloss:0.35408 validation-auc:0.96774 validation-aucpr:0.96779
[40] validation-logloss:0.34975 validation-auc:0.96796 validation-aucpr:0.96775
[41] validation-logloss:0.34598 validation-auc:0.96781 validation-aucpr:0.96721
[42] validation-logloss:0.34184 validation-auc:0.96805 validation-aucpr:0.96921
[43] validation-logloss:0.33816 validation-auc:0.96820 validation-aucpr:0.97004
[44] validation-logloss:0.33485 validation-auc:0.96802 validation-aucpr:0.96986
[45] validation-logloss:0.33116 validation-auc:0.96802 validation-aucpr:0.96992
[46] validation-logloss:0.32810 validation-auc:0.96807 validation-aucpr:0.96954
[47] validation-logloss:0.32490 validation-auc:0.96811 validation-aucpr:0.96906
{'best_iteration': '43', 'best_score': '0.9700397161982565'}
Trial 27, Fold 3: Log loss = 0.32490451688075633, Average precision = 0.9693429881943123, ROC-AUC = 0.9681149536265663, Elapsed Time = 13.689646200000425 seconds
Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 27, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67743 validation-auc:0.81076 validation-aucpr:0.71757
[1] validation-logloss:0.66216 validation-auc:0.89878 validation-aucpr:0.83078
[2] validation-logloss:0.64765 validation-auc:0.93143 validation-aucpr:0.90569
[3] validation-logloss:0.63401 validation-auc:0.94414 validation-aucpr:0.93825
[4] validation-logloss:0.62057 validation-auc:0.95023 validation-aucpr:0.94818
[5] validation-logloss:0.60790 validation-auc:0.95327 validation-aucpr:0.94992
[6] validation-logloss:0.59554 validation-auc:0.95592 validation-aucpr:0.95984
[7] validation-logloss:0.58397 validation-auc:0.95867 validation-aucpr:0.96543
[8] validation-logloss:0.57317 validation-auc:0.95943 validation-aucpr:0.96475
[9] validation-logloss:0.56250 validation-auc:0.96107 validation-aucpr:0.96689
[10] validation-logloss:0.55227 validation-auc:0.96284 validation-aucpr:0.96913
[11] validation-logloss:0.54184 validation-auc:0.96332 validation-aucpr:0.96981
[12] validation-logloss:0.53188 validation-auc:0.96431 validation-aucpr:0.97036
[13] validation-logloss:0.52222 validation-auc:0.96476 validation-aucpr:0.97071
[14] validation-logloss:0.51292 validation-auc:0.96536 validation-aucpr:0.97109
[15] validation-logloss:0.50398 validation-auc:0.96553 validation-aucpr:0.97130
[16] validation-logloss:0.49560 validation-auc:0.96553 validation-aucpr:0.97135
[17] validation-logloss:0.48745 validation-auc:0.96538 validation-aucpr:0.97099
[18] validation-logloss:0.47929 validation-auc:0.96561 validation-aucpr:0.97158
[19] validation-logloss:0.47114 validation-auc:0.96582 validation-aucpr:0.97170
[20] validation-logloss:0.46362 validation-auc:0.96625 validation-aucpr:0.97191
[21] validation-logloss:0.45683 validation-auc:0.96628 validation-aucpr:0.97182
[22] validation-logloss:0.44954 validation-auc:0.96640 validation-aucpr:0.97198
[23] validation-logloss:0.44249 validation-auc:0.96643 validation-aucpr:0.97197
[24] validation-logloss:0.43555 validation-auc:0.96692 validation-aucpr:0.97235
[25] validation-logloss:0.42903 validation-auc:0.96713 validation-aucpr:0.97245
[26] validation-logloss:0.42291 validation-auc:0.96725 validation-aucpr:0.97252
[27] validation-logloss:0.41705 validation-auc:0.96701 validation-aucpr:0.97235
[28] validation-logloss:0.41130 validation-auc:0.96701 validation-aucpr:0.97239
[29] validation-logloss:0.40564 validation-auc:0.96711 validation-aucpr:0.97246
[30] validation-logloss:0.40002 validation-auc:0.96723 validation-aucpr:0.97256
[31] validation-logloss:0.39462 validation-auc:0.96709 validation-aucpr:0.97251
[32] validation-logloss:0.38943 validation-auc:0.96708 validation-aucpr:0.97249
[33] validation-logloss:0.38404 validation-auc:0.96728 validation-aucpr:0.97264
[34] validation-logloss:0.37898 validation-auc:0.96734 validation-aucpr:0.97288
[35] validation-logloss:0.37418 validation-auc:0.96724 validation-aucpr:0.97285
[36] validation-logloss:0.36968 validation-auc:0.96734 validation-aucpr:0.97287
[37] validation-logloss:0.36536 validation-auc:0.96737 validation-aucpr:0.97289
[38] validation-logloss:0.36079 validation-auc:0.96746 validation-aucpr:0.97294
[39] validation-logloss:0.35652 validation-auc:0.96745 validation-aucpr:0.97293
[40] validation-logloss:0.35209 validation-auc:0.96760 validation-aucpr:0.97305
[41] validation-logloss:0.34816 validation-auc:0.96741 validation-aucpr:0.97290
[42] validation-logloss:0.34414 validation-auc:0.96743 validation-aucpr:0.97290
[43] validation-logloss:0.34052 validation-auc:0.96740 validation-aucpr:0.97289
[44] validation-logloss:0.33680 validation-auc:0.96750 validation-aucpr:0.97296
[45] validation-logloss:0.33305 validation-auc:0.96747 validation-aucpr:0.97292
[46] validation-logloss:0.32903 validation-auc:0.96780 validation-aucpr:0.97315
[47] validation-logloss:0.32529 validation-auc:0.96809 validation-aucpr:0.97334
{'best_iteration': '47', 'best_score': '0.9733443455114036'}
Trial 27, Fold 4: Log loss = 0.32529408301928064, Average precision = 0.973338719744536, ROC-AUC = 0.9680886988514298, Elapsed Time = 13.645755100000315 seconds
Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 27, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67738 validation-auc:0.81385 validation-aucpr:0.73173
[1] validation-logloss:0.66237 validation-auc:0.90118 validation-aucpr:0.84005
[2] validation-logloss:0.64846 validation-auc:0.92205 validation-aucpr:0.89053
[3] validation-logloss:0.63505 validation-auc:0.93435 validation-aucpr:0.92671
[4] validation-logloss:0.62169 validation-auc:0.93970 validation-aucpr:0.93789
[5] validation-logloss:0.60894 validation-auc:0.94506 validation-aucpr:0.94859
[6] validation-logloss:0.59665 validation-auc:0.95013 validation-aucpr:0.95372
[7] validation-logloss:0.58482 validation-auc:0.95344 validation-aucpr:0.95548
[8] validation-logloss:0.57389 validation-auc:0.95436 validation-aucpr:0.95889
[9] validation-logloss:0.56268 validation-auc:0.95629 validation-aucpr:0.96026
[10] validation-logloss:0.55208 validation-auc:0.95708 validation-aucpr:0.96116
[11] validation-logloss:0.54197 validation-auc:0.95843 validation-aucpr:0.96321
[12] validation-logloss:0.53196 validation-auc:0.95966 validation-aucpr:0.96381
[13] validation-logloss:0.52262 validation-auc:0.96025 validation-aucpr:0.96402
[14] validation-logloss:0.51390 validation-auc:0.96038 validation-aucpr:0.96556
[15] validation-logloss:0.50478 validation-auc:0.96132 validation-aucpr:0.96608
[16] validation-logloss:0.49603 validation-auc:0.96212 validation-aucpr:0.96658
[17] validation-logloss:0.48764 validation-auc:0.96282 validation-aucpr:0.96701
[18] validation-logloss:0.47971 validation-auc:0.96276 validation-aucpr:0.96692
[19] validation-logloss:0.47242 validation-auc:0.96261 validation-aucpr:0.96620
[20] validation-logloss:0.46483 validation-auc:0.96338 validation-aucpr:0.96802
[21] validation-logloss:0.45752 validation-auc:0.96341 validation-aucpr:0.96798
[22] validation-logloss:0.45059 validation-auc:0.96360 validation-aucpr:0.96803
[23] validation-logloss:0.44397 validation-auc:0.96331 validation-aucpr:0.96787
[24] validation-logloss:0.43755 validation-auc:0.96321 validation-aucpr:0.96770
[25] validation-logloss:0.43091 validation-auc:0.96369 validation-aucpr:0.96790
[26] validation-logloss:0.42472 validation-auc:0.96361 validation-aucpr:0.96895
[27] validation-logloss:0.41870 validation-auc:0.96364 validation-aucpr:0.96895
[28] validation-logloss:0.41276 validation-auc:0.96368 validation-aucpr:0.96899
[29] validation-logloss:0.40704 validation-auc:0.96383 validation-aucpr:0.96911
[30] validation-logloss:0.40219 validation-auc:0.96372 validation-aucpr:0.96847
[31] validation-logloss:0.39687 validation-auc:0.96387 validation-aucpr:0.96860
[32] validation-logloss:0.39155 validation-auc:0.96408 validation-aucpr:0.96882
[33] validation-logloss:0.38672 validation-auc:0.96430 validation-aucpr:0.96881
[34] validation-logloss:0.38164 validation-auc:0.96450 validation-aucpr:0.96895
[35] validation-logloss:0.37686 validation-auc:0.96452 validation-aucpr:0.96902
[36] validation-logloss:0.37210 validation-auc:0.96465 validation-aucpr:0.96915
[37] validation-logloss:0.36770 validation-auc:0.96477 validation-aucpr:0.96914
[38] validation-logloss:0.36353 validation-auc:0.96478 validation-aucpr:0.96915
[39] validation-logloss:0.35957 validation-auc:0.96477 validation-aucpr:0.96868
[40] validation-logloss:0.35559 validation-auc:0.96487 validation-aucpr:0.96852
[41] validation-logloss:0.35143 validation-auc:0.96494 validation-aucpr:0.96859
[42] validation-logloss:0.34795 validation-auc:0.96491 validation-aucpr:0.96830
[43] validation-logloss:0.34400 validation-auc:0.96510 validation-aucpr:0.96823
[44] validation-logloss:0.34021 validation-auc:0.96519 validation-aucpr:0.96818
[45] validation-logloss:0.33661 validation-auc:0.96519 validation-aucpr:0.96822
[46] validation-logloss:0.33316 validation-auc:0.96532 validation-aucpr:0.96853
[47] validation-logloss:0.32985 validation-auc:0.96533 validation-aucpr:0.96866
{'best_iteration': '38', 'best_score': '0.9691535642346878'}
Trial 27, Fold 5: Log loss = 0.32985425076024333, Average precision = 0.9688231578351952, ROC-AUC = 0.9653324415212828, Elapsed Time = 14.10808200000065 seconds
Optimization Progress: 28%|##8 | 28/100 [48:52<3:03:46, 153.15s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 28, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67453 validation-auc:0.92966 validation-aucpr:0.92412
[1] validation-logloss:0.65734 validation-auc:0.94220 validation-aucpr:0.94079
[2] validation-logloss:0.64190 validation-auc:0.94381 validation-aucpr:0.94793
[3] validation-logloss:0.62513 validation-auc:0.95034 validation-aucpr:0.95588
[4] validation-logloss:0.61047 validation-auc:0.95093 validation-aucpr:0.95709
[5] validation-logloss:0.59542 validation-auc:0.95285 validation-aucpr:0.95884
[6] validation-logloss:0.58235 validation-auc:0.95329 validation-aucpr:0.95917
[7] validation-logloss:0.56761 validation-auc:0.95724 validation-aucpr:0.96378
[8] validation-logloss:0.55330 validation-auc:0.95864 validation-aucpr:0.96548
[9] validation-logloss:0.54248 validation-auc:0.95811 validation-aucpr:0.96489
[10] validation-logloss:0.53158 validation-auc:0.95876 validation-aucpr:0.96530
[11] validation-logloss:0.51977 validation-auc:0.95895 validation-aucpr:0.96559
[12] validation-logloss:0.50953 validation-auc:0.95900 validation-aucpr:0.96570
[13] validation-logloss:0.50093 validation-auc:0.95857 validation-aucpr:0.96533
[14] validation-logloss:0.49037 validation-auc:0.95885 validation-aucpr:0.96570
[15] validation-logloss:0.48179 validation-auc:0.95892 validation-aucpr:0.96568
[16] validation-logloss:0.47358 validation-auc:0.95888 validation-aucpr:0.96558
[17] validation-logloss:0.46601 validation-auc:0.95864 validation-aucpr:0.96541
[18] validation-logloss:0.45761 validation-auc:0.95848 validation-aucpr:0.96536
[19] validation-logloss:0.44815 validation-auc:0.95907 validation-aucpr:0.96591
[20] validation-logloss:0.43952 validation-auc:0.95952 validation-aucpr:0.96641
[21] validation-logloss:0.43300 validation-auc:0.95957 validation-aucpr:0.96643
[22] validation-logloss:0.42651 validation-auc:0.95975 validation-aucpr:0.96658
[23] validation-logloss:0.41861 validation-auc:0.95982 validation-aucpr:0.96674
[24] validation-logloss:0.41035 validation-auc:0.96038 validation-aucpr:0.96730
{'best_iteration': '24', 'best_score': '0.967297137732974'}
Trial 28, Fold 1: Log loss = 0.41034771616691573, Average precision = 0.9673015836050309, ROC-AUC = 0.9603838517992763, Elapsed Time = 0.9977183999999397 seconds
Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 28, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67493 validation-auc:0.92659 validation-aucpr:0.91603
[1] validation-logloss:0.65440 validation-auc:0.95558 validation-aucpr:0.96034
[2] validation-logloss:0.63804 validation-auc:0.95563 validation-aucpr:0.96028
[3] validation-logloss:0.62193 validation-auc:0.95796 validation-aucpr:0.96183
[4] validation-logloss:0.60719 validation-auc:0.95712 validation-aucpr:0.96109
[5] validation-logloss:0.59324 validation-auc:0.95693 validation-aucpr:0.96071
[6] validation-logloss:0.57882 validation-auc:0.95797 validation-aucpr:0.96157
[7] validation-logloss:0.56650 validation-auc:0.95736 validation-aucpr:0.96121
[8] validation-logloss:0.55490 validation-auc:0.95750 validation-aucpr:0.96111
[9] validation-logloss:0.54254 validation-auc:0.95863 validation-aucpr:0.96241
[10] validation-logloss:0.53208 validation-auc:0.95793 validation-aucpr:0.96168
[11] validation-logloss:0.51964 validation-auc:0.95966 validation-aucpr:0.96375
[12] validation-logloss:0.50705 validation-auc:0.96118 validation-aucpr:0.96578
[13] validation-logloss:0.49555 validation-auc:0.96162 validation-aucpr:0.96616
[14] validation-logloss:0.48666 validation-auc:0.96141 validation-aucpr:0.96585
[15] validation-logloss:0.47513 validation-auc:0.96223 validation-aucpr:0.96669
[16] validation-logloss:0.46474 validation-auc:0.96290 validation-aucpr:0.96736
[17] validation-logloss:0.45657 validation-auc:0.96301 validation-aucpr:0.96737
[18] validation-logloss:0.44932 validation-auc:0.96257 validation-aucpr:0.96691
[19] validation-logloss:0.44048 validation-auc:0.96280 validation-aucpr:0.96714
[20] validation-logloss:0.43353 validation-auc:0.96272 validation-aucpr:0.96704
[21] validation-logloss:0.42738 validation-auc:0.96251 validation-aucpr:0.96683
[22] validation-logloss:0.42151 validation-auc:0.96242 validation-aucpr:0.96672
[23] validation-logloss:0.41597 validation-auc:0.96235 validation-aucpr:0.96664
[24] validation-logloss:0.41013 validation-auc:0.96245 validation-aucpr:0.96669
{'best_iteration': '17', 'best_score': '0.9673667641165824'}
Trial 28, Fold 2: Log loss = 0.4101304550323175, Average precision = 0.9666330371412952, ROC-AUC = 0.9624493276207374, Elapsed Time = 1.2822998999999982 seconds
Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 28, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67449 validation-auc:0.93199 validation-aucpr:0.92687
[1] validation-logloss:0.65477 validation-auc:0.95745 validation-aucpr:0.96278
[2] validation-logloss:0.63839 validation-auc:0.95725 validation-aucpr:0.96317
[3] validation-logloss:0.62300 validation-auc:0.95666 validation-aucpr:0.96261
[4] validation-logloss:0.60752 validation-auc:0.95706 validation-aucpr:0.96320
[5] validation-logloss:0.59387 validation-auc:0.95680 validation-aucpr:0.96336
[6] validation-logloss:0.58044 validation-auc:0.95810 validation-aucpr:0.96379
[7] validation-logloss:0.56757 validation-auc:0.95870 validation-aucpr:0.96417
[8] validation-logloss:0.55673 validation-auc:0.95799 validation-aucpr:0.96354
[9] validation-logloss:0.54440 validation-auc:0.95855 validation-aucpr:0.96395
[10] validation-logloss:0.53330 validation-auc:0.95814 validation-aucpr:0.96352
[11] validation-logloss:0.52290 validation-auc:0.95761 validation-aucpr:0.96295
[12] validation-logloss:0.51239 validation-auc:0.95829 validation-aucpr:0.96379
[13] validation-logloss:0.50305 validation-auc:0.95823 validation-aucpr:0.96375
[14] validation-logloss:0.49428 validation-auc:0.95778 validation-aucpr:0.96334
[15] validation-logloss:0.48541 validation-auc:0.95800 validation-aucpr:0.96341
[16] validation-logloss:0.47637 validation-auc:0.95854 validation-aucpr:0.96369
[17] validation-logloss:0.46880 validation-auc:0.95807 validation-aucpr:0.96322
[18] validation-logloss:0.46171 validation-auc:0.95788 validation-aucpr:0.96306
[19] validation-logloss:0.45486 validation-auc:0.95767 validation-aucpr:0.96290
[20] validation-logloss:0.44828 validation-auc:0.95755 validation-aucpr:0.96278
[21] validation-logloss:0.43986 validation-auc:0.95857 validation-aucpr:0.96410
[22] validation-logloss:0.43406 validation-auc:0.95835 validation-aucpr:0.96395
[23] validation-logloss:0.42816 validation-auc:0.95807 validation-aucpr:0.96371
[24] validation-logloss:0.42208 validation-auc:0.95836 validation-aucpr:0.96387
{'best_iteration': '7', 'best_score': '0.9641735778234024'}
Trial 28, Fold 3: Log loss = 0.42207755835198346, Average precision = 0.9638774698876118, ROC-AUC = 0.9583618753069894, Elapsed Time = 1.3180508999994345 seconds
Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 28, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67487 validation-auc:0.92848 validation-aucpr:0.92519
[1] validation-logloss:0.65758 validation-auc:0.93797 validation-aucpr:0.93847
[2] validation-logloss:0.64007 validation-auc:0.95223 validation-aucpr:0.95651
[3] validation-logloss:0.62384 validation-auc:0.95442 validation-aucpr:0.96065
[4] validation-logloss:0.60735 validation-auc:0.95637 validation-aucpr:0.96316
[5] validation-logloss:0.59402 validation-auc:0.95596 validation-aucpr:0.96283
[6] validation-logloss:0.58079 validation-auc:0.95533 validation-aucpr:0.96204
[7] validation-logloss:0.56572 validation-auc:0.95708 validation-aucpr:0.96393
[8] validation-logloss:0.55312 validation-auc:0.95708 validation-aucpr:0.96397
[9] validation-logloss:0.53975 validation-auc:0.95803 validation-aucpr:0.96481
[10] validation-logloss:0.52881 validation-auc:0.95869 validation-aucpr:0.96522
[11] validation-logloss:0.51717 validation-auc:0.95914 validation-aucpr:0.96566
[12] validation-logloss:0.50687 validation-auc:0.95932 validation-aucpr:0.96581
[13] validation-logloss:0.49786 validation-auc:0.95874 validation-aucpr:0.96533
[14] validation-logloss:0.48824 validation-auc:0.95920 validation-aucpr:0.96564
[15] validation-logloss:0.47747 validation-auc:0.96011 validation-aucpr:0.96646
[16] validation-logloss:0.46911 validation-auc:0.96021 validation-aucpr:0.96650
[17] validation-logloss:0.46103 validation-auc:0.96070 validation-aucpr:0.96690
[18] validation-logloss:0.45382 validation-auc:0.96053 validation-aucpr:0.96667
[19] validation-logloss:0.44491 validation-auc:0.96111 validation-aucpr:0.96724
[20] validation-logloss:0.43808 validation-auc:0.96107 validation-aucpr:0.96717
[21] validation-logloss:0.42941 validation-auc:0.96140 validation-aucpr:0.96752
[22] validation-logloss:0.42274 validation-auc:0.96152 validation-aucpr:0.96760
[23] validation-logloss:0.41489 validation-auc:0.96189 validation-aucpr:0.96801
[24] validation-logloss:0.40935 validation-auc:0.96188 validation-aucpr:0.96799
{'best_iteration': '23', 'best_score': '0.9680064618656231'}
Trial 28, Fold 4: Log loss = 0.409350621791954, Average precision = 0.9679871158277571, ROC-AUC = 0.9618803664122502, Elapsed Time = 1.2848967999998422 seconds
Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 28, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67495 validation-auc:0.91969 validation-aucpr:0.90610
[1] validation-logloss:0.65813 validation-auc:0.93588 validation-aucpr:0.93828
[2] validation-logloss:0.64248 validation-auc:0.93797 validation-aucpr:0.94127
[3] validation-logloss:0.62754 validation-auc:0.93957 validation-aucpr:0.94455
[4] validation-logloss:0.61008 validation-auc:0.94994 validation-aucpr:0.95687
[5] validation-logloss:0.59344 validation-auc:0.95363 validation-aucpr:0.96018
[6] validation-logloss:0.58120 validation-auc:0.95313 validation-aucpr:0.96021
[7] validation-logloss:0.56863 validation-auc:0.95286 validation-aucpr:0.96003
[8] validation-logloss:0.55453 validation-auc:0.95443 validation-aucpr:0.96131
[9] validation-logloss:0.54319 validation-auc:0.95455 validation-aucpr:0.96132
[10] validation-logloss:0.53230 validation-auc:0.95490 validation-aucpr:0.96160
[11] validation-logloss:0.52226 validation-auc:0.95486 validation-aucpr:0.96151
[12] validation-logloss:0.51321 validation-auc:0.95474 validation-aucpr:0.96134
[13] validation-logloss:0.50445 validation-auc:0.95453 validation-aucpr:0.96117
[14] validation-logloss:0.49649 validation-auc:0.95444 validation-aucpr:0.96145
[15] validation-logloss:0.48890 validation-auc:0.95432 validation-aucpr:0.96129
[16] validation-logloss:0.48053 validation-auc:0.95437 validation-aucpr:0.96124
[17] validation-logloss:0.47301 validation-auc:0.95430 validation-aucpr:0.96114
[18] validation-logloss:0.46379 validation-auc:0.95513 validation-aucpr:0.96189
[19] validation-logloss:0.45706 validation-auc:0.95482 validation-aucpr:0.96159
[20] validation-logloss:0.44790 validation-auc:0.95561 validation-aucpr:0.96244
[21] validation-logloss:0.44038 validation-auc:0.95607 validation-aucpr:0.96272
[22] validation-logloss:0.43436 validation-auc:0.95596 validation-aucpr:0.96254
[23] validation-logloss:0.42855 validation-auc:0.95596 validation-aucpr:0.96258
[24] validation-logloss:0.42321 validation-auc:0.95581 validation-aucpr:0.96247
{'best_iteration': '21', 'best_score': '0.9627214154090976'}
Trial 28, Fold 5: Log loss = 0.42320609848369695, Average precision = 0.9624687066772707, ROC-AUC = 0.9558063281496758, Elapsed Time = 1.2925592999999935 seconds
Optimization Progress: 29%|##9 | 29/100 [49:06<2:11:52, 111.45s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 29, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64541 validation-auc:0.91169 validation-aucpr:0.91761
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60390 validation-auc:0.93151 validation-aucpr:0.93490
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56258 validation-auc:0.94565 validation-aucpr:0.95033
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53228 validation-auc:0.94971 validation-aucpr:0.95664
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50397 validation-auc:0.95238 validation-aucpr:0.95835
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.48025 validation-auc:0.95444 validation-aucpr:0.95879
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.45484 validation-auc:0.95602 validation-aucpr:0.96062
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.43727 validation-auc:0.95625 validation-aucpr:0.96108
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.41788 validation-auc:0.95818 validation-aucpr:0.96269
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.40385 validation-auc:0.95779 validation-aucpr:0.96240
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.38933 validation-auc:0.95868 validation-aucpr:0.96319
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.37717 validation-auc:0.95881 validation-aucpr:0.96220
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.36023 validation-auc:0.96053 validation-aucpr:0.96401
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35017 validation-auc:0.96065 validation-aucpr:0.96393
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34203 validation-auc:0.96039 validation-aucpr:0.96372
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33393 validation-auc:0.96071 validation-aucpr:0.96352
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.32278 validation-auc:0.96130 validation-aucpr:0.96421
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.31714 validation-auc:0.96100 validation-aucpr:0.96305
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.31066 validation-auc:0.96119 validation-aucpr:0.96318
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.30438 validation-auc:0.96131 validation-aucpr:0.96372
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.29466 validation-auc:0.96206 validation-aucpr:0.96483
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.28839 validation-auc:0.96270 validation-aucpr:0.96702
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.28440 validation-auc:0.96293 validation-aucpr:0.96716
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.27877 validation-auc:0.96347 validation-aucpr:0.96743
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.27457 validation-auc:0.96363 validation-aucpr:0.96750
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.27128 validation-auc:0.96368 validation-aucpr:0.96756
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.26828 validation-auc:0.96387 validation-aucpr:0.96876
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.26574 validation-auc:0.96382 validation-aucpr:0.96772
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.26306 validation-auc:0.96390 validation-aucpr:0.96766
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.26011 validation-auc:0.96420 validation-aucpr:0.96892
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.25810 validation-auc:0.96391 validation-aucpr:0.96855
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.25614 validation-auc:0.96390 validation-aucpr:0.96878
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.25481 validation-auc:0.96400 validation-aucpr:0.96956
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.25265 validation-auc:0.96420 validation-aucpr:0.96960
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.24764 validation-auc:0.96488 validation-aucpr:0.97028
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.24315 validation-auc:0.96533 validation-aucpr:0.97079
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.24109 validation-auc:0.96570 validation-aucpr:0.97108
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.23990 validation-auc:0.96591 validation-aucpr:0.97124
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.23887 validation-auc:0.96590 validation-aucpr:0.97122
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.23697 validation-auc:0.96600 validation-aucpr:0.97138
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.23386 validation-auc:0.96620 validation-aucpr:0.97163
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23076 validation-auc:0.96660 validation-aucpr:0.97203
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.22987 validation-auc:0.96673 validation-aucpr:0.97209
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.22815 validation-auc:0.96679 validation-aucpr:0.97213
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.22617 validation-auc:0.96694 validation-aucpr:0.97227
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.22627 validation-auc:0.96680 validation-aucpr:0.97210
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.22582 validation-auc:0.96678 validation-aucpr:0.97208
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.22572 validation-auc:0.96665 validation-aucpr:0.97185
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.22484 validation-auc:0.96671 validation-aucpr:0.97197
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22269 validation-auc:0.96704 validation-aucpr:0.97232
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22300 validation-auc:0.96678 validation-aucpr:0.97210
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22248 validation-auc:0.96691 validation-aucpr:0.97215
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22211 validation-auc:0.96701 validation-aucpr:0.97218
[18:48:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.22179 validation-auc:0.96700 validation-aucpr:0.97213
{'best_iteration': '49', 'best_score': '0.9723227413480227'}
Trial 29, Fold 1: Log loss = 0.2217877447091866, Average precision = 0.972137945326637, ROC-AUC = 0.9670048524779781, Elapsed Time = 6.752285800001118 seconds
Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 29, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64490 validation-auc:0.91025 validation-aucpr:0.89715
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59718 validation-auc:0.94638 validation-aucpr:0.94511
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56146 validation-auc:0.95100 validation-aucpr:0.95479
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53075 validation-auc:0.95366 validation-aucpr:0.95701
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50243 validation-auc:0.95516 validation-aucpr:0.95991
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.47286 validation-auc:0.95755 validation-aucpr:0.96278
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.45099 validation-auc:0.95856 validation-aucpr:0.96356
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.43191 validation-auc:0.95870 validation-aucpr:0.96129
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40901 validation-auc:0.96055 validation-aucpr:0.96300
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.39460 validation-auc:0.96081 validation-aucpr:0.96304
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.38144 validation-auc:0.96124 validation-aucpr:0.96586
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.36900 validation-auc:0.96137 validation-aucpr:0.96592
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.35892 validation-auc:0.96110 validation-aucpr:0.96563
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.34433 validation-auc:0.96190 validation-aucpr:0.96650
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.33530 validation-auc:0.96207 validation-aucpr:0.96607
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.32704 validation-auc:0.96184 validation-aucpr:0.96605
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.31647 validation-auc:0.96227 validation-aucpr:0.96651
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.30982 validation-auc:0.96262 validation-aucpr:0.96681
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.30323 validation-auc:0.96276 validation-aucpr:0.96685
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.29368 validation-auc:0.96338 validation-aucpr:0.96740
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.28486 validation-auc:0.96413 validation-aucpr:0.96818
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.27980 validation-auc:0.96427 validation-aucpr:0.96827
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.27518 validation-auc:0.96453 validation-aucpr:0.96854
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.26847 validation-auc:0.96491 validation-aucpr:0.96882
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.26388 validation-auc:0.96527 validation-aucpr:0.96918
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.26109 validation-auc:0.96506 validation-aucpr:0.96895
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.25792 validation-auc:0.96526 validation-aucpr:0.96906
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.25419 validation-auc:0.96561 validation-aucpr:0.96941
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.24922 validation-auc:0.96584 validation-aucpr:0.96966
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24651 validation-auc:0.96613 validation-aucpr:0.96981
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.24519 validation-auc:0.96592 validation-aucpr:0.96970
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.24185 validation-auc:0.96668 validation-aucpr:0.97060
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23883 validation-auc:0.96671 validation-aucpr:0.97075
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.23528 validation-auc:0.96686 validation-aucpr:0.97077
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.23331 validation-auc:0.96702 validation-aucpr:0.97079
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22974 validation-auc:0.96736 validation-aucpr:0.97121
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22777 validation-auc:0.96776 validation-aucpr:0.97112
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.22637 validation-auc:0.96798 validation-aucpr:0.97108
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.22523 validation-auc:0.96796 validation-aucpr:0.97093
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.22371 validation-auc:0.96822 validation-aucpr:0.97094
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.22283 validation-auc:0.96808 validation-aucpr:0.97074
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.22280 validation-auc:0.96778 validation-aucpr:0.97048
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.22129 validation-auc:0.96768 validation-aucpr:0.97051
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.21993 validation-auc:0.96750 validation-aucpr:0.97077
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.21782 validation-auc:0.96776 validation-aucpr:0.97092
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.21496 validation-auc:0.96826 validation-aucpr:0.97128
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.21431 validation-auc:0.96839 validation-aucpr:0.97142
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.21447 validation-auc:0.96830 validation-aucpr:0.97150
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.21373 validation-auc:0.96839 validation-aucpr:0.97154
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.21232 validation-auc:0.96859 validation-aucpr:0.97166
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.21266 validation-auc:0.96828 validation-aucpr:0.97137
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.21169 validation-auc:0.96818 validation-aucpr:0.97115
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.21162 validation-auc:0.96829 validation-aucpr:0.97087
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.21039 validation-auc:0.96847 validation-aucpr:0.97084
{'best_iteration': '49', 'best_score': '0.9716618943586721'}
Trial 29, Fold 2: Log loss = 0.21039004558810057, Average precision = 0.970847171830053, ROC-AUC = 0.968472955964917, Elapsed Time = 6.9477287999998225 seconds
Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 29, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64386 validation-auc:0.92319 validation-aucpr:0.92384
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60188 validation-auc:0.93882 validation-aucpr:0.93939
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56659 validation-auc:0.94351 validation-aucpr:0.94409
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53346 validation-auc:0.94853 validation-aucpr:0.95234
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50650 validation-auc:0.94981 validation-aucpr:0.95381
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.48344 validation-auc:0.95081 validation-aucpr:0.95484
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.46198 validation-auc:0.95191 validation-aucpr:0.95633
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.44093 validation-auc:0.95335 validation-aucpr:0.95589
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.41706 validation-auc:0.95852 validation-aucpr:0.96101
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.39609 validation-auc:0.96103 validation-aucpr:0.96550
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37858 validation-auc:0.96179 validation-aucpr:0.96620
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.36437 validation-auc:0.96261 validation-aucpr:0.96691
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.35270 validation-auc:0.96273 validation-aucpr:0.96611
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.34169 validation-auc:0.96298 validation-aucpr:0.96487
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.33360 validation-auc:0.96263 validation-aucpr:0.96502
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.32453 validation-auc:0.96290 validation-aucpr:0.96526
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.31707 validation-auc:0.96315 validation-aucpr:0.96400
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.31041 validation-auc:0.96328 validation-aucpr:0.96391
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.30402 validation-auc:0.96327 validation-aucpr:0.96390
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.29938 validation-auc:0.96318 validation-aucpr:0.96362
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.29085 validation-auc:0.96370 validation-aucpr:0.96493
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.28595 validation-auc:0.96380 validation-aucpr:0.96560
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.28230 validation-auc:0.96380 validation-aucpr:0.96590
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.27932 validation-auc:0.96386 validation-aucpr:0.96738
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.27579 validation-auc:0.96382 validation-aucpr:0.96731
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.27278 validation-auc:0.96373 validation-aucpr:0.96769
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.27058 validation-auc:0.96360 validation-aucpr:0.96757
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.26561 validation-auc:0.96404 validation-aucpr:0.96790
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.26381 validation-auc:0.96376 validation-aucpr:0.96706
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.26137 validation-auc:0.96371 validation-aucpr:0.96715
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.25954 validation-auc:0.96344 validation-aucpr:0.96668
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.25729 validation-auc:0.96355 validation-aucpr:0.96677
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.25236 validation-auc:0.96430 validation-aucpr:0.96910
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.25129 validation-auc:0.96413 validation-aucpr:0.96895
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.24862 validation-auc:0.96433 validation-aucpr:0.96923
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.24413 validation-auc:0.96474 validation-aucpr:0.96958
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.24228 validation-auc:0.96499 validation-aucpr:0.96976
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.24063 validation-auc:0.96532 validation-aucpr:0.96993
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.23938 validation-auc:0.96534 validation-aucpr:0.97006
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.23800 validation-auc:0.96548 validation-aucpr:0.97012
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.23678 validation-auc:0.96573 validation-aucpr:0.97037
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23572 validation-auc:0.96585 validation-aucpr:0.97055
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23442 validation-auc:0.96585 validation-aucpr:0.97056
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23143 validation-auc:0.96616 validation-aucpr:0.97085
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23070 validation-auc:0.96622 validation-aucpr:0.97088
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.22768 validation-auc:0.96647 validation-aucpr:0.97095
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.22714 validation-auc:0.96647 validation-aucpr:0.97094
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.22464 validation-auc:0.96678 validation-aucpr:0.97116
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.22358 validation-auc:0.96696 validation-aucpr:0.97143
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22294 validation-auc:0.96704 validation-aucpr:0.97100
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22254 validation-auc:0.96717 validation-aucpr:0.97115
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22258 validation-auc:0.96711 validation-aucpr:0.97139
[18:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22249 validation-auc:0.96704 validation-aucpr:0.97134
[18:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.22047 validation-auc:0.96717 validation-aucpr:0.97147
{'best_iteration': '53', 'best_score': '0.9714691673218635'}
Trial 29, Fold 3: Log loss = 0.220469281968947, Average precision = 0.9714746681701518, ROC-AUC = 0.9671723160713082, Elapsed Time = 7.089355300000534 seconds
Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 29, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64628 validation-auc:0.89556 validation-aucpr:0.91012
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59900 validation-auc:0.94428 validation-aucpr:0.94372
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56390 validation-auc:0.94788 validation-aucpr:0.94820
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53289 validation-auc:0.95223 validation-aucpr:0.95777
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50459 validation-auc:0.95366 validation-aucpr:0.95973
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.47957 validation-auc:0.95403 validation-aucpr:0.96028
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.45126 validation-auc:0.95756 validation-aucpr:0.96386
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.43384 validation-auc:0.95720 validation-aucpr:0.96336
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.41201 validation-auc:0.95891 validation-aucpr:0.96527
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.39652 validation-auc:0.95881 validation-aucpr:0.96527
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.38243 validation-auc:0.95865 validation-aucpr:0.96514
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.36925 validation-auc:0.95956 validation-aucpr:0.96582
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.35757 validation-auc:0.95997 validation-aucpr:0.96634
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.34414 validation-auc:0.96084 validation-aucpr:0.96712
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.33558 validation-auc:0.96098 validation-aucpr:0.96723
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.32237 validation-auc:0.96205 validation-aucpr:0.96822
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.31110 validation-auc:0.96301 validation-aucpr:0.96898
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.30415 validation-auc:0.96278 validation-aucpr:0.96879
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.29423 validation-auc:0.96340 validation-aucpr:0.96939
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.28877 validation-auc:0.96338 validation-aucpr:0.96944
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.28283 validation-auc:0.96365 validation-aucpr:0.96962
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.27796 validation-auc:0.96396 validation-aucpr:0.96987
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.27411 validation-auc:0.96405 validation-aucpr:0.96989
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.26754 validation-auc:0.96453 validation-aucpr:0.97037
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.26421 validation-auc:0.96453 validation-aucpr:0.97036
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.26074 validation-auc:0.96448 validation-aucpr:0.97029
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.25768 validation-auc:0.96467 validation-aucpr:0.97043
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.25188 validation-auc:0.96511 validation-aucpr:0.97088
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.24964 validation-auc:0.96517 validation-aucpr:0.97089
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24743 validation-auc:0.96530 validation-aucpr:0.97096
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.24537 validation-auc:0.96529 validation-aucpr:0.97096
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.24115 validation-auc:0.96558 validation-aucpr:0.97128
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23780 validation-auc:0.96582 validation-aucpr:0.97150
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.23609 validation-auc:0.96586 validation-aucpr:0.97151
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.23350 validation-auc:0.96578 validation-aucpr:0.97135
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.23237 validation-auc:0.96575 validation-aucpr:0.97140
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.23065 validation-auc:0.96601 validation-aucpr:0.97159
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.22776 validation-auc:0.96638 validation-aucpr:0.97196
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.22684 validation-auc:0.96641 validation-aucpr:0.97199
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.22497 validation-auc:0.96644 validation-aucpr:0.97203
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.22292 validation-auc:0.96694 validation-aucpr:0.97233
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.22095 validation-auc:0.96721 validation-aucpr:0.97254
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.22040 validation-auc:0.96711 validation-aucpr:0.97249
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.21913 validation-auc:0.96738 validation-aucpr:0.97269
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.21883 validation-auc:0.96733 validation-aucpr:0.97264
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.21750 validation-auc:0.96736 validation-aucpr:0.97269
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.21688 validation-auc:0.96748 validation-aucpr:0.97276
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.21571 validation-auc:0.96783 validation-aucpr:0.97300
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.21368 validation-auc:0.96821 validation-aucpr:0.97328
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.21324 validation-auc:0.96831 validation-aucpr:0.97339
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.21172 validation-auc:0.96869 validation-aucpr:0.97368
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.21151 validation-auc:0.96865 validation-aucpr:0.97367
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.21170 validation-auc:0.96838 validation-aucpr:0.97348
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.21202 validation-auc:0.96815 validation-aucpr:0.97323
{'best_iteration': '50', 'best_score': '0.9736780566332014'}
Trial 29, Fold 4: Log loss = 0.21202301627995604, Average precision = 0.9732314089812211, ROC-AUC = 0.968145179557978, Elapsed Time = 9.093965699999899 seconds
Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 29, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64529 validation-auc:0.90132 validation-aucpr:0.91196
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60198 validation-auc:0.93659 validation-aucpr:0.93723
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56527 validation-auc:0.94311 validation-aucpr:0.94273
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53389 validation-auc:0.94624 validation-aucpr:0.94825
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50769 validation-auc:0.94717 validation-aucpr:0.95187
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.48347 validation-auc:0.95008 validation-aucpr:0.95406
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.46231 validation-auc:0.95062 validation-aucpr:0.95503
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.44375 validation-auc:0.95115 validation-aucpr:0.95522
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.42109 validation-auc:0.95512 validation-aucpr:0.95981
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.40650 validation-auc:0.95518 validation-aucpr:0.96009
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.39462 validation-auc:0.95529 validation-aucpr:0.96068
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.37714 validation-auc:0.95733 validation-aucpr:0.96316
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.36635 validation-auc:0.95690 validation-aucpr:0.96276
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35676 validation-auc:0.95711 validation-aucpr:0.96284
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34751 validation-auc:0.95718 validation-aucpr:0.96300
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33915 validation-auc:0.95730 validation-aucpr:0.96305
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.33291 validation-auc:0.95688 validation-aucpr:0.96274
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.32761 validation-auc:0.95670 validation-aucpr:0.96259
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.31691 validation-auc:0.95790 validation-aucpr:0.96297
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.31259 validation-auc:0.95757 validation-aucpr:0.96298
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.30643 validation-auc:0.95817 validation-aucpr:0.96298
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.30059 validation-auc:0.95866 validation-aucpr:0.96323
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.29786 validation-auc:0.95810 validation-aucpr:0.96202
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.28972 validation-auc:0.95917 validation-aucpr:0.96395
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.28543 validation-auc:0.95940 validation-aucpr:0.96381
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.28207 validation-auc:0.95944 validation-aucpr:0.96413
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.27893 validation-auc:0.95961 validation-aucpr:0.96416
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.27676 validation-auc:0.95941 validation-aucpr:0.96401
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.27162 validation-auc:0.95997 validation-aucpr:0.96443
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.26585 validation-auc:0.96053 validation-aucpr:0.96585
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.26275 validation-auc:0.96089 validation-aucpr:0.96607
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.26138 validation-auc:0.96084 validation-aucpr:0.96527
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.25685 validation-auc:0.96118 validation-aucpr:0.96511
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.25283 validation-auc:0.96169 validation-aucpr:0.96662
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.24905 validation-auc:0.96202 validation-aucpr:0.96667
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.24578 validation-auc:0.96216 validation-aucpr:0.96671
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.24385 validation-auc:0.96239 validation-aucpr:0.96697
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.24066 validation-auc:0.96285 validation-aucpr:0.96735
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.23951 validation-auc:0.96292 validation-aucpr:0.96729
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.23658 validation-auc:0.96348 validation-aucpr:0.96777
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.23554 validation-auc:0.96363 validation-aucpr:0.96805
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23492 validation-auc:0.96364 validation-aucpr:0.96807
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23292 validation-auc:0.96398 validation-aucpr:0.96795
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23274 validation-auc:0.96380 validation-aucpr:0.96787
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23152 validation-auc:0.96393 validation-aucpr:0.96792
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23065 validation-auc:0.96412 validation-aucpr:0.96807
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23042 validation-auc:0.96413 validation-aucpr:0.96830
[18:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.22993 validation-auc:0.96429 validation-aucpr:0.96835
[18:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.22977 validation-auc:0.96417 validation-aucpr:0.96822
[18:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22903 validation-auc:0.96428 validation-aucpr:0.96845
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22735 validation-auc:0.96465 validation-aucpr:0.96932
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22616 validation-auc:0.96502 validation-aucpr:0.96959
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22491 validation-auc:0.96514 validation-aucpr:0.96973
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.22447 validation-auc:0.96525 validation-aucpr:0.96987
{'best_iteration': '53', 'best_score': '0.969869479613832'}
Trial 29, Fold 5: Log loss = 0.2244748179461408, Average precision = 0.9698752741540864, ROC-AUC = 0.9652471248351076, Elapsed Time = 9.2844757999992 seconds
Optimization Progress: 30%|### | 30/100 [49:53<1:47:34, 92.21s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 30, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68429 validation-auc:0.94592 validation-aucpr:0.95150
[1] validation-logloss:0.67552 validation-auc:0.93777 validation-aucpr:0.94722
[2] validation-logloss:0.66675 validation-auc:0.94673 validation-aucpr:0.94831
[3] validation-logloss:0.65861 validation-auc:0.95024 validation-aucpr:0.95688
[4] validation-logloss:0.65139 validation-auc:0.95150 validation-aucpr:0.95454
[5] validation-logloss:0.64332 validation-auc:0.95423 validation-aucpr:0.96099
[6] validation-logloss:0.63530 validation-auc:0.95723 validation-aucpr:0.96523
[7] validation-logloss:0.62740 validation-auc:0.95863 validation-aucpr:0.96553
[8] validation-logloss:0.61993 validation-auc:0.95944 validation-aucpr:0.96701
[9] validation-logloss:0.61339 validation-auc:0.95979 validation-aucpr:0.96727
[10] validation-logloss:0.60710 validation-auc:0.96040 validation-aucpr:0.96584
[11] validation-logloss:0.60038 validation-auc:0.96130 validation-aucpr:0.96669
[12] validation-logloss:0.59437 validation-auc:0.96237 validation-aucpr:0.96906
[13] validation-logloss:0.58734 validation-auc:0.96312 validation-aucpr:0.96844
[14] validation-logloss:0.58055 validation-auc:0.96386 validation-aucpr:0.96908
[15] validation-logloss:0.57401 validation-auc:0.96429 validation-aucpr:0.96993
[16] validation-logloss:0.56841 validation-auc:0.96440 validation-aucpr:0.97014
[17] validation-logloss:0.56276 validation-auc:0.96505 validation-aucpr:0.97077
[18] validation-logloss:0.55744 validation-auc:0.96532 validation-aucpr:0.97094
[19] validation-logloss:0.55132 validation-auc:0.96545 validation-aucpr:0.97093
[20] validation-logloss:0.54604 validation-auc:0.96553 validation-aucpr:0.97092
[21] validation-logloss:0.54070 validation-auc:0.96584 validation-aucpr:0.97118
[22] validation-logloss:0.53573 validation-auc:0.96563 validation-aucpr:0.97098
[23] validation-logloss:0.53025 validation-auc:0.96542 validation-aucpr:0.97088
[24] validation-logloss:0.52468 validation-auc:0.96565 validation-aucpr:0.97105
[25] validation-logloss:0.51943 validation-auc:0.96566 validation-aucpr:0.97112
[26] validation-logloss:0.51424 validation-auc:0.96584 validation-aucpr:0.97123
[27] validation-logloss:0.50921 validation-auc:0.96563 validation-aucpr:0.97109
[28] validation-logloss:0.50410 validation-auc:0.96567 validation-aucpr:0.97114
[29] validation-logloss:0.50007 validation-auc:0.96536 validation-aucpr:0.97089
[30] validation-logloss:0.49528 validation-auc:0.96537 validation-aucpr:0.97086
[31] validation-logloss:0.49038 validation-auc:0.96559 validation-aucpr:0.97102
[32] validation-logloss:0.48545 validation-auc:0.96580 validation-aucpr:0.97126
[33] validation-logloss:0.48088 validation-auc:0.96576 validation-aucpr:0.97130
[34] validation-logloss:0.47632 validation-auc:0.96618 validation-aucpr:0.97157
[35] validation-logloss:0.47214 validation-auc:0.96644 validation-aucpr:0.97172
[36] validation-logloss:0.46841 validation-auc:0.96627 validation-aucpr:0.97160
[37] validation-logloss:0.46417 validation-auc:0.96637 validation-aucpr:0.97169
[38] validation-logloss:0.46038 validation-auc:0.96639 validation-aucpr:0.97168
[39] validation-logloss:0.45608 validation-auc:0.96650 validation-aucpr:0.97175
[40] validation-logloss:0.45191 validation-auc:0.96643 validation-aucpr:0.97156
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[75] validation-logloss:0.35178 validation-auc:0.96737 validation-aucpr:0.97193
[76] validation-logloss:0.34978 validation-auc:0.96735 validation-aucpr:0.97190
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[78] validation-logloss:0.34553 validation-auc:0.96737 validation-aucpr:0.97192
[79] validation-logloss:0.34330 validation-auc:0.96739 validation-aucpr:0.97194
[80] validation-logloss:0.34108 validation-auc:0.96743 validation-aucpr:0.97198
[81] validation-logloss:0.33942 validation-auc:0.96744 validation-aucpr:0.97189
[82] validation-logloss:0.33732 validation-auc:0.96746 validation-aucpr:0.97191
[83] validation-logloss:0.33519 validation-auc:0.96746 validation-aucpr:0.97195
[84] validation-logloss:0.33347 validation-auc:0.96745 validation-aucpr:0.97195
[85] validation-logloss:0.33180 validation-auc:0.96749 validation-aucpr:0.97199
[86] validation-logloss:0.33005 validation-auc:0.96758 validation-aucpr:0.97196
[87] validation-logloss:0.32842 validation-auc:0.96757 validation-aucpr:0.97191
[88] validation-logloss:0.32691 validation-auc:0.96755 validation-aucpr:0.97186
[89] validation-logloss:0.32524 validation-auc:0.96766 validation-aucpr:0.97191
[90] validation-logloss:0.32311 validation-auc:0.96779 validation-aucpr:0.97203
[91] validation-logloss:0.32119 validation-auc:0.96784 validation-aucpr:0.97207
[92] validation-logloss:0.31967 validation-auc:0.96783 validation-aucpr:0.97206
[93] validation-logloss:0.31793 validation-auc:0.96785 validation-aucpr:0.97211
{'best_iteration': '93', 'best_score': '0.9721051971930318'}
Trial 30, Fold 1: Log loss = 0.31792534158671387, Average precision = 0.9721165311715719, ROC-AUC = 0.9678542751035975, Elapsed Time = 2.6847978999994666 seconds
Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 30, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68365 validation-auc:0.94604 validation-aucpr:0.95049
[1] validation-logloss:0.67572 validation-auc:0.94530 validation-aucpr:0.94160
[2] validation-logloss:0.66691 validation-auc:0.95222 validation-aucpr:0.94586
[3] validation-logloss:0.65860 validation-auc:0.95589 validation-aucpr:0.94929
[4] validation-logloss:0.65023 validation-auc:0.95917 validation-aucpr:0.95637
[5] validation-logloss:0.64297 validation-auc:0.96098 validation-aucpr:0.96079
[6] validation-logloss:0.63576 validation-auc:0.96114 validation-aucpr:0.95928
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[10] validation-logloss:0.60804 validation-auc:0.96356 validation-aucpr:0.96631
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[12] validation-logloss:0.59564 validation-auc:0.96426 validation-aucpr:0.96675
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[14] validation-logloss:0.58317 validation-auc:0.96439 validation-aucpr:0.96662
[15] validation-logloss:0.57749 validation-auc:0.96410 validation-aucpr:0.96610
[16] validation-logloss:0.57090 validation-auc:0.96498 validation-aucpr:0.96923
[17] validation-logloss:0.56455 validation-auc:0.96538 validation-aucpr:0.96953
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[28] validation-logloss:0.50583 validation-auc:0.96709 validation-aucpr:0.97109
[29] validation-logloss:0.50127 validation-auc:0.96707 validation-aucpr:0.97107
[30] validation-logloss:0.49647 validation-auc:0.96730 validation-aucpr:0.97126
[31] validation-logloss:0.49155 validation-auc:0.96760 validation-aucpr:0.97151
[32] validation-logloss:0.48744 validation-auc:0.96764 validation-aucpr:0.97146
[33] validation-logloss:0.48312 validation-auc:0.96791 validation-aucpr:0.97167
[34] validation-logloss:0.47846 validation-auc:0.96789 validation-aucpr:0.97166
[35] validation-logloss:0.47445 validation-auc:0.96760 validation-aucpr:0.97146
[36] validation-logloss:0.47048 validation-auc:0.96763 validation-aucpr:0.97155
[37] validation-logloss:0.46636 validation-auc:0.96774 validation-aucpr:0.97165
[38] validation-logloss:0.46207 validation-auc:0.96798 validation-aucpr:0.97181
[39] validation-logloss:0.45823 validation-auc:0.96821 validation-aucpr:0.97197
[40] validation-logloss:0.45420 validation-auc:0.96827 validation-aucpr:0.97206
[41] validation-logloss:0.45065 validation-auc:0.96828 validation-aucpr:0.97205
[42] validation-logloss:0.44746 validation-auc:0.96802 validation-aucpr:0.97183
[43] validation-logloss:0.44361 validation-auc:0.96794 validation-aucpr:0.97174
[44] validation-logloss:0.44031 validation-auc:0.96803 validation-aucpr:0.97174
[45] validation-logloss:0.43642 validation-auc:0.96826 validation-aucpr:0.97190
[46] validation-logloss:0.43256 validation-auc:0.96825 validation-aucpr:0.97194
[47] validation-logloss:0.42921 validation-auc:0.96842 validation-aucpr:0.97204
[48] validation-logloss:0.42610 validation-auc:0.96845 validation-aucpr:0.97206
[49] validation-logloss:0.42307 validation-auc:0.96852 validation-aucpr:0.97241
[50] validation-logloss:0.41996 validation-auc:0.96847 validation-aucpr:0.97237
[51] validation-logloss:0.41643 validation-auc:0.96855 validation-aucpr:0.97247
[52] validation-logloss:0.41358 validation-auc:0.96848 validation-aucpr:0.97239
[53] validation-logloss:0.41055 validation-auc:0.96855 validation-aucpr:0.97241
[54] validation-logloss:0.40719 validation-auc:0.96855 validation-aucpr:0.97244
[55] validation-logloss:0.40378 validation-auc:0.96861 validation-aucpr:0.97252
[56] validation-logloss:0.40052 validation-auc:0.96877 validation-aucpr:0.97266
[57] validation-logloss:0.39732 validation-auc:0.96879 validation-aucpr:0.97269
[58] validation-logloss:0.39411 validation-auc:0.96880 validation-aucpr:0.97270
[59] validation-logloss:0.39130 validation-auc:0.96902 validation-aucpr:0.97284
[60] validation-logloss:0.38876 validation-auc:0.96907 validation-aucpr:0.97287
[61] validation-logloss:0.38592 validation-auc:0.96904 validation-aucpr:0.97286
[62] validation-logloss:0.38348 validation-auc:0.96902 validation-aucpr:0.97282
[63] validation-logloss:0.38096 validation-auc:0.96905 validation-aucpr:0.97285
[64] validation-logloss:0.37865 validation-auc:0.96914 validation-aucpr:0.97291
[65] validation-logloss:0.37629 validation-auc:0.96906 validation-aucpr:0.97285
[66] validation-logloss:0.37340 validation-auc:0.96917 validation-aucpr:0.97294
[67] validation-logloss:0.37049 validation-auc:0.96926 validation-aucpr:0.97303
[68] validation-logloss:0.36816 validation-auc:0.96932 validation-aucpr:0.97309
[69] validation-logloss:0.36543 validation-auc:0.96931 validation-aucpr:0.97308
[70] validation-logloss:0.36335 validation-auc:0.96927 validation-aucpr:0.97306
[71] validation-logloss:0.36117 validation-auc:0.96936 validation-aucpr:0.97312
[72] validation-logloss:0.35853 validation-auc:0.96937 validation-aucpr:0.97314
[73] validation-logloss:0.35584 validation-auc:0.96960 validation-aucpr:0.97333
[74] validation-logloss:0.35337 validation-auc:0.96967 validation-aucpr:0.97337
[75] validation-logloss:0.35132 validation-auc:0.96964 validation-aucpr:0.97335
[76] validation-logloss:0.34943 validation-auc:0.96954 validation-aucpr:0.97326
[77] validation-logloss:0.34704 validation-auc:0.96961 validation-aucpr:0.97331
[78] validation-logloss:0.34465 validation-auc:0.96956 validation-aucpr:0.97328
[79] validation-logloss:0.34274 validation-auc:0.96949 validation-aucpr:0.97323
[80] validation-logloss:0.34039 validation-auc:0.96957 validation-aucpr:0.97330
[81] validation-logloss:0.33814 validation-auc:0.96965 validation-aucpr:0.97338
[82] validation-logloss:0.33642 validation-auc:0.96960 validation-aucpr:0.97334
[83] validation-logloss:0.33423 validation-auc:0.96971 validation-aucpr:0.97343
[84] validation-logloss:0.33225 validation-auc:0.96965 validation-aucpr:0.97339
[85] validation-logloss:0.33006 validation-auc:0.96971 validation-aucpr:0.97346
[86] validation-logloss:0.32813 validation-auc:0.96970 validation-aucpr:0.97345
[87] validation-logloss:0.32600 validation-auc:0.96969 validation-aucpr:0.97346
[88] validation-logloss:0.32441 validation-auc:0.96962 validation-aucpr:0.97341
[89] validation-logloss:0.32238 validation-auc:0.96966 validation-aucpr:0.97344
[90] validation-logloss:0.32048 validation-auc:0.96966 validation-aucpr:0.97341
[91] validation-logloss:0.31844 validation-auc:0.96977 validation-aucpr:0.97348
[92] validation-logloss:0.31664 validation-auc:0.96981 validation-aucpr:0.97352
[93] validation-logloss:0.31517 validation-auc:0.96981 validation-aucpr:0.97351
{'best_iteration': '92', 'best_score': '0.9735212751789194'}
Trial 30, Fold 2: Log loss = 0.3151728598347162, Average precision = 0.97351458863413, ROC-AUC = 0.9698109920632872, Elapsed Time = 2.9415685000003577 seconds
Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 30, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68408 validation-auc:0.94187 validation-aucpr:0.94867
[1] validation-logloss:0.67524 validation-auc:0.94617 validation-aucpr:0.94137
[2] validation-logloss:0.66632 validation-auc:0.95575 validation-aucpr:0.95035
[3] validation-logloss:0.65793 validation-auc:0.96003 validation-aucpr:0.96118
[4] validation-logloss:0.64957 validation-auc:0.96238 validation-aucpr:0.96455
[5] validation-logloss:0.64207 validation-auc:0.96387 validation-aucpr:0.96392
[6] validation-logloss:0.63507 validation-auc:0.96440 validation-aucpr:0.96713
[7] validation-logloss:0.62749 validation-auc:0.96389 validation-aucpr:0.96640
[8] validation-logloss:0.62102 validation-auc:0.96349 validation-aucpr:0.96595
[9] validation-logloss:0.61401 validation-auc:0.96381 validation-aucpr:0.96586
[10] validation-logloss:0.60659 validation-auc:0.96456 validation-aucpr:0.96801
[11] validation-logloss:0.59936 validation-auc:0.96502 validation-aucpr:0.96831
[12] validation-logloss:0.59221 validation-auc:0.96563 validation-aucpr:0.97013
[13] validation-logloss:0.58612 validation-auc:0.96543 validation-aucpr:0.97010
[14] validation-logloss:0.57933 validation-auc:0.96590 validation-aucpr:0.97037
[15] validation-logloss:0.57365 validation-auc:0.96584 validation-aucpr:0.97090
[16] validation-logloss:0.56810 validation-auc:0.96577 validation-aucpr:0.97072
[17] validation-logloss:0.56177 validation-auc:0.96637 validation-aucpr:0.97125
[18] validation-logloss:0.55568 validation-auc:0.96698 validation-aucpr:0.97236
[19] validation-logloss:0.54971 validation-auc:0.96694 validation-aucpr:0.97227
[20] validation-logloss:0.54382 validation-auc:0.96721 validation-aucpr:0.97253
[21] validation-logloss:0.53799 validation-auc:0.96767 validation-aucpr:0.97289
[22] validation-logloss:0.53244 validation-auc:0.96710 validation-aucpr:0.97250
[23] validation-logloss:0.52740 validation-auc:0.96719 validation-aucpr:0.97251
[24] validation-logloss:0.52207 validation-auc:0.96704 validation-aucpr:0.97234
[25] validation-logloss:0.51728 validation-auc:0.96701 validation-aucpr:0.97233
[26] validation-logloss:0.51189 validation-auc:0.96722 validation-aucpr:0.97246
[27] validation-logloss:0.50708 validation-auc:0.96742 validation-aucpr:0.97263
[28] validation-logloss:0.50200 validation-auc:0.96738 validation-aucpr:0.97210
[29] validation-logloss:0.49688 validation-auc:0.96758 validation-aucpr:0.97225
[30] validation-logloss:0.49187 validation-auc:0.96786 validation-aucpr:0.97251
[31] validation-logloss:0.48776 validation-auc:0.96775 validation-aucpr:0.97230
[32] validation-logloss:0.48288 validation-auc:0.96808 validation-aucpr:0.97260
[33] validation-logloss:0.47830 validation-auc:0.96819 validation-aucpr:0.97268
[34] validation-logloss:0.47379 validation-auc:0.96802 validation-aucpr:0.97254
[35] validation-logloss:0.46974 validation-auc:0.96815 validation-aucpr:0.97261
[36] validation-logloss:0.46537 validation-auc:0.96806 validation-aucpr:0.97118
[37] validation-logloss:0.46151 validation-auc:0.96815 validation-aucpr:0.97130
[38] validation-logloss:0.45769 validation-auc:0.96810 validation-aucpr:0.97128
[39] validation-logloss:0.45390 validation-auc:0.96821 validation-aucpr:0.97132
[40] validation-logloss:0.45021 validation-auc:0.96844 validation-aucpr:0.97143
[41] validation-logloss:0.44612 validation-auc:0.96829 validation-aucpr:0.97137
[42] validation-logloss:0.44195 validation-auc:0.96845 validation-aucpr:0.97145
[43] validation-logloss:0.43792 validation-auc:0.96870 validation-aucpr:0.97165
[44] validation-logloss:0.43407 validation-auc:0.96872 validation-aucpr:0.97169
[45] validation-logloss:0.43017 validation-auc:0.96884 validation-aucpr:0.97173
[46] validation-logloss:0.42701 validation-auc:0.96870 validation-aucpr:0.97165
[47] validation-logloss:0.42382 validation-auc:0.96870 validation-aucpr:0.97173
[48] validation-logloss:0.42090 validation-auc:0.96856 validation-aucpr:0.97160
[49] validation-logloss:0.41724 validation-auc:0.96862 validation-aucpr:0.97168
[50] validation-logloss:0.41368 validation-auc:0.96859 validation-aucpr:0.97169
[51] validation-logloss:0.41085 validation-auc:0.96855 validation-aucpr:0.97160
[52] validation-logloss:0.40742 validation-auc:0.96856 validation-aucpr:0.97166
[53] validation-logloss:0.40458 validation-auc:0.96849 validation-aucpr:0.97156
[54] validation-logloss:0.40118 validation-auc:0.96860 validation-aucpr:0.97154
[55] validation-logloss:0.39834 validation-auc:0.96862 validation-aucpr:0.97144
[56] validation-logloss:0.39559 validation-auc:0.96866 validation-aucpr:0.97140
[57] validation-logloss:0.39294 validation-auc:0.96856 validation-aucpr:0.97128
[58] validation-logloss:0.38978 validation-auc:0.96864 validation-aucpr:0.97119
[59] validation-logloss:0.38675 validation-auc:0.96874 validation-aucpr:0.97126
[60] validation-logloss:0.38366 validation-auc:0.96883 validation-aucpr:0.97134
[61] validation-logloss:0.38078 validation-auc:0.96886 validation-aucpr:0.97138
[62] validation-logloss:0.37849 validation-auc:0.96880 validation-aucpr:0.97158
[63] validation-logloss:0.37571 validation-auc:0.96881 validation-aucpr:0.97180
[64] validation-logloss:0.37290 validation-auc:0.96881 validation-aucpr:0.97171
[65] validation-logloss:0.37057 validation-auc:0.96886 validation-aucpr:0.97186
[66] validation-logloss:0.36798 validation-auc:0.96894 validation-aucpr:0.97193
[67] validation-logloss:0.36564 validation-auc:0.96896 validation-aucpr:0.97199
[68] validation-logloss:0.36332 validation-auc:0.96906 validation-aucpr:0.97205
[69] validation-logloss:0.36059 validation-auc:0.96917 validation-aucpr:0.97196
[70] validation-logloss:0.35849 validation-auc:0.96918 validation-aucpr:0.97240
[71] validation-logloss:0.35635 validation-auc:0.96926 validation-aucpr:0.97251
{'best_iteration': '21', 'best_score': '0.9728873063164621'}
Trial 30, Fold 3: Log loss = 0.35634576391738043, Average precision = 0.9725254290879971, ROC-AUC = 0.9692625516464255, Elapsed Time = 2.331017199998314 seconds
Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 30, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68408 validation-auc:0.93053 validation-aucpr:0.92858
[1] validation-logloss:0.67602 validation-auc:0.94184 validation-aucpr:0.94726
[2] validation-logloss:0.66734 validation-auc:0.94964 validation-aucpr:0.94641
[3] validation-logloss:0.66002 validation-auc:0.95045 validation-aucpr:0.95206
[4] validation-logloss:0.65255 validation-auc:0.95388 validation-aucpr:0.95359
[5] validation-logloss:0.64522 validation-auc:0.95537 validation-aucpr:0.95826
[6] validation-logloss:0.63706 validation-auc:0.95776 validation-aucpr:0.96238
[7] validation-logloss:0.62999 validation-auc:0.95796 validation-aucpr:0.96265
[8] validation-logloss:0.62335 validation-auc:0.95790 validation-aucpr:0.96314
[9] validation-logloss:0.61658 validation-auc:0.95907 validation-aucpr:0.96494
[10] validation-logloss:0.60931 validation-auc:0.96047 validation-aucpr:0.96625
[11] validation-logloss:0.60196 validation-auc:0.96131 validation-aucpr:0.96660
[12] validation-logloss:0.59566 validation-auc:0.96191 validation-aucpr:0.96711
[13] validation-logloss:0.58955 validation-auc:0.96224 validation-aucpr:0.96656
[14] validation-logloss:0.58364 validation-auc:0.96271 validation-aucpr:0.96855
[15] validation-logloss:0.57782 validation-auc:0.96260 validation-aucpr:0.96845
[16] validation-logloss:0.57132 validation-auc:0.96341 validation-aucpr:0.96908
[17] validation-logloss:0.56494 validation-auc:0.96372 validation-aucpr:0.96948
[18] validation-logloss:0.55875 validation-auc:0.96405 validation-aucpr:0.96983
[19] validation-logloss:0.55350 validation-auc:0.96416 validation-aucpr:0.96997
[20] validation-logloss:0.54836 validation-auc:0.96413 validation-aucpr:0.96993
[21] validation-logloss:0.54326 validation-auc:0.96422 validation-aucpr:0.97000
[22] validation-logloss:0.53830 validation-auc:0.96416 validation-aucpr:0.96996
[23] validation-logloss:0.53344 validation-auc:0.96406 validation-aucpr:0.96985
[24] validation-logloss:0.52802 validation-auc:0.96401 validation-aucpr:0.96992
[25] validation-logloss:0.52335 validation-auc:0.96404 validation-aucpr:0.96996
[26] validation-logloss:0.51893 validation-auc:0.96395 validation-aucpr:0.96989
[27] validation-logloss:0.51432 validation-auc:0.96402 validation-aucpr:0.96990
[28] validation-logloss:0.50917 validation-auc:0.96437 validation-aucpr:0.97021
[29] validation-logloss:0.50445 validation-auc:0.96439 validation-aucpr:0.97023
[30] validation-logloss:0.49963 validation-auc:0.96440 validation-aucpr:0.97025
[31] validation-logloss:0.49506 validation-auc:0.96445 validation-aucpr:0.97033
[32] validation-logloss:0.49094 validation-auc:0.96459 validation-aucpr:0.97040
[33] validation-logloss:0.48682 validation-auc:0.96440 validation-aucpr:0.97025
[34] validation-logloss:0.48220 validation-auc:0.96462 validation-aucpr:0.97043
[35] validation-logloss:0.47819 validation-auc:0.96489 validation-aucpr:0.97062
[36] validation-logloss:0.47362 validation-auc:0.96509 validation-aucpr:0.97084
[37] validation-logloss:0.47000 validation-auc:0.96508 validation-aucpr:0.97081
[38] validation-logloss:0.46619 validation-auc:0.96512 validation-aucpr:0.97081
[39] validation-logloss:0.46245 validation-auc:0.96517 validation-aucpr:0.97081
[40] validation-logloss:0.45866 validation-auc:0.96523 validation-aucpr:0.97086
[41] validation-logloss:0.45454 validation-auc:0.96526 validation-aucpr:0.97091
[42] validation-logloss:0.45097 validation-auc:0.96531 validation-aucpr:0.97093
[43] validation-logloss:0.44752 validation-auc:0.96538 validation-aucpr:0.97095
[44] validation-logloss:0.44336 validation-auc:0.96564 validation-aucpr:0.97119
[45] validation-logloss:0.43937 validation-auc:0.96570 validation-aucpr:0.97127
[46] validation-logloss:0.43563 validation-auc:0.96581 validation-aucpr:0.97139
[47] validation-logloss:0.43250 validation-auc:0.96577 validation-aucpr:0.97139
[48] validation-logloss:0.42861 validation-auc:0.96600 validation-aucpr:0.97157
[49] validation-logloss:0.42487 validation-auc:0.96607 validation-aucpr:0.97163
[50] validation-logloss:0.42175 validation-auc:0.96612 validation-aucpr:0.97168
[51] validation-logloss:0.41890 validation-auc:0.96605 validation-aucpr:0.97163
[52] validation-logloss:0.41532 validation-auc:0.96618 validation-aucpr:0.97174
[53] validation-logloss:0.41198 validation-auc:0.96630 validation-aucpr:0.97186
[54] validation-logloss:0.40912 validation-auc:0.96622 validation-aucpr:0.97179
[55] validation-logloss:0.40595 validation-auc:0.96621 validation-aucpr:0.97181
[56] validation-logloss:0.40323 validation-auc:0.96614 validation-aucpr:0.97174
[57] validation-logloss:0.40033 validation-auc:0.96620 validation-aucpr:0.97175
[58] validation-logloss:0.39753 validation-auc:0.96628 validation-aucpr:0.97177
[59] validation-logloss:0.39482 validation-auc:0.96628 validation-aucpr:0.97175
[60] validation-logloss:0.39217 validation-auc:0.96629 validation-aucpr:0.97175
[61] validation-logloss:0.38975 validation-auc:0.96624 validation-aucpr:0.97172
[62] validation-logloss:0.38734 validation-auc:0.96618 validation-aucpr:0.97167
[63] validation-logloss:0.38437 validation-auc:0.96633 validation-aucpr:0.97179
[64] validation-logloss:0.38173 validation-auc:0.96644 validation-aucpr:0.97187
[65] validation-logloss:0.37886 validation-auc:0.96645 validation-aucpr:0.97190
[66] validation-logloss:0.37653 validation-auc:0.96645 validation-aucpr:0.97190
[67] validation-logloss:0.37438 validation-auc:0.96646 validation-aucpr:0.97188
[68] validation-logloss:0.37207 validation-auc:0.96645 validation-aucpr:0.97190
[69] validation-logloss:0.36934 validation-auc:0.96649 validation-aucpr:0.97196
[70] validation-logloss:0.36648 validation-auc:0.96668 validation-aucpr:0.97211
[71] validation-logloss:0.36382 validation-auc:0.96681 validation-aucpr:0.97224
[72] validation-logloss:0.36169 validation-auc:0.96679 validation-aucpr:0.97222
[73] validation-logloss:0.35899 validation-auc:0.96694 validation-aucpr:0.97234
[74] validation-logloss:0.35707 validation-auc:0.96689 validation-aucpr:0.97229
[75] validation-logloss:0.35464 validation-auc:0.96695 validation-aucpr:0.97234
[76] validation-logloss:0.35221 validation-auc:0.96708 validation-aucpr:0.97244
[77] validation-logloss:0.35029 validation-auc:0.96711 validation-aucpr:0.97247
[78] validation-logloss:0.34794 validation-auc:0.96713 validation-aucpr:0.97251
[79] validation-logloss:0.34612 validation-auc:0.96707 validation-aucpr:0.97246
[80] validation-logloss:0.34386 validation-auc:0.96713 validation-aucpr:0.97253
[81] validation-logloss:0.34157 validation-auc:0.96717 validation-aucpr:0.97256
[82] validation-logloss:0.33969 validation-auc:0.96733 validation-aucpr:0.97266
[83] validation-logloss:0.33794 validation-auc:0.96729 validation-aucpr:0.97263
[84] validation-logloss:0.33621 validation-auc:0.96718 validation-aucpr:0.97255
[85] validation-logloss:0.33440 validation-auc:0.96713 validation-aucpr:0.97252
[86] validation-logloss:0.33270 validation-auc:0.96717 validation-aucpr:0.97253
[87] validation-logloss:0.33074 validation-auc:0.96729 validation-aucpr:0.97261
[88] validation-logloss:0.32873 validation-auc:0.96735 validation-aucpr:0.97266
[89] validation-logloss:0.32663 validation-auc:0.96743 validation-aucpr:0.97272
[90] validation-logloss:0.32507 validation-auc:0.96744 validation-aucpr:0.97273
[91] validation-logloss:0.32368 validation-auc:0.96751 validation-aucpr:0.97276
[92] validation-logloss:0.32184 validation-auc:0.96751 validation-aucpr:0.97277
[93] validation-logloss:0.32051 validation-auc:0.96739 validation-aucpr:0.97268
{'best_iteration': '92', 'best_score': '0.9727730022275809'}
Trial 30, Fold 4: Log loss = 0.3205108791725433, Average precision = 0.9726860324319256, ROC-AUC = 0.9673858195574059, Elapsed Time = 2.7896488000005775 seconds
Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 30, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68389 validation-auc:0.94408 validation-aucpr:0.95018
[1] validation-logloss:0.67593 validation-auc:0.94630 validation-aucpr:0.94872
[2] validation-logloss:0.66727 validation-auc:0.95062 validation-aucpr:0.95591
[3] validation-logloss:0.65885 validation-auc:0.95609 validation-aucpr:0.95779
[4] validation-logloss:0.65153 validation-auc:0.95795 validation-aucpr:0.95871
[5] validation-logloss:0.64346 validation-auc:0.95900 validation-aucpr:0.96111
[6] validation-logloss:0.63639 validation-auc:0.96002 validation-aucpr:0.96209
[7] validation-logloss:0.62865 validation-auc:0.96094 validation-aucpr:0.96319
[8] validation-logloss:0.62186 validation-auc:0.96195 validation-aucpr:0.96294
[9] validation-logloss:0.61413 validation-auc:0.96361 validation-aucpr:0.96569
[10] validation-logloss:0.60685 validation-auc:0.96372 validation-aucpr:0.96641
[11] validation-logloss:0.59983 validation-auc:0.96387 validation-aucpr:0.96739
[12] validation-logloss:0.59367 validation-auc:0.96366 validation-aucpr:0.96683
[13] validation-logloss:0.58787 validation-auc:0.96348 validation-aucpr:0.96616
[14] validation-logloss:0.58193 validation-auc:0.96344 validation-aucpr:0.96696
[15] validation-logloss:0.57639 validation-auc:0.96299 validation-aucpr:0.96664
[16] validation-logloss:0.56976 validation-auc:0.96373 validation-aucpr:0.96727
[17] validation-logloss:0.56419 validation-auc:0.96385 validation-aucpr:0.96726
[18] validation-logloss:0.55887 validation-auc:0.96372 validation-aucpr:0.96708
[19] validation-logloss:0.55282 validation-auc:0.96387 validation-aucpr:0.96720
[20] validation-logloss:0.54697 validation-auc:0.96419 validation-aucpr:0.96745
[21] validation-logloss:0.54168 validation-auc:0.96421 validation-aucpr:0.96748
[22] validation-logloss:0.53664 validation-auc:0.96444 validation-aucpr:0.96754
[23] validation-logloss:0.53165 validation-auc:0.96457 validation-aucpr:0.96811
[24] validation-logloss:0.52697 validation-auc:0.96463 validation-aucpr:0.96805
[25] validation-logloss:0.52145 validation-auc:0.96501 validation-aucpr:0.96835
[26] validation-logloss:0.51586 validation-auc:0.96520 validation-aucpr:0.96882
[27] validation-logloss:0.51060 validation-auc:0.96526 validation-aucpr:0.96894
[28] validation-logloss:0.50598 validation-auc:0.96516 validation-aucpr:0.96883
[29] validation-logloss:0.50148 validation-auc:0.96516 validation-aucpr:0.96883
[30] validation-logloss:0.49719 validation-auc:0.96503 validation-aucpr:0.96871
[31] validation-logloss:0.49243 validation-auc:0.96515 validation-aucpr:0.96879
[32] validation-logloss:0.48826 validation-auc:0.96527 validation-aucpr:0.96877
[33] validation-logloss:0.48420 validation-auc:0.96508 validation-aucpr:0.96865
[34] validation-logloss:0.47947 validation-auc:0.96535 validation-aucpr:0.96884
[35] validation-logloss:0.47481 validation-auc:0.96546 validation-aucpr:0.96896
[36] validation-logloss:0.47024 validation-auc:0.96562 validation-aucpr:0.96916
[37] validation-logloss:0.46582 validation-auc:0.96589 validation-aucpr:0.96938
[38] validation-logloss:0.46197 validation-auc:0.96607 validation-aucpr:0.96938
[39] validation-logloss:0.45774 validation-auc:0.96611 validation-aucpr:0.96936
[40] validation-logloss:0.45422 validation-auc:0.96600 validation-aucpr:0.96922
[41] validation-logloss:0.45013 validation-auc:0.96619 validation-aucpr:0.97111
[42] validation-logloss:0.44669 validation-auc:0.96600 validation-aucpr:0.97095
[43] validation-logloss:0.44339 validation-auc:0.96590 validation-aucpr:0.97086
[44] validation-logloss:0.43950 validation-auc:0.96602 validation-aucpr:0.97102
[45] validation-logloss:0.43635 validation-auc:0.96585 validation-aucpr:0.97089
[46] validation-logloss:0.43302 validation-auc:0.96593 validation-aucpr:0.97089
[47] validation-logloss:0.42971 validation-auc:0.96595 validation-aucpr:0.97091
[48] validation-logloss:0.42652 validation-auc:0.96591 validation-aucpr:0.97085
[49] validation-logloss:0.42299 validation-auc:0.96591 validation-aucpr:0.97085
[50] validation-logloss:0.41946 validation-auc:0.96594 validation-aucpr:0.97089
[51] validation-logloss:0.41602 validation-auc:0.96601 validation-aucpr:0.97104
[52] validation-logloss:0.41316 validation-auc:0.96593 validation-aucpr:0.97095
[53] validation-logloss:0.40987 validation-auc:0.96589 validation-aucpr:0.97093
[54] validation-logloss:0.40726 validation-auc:0.96585 validation-aucpr:0.97083
[55] validation-logloss:0.40409 validation-auc:0.96587 validation-aucpr:0.97085
[56] validation-logloss:0.40149 validation-auc:0.96583 validation-aucpr:0.97076
[57] validation-logloss:0.39861 validation-auc:0.96587 validation-aucpr:0.97076
[58] validation-logloss:0.39560 validation-auc:0.96590 validation-aucpr:0.97083
[59] validation-logloss:0.39256 validation-auc:0.96592 validation-aucpr:0.97085
[60] validation-logloss:0.38969 validation-auc:0.96604 validation-aucpr:0.97098
[61] validation-logloss:0.38714 validation-auc:0.96606 validation-aucpr:0.97099
[62] validation-logloss:0.38429 validation-auc:0.96611 validation-aucpr:0.97106
[63] validation-logloss:0.38189 validation-auc:0.96613 validation-aucpr:0.97144
[64] validation-logloss:0.37956 validation-auc:0.96613 validation-aucpr:0.97129
[65] validation-logloss:0.37674 validation-auc:0.96623 validation-aucpr:0.97139
[66] validation-logloss:0.37406 validation-auc:0.96631 validation-aucpr:0.97144
[67] validation-logloss:0.37136 validation-auc:0.96635 validation-aucpr:0.97146
[68] validation-logloss:0.36898 validation-auc:0.96637 validation-aucpr:0.97148
[69] validation-logloss:0.36681 validation-auc:0.96639 validation-aucpr:0.97148
[70] validation-logloss:0.36432 validation-auc:0.96647 validation-aucpr:0.97155
[71] validation-logloss:0.36216 validation-auc:0.96642 validation-aucpr:0.97151
[72] validation-logloss:0.36015 validation-auc:0.96633 validation-aucpr:0.97129
[73] validation-logloss:0.35765 validation-auc:0.96644 validation-aucpr:0.97136
[74] validation-logloss:0.35565 validation-auc:0.96635 validation-aucpr:0.97129
[75] validation-logloss:0.35367 validation-auc:0.96625 validation-aucpr:0.97119
[76] validation-logloss:0.35145 validation-auc:0.96624 validation-aucpr:0.97121
[77] validation-logloss:0.34941 validation-auc:0.96630 validation-aucpr:0.97126
[78] validation-logloss:0.34759 validation-auc:0.96626 validation-aucpr:0.97123
[79] validation-logloss:0.34539 validation-auc:0.96626 validation-aucpr:0.97122
[80] validation-logloss:0.34306 validation-auc:0.96640 validation-aucpr:0.97132
[81] validation-logloss:0.34139 validation-auc:0.96634 validation-aucpr:0.97128
[82] validation-logloss:0.33913 validation-auc:0.96646 validation-aucpr:0.97139
[83] validation-logloss:0.33745 validation-auc:0.96637 validation-aucpr:0.97129
[84] validation-logloss:0.33536 validation-auc:0.96636 validation-aucpr:0.97129
[85] validation-logloss:0.33366 validation-auc:0.96635 validation-aucpr:0.97126
[86] validation-logloss:0.33201 validation-auc:0.96634 validation-aucpr:0.97123
[87] validation-logloss:0.33025 validation-auc:0.96644 validation-aucpr:0.97128
[88] validation-logloss:0.32833 validation-auc:0.96647 validation-aucpr:0.97132
[89] validation-logloss:0.32614 validation-auc:0.96663 validation-aucpr:0.97144
[90] validation-logloss:0.32447 validation-auc:0.96673 validation-aucpr:0.97150
[91] validation-logloss:0.32296 validation-auc:0.96674 validation-aucpr:0.97150
[92] validation-logloss:0.32118 validation-auc:0.96676 validation-aucpr:0.97146
[93] validation-logloss:0.31976 validation-auc:0.96669 validation-aucpr:0.97140
{'best_iteration': '70', 'best_score': '0.9715477136630459'}
Trial 30, Fold 5: Log loss = 0.3197561209559464, Average precision = 0.9714092461106858, ROC-AUC = 0.9666948330639318, Elapsed Time = 2.8984010000003764 seconds
Optimization Progress: 31%|###1 | 31/100 [50:17<1:22:15, 71.52s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 31, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66183 validation-auc:0.95646 validation-aucpr:0.95706
[1] validation-logloss:0.63297 validation-auc:0.96248 validation-aucpr:0.96496
[2] validation-logloss:0.60646 validation-auc:0.96382 validation-aucpr:0.96777
[3] validation-logloss:0.58199 validation-auc:0.96545 validation-aucpr:0.97079
[4] validation-logloss:0.55933 validation-auc:0.96640 validation-aucpr:0.97186
[5] validation-logloss:0.53802 validation-auc:0.96680 validation-aucpr:0.97221
[6] validation-logloss:0.51885 validation-auc:0.96668 validation-aucpr:0.97209
[7] validation-logloss:0.50046 validation-auc:0.96686 validation-aucpr:0.97228
[8] validation-logloss:0.48402 validation-auc:0.96716 validation-aucpr:0.97265
[9] validation-logloss:0.46789 validation-auc:0.96732 validation-aucpr:0.97279
[10] validation-logloss:0.45298 validation-auc:0.96740 validation-aucpr:0.97285
[11] validation-logloss:0.43896 validation-auc:0.96748 validation-aucpr:0.97287
[12] validation-logloss:0.42568 validation-auc:0.96771 validation-aucpr:0.97302
[13] validation-logloss:0.41462 validation-auc:0.96823 validation-aucpr:0.97348
[14] validation-logloss:0.40427 validation-auc:0.96817 validation-aucpr:0.97349
[15] validation-logloss:0.39318 validation-auc:0.96815 validation-aucpr:0.97348
[16] validation-logloss:0.38312 validation-auc:0.96809 validation-aucpr:0.97343
[17] validation-logloss:0.37376 validation-auc:0.96805 validation-aucpr:0.97338
[18] validation-logloss:0.36441 validation-auc:0.96819 validation-aucpr:0.97346
[19] validation-logloss:0.35571 validation-auc:0.96843 validation-aucpr:0.97361
[20] validation-logloss:0.34753 validation-auc:0.96842 validation-aucpr:0.97358
[21] validation-logloss:0.33995 validation-auc:0.96861 validation-aucpr:0.97403
[22] validation-logloss:0.33281 validation-auc:0.96855 validation-aucpr:0.97398
[23] validation-logloss:0.32569 validation-auc:0.96871 validation-aucpr:0.97410
[24] validation-logloss:0.31917 validation-auc:0.96888 validation-aucpr:0.97422
[25] validation-logloss:0.31279 validation-auc:0.96915 validation-aucpr:0.97443
[26] validation-logloss:0.30690 validation-auc:0.96927 validation-aucpr:0.97452
[27] validation-logloss:0.30141 validation-auc:0.96953 validation-aucpr:0.97463
[28] validation-logloss:0.29670 validation-auc:0.96967 validation-aucpr:0.97471
[29] validation-logloss:0.29209 validation-auc:0.96985 validation-aucpr:0.97484
[30] validation-logloss:0.28713 validation-auc:0.97002 validation-aucpr:0.97495
[31] validation-logloss:0.28249 validation-auc:0.97013 validation-aucpr:0.97502
[32] validation-logloss:0.27809 validation-auc:0.97021 validation-aucpr:0.97505
[33] validation-logloss:0.27414 validation-auc:0.97021 validation-aucpr:0.97509
[34] validation-logloss:0.27030 validation-auc:0.97021 validation-aucpr:0.97509
[35] validation-logloss:0.26659 validation-auc:0.97032 validation-aucpr:0.97517
[36] validation-logloss:0.26306 validation-auc:0.97037 validation-aucpr:0.97522
[37] validation-logloss:0.25957 validation-auc:0.97042 validation-aucpr:0.97526
[38] validation-logloss:0.25630 validation-auc:0.97057 validation-aucpr:0.97535
[39] validation-logloss:0.25326 validation-auc:0.97067 validation-aucpr:0.97541
[40] validation-logloss:0.25033 validation-auc:0.97084 validation-aucpr:0.97561
[41] validation-logloss:0.24780 validation-auc:0.97093 validation-aucpr:0.97564
[42] validation-logloss:0.24514 validation-auc:0.97099 validation-aucpr:0.97567
{'best_iteration': '42', 'best_score': '0.975667931459085'}
Trial 31, Fold 1: Log loss = 0.24514445785594513, Average precision = 0.9756598868388844, ROC-AUC = 0.9709922248032004, Elapsed Time = 1.0101247000002331 seconds
Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 31, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66193 validation-auc:0.95879 validation-aucpr:0.95950
[1] validation-logloss:0.63288 validation-auc:0.96351 validation-aucpr:0.96590
[2] validation-logloss:0.60870 validation-auc:0.96519 validation-aucpr:0.96779
[3] validation-logloss:0.58404 validation-auc:0.96680 validation-aucpr:0.96988
[4] validation-logloss:0.56373 validation-auc:0.96679 validation-aucpr:0.97063
[5] validation-logloss:0.54220 validation-auc:0.96742 validation-aucpr:0.97119
[6] validation-logloss:0.52272 validation-auc:0.96782 validation-aucpr:0.97166
[7] validation-logloss:0.50640 validation-auc:0.96780 validation-aucpr:0.97158
[8] validation-logloss:0.49092 validation-auc:0.96778 validation-aucpr:0.97148
[9] validation-logloss:0.47483 validation-auc:0.96831 validation-aucpr:0.97197
[10] validation-logloss:0.45941 validation-auc:0.96870 validation-aucpr:0.97224
[11] validation-logloss:0.44531 validation-auc:0.96861 validation-aucpr:0.97214
[12] validation-logloss:0.43158 validation-auc:0.96909 validation-aucpr:0.97255
[13] validation-logloss:0.41885 validation-auc:0.96938 validation-aucpr:0.97280
[14] validation-logloss:0.40668 validation-auc:0.96967 validation-aucpr:0.97309
[15] validation-logloss:0.39536 validation-auc:0.96979 validation-aucpr:0.97316
[16] validation-logloss:0.38460 validation-auc:0.96995 validation-aucpr:0.97328
[17] validation-logloss:0.37501 validation-auc:0.96990 validation-aucpr:0.97323
[18] validation-logloss:0.36567 validation-auc:0.96984 validation-aucpr:0.97320
[19] validation-logloss:0.35660 validation-auc:0.96994 validation-aucpr:0.97329
[20] validation-logloss:0.34823 validation-auc:0.96999 validation-aucpr:0.97324
[21] validation-logloss:0.34131 validation-auc:0.97012 validation-aucpr:0.97333
[22] validation-logloss:0.33377 validation-auc:0.97014 validation-aucpr:0.97332
[23] validation-logloss:0.32677 validation-auc:0.97004 validation-aucpr:0.97324
[24] validation-logloss:0.31991 validation-auc:0.97007 validation-aucpr:0.97326
[25] validation-logloss:0.31332 validation-auc:0.97008 validation-aucpr:0.97327
[26] validation-logloss:0.30730 validation-auc:0.97026 validation-aucpr:0.97338
[27] validation-logloss:0.30129 validation-auc:0.97039 validation-aucpr:0.97348
[28] validation-logloss:0.29576 validation-auc:0.97044 validation-aucpr:0.97348
[29] validation-logloss:0.29055 validation-auc:0.97046 validation-aucpr:0.97350
[30] validation-logloss:0.28568 validation-auc:0.97061 validation-aucpr:0.97359
[31] validation-logloss:0.28150 validation-auc:0.97068 validation-aucpr:0.97367
[32] validation-logloss:0.27683 validation-auc:0.97074 validation-aucpr:0.97372
[33] validation-logloss:0.27249 validation-auc:0.97075 validation-aucpr:0.97374
[34] validation-logloss:0.26876 validation-auc:0.97091 validation-aucpr:0.97387
[35] validation-logloss:0.26493 validation-auc:0.97096 validation-aucpr:0.97389
[36] validation-logloss:0.26150 validation-auc:0.97079 validation-aucpr:0.97377
[37] validation-logloss:0.25798 validation-auc:0.97081 validation-aucpr:0.97377
[38] validation-logloss:0.25468 validation-auc:0.97086 validation-aucpr:0.97413
[39] validation-logloss:0.25148 validation-auc:0.97095 validation-aucpr:0.97421
[40] validation-logloss:0.24849 validation-auc:0.97098 validation-aucpr:0.97423
[41] validation-logloss:0.24613 validation-auc:0.97100 validation-aucpr:0.97425
[42] validation-logloss:0.24319 validation-auc:0.97103 validation-aucpr:0.97428
{'best_iteration': '42', 'best_score': '0.9742822625501332'}
Trial 31, Fold 2: Log loss = 0.2431944995877271, Average precision = 0.9742661387249827, ROC-AUC = 0.9710278575549607, Elapsed Time = 1.205074200001036 seconds
Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 31, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66155 validation-auc:0.96269 validation-aucpr:0.96502
[1] validation-logloss:0.63567 validation-auc:0.96477 validation-aucpr:0.96781
[2] validation-logloss:0.60879 validation-auc:0.96584 validation-aucpr:0.97005
[3] validation-logloss:0.58748 validation-auc:0.96561 validation-aucpr:0.96964
[4] validation-logloss:0.56424 validation-auc:0.96632 validation-aucpr:0.97021
[5] validation-logloss:0.54594 validation-auc:0.96677 validation-aucpr:0.97070
[6] validation-logloss:0.52830 validation-auc:0.96649 validation-aucpr:0.97038
[7] validation-logloss:0.50908 validation-auc:0.96715 validation-aucpr:0.97099
[8] validation-logloss:0.49107 validation-auc:0.96772 validation-aucpr:0.97144
[9] validation-logloss:0.47687 validation-auc:0.96786 validation-aucpr:0.97150
[10] validation-logloss:0.46118 validation-auc:0.96811 validation-aucpr:0.97178
[11] validation-logloss:0.44636 validation-auc:0.96854 validation-aucpr:0.97242
[12] validation-logloss:0.43416 validation-auc:0.96864 validation-aucpr:0.97249
[13] validation-logloss:0.42113 validation-auc:0.96879 validation-aucpr:0.97262
[14] validation-logloss:0.41051 validation-auc:0.96881 validation-aucpr:0.97259
[15] validation-logloss:0.40051 validation-auc:0.96874 validation-aucpr:0.97252
[16] validation-logloss:0.38962 validation-auc:0.96897 validation-aucpr:0.97278
[17] validation-logloss:0.37920 validation-auc:0.96910 validation-aucpr:0.97290
[18] validation-logloss:0.36940 validation-auc:0.96930 validation-aucpr:0.97337
[19] validation-logloss:0.36014 validation-auc:0.96948 validation-aucpr:0.97351
[20] validation-logloss:0.35126 validation-auc:0.96980 validation-aucpr:0.97381
[21] validation-logloss:0.34316 validation-auc:0.96990 validation-aucpr:0.97390
[22] validation-logloss:0.33556 validation-auc:0.96992 validation-aucpr:0.97393
[23] validation-logloss:0.32804 validation-auc:0.97010 validation-aucpr:0.97409
[24] validation-logloss:0.32107 validation-auc:0.97012 validation-aucpr:0.97412
[25] validation-logloss:0.31442 validation-auc:0.97027 validation-aucpr:0.97425
[26] validation-logloss:0.30908 validation-auc:0.97041 validation-aucpr:0.97433
[27] validation-logloss:0.30309 validation-auc:0.97062 validation-aucpr:0.97446
[28] validation-logloss:0.29742 validation-auc:0.97069 validation-aucpr:0.97450
[29] validation-logloss:0.29303 validation-auc:0.97067 validation-aucpr:0.97447
[30] validation-logloss:0.28786 validation-auc:0.97070 validation-aucpr:0.97452
[31] validation-logloss:0.28300 validation-auc:0.97083 validation-aucpr:0.97463
[32] validation-logloss:0.27842 validation-auc:0.97087 validation-aucpr:0.97465
[33] validation-logloss:0.27487 validation-auc:0.97086 validation-aucpr:0.97463
[34] validation-logloss:0.27081 validation-auc:0.97095 validation-aucpr:0.97472
[35] validation-logloss:0.26673 validation-auc:0.97105 validation-aucpr:0.97480
[36] validation-logloss:0.26310 validation-auc:0.97109 validation-aucpr:0.97483
[37] validation-logloss:0.25937 validation-auc:0.97118 validation-aucpr:0.97490
[38] validation-logloss:0.25586 validation-auc:0.97121 validation-aucpr:0.97492
[39] validation-logloss:0.25267 validation-auc:0.97129 validation-aucpr:0.97497
[40] validation-logloss:0.24958 validation-auc:0.97127 validation-aucpr:0.97493
[41] validation-logloss:0.24677 validation-auc:0.97125 validation-aucpr:0.97490
[42] validation-logloss:0.24382 validation-auc:0.97133 validation-aucpr:0.97495
{'best_iteration': '39', 'best_score': '0.9749729169827692'}
Trial 31, Fold 3: Log loss = 0.24381864069012466, Average precision = 0.9749243711198644, ROC-AUC = 0.9713275057545434, Elapsed Time = 1.1850333000002138 seconds
Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 31, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66180 validation-auc:0.96164 validation-aucpr:0.96373
[1] validation-logloss:0.63291 validation-auc:0.96602 validation-aucpr:0.96790
[2] validation-logloss:0.60627 validation-auc:0.96732 validation-aucpr:0.96907
[3] validation-logloss:0.58496 validation-auc:0.96733 validation-aucpr:0.97206
[4] validation-logloss:0.56435 validation-auc:0.96676 validation-aucpr:0.97179
[5] validation-logloss:0.54289 validation-auc:0.96763 validation-aucpr:0.97264
[6] validation-logloss:0.52277 validation-auc:0.96805 validation-aucpr:0.97296
[7] validation-logloss:0.50653 validation-auc:0.96741 validation-aucpr:0.97257
[8] validation-logloss:0.49114 validation-auc:0.96726 validation-aucpr:0.97242
[9] validation-logloss:0.47453 validation-auc:0.96751 validation-aucpr:0.97262
[10] validation-logloss:0.45930 validation-auc:0.96778 validation-aucpr:0.97281
[11] validation-logloss:0.44644 validation-auc:0.96761 validation-aucpr:0.97271
[12] validation-logloss:0.43268 validation-auc:0.96792 validation-aucpr:0.97297
[13] validation-logloss:0.41970 validation-auc:0.96842 validation-aucpr:0.97337
[14] validation-logloss:0.40759 validation-auc:0.96896 validation-aucpr:0.97379
[15] validation-logloss:0.39630 validation-auc:0.96909 validation-aucpr:0.97391
[16] validation-logloss:0.38556 validation-auc:0.96949 validation-aucpr:0.97420
[17] validation-logloss:0.37557 validation-auc:0.96959 validation-aucpr:0.97426
[18] validation-logloss:0.36609 validation-auc:0.96971 validation-aucpr:0.97437
[19] validation-logloss:0.35856 validation-auc:0.96950 validation-aucpr:0.97418
[20] validation-logloss:0.35117 validation-auc:0.96946 validation-aucpr:0.97416
[21] validation-logloss:0.34290 validation-auc:0.96984 validation-aucpr:0.97448
[22] validation-logloss:0.33518 validation-auc:0.96988 validation-aucpr:0.97455
[23] validation-logloss:0.32880 validation-auc:0.96980 validation-aucpr:0.97448
[24] validation-logloss:0.32177 validation-auc:0.97003 validation-aucpr:0.97465
[25] validation-logloss:0.31535 validation-auc:0.97016 validation-aucpr:0.97475
[26] validation-logloss:0.30905 validation-auc:0.97040 validation-aucpr:0.97499
[27] validation-logloss:0.30311 validation-auc:0.97059 validation-aucpr:0.97513
[28] validation-logloss:0.29748 validation-auc:0.97073 validation-aucpr:0.97525
[29] validation-logloss:0.29216 validation-auc:0.97082 validation-aucpr:0.97534
[30] validation-logloss:0.28697 validation-auc:0.97104 validation-aucpr:0.97550
[31] validation-logloss:0.28216 validation-auc:0.97124 validation-aucpr:0.97566
[32] validation-logloss:0.27752 validation-auc:0.97130 validation-aucpr:0.97572
[33] validation-logloss:0.27324 validation-auc:0.97133 validation-aucpr:0.97576
[34] validation-logloss:0.26915 validation-auc:0.97133 validation-aucpr:0.97576
[35] validation-logloss:0.26526 validation-auc:0.97132 validation-aucpr:0.97576
[36] validation-logloss:0.26158 validation-auc:0.97134 validation-aucpr:0.97577
[37] validation-logloss:0.25805 validation-auc:0.97143 validation-aucpr:0.97584
[38] validation-logloss:0.25464 validation-auc:0.97155 validation-aucpr:0.97590
[39] validation-logloss:0.25141 validation-auc:0.97162 validation-aucpr:0.97596
[40] validation-logloss:0.24850 validation-auc:0.97165 validation-aucpr:0.97597
[41] validation-logloss:0.24565 validation-auc:0.97172 validation-aucpr:0.97601
[42] validation-logloss:0.24301 validation-auc:0.97172 validation-aucpr:0.97602
{'best_iteration': '42', 'best_score': '0.9760215168892972'}
Trial 31, Fold 4: Log loss = 0.24300942956000274, Average precision = 0.9760034541082719, ROC-AUC = 0.9717249237584973, Elapsed Time = 1.2454550999991625 seconds
Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 31, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66161 validation-auc:0.96173 validation-aucpr:0.96283
[1] validation-logloss:0.63570 validation-auc:0.96160 validation-aucpr:0.96336
[2] validation-logloss:0.61121 validation-auc:0.96395 validation-aucpr:0.96851
[3] validation-logloss:0.58782 validation-auc:0.96463 validation-aucpr:0.96896
[4] validation-logloss:0.56466 validation-auc:0.96560 validation-aucpr:0.96974
[5] validation-logloss:0.54307 validation-auc:0.96626 validation-aucpr:0.97044
[6] validation-logloss:0.52326 validation-auc:0.96670 validation-aucpr:0.97072
[7] validation-logloss:0.50682 validation-auc:0.96592 validation-aucpr:0.96995
[8] validation-logloss:0.49165 validation-auc:0.96597 validation-aucpr:0.97014
[9] validation-logloss:0.47517 validation-auc:0.96650 validation-aucpr:0.97123
[10] validation-logloss:0.46250 validation-auc:0.96624 validation-aucpr:0.97094
[11] validation-logloss:0.45012 validation-auc:0.96612 validation-aucpr:0.97084
[12] validation-logloss:0.43659 validation-auc:0.96648 validation-aucpr:0.97103
[13] validation-logloss:0.42372 validation-auc:0.96679 validation-aucpr:0.97130
[14] validation-logloss:0.41322 validation-auc:0.96685 validation-aucpr:0.97129
[15] validation-logloss:0.40190 validation-auc:0.96714 validation-aucpr:0.97154
[16] validation-logloss:0.39123 validation-auc:0.96727 validation-aucpr:0.97167
[17] validation-logloss:0.38092 validation-auc:0.96778 validation-aucpr:0.97206
[18] validation-logloss:0.37132 validation-auc:0.96803 validation-aucpr:0.97229
[19] validation-logloss:0.36227 validation-auc:0.96819 validation-aucpr:0.97242
[20] validation-logloss:0.35382 validation-auc:0.96848 validation-aucpr:0.97266
[21] validation-logloss:0.34570 validation-auc:0.96872 validation-aucpr:0.97283
[22] validation-logloss:0.33924 validation-auc:0.96875 validation-aucpr:0.97283
[23] validation-logloss:0.33197 validation-auc:0.96898 validation-aucpr:0.97300
[24] validation-logloss:0.32501 validation-auc:0.96919 validation-aucpr:0.97316
[25] validation-logloss:0.31836 validation-auc:0.96936 validation-aucpr:0.97328
[26] validation-logloss:0.31239 validation-auc:0.96947 validation-aucpr:0.97336
[27] validation-logloss:0.30660 validation-auc:0.96958 validation-aucpr:0.97344
[28] validation-logloss:0.30114 validation-auc:0.96962 validation-aucpr:0.97348
[29] validation-logloss:0.29596 validation-auc:0.96968 validation-aucpr:0.97353
[30] validation-logloss:0.29106 validation-auc:0.96978 validation-aucpr:0.97358
[31] validation-logloss:0.28623 validation-auc:0.96996 validation-aucpr:0.97372
[32] validation-logloss:0.28176 validation-auc:0.97005 validation-aucpr:0.97379
[33] validation-logloss:0.27762 validation-auc:0.97009 validation-aucpr:0.97382
[34] validation-logloss:0.27364 validation-auc:0.97009 validation-aucpr:0.97385
[35] validation-logloss:0.26978 validation-auc:0.97014 validation-aucpr:0.97387
[36] validation-logloss:0.26623 validation-auc:0.97027 validation-aucpr:0.97395
[37] validation-logloss:0.26279 validation-auc:0.97047 validation-aucpr:0.97443
[38] validation-logloss:0.25942 validation-auc:0.97066 validation-aucpr:0.97455
[39] validation-logloss:0.25635 validation-auc:0.97080 validation-aucpr:0.97463
[40] validation-logloss:0.25332 validation-auc:0.97090 validation-aucpr:0.97470
[41] validation-logloss:0.25038 validation-auc:0.97100 validation-aucpr:0.97479
[42] validation-logloss:0.24800 validation-auc:0.97108 validation-aucpr:0.97485
{'best_iteration': '42', 'best_score': '0.974853958215763'}
Trial 31, Fold 5: Log loss = 0.24799681727472822, Average precision = 0.9748078353358505, ROC-AUC = 0.9710814335878715, Elapsed Time = 1.209885000000213 seconds
Optimization Progress: 32%|###2 | 32/100 [50:30<1:01:27, 54.22s/it]
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 32, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67263 validation-auc:0.94340 validation-aucpr:0.95202
[1] validation-logloss:0.65321 validation-auc:0.95034 validation-aucpr:0.95581
[2] validation-logloss:0.63598 validation-auc:0.95080 validation-aucpr:0.95973
[3] validation-logloss:0.61883 validation-auc:0.95193 validation-aucpr:0.96076
[4] validation-logloss:0.60229 validation-auc:0.95492 validation-aucpr:0.96341
[5] validation-logloss:0.58667 validation-auc:0.95776 validation-aucpr:0.96592
[6] validation-logloss:0.57125 validation-auc:0.95920 validation-aucpr:0.96671
[7] validation-logloss:0.55687 validation-auc:0.96026 validation-aucpr:0.96781
[8] validation-logloss:0.54337 validation-auc:0.96070 validation-aucpr:0.96799
[9] validation-logloss:0.53106 validation-auc:0.96176 validation-aucpr:0.96795
[10] validation-logloss:0.51879 validation-auc:0.96239 validation-aucpr:0.96917
[11] validation-logloss:0.50777 validation-auc:0.96299 validation-aucpr:0.96996
[12] validation-logloss:0.49780 validation-auc:0.96312 validation-aucpr:0.97013
[13] validation-logloss:0.48833 validation-auc:0.96363 validation-aucpr:0.97045
[14] validation-logloss:0.47754 validation-auc:0.96423 validation-aucpr:0.97088
[15] validation-logloss:0.46756 validation-auc:0.96454 validation-aucpr:0.97113
[16] validation-logloss:0.45813 validation-auc:0.96443 validation-aucpr:0.97110
[17] validation-logloss:0.44947 validation-auc:0.96494 validation-aucpr:0.97145
[18] validation-logloss:0.44132 validation-auc:0.96553 validation-aucpr:0.97181
[19] validation-logloss:0.43318 validation-auc:0.96540 validation-aucpr:0.97173
[20] validation-logloss:0.42591 validation-auc:0.96520 validation-aucpr:0.97150
[21] validation-logloss:0.41936 validation-auc:0.96491 validation-aucpr:0.97136
[22] validation-logloss:0.41177 validation-auc:0.96509 validation-aucpr:0.97151
[23] validation-logloss:0.40542 validation-auc:0.96507 validation-aucpr:0.97148
[24] validation-logloss:0.39810 validation-auc:0.96544 validation-aucpr:0.97167
[25] validation-logloss:0.39183 validation-auc:0.96581 validation-aucpr:0.97190
[26] validation-logloss:0.38546 validation-auc:0.96588 validation-aucpr:0.97188
[27] validation-logloss:0.37882 validation-auc:0.96594 validation-aucpr:0.97199
[28] validation-logloss:0.37264 validation-auc:0.96584 validation-aucpr:0.97195
[29] validation-logloss:0.36749 validation-auc:0.96590 validation-aucpr:0.97194
[30] validation-logloss:0.36203 validation-auc:0.96596 validation-aucpr:0.97202
[31] validation-logloss:0.35632 validation-auc:0.96619 validation-aucpr:0.97229
[32] validation-logloss:0.35195 validation-auc:0.96610 validation-aucpr:0.97221
[33] validation-logloss:0.34733 validation-auc:0.96600 validation-aucpr:0.97213
[34] validation-logloss:0.34201 validation-auc:0.96642 validation-aucpr:0.97235
[35] validation-logloss:0.33734 validation-auc:0.96656 validation-aucpr:0.97242
[36] validation-logloss:0.33317 validation-auc:0.96646 validation-aucpr:0.97237
[37] validation-logloss:0.32841 validation-auc:0.96671 validation-aucpr:0.97261
[38] validation-logloss:0.32478 validation-auc:0.96656 validation-aucpr:0.97252
[39] validation-logloss:0.32075 validation-auc:0.96667 validation-aucpr:0.97261
[40] validation-logloss:0.31663 validation-auc:0.96684 validation-aucpr:0.97271
[41] validation-logloss:0.31292 validation-auc:0.96703 validation-aucpr:0.97279
[42] validation-logloss:0.30980 validation-auc:0.96715 validation-aucpr:0.97284
[43] validation-logloss:0.30637 validation-auc:0.96700 validation-aucpr:0.97273
[44] validation-logloss:0.30303 validation-auc:0.96718 validation-aucpr:0.97283
[45] validation-logloss:0.30017 validation-auc:0.96713 validation-aucpr:0.97278
[46] validation-logloss:0.29722 validation-auc:0.96719 validation-aucpr:0.97284
[47] validation-logloss:0.29425 validation-auc:0.96728 validation-aucpr:0.97288
[48] validation-logloss:0.29113 validation-auc:0.96732 validation-aucpr:0.97294
[49] validation-logloss:0.28814 validation-auc:0.96732 validation-aucpr:0.97293
[50] validation-logloss:0.28582 validation-auc:0.96730 validation-aucpr:0.97290
[51] validation-logloss:0.28280 validation-auc:0.96748 validation-aucpr:0.97303
[52] validation-logloss:0.28036 validation-auc:0.96770 validation-aucpr:0.97316
[53] validation-logloss:0.27775 validation-auc:0.96772 validation-aucpr:0.97320
[54] validation-logloss:0.27522 validation-auc:0.96774 validation-aucpr:0.97323
[55] validation-logloss:0.27307 validation-auc:0.96779 validation-aucpr:0.97327
[56] validation-logloss:0.27052 validation-auc:0.96775 validation-aucpr:0.97324
[57] validation-logloss:0.26850 validation-auc:0.96760 validation-aucpr:0.97314
[58] validation-logloss:0.26641 validation-auc:0.96754 validation-aucpr:0.97311
[59] validation-logloss:0.26416 validation-auc:0.96761 validation-aucpr:0.97317
[60] validation-logloss:0.26259 validation-auc:0.96757 validation-aucpr:0.97315
[61] validation-logloss:0.26027 validation-auc:0.96777 validation-aucpr:0.97329
[62] validation-logloss:0.25813 validation-auc:0.96778 validation-aucpr:0.97330
[63] validation-logloss:0.25674 validation-auc:0.96759 validation-aucpr:0.97312
[64] validation-logloss:0.25505 validation-auc:0.96765 validation-aucpr:0.97318
[65] validation-logloss:0.25362 validation-auc:0.96775 validation-aucpr:0.97322
[66] validation-logloss:0.25198 validation-auc:0.96783 validation-aucpr:0.97327
[67] validation-logloss:0.25036 validation-auc:0.96784 validation-aucpr:0.97327
[68] validation-logloss:0.24911 validation-auc:0.96772 validation-aucpr:0.97319
[69] validation-logloss:0.24788 validation-auc:0.96770 validation-aucpr:0.97314
[70] validation-logloss:0.24638 validation-auc:0.96768 validation-aucpr:0.97311
[71] validation-logloss:0.24509 validation-auc:0.96758 validation-aucpr:0.97310
[72] validation-logloss:0.24377 validation-auc:0.96755 validation-aucpr:0.97309
[73] validation-logloss:0.24245 validation-auc:0.96759 validation-aucpr:0.97313
[74] validation-logloss:0.24113 validation-auc:0.96753 validation-aucpr:0.97308
[75] validation-logloss:0.24001 validation-auc:0.96757 validation-aucpr:0.97309
[76] validation-logloss:0.23915 validation-auc:0.96739 validation-aucpr:0.97295
[77] validation-logloss:0.23776 validation-auc:0.96748 validation-aucpr:0.97303
[78] validation-logloss:0.23676 validation-auc:0.96758 validation-aucpr:0.97311
[79] validation-logloss:0.23585 validation-auc:0.96760 validation-aucpr:0.97311
[80] validation-logloss:0.23479 validation-auc:0.96769 validation-aucpr:0.97319
[81] validation-logloss:0.23382 validation-auc:0.96765 validation-aucpr:0.97316
[82] validation-logloss:0.23265 validation-auc:0.96776 validation-aucpr:0.97326
[83] validation-logloss:0.23180 validation-auc:0.96756 validation-aucpr:0.97312
[84] validation-logloss:0.23077 validation-auc:0.96755 validation-aucpr:0.97309
[85] validation-logloss:0.23012 validation-auc:0.96753 validation-aucpr:0.97305
[86] validation-logloss:0.22915 validation-auc:0.96757 validation-aucpr:0.97302
[87] validation-logloss:0.22848 validation-auc:0.96755 validation-aucpr:0.97301
[88] validation-logloss:0.22776 validation-auc:0.96749 validation-aucpr:0.97296
[89] validation-logloss:0.22667 validation-auc:0.96768 validation-aucpr:0.97313
[90] validation-logloss:0.22566 validation-auc:0.96784 validation-aucpr:0.97322
[91] validation-logloss:0.22487 validation-auc:0.96790 validation-aucpr:0.97326
[92] validation-logloss:0.22411 validation-auc:0.96792 validation-aucpr:0.97329
[93] validation-logloss:0.22307 validation-auc:0.96807 validation-aucpr:0.97337
[94] validation-logloss:0.22263 validation-auc:0.96794 validation-aucpr:0.97341
[95] validation-logloss:0.22192 validation-auc:0.96796 validation-aucpr:0.97341
[96] validation-logloss:0.22136 validation-auc:0.96796 validation-aucpr:0.97340
[97] validation-logloss:0.22077 validation-auc:0.96801 validation-aucpr:0.97346
{'best_iteration': '97', 'best_score': '0.9734587937630889'}
Trial 32, Fold 1: Log loss = 0.22077211741617622, Average precision = 0.973463242617084, ROC-AUC = 0.9680081181323278, Elapsed Time = 1103.3959042000006 seconds
Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 32, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67236 validation-auc:0.94406 validation-aucpr:0.94290
[1] validation-logloss:0.65459 validation-auc:0.94981 validation-aucpr:0.94874
[2] validation-logloss:0.63797 validation-auc:0.95234 validation-aucpr:0.95579
[3] validation-logloss:0.62092 validation-auc:0.95669 validation-aucpr:0.96050
[4] validation-logloss:0.60429 validation-auc:0.95935 validation-aucpr:0.96403
[5] validation-logloss:0.58879 validation-auc:0.96113 validation-aucpr:0.96654
[6] validation-logloss:0.57537 validation-auc:0.96182 validation-aucpr:0.96684
[7] validation-logloss:0.56189 validation-auc:0.96251 validation-aucpr:0.96726
[8] validation-logloss:0.54936 validation-auc:0.96338 validation-aucpr:0.96795
[9] validation-logloss:0.53656 validation-auc:0.96385 validation-aucpr:0.96843
[10] validation-logloss:0.52506 validation-auc:0.96410 validation-aucpr:0.96852
[11] validation-logloss:0.51309 validation-auc:0.96508 validation-aucpr:0.96943
[12] validation-logloss:0.50140 validation-auc:0.96565 validation-aucpr:0.96975
[13] validation-logloss:0.49165 validation-auc:0.96541 validation-aucpr:0.96956
[14] validation-logloss:0.48070 validation-auc:0.96607 validation-aucpr:0.97016
[15] validation-logloss:0.47181 validation-auc:0.96606 validation-aucpr:0.97006
[16] validation-logloss:0.46168 validation-auc:0.96680 validation-aucpr:0.97063
[17] validation-logloss:0.45189 validation-auc:0.96697 validation-aucpr:0.97077
[18] validation-logloss:0.44430 validation-auc:0.96686 validation-aucpr:0.97082
[19] validation-logloss:0.43688 validation-auc:0.96698 validation-aucpr:0.97084
[20] validation-logloss:0.42863 validation-auc:0.96713 validation-aucpr:0.97096
[21] validation-logloss:0.42035 validation-auc:0.96757 validation-aucpr:0.97131
[22] validation-logloss:0.41283 validation-auc:0.96784 validation-aucpr:0.97176
[23] validation-logloss:0.40501 validation-auc:0.96794 validation-aucpr:0.97190
[24] validation-logloss:0.39759 validation-auc:0.96808 validation-aucpr:0.97210
[25] validation-logloss:0.39053 validation-auc:0.96844 validation-aucpr:0.97236
[26] validation-logloss:0.38468 validation-auc:0.96824 validation-aucpr:0.97220
[27] validation-logloss:0.37795 validation-auc:0.96825 validation-aucpr:0.97230
[28] validation-logloss:0.37175 validation-auc:0.96836 validation-aucpr:0.97242
[29] validation-logloss:0.36568 validation-auc:0.96833 validation-aucpr:0.97236
[30] validation-logloss:0.35981 validation-auc:0.96845 validation-aucpr:0.97251
[31] validation-logloss:0.35390 validation-auc:0.96900 validation-aucpr:0.97285
[32] validation-logloss:0.34880 validation-auc:0.96930 validation-aucpr:0.97307
[33] validation-logloss:0.34411 validation-auc:0.96914 validation-aucpr:0.97291
[34] validation-logloss:0.33889 validation-auc:0.96923 validation-aucpr:0.97304
[35] validation-logloss:0.33466 validation-auc:0.96926 validation-aucpr:0.97301
[36] validation-logloss:0.33082 validation-auc:0.96913 validation-aucpr:0.97290
[37] validation-logloss:0.32627 validation-auc:0.96930 validation-aucpr:0.97304
[38] validation-logloss:0.32169 validation-auc:0.96943 validation-aucpr:0.97316
[39] validation-logloss:0.31729 validation-auc:0.96945 validation-aucpr:0.97320
[40] validation-logloss:0.31383 validation-auc:0.96952 validation-aucpr:0.97325
[41] validation-logloss:0.31043 validation-auc:0.96947 validation-aucpr:0.97317
[42] validation-logloss:0.30689 validation-auc:0.96938 validation-aucpr:0.97312
[43] validation-logloss:0.30375 validation-auc:0.96930 validation-aucpr:0.97287
[44] validation-logloss:0.30044 validation-auc:0.96919 validation-aucpr:0.97278
[45] validation-logloss:0.29766 validation-auc:0.96908 validation-aucpr:0.97272
[46] validation-logloss:0.29488 validation-auc:0.96907 validation-aucpr:0.97270
[47] validation-logloss:0.29180 validation-auc:0.96907 validation-aucpr:0.97275
[48] validation-logloss:0.28907 validation-auc:0.96914 validation-aucpr:0.97275
[49] validation-logloss:0.28583 validation-auc:0.96921 validation-aucpr:0.97284
[50] validation-logloss:0.28266 validation-auc:0.96921 validation-aucpr:0.97286
[51] validation-logloss:0.28006 validation-auc:0.96931 validation-aucpr:0.97294
[52] validation-logloss:0.27766 validation-auc:0.96937 validation-aucpr:0.97296
[53] validation-logloss:0.27507 validation-auc:0.96943 validation-aucpr:0.97307
[54] validation-logloss:0.27273 validation-auc:0.96954 validation-aucpr:0.97312
[55] validation-logloss:0.26995 validation-auc:0.96970 validation-aucpr:0.97323
[56] validation-logloss:0.26750 validation-auc:0.96984 validation-aucpr:0.97325
[57] validation-logloss:0.26504 validation-auc:0.96976 validation-aucpr:0.97322
[58] validation-logloss:0.26259 validation-auc:0.96978 validation-aucpr:0.97320
[59] validation-logloss:0.25992 validation-auc:0.97011 validation-aucpr:0.97345
[60] validation-logloss:0.25797 validation-auc:0.97010 validation-aucpr:0.97349
[61] validation-logloss:0.25566 validation-auc:0.97018 validation-aucpr:0.97352
[62] validation-logloss:0.25362 validation-auc:0.97024 validation-aucpr:0.97351
[63] validation-logloss:0.25214 validation-auc:0.97016 validation-aucpr:0.97342
[64] validation-logloss:0.25019 validation-auc:0.97014 validation-aucpr:0.97327
[65] validation-logloss:0.24815 validation-auc:0.97028 validation-aucpr:0.97338
[66] validation-logloss:0.24665 validation-auc:0.97043 validation-aucpr:0.97414
[67] validation-logloss:0.24538 validation-auc:0.97023 validation-aucpr:0.97401
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[70] validation-logloss:0.23994 validation-auc:0.97068 validation-aucpr:0.97444
[71] validation-logloss:0.23848 validation-auc:0.97072 validation-aucpr:0.97446
[72] validation-logloss:0.23680 validation-auc:0.97087 validation-aucpr:0.97457
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[74] validation-logloss:0.23459 validation-auc:0.97079 validation-aucpr:0.97448
[75] validation-logloss:0.23351 validation-auc:0.97062 validation-aucpr:0.97436
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[79] validation-logloss:0.22783 validation-auc:0.97074 validation-aucpr:0.97459
[80] validation-logloss:0.22678 validation-auc:0.97080 validation-aucpr:0.97464
[81] validation-logloss:0.22587 validation-auc:0.97076 validation-aucpr:0.97463
[82] validation-logloss:0.22439 validation-auc:0.97095 validation-aucpr:0.97475
[83] validation-logloss:0.22338 validation-auc:0.97096 validation-aucpr:0.97475
[84] validation-logloss:0.22235 validation-auc:0.97102 validation-aucpr:0.97478
[85] validation-logloss:0.22108 validation-auc:0.97108 validation-aucpr:0.97484
[86] validation-logloss:0.22028 validation-auc:0.97108 validation-aucpr:0.97485
[87] validation-logloss:0.21938 validation-auc:0.97099 validation-aucpr:0.97476
[88] validation-logloss:0.21843 validation-auc:0.97095 validation-aucpr:0.97473
[89] validation-logloss:0.21772 validation-auc:0.97095 validation-aucpr:0.97475
[90] validation-logloss:0.21681 validation-auc:0.97092 validation-aucpr:0.97473
[91] validation-logloss:0.21563 validation-auc:0.97107 validation-aucpr:0.97483
[92] validation-logloss:0.21493 validation-auc:0.97110 validation-aucpr:0.97485
[93] validation-logloss:0.21439 validation-auc:0.97106 validation-aucpr:0.97484
[94] validation-logloss:0.21366 validation-auc:0.97098 validation-aucpr:0.97479
[95] validation-logloss:0.21289 validation-auc:0.97099 validation-aucpr:0.97478
[96] validation-logloss:0.21219 validation-auc:0.97088 validation-aucpr:0.97469
[97] validation-logloss:0.21159 validation-auc:0.97079 validation-aucpr:0.97462
{'best_iteration': '92', 'best_score': '0.9748518836757191'}
Trial 32, Fold 2: Log loss = 0.21159174969704342, Average precision = 0.9746231769873027, ROC-AUC = 0.9707887030450622, Elapsed Time = 1091.1235703999992 seconds
Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 32, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67319 validation-auc:0.93892 validation-aucpr:0.94781
[1] validation-logloss:0.65596 validation-auc:0.94305 validation-aucpr:0.95011
[2] validation-logloss:0.63705 validation-auc:0.95411 validation-aucpr:0.95880
[3] validation-logloss:0.62103 validation-auc:0.95509 validation-aucpr:0.95783
[4] validation-logloss:0.60463 validation-auc:0.95661 validation-aucpr:0.96193
[5] validation-logloss:0.58850 validation-auc:0.95903 validation-aucpr:0.96166
[6] validation-logloss:0.57324 validation-auc:0.96114 validation-aucpr:0.96680
[7] validation-logloss:0.55857 validation-auc:0.96288 validation-aucpr:0.96801
[8] validation-logloss:0.54550 validation-auc:0.96333 validation-aucpr:0.96792
[9] validation-logloss:0.53384 validation-auc:0.96418 validation-aucpr:0.96868
[10] validation-logloss:0.52177 validation-auc:0.96367 validation-aucpr:0.96672
[11] validation-logloss:0.51114 validation-auc:0.96382 validation-aucpr:0.96594
[12] validation-logloss:0.50104 validation-auc:0.96447 validation-aucpr:0.96555
[13] validation-logloss:0.49117 validation-auc:0.96460 validation-aucpr:0.96421
[14] validation-logloss:0.47992 validation-auc:0.96512 validation-aucpr:0.96523
[15] validation-logloss:0.47002 validation-auc:0.96590 validation-aucpr:0.96521
[16] validation-logloss:0.45997 validation-auc:0.96655 validation-aucpr:0.96547
[17] validation-logloss:0.45123 validation-auc:0.96652 validation-aucpr:0.96545
[18] validation-logloss:0.44259 validation-auc:0.96727 validation-aucpr:0.96731
[19] validation-logloss:0.43373 validation-auc:0.96761 validation-aucpr:0.96870
[20] validation-logloss:0.42554 validation-auc:0.96755 validation-aucpr:0.96887
[21] validation-logloss:0.41869 validation-auc:0.96719 validation-aucpr:0.96825
[22] validation-logloss:0.41049 validation-auc:0.96737 validation-aucpr:0.96812
[23] validation-logloss:0.40373 validation-auc:0.96742 validation-aucpr:0.96792
[24] validation-logloss:0.39670 validation-auc:0.96785 validation-aucpr:0.96815
[25] validation-logloss:0.38985 validation-auc:0.96790 validation-aucpr:0.96993
[26] validation-logloss:0.38364 validation-auc:0.96818 validation-aucpr:0.97015
[27] validation-logloss:0.37767 validation-auc:0.96831 validation-aucpr:0.96966
[28] validation-logloss:0.37192 validation-auc:0.96875 validation-aucpr:0.97246
[29] validation-logloss:0.36617 validation-auc:0.96880 validation-aucpr:0.97245
[30] validation-logloss:0.36036 validation-auc:0.96878 validation-aucpr:0.97251
[31] validation-logloss:0.35542 validation-auc:0.96875 validation-aucpr:0.97258
[32] validation-logloss:0.35057 validation-auc:0.96883 validation-aucpr:0.97256
[33] validation-logloss:0.34508 validation-auc:0.96901 validation-aucpr:0.97266
[34] validation-logloss:0.34013 validation-auc:0.96919 validation-aucpr:0.97367
[35] validation-logloss:0.33609 validation-auc:0.96910 validation-aucpr:0.97368
[36] validation-logloss:0.33108 validation-auc:0.96921 validation-aucpr:0.97379
[37] validation-logloss:0.32745 validation-auc:0.96936 validation-aucpr:0.97383
[38] validation-logloss:0.32347 validation-auc:0.96936 validation-aucpr:0.97387
[39] validation-logloss:0.32001 validation-auc:0.96916 validation-aucpr:0.97369
[40] validation-logloss:0.31611 validation-auc:0.96912 validation-aucpr:0.97366
[41] validation-logloss:0.31237 validation-auc:0.96922 validation-aucpr:0.97371
[42] validation-logloss:0.30847 validation-auc:0.96931 validation-aucpr:0.97377
[43] validation-logloss:0.30443 validation-auc:0.96952 validation-aucpr:0.97400
[44] validation-logloss:0.30140 validation-auc:0.96946 validation-aucpr:0.97390
[45] validation-logloss:0.29793 validation-auc:0.96938 validation-aucpr:0.97379
[46] validation-logloss:0.29521 validation-auc:0.96928 validation-aucpr:0.97371
[47] validation-logloss:0.29220 validation-auc:0.96927 validation-aucpr:0.97368
[48] validation-logloss:0.28887 validation-auc:0.96950 validation-aucpr:0.97392
[49] validation-logloss:0.28610 validation-auc:0.96980 validation-aucpr:0.97403
[50] validation-logloss:0.28256 validation-auc:0.96989 validation-aucpr:0.97413
[51] validation-logloss:0.27977 validation-auc:0.96989 validation-aucpr:0.97416
[52] validation-logloss:0.27672 validation-auc:0.97013 validation-aucpr:0.97430
[53] validation-logloss:0.27425 validation-auc:0.97021 validation-aucpr:0.97435
[54] validation-logloss:0.27142 validation-auc:0.97024 validation-aucpr:0.97437
[55] validation-logloss:0.26892 validation-auc:0.97026 validation-aucpr:0.97437
[56] validation-logloss:0.26669 validation-auc:0.97038 validation-aucpr:0.97445
[57] validation-logloss:0.26431 validation-auc:0.97048 validation-aucpr:0.97458
[58] validation-logloss:0.26197 validation-auc:0.97051 validation-aucpr:0.97459
[59] validation-logloss:0.25953 validation-auc:0.97062 validation-aucpr:0.97472
[60] validation-logloss:0.25734 validation-auc:0.97047 validation-aucpr:0.97462
[61] validation-logloss:0.25582 validation-auc:0.97035 validation-aucpr:0.97449
[62] validation-logloss:0.25389 validation-auc:0.97049 validation-aucpr:0.97454
[63] validation-logloss:0.25223 validation-auc:0.97038 validation-aucpr:0.97444
[64] validation-logloss:0.25081 validation-auc:0.97034 validation-aucpr:0.97439
[65] validation-logloss:0.24915 validation-auc:0.97042 validation-aucpr:0.97443
[66] validation-logloss:0.24712 validation-auc:0.97056 validation-aucpr:0.97460
[67] validation-logloss:0.24553 validation-auc:0.97056 validation-aucpr:0.97459
[68] validation-logloss:0.24357 validation-auc:0.97070 validation-aucpr:0.97470
[69] validation-logloss:0.24178 validation-auc:0.97078 validation-aucpr:0.97474
[70] validation-logloss:0.24051 validation-auc:0.97071 validation-aucpr:0.97466
[71] validation-logloss:0.23910 validation-auc:0.97078 validation-aucpr:0.97471
[72] validation-logloss:0.23802 validation-auc:0.97067 validation-aucpr:0.97467
[73] validation-logloss:0.23649 validation-auc:0.97069 validation-aucpr:0.97469
[74] validation-logloss:0.23495 validation-auc:0.97070 validation-aucpr:0.97471
[75] validation-logloss:0.23367 validation-auc:0.97073 validation-aucpr:0.97472
[76] validation-logloss:0.23249 validation-auc:0.97076 validation-aucpr:0.97477
[77] validation-logloss:0.23150 validation-auc:0.97076 validation-aucpr:0.97479
[78] validation-logloss:0.23021 validation-auc:0.97092 validation-aucpr:0.97491
[79] validation-logloss:0.22881 validation-auc:0.97093 validation-aucpr:0.97488
[80] validation-logloss:0.22747 validation-auc:0.97103 validation-aucpr:0.97497
[81] validation-logloss:0.22623 validation-auc:0.97102 validation-aucpr:0.97498
[82] validation-logloss:0.22514 validation-auc:0.97106 validation-aucpr:0.97504
[83] validation-logloss:0.22406 validation-auc:0.97103 validation-aucpr:0.97501
[84] validation-logloss:0.22272 validation-auc:0.97117 validation-aucpr:0.97511
[85] validation-logloss:0.22178 validation-auc:0.97120 validation-aucpr:0.97517
[86] validation-logloss:0.22110 validation-auc:0.97115 validation-aucpr:0.97511
[87] validation-logloss:0.22017 validation-auc:0.97111 validation-aucpr:0.97507
[88] validation-logloss:0.21917 validation-auc:0.97123 validation-aucpr:0.97513
[89] validation-logloss:0.21807 validation-auc:0.97139 validation-aucpr:0.97523
[90] validation-logloss:0.21737 validation-auc:0.97135 validation-aucpr:0.97519
[91] validation-logloss:0.21643 validation-auc:0.97143 validation-aucpr:0.97530
[92] validation-logloss:0.21574 validation-auc:0.97147 validation-aucpr:0.97529
[93] validation-logloss:0.21508 validation-auc:0.97142 validation-aucpr:0.97524
[94] validation-logloss:0.21419 validation-auc:0.97154 validation-aucpr:0.97541
[95] validation-logloss:0.21351 validation-auc:0.97158 validation-aucpr:0.97539
[96] validation-logloss:0.21322 validation-auc:0.97144 validation-aucpr:0.97528
[97] validation-logloss:0.21248 validation-auc:0.97140 validation-aucpr:0.97527
{'best_iteration': '94', 'best_score': '0.9754120472963778'}
Trial 32, Fold 3: Log loss = 0.21247699408509904, Average precision = 0.9752734811271323, ROC-AUC = 0.9714012433666246, Elapsed Time = 1086.049289999999 seconds
Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 32, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67231 validation-auc:0.94524 validation-aucpr:0.94865
[1] validation-logloss:0.65399 validation-auc:0.94796 validation-aucpr:0.95001
[2] validation-logloss:0.63804 validation-auc:0.94682 validation-aucpr:0.95165
[3] validation-logloss:0.62247 validation-auc:0.94910 validation-aucpr:0.94988
[4] validation-logloss:0.60601 validation-auc:0.95421 validation-aucpr:0.96119
[5] validation-logloss:0.59011 validation-auc:0.95657 validation-aucpr:0.96393
[6] validation-logloss:0.57554 validation-auc:0.95806 validation-aucpr:0.96484
[7] validation-logloss:0.56092 validation-auc:0.95983 validation-aucpr:0.96728
[8] validation-logloss:0.54766 validation-auc:0.96045 validation-aucpr:0.96747
[9] validation-logloss:0.53475 validation-auc:0.96090 validation-aucpr:0.96777
[10] validation-logloss:0.52325 validation-auc:0.96163 validation-aucpr:0.96825
[11] validation-logloss:0.51122 validation-auc:0.96215 validation-aucpr:0.96869
[12] validation-logloss:0.50056 validation-auc:0.96278 validation-aucpr:0.96919
[13] validation-logloss:0.49136 validation-auc:0.96235 validation-aucpr:0.96880
[14] validation-logloss:0.48030 validation-auc:0.96273 validation-aucpr:0.96918
[15] validation-logloss:0.47049 validation-auc:0.96284 validation-aucpr:0.96921
[16] validation-logloss:0.46138 validation-auc:0.96327 validation-aucpr:0.96951
[17] validation-logloss:0.45193 validation-auc:0.96396 validation-aucpr:0.97008
[18] validation-logloss:0.44298 validation-auc:0.96435 validation-aucpr:0.97035
[19] validation-logloss:0.43554 validation-auc:0.96434 validation-aucpr:0.97036
[20] validation-logloss:0.42842 validation-auc:0.96446 validation-aucpr:0.97049
[21] validation-logloss:0.42138 validation-auc:0.96458 validation-aucpr:0.97059
[22] validation-logloss:0.41421 validation-auc:0.96488 validation-aucpr:0.97083
[23] validation-logloss:0.40668 validation-auc:0.96497 validation-aucpr:0.97092
[24] validation-logloss:0.39907 validation-auc:0.96552 validation-aucpr:0.97136
[25] validation-logloss:0.39262 validation-auc:0.96559 validation-aucpr:0.97141
[26] validation-logloss:0.38704 validation-auc:0.96544 validation-aucpr:0.97129
[27] validation-logloss:0.38036 validation-auc:0.96570 validation-aucpr:0.97146
[28] validation-logloss:0.37498 validation-auc:0.96539 validation-aucpr:0.97116
[29] validation-logloss:0.36901 validation-auc:0.96535 validation-aucpr:0.97116
[30] validation-logloss:0.36331 validation-auc:0.96565 validation-aucpr:0.97143
[31] validation-logloss:0.35736 validation-auc:0.96607 validation-aucpr:0.97175
[32] validation-logloss:0.35173 validation-auc:0.96651 validation-aucpr:0.97206
[33] validation-logloss:0.34719 validation-auc:0.96655 validation-aucpr:0.97215
[34] validation-logloss:0.34217 validation-auc:0.96680 validation-aucpr:0.97232
[35] validation-logloss:0.33729 validation-auc:0.96683 validation-aucpr:0.97236
[36] validation-logloss:0.33296 validation-auc:0.96667 validation-aucpr:0.97225
[37] validation-logloss:0.32852 validation-auc:0.96687 validation-aucpr:0.97238
[38] validation-logloss:0.32481 validation-auc:0.96694 validation-aucpr:0.97241
[39] validation-logloss:0.32075 validation-auc:0.96679 validation-aucpr:0.97229
[40] validation-logloss:0.31696 validation-auc:0.96673 validation-aucpr:0.97228
[41] validation-logloss:0.31375 validation-auc:0.96669 validation-aucpr:0.97225
[42] validation-logloss:0.30988 validation-auc:0.96682 validation-aucpr:0.97236
[43] validation-logloss:0.30662 validation-auc:0.96673 validation-aucpr:0.97229
[44] validation-logloss:0.30328 validation-auc:0.96678 validation-aucpr:0.97231
[45] validation-logloss:0.29965 validation-auc:0.96703 validation-aucpr:0.97248
[46] validation-logloss:0.29624 validation-auc:0.96708 validation-aucpr:0.97250
[47] validation-logloss:0.29295 validation-auc:0.96706 validation-aucpr:0.97250
[48] validation-logloss:0.28965 validation-auc:0.96726 validation-aucpr:0.97267
[49] validation-logloss:0.28671 validation-auc:0.96727 validation-aucpr:0.97265
[50] validation-logloss:0.28357 validation-auc:0.96745 validation-aucpr:0.97281
[51] validation-logloss:0.28071 validation-auc:0.96744 validation-aucpr:0.97281
[52] validation-logloss:0.27840 validation-auc:0.96752 validation-aucpr:0.97284
[53] validation-logloss:0.27567 validation-auc:0.96765 validation-aucpr:0.97291
[54] validation-logloss:0.27301 validation-auc:0.96780 validation-aucpr:0.97301
[55] validation-logloss:0.27048 validation-auc:0.96791 validation-aucpr:0.97309
[56] validation-logloss:0.26806 validation-auc:0.96784 validation-aucpr:0.97309
[57] validation-logloss:0.26597 validation-auc:0.96789 validation-aucpr:0.97313
[58] validation-logloss:0.26364 validation-auc:0.96802 validation-aucpr:0.97323
[59] validation-logloss:0.26186 validation-auc:0.96793 validation-aucpr:0.97317
[60] validation-logloss:0.26017 validation-auc:0.96778 validation-aucpr:0.97308
[61] validation-logloss:0.25807 validation-auc:0.96784 validation-aucpr:0.97310
[62] validation-logloss:0.25641 validation-auc:0.96785 validation-aucpr:0.97312
[63] validation-logloss:0.25453 validation-auc:0.96785 validation-aucpr:0.97315
[64] validation-logloss:0.25325 validation-auc:0.96762 validation-aucpr:0.97299
[65] validation-logloss:0.25132 validation-auc:0.96765 validation-aucpr:0.97302
[66] validation-logloss:0.24974 validation-auc:0.96765 validation-aucpr:0.97304
[67] validation-logloss:0.24811 validation-auc:0.96772 validation-aucpr:0.97308
[68] validation-logloss:0.24646 validation-auc:0.96775 validation-aucpr:0.97311
[69] validation-logloss:0.24493 validation-auc:0.96781 validation-aucpr:0.97315
[70] validation-logloss:0.24330 validation-auc:0.96787 validation-aucpr:0.97318
[71] validation-logloss:0.24159 validation-auc:0.96793 validation-aucpr:0.97327
[72] validation-logloss:0.24032 validation-auc:0.96800 validation-aucpr:0.97331
[73] validation-logloss:0.23899 validation-auc:0.96813 validation-aucpr:0.97342
[74] validation-logloss:0.23771 validation-auc:0.96810 validation-aucpr:0.97340
[75] validation-logloss:0.23639 validation-auc:0.96811 validation-aucpr:0.97340
[76] validation-logloss:0.23500 validation-auc:0.96818 validation-aucpr:0.97345
[77] validation-logloss:0.23383 validation-auc:0.96824 validation-aucpr:0.97349
[78] validation-logloss:0.23314 validation-auc:0.96813 validation-aucpr:0.97340
[79] validation-logloss:0.23194 validation-auc:0.96818 validation-aucpr:0.97346
[80] validation-logloss:0.23113 validation-auc:0.96816 validation-aucpr:0.97344
[81] validation-logloss:0.23001 validation-auc:0.96813 validation-aucpr:0.97344
[82] validation-logloss:0.22882 validation-auc:0.96825 validation-aucpr:0.97350
[83] validation-logloss:0.22773 validation-auc:0.96836 validation-aucpr:0.97357
[84] validation-logloss:0.22709 validation-auc:0.96834 validation-aucpr:0.97354
[85] validation-logloss:0.22644 validation-auc:0.96837 validation-aucpr:0.97352
[86] validation-logloss:0.22556 validation-auc:0.96832 validation-aucpr:0.97349
[87] validation-logloss:0.22452 validation-auc:0.96836 validation-aucpr:0.97352
[88] validation-logloss:0.22396 validation-auc:0.96833 validation-aucpr:0.97350
[89] validation-logloss:0.22279 validation-auc:0.96841 validation-aucpr:0.97357
[90] validation-logloss:0.22209 validation-auc:0.96842 validation-aucpr:0.97361
[91] validation-logloss:0.22123 validation-auc:0.96847 validation-aucpr:0.97366
[92] validation-logloss:0.22042 validation-auc:0.96857 validation-aucpr:0.97374
[93] validation-logloss:0.21988 validation-auc:0.96856 validation-aucpr:0.97373
[94] validation-logloss:0.21943 validation-auc:0.96852 validation-aucpr:0.97370
[95] validation-logloss:0.21840 validation-auc:0.96868 validation-aucpr:0.97381
[96] validation-logloss:0.21767 validation-auc:0.96872 validation-aucpr:0.97383
[97] validation-logloss:0.21687 validation-auc:0.96880 validation-aucpr:0.97390
{'best_iteration': '97', 'best_score': '0.9738957097915883'}
Trial 32, Fold 4: Log loss = 0.21687468327294243, Average precision = 0.9738999480902333, ROC-AUC = 0.9687965705034205, Elapsed Time = 1075.9968526999983 seconds
Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 32, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67284 validation-auc:0.93883 validation-aucpr:0.94575
[1] validation-logloss:0.65434 validation-auc:0.94729 validation-aucpr:0.95476
[2] validation-logloss:0.63869 validation-auc:0.94707 validation-aucpr:0.95243
[3] validation-logloss:0.62192 validation-auc:0.95017 validation-aucpr:0.95572
[4] validation-logloss:0.60565 validation-auc:0.95491 validation-aucpr:0.96287
[5] validation-logloss:0.58926 validation-auc:0.95653 validation-aucpr:0.96382
[6] validation-logloss:0.57363 validation-auc:0.95766 validation-aucpr:0.96465
[7] validation-logloss:0.55965 validation-auc:0.95898 validation-aucpr:0.96554
[8] validation-logloss:0.54637 validation-auc:0.95914 validation-aucpr:0.96576
[9] validation-logloss:0.53326 validation-auc:0.96037 validation-aucpr:0.96724
[10] validation-logloss:0.52142 validation-auc:0.96102 validation-aucpr:0.96757
[11] validation-logloss:0.51024 validation-auc:0.96143 validation-aucpr:0.96785
[12] validation-logloss:0.49925 validation-auc:0.96201 validation-aucpr:0.96835
[13] validation-logloss:0.48992 validation-auc:0.96172 validation-aucpr:0.96806
[14] validation-logloss:0.48136 validation-auc:0.96132 validation-aucpr:0.96769
[15] validation-logloss:0.47188 validation-auc:0.96192 validation-aucpr:0.96803
[16] validation-logloss:0.46233 validation-auc:0.96246 validation-aucpr:0.96843
[17] validation-logloss:0.45301 validation-auc:0.96301 validation-aucpr:0.96876
[18] validation-logloss:0.44538 validation-auc:0.96267 validation-aucpr:0.96817
[19] validation-logloss:0.43757 validation-auc:0.96269 validation-aucpr:0.96818
[20] validation-logloss:0.43047 validation-auc:0.96251 validation-aucpr:0.96803
[21] validation-logloss:0.42303 validation-auc:0.96268 validation-aucpr:0.96822
[22] validation-logloss:0.41592 validation-auc:0.96323 validation-aucpr:0.96865
[23] validation-logloss:0.40849 validation-auc:0.96340 validation-aucpr:0.96882
[24] validation-logloss:0.40118 validation-auc:0.96379 validation-aucpr:0.96914
[25] validation-logloss:0.39497 validation-auc:0.96395 validation-aucpr:0.96927
[26] validation-logloss:0.38834 validation-auc:0.96423 validation-aucpr:0.96945
[27] validation-logloss:0.38222 validation-auc:0.96434 validation-aucpr:0.96949
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[40] validation-logloss:0.31849 validation-auc:0.96674 validation-aucpr:0.97154
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[59] validation-logloss:0.26501 validation-auc:0.96786 validation-aucpr:0.97224
[60] validation-logloss:0.26306 validation-auc:0.96783 validation-aucpr:0.97220
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[64] validation-logloss:0.25621 validation-auc:0.96767 validation-aucpr:0.97200
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[66] validation-logloss:0.25262 validation-auc:0.96774 validation-aucpr:0.97206
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[68] validation-logloss:0.24947 validation-auc:0.96768 validation-aucpr:0.97199
[69] validation-logloss:0.24798 validation-auc:0.96769 validation-aucpr:0.97202
[70] validation-logloss:0.24641 validation-auc:0.96780 validation-aucpr:0.97216
[71] validation-logloss:0.24516 validation-auc:0.96777 validation-aucpr:0.97220
[72] validation-logloss:0.24358 validation-auc:0.96787 validation-aucpr:0.97226
[73] validation-logloss:0.24212 validation-auc:0.96788 validation-aucpr:0.97228
[74] validation-logloss:0.24075 validation-auc:0.96786 validation-aucpr:0.97227
[75] validation-logloss:0.23973 validation-auc:0.96787 validation-aucpr:0.97218
[76] validation-logloss:0.23823 validation-auc:0.96798 validation-aucpr:0.97230
[77] validation-logloss:0.23652 validation-auc:0.96819 validation-aucpr:0.97245
[78] validation-logloss:0.23531 validation-auc:0.96821 validation-aucpr:0.97248
[79] validation-logloss:0.23460 validation-auc:0.96801 validation-aucpr:0.97258
[80] validation-logloss:0.23365 validation-auc:0.96800 validation-aucpr:0.97258
[81] validation-logloss:0.23254 validation-auc:0.96808 validation-aucpr:0.97262
[82] validation-logloss:0.23124 validation-auc:0.96823 validation-aucpr:0.97271
[83] validation-logloss:0.23023 validation-auc:0.96824 validation-aucpr:0.97274
[84] validation-logloss:0.22957 validation-auc:0.96818 validation-aucpr:0.97269
[85] validation-logloss:0.22865 validation-auc:0.96822 validation-aucpr:0.97276
[86] validation-logloss:0.22773 validation-auc:0.96824 validation-aucpr:0.97277
[87] validation-logloss:0.22670 validation-auc:0.96826 validation-aucpr:0.97277
[88] validation-logloss:0.22592 validation-auc:0.96831 validation-aucpr:0.97279
[89] validation-logloss:0.22500 validation-auc:0.96844 validation-aucpr:0.97287
[90] validation-logloss:0.22445 validation-auc:0.96843 validation-aucpr:0.97279
[91] validation-logloss:0.22376 validation-auc:0.96842 validation-aucpr:0.97277
[92] validation-logloss:0.22315 validation-auc:0.96843 validation-aucpr:0.97275
[93] validation-logloss:0.22255 validation-auc:0.96840 validation-aucpr:0.97271
[94] validation-logloss:0.22144 validation-auc:0.96863 validation-aucpr:0.97287
[95] validation-logloss:0.22074 validation-auc:0.96865 validation-aucpr:0.97287
[96] validation-logloss:0.22032 validation-auc:0.96856 validation-aucpr:0.97277
[97] validation-logloss:0.21967 validation-auc:0.96859 validation-aucpr:0.97277
{'best_iteration': '89', 'best_score': '0.972873718449354'}
Trial 32, Fold 5: Log loss = 0.21967443811519236, Average precision = 0.9727732177067483, ROC-AUC = 0.9685915686602381, Elapsed Time = 1074.4078993000003 seconds
Optimization Progress: 33%|###3 | 33/100 [2:21:10<31:04:44, 1669.92s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 33, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68334 validation-auc:0.96185 validation-aucpr:0.96505
[1] validation-logloss:0.67396 validation-auc:0.96582 validation-aucpr:0.97007
[2] validation-logloss:0.66562 validation-auc:0.96601 validation-aucpr:0.97066
[3] validation-logloss:0.65712 validation-auc:0.96579 validation-aucpr:0.97046
[4] validation-logloss:0.64923 validation-auc:0.96559 validation-aucpr:0.97019
[5] validation-logloss:0.64059 validation-auc:0.96629 validation-aucpr:0.97070
[6] validation-logloss:0.63328 validation-auc:0.96574 validation-aucpr:0.97018
[7] validation-logloss:0.62495 validation-auc:0.96681 validation-aucpr:0.97111
[8] validation-logloss:0.61692 validation-auc:0.96710 validation-aucpr:0.97144
[9] validation-logloss:0.60912 validation-auc:0.96732 validation-aucpr:0.97173
[10] validation-logloss:0.60147 validation-auc:0.96756 validation-aucpr:0.97193
[11] validation-logloss:0.59395 validation-auc:0.96784 validation-aucpr:0.97222
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[72] validation-logloss:0.33878 validation-auc:0.97066 validation-aucpr:0.97524
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[74] validation-logloss:0.33427 validation-auc:0.97069 validation-aucpr:0.97525
[75] validation-logloss:0.33203 validation-auc:0.97074 validation-aucpr:0.97530
[76] validation-logloss:0.32990 validation-auc:0.97072 validation-aucpr:0.97529
[77] validation-logloss:0.32772 validation-auc:0.97074 validation-aucpr:0.97529
[78] validation-logloss:0.32565 validation-auc:0.97074 validation-aucpr:0.97529
[79] validation-logloss:0.32358 validation-auc:0.97077 validation-aucpr:0.97532
[80] validation-logloss:0.32156 validation-auc:0.97079 validation-aucpr:0.97532
[81] validation-logloss:0.31955 validation-auc:0.97079 validation-aucpr:0.97532
[82] validation-logloss:0.31753 validation-auc:0.97085 validation-aucpr:0.97536
[83] validation-logloss:0.31560 validation-auc:0.97086 validation-aucpr:0.97538
[84] validation-logloss:0.31362 validation-auc:0.97096 validation-aucpr:0.97545
[85] validation-logloss:0.31186 validation-auc:0.97094 validation-aucpr:0.97542
[86] validation-logloss:0.30996 validation-auc:0.97098 validation-aucpr:0.97545
[87] validation-logloss:0.30820 validation-auc:0.97097 validation-aucpr:0.97543
[88] validation-logloss:0.30645 validation-auc:0.97099 validation-aucpr:0.97545
[89] validation-logloss:0.30485 validation-auc:0.97104 validation-aucpr:0.97548
[90] validation-logloss:0.30307 validation-auc:0.97106 validation-aucpr:0.97548
[91] validation-logloss:0.30129 validation-auc:0.97111 validation-aucpr:0.97551
[92] validation-logloss:0.29960 validation-auc:0.97114 validation-aucpr:0.97553
[93] validation-logloss:0.29799 validation-auc:0.97113 validation-aucpr:0.97554
[94] validation-logloss:0.29630 validation-auc:0.97121 validation-aucpr:0.97559
{'best_iteration': '94', 'best_score': '0.9755942644594567'}
Trial 33, Fold 1: Log loss = 0.2962993850750307, Average precision = 0.9755984835078575, ROC-AUC = 0.9712136542110571, Elapsed Time = 2.482240000001184 seconds
Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 33, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68357 validation-auc:0.96349 validation-aucpr:0.96723
[1] validation-logloss:0.67503 validation-auc:0.96605 validation-aucpr:0.96927
[2] validation-logloss:0.66588 validation-auc:0.96734 validation-aucpr:0.97044
[3] validation-logloss:0.65692 validation-auc:0.96852 validation-aucpr:0.97158
[4] validation-logloss:0.64816 validation-auc:0.96976 validation-aucpr:0.97314
[5] validation-logloss:0.63948 validation-auc:0.96993 validation-aucpr:0.97326
[6] validation-logloss:0.63122 validation-auc:0.97000 validation-aucpr:0.97323
[7] validation-logloss:0.62301 validation-auc:0.97030 validation-aucpr:0.97356
[8] validation-logloss:0.61506 validation-auc:0.97025 validation-aucpr:0.97350
[9] validation-logloss:0.60733 validation-auc:0.97022 validation-aucpr:0.97341
[10] validation-logloss:0.60051 validation-auc:0.97040 validation-aucpr:0.97361
[11] validation-logloss:0.59314 validation-auc:0.97071 validation-aucpr:0.97385
[12] validation-logloss:0.58588 validation-auc:0.97085 validation-aucpr:0.97396
[13] validation-logloss:0.57869 validation-auc:0.97097 validation-aucpr:0.97405
[14] validation-logloss:0.57213 validation-auc:0.97093 validation-aucpr:0.97403
[15] validation-logloss:0.56526 validation-auc:0.97096 validation-aucpr:0.97403
[16] validation-logloss:0.55869 validation-auc:0.97071 validation-aucpr:0.97372
[17] validation-logloss:0.55209 validation-auc:0.97096 validation-aucpr:0.97390
[18] validation-logloss:0.54562 validation-auc:0.97109 validation-aucpr:0.97399
[19] validation-logloss:0.53934 validation-auc:0.97103 validation-aucpr:0.97394
[20] validation-logloss:0.53323 validation-auc:0.97097 validation-aucpr:0.97388
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[25] validation-logloss:0.50438 validation-auc:0.97090 validation-aucpr:0.97383
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[28] validation-logloss:0.48831 validation-auc:0.97111 validation-aucpr:0.97400
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[30] validation-logloss:0.47818 validation-auc:0.97121 validation-aucpr:0.97406
[31] validation-logloss:0.47329 validation-auc:0.97130 validation-aucpr:0.97412
[32] validation-logloss:0.46851 validation-auc:0.97126 validation-aucpr:0.97409
[33] validation-logloss:0.46394 validation-auc:0.97114 validation-aucpr:0.97400
[34] validation-logloss:0.45930 validation-auc:0.97116 validation-aucpr:0.97401
[35] validation-logloss:0.45477 validation-auc:0.97118 validation-aucpr:0.97404
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[79] validation-logloss:0.32155 validation-auc:0.97187 validation-aucpr:0.97452
[80] validation-logloss:0.31950 validation-auc:0.97183 validation-aucpr:0.97449
[81] validation-logloss:0.31742 validation-auc:0.97189 validation-aucpr:0.97453
[82] validation-logloss:0.31564 validation-auc:0.97189 validation-aucpr:0.97453
[83] validation-logloss:0.31362 validation-auc:0.97191 validation-aucpr:0.97455
[84] validation-logloss:0.31167 validation-auc:0.97193 validation-aucpr:0.97456
[85] validation-logloss:0.30973 validation-auc:0.97193 validation-aucpr:0.97456
[86] validation-logloss:0.30783 validation-auc:0.97194 validation-aucpr:0.97458
[87] validation-logloss:0.30591 validation-auc:0.97200 validation-aucpr:0.97462
[88] validation-logloss:0.30411 validation-auc:0.97200 validation-aucpr:0.97463
[89] validation-logloss:0.30228 validation-auc:0.97205 validation-aucpr:0.97467
[90] validation-logloss:0.30049 validation-auc:0.97212 validation-aucpr:0.97472
[91] validation-logloss:0.29872 validation-auc:0.97216 validation-aucpr:0.97474
[92] validation-logloss:0.29700 validation-auc:0.97214 validation-aucpr:0.97473
[93] validation-logloss:0.29530 validation-auc:0.97216 validation-aucpr:0.97474
[94] validation-logloss:0.29366 validation-auc:0.97215 validation-aucpr:0.97473
{'best_iteration': '93', 'best_score': '0.9747449932848273'}
Trial 33, Fold 2: Log loss = 0.2936584776398004, Average precision = 0.9746742058070549, ROC-AUC = 0.9721450247515813, Elapsed Time = 2.6043852000002516 seconds
Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 33, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68338 validation-auc:0.96407 validation-aucpr:0.96861
[1] validation-logloss:0.67388 validation-auc:0.96621 validation-aucpr:0.97075
[2] validation-logloss:0.66461 validation-auc:0.96840 validation-aucpr:0.97245
[3] validation-logloss:0.65551 validation-auc:0.96954 validation-aucpr:0.97351
[4] validation-logloss:0.64669 validation-auc:0.97044 validation-aucpr:0.97410
[5] validation-logloss:0.63803 validation-auc:0.97051 validation-aucpr:0.97419
[6] validation-logloss:0.62958 validation-auc:0.97094 validation-aucpr:0.97443
[7] validation-logloss:0.62138 validation-auc:0.97061 validation-aucpr:0.97417
[8] validation-logloss:0.61445 validation-auc:0.97086 validation-aucpr:0.97523
[9] validation-logloss:0.60675 validation-auc:0.97069 validation-aucpr:0.97510
[10] validation-logloss:0.59922 validation-auc:0.97059 validation-aucpr:0.97500
[11] validation-logloss:0.59168 validation-auc:0.97083 validation-aucpr:0.97517
[12] validation-logloss:0.58435 validation-auc:0.97091 validation-aucpr:0.97524
[13] validation-logloss:0.57723 validation-auc:0.97099 validation-aucpr:0.97529
[14] validation-logloss:0.57035 validation-auc:0.97091 validation-aucpr:0.97526
[15] validation-logloss:0.56352 validation-auc:0.97089 validation-aucpr:0.97525
[16] validation-logloss:0.55682 validation-auc:0.97089 validation-aucpr:0.97521
[17] validation-logloss:0.55026 validation-auc:0.97095 validation-aucpr:0.97526
[18] validation-logloss:0.54405 validation-auc:0.97102 validation-aucpr:0.97526
[19] validation-logloss:0.53793 validation-auc:0.97088 validation-aucpr:0.97509
[20] validation-logloss:0.53184 validation-auc:0.97085 validation-aucpr:0.97507
[21] validation-logloss:0.52597 validation-auc:0.97078 validation-aucpr:0.97502
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[28] validation-logloss:0.48877 validation-auc:0.97118 validation-aucpr:0.97533
[29] validation-logloss:0.48366 validation-auc:0.97121 validation-aucpr:0.97537
[30] validation-logloss:0.47857 validation-auc:0.97128 validation-aucpr:0.97543
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[32] validation-logloss:0.46936 validation-auc:0.97138 validation-aucpr:0.97557
[33] validation-logloss:0.46523 validation-auc:0.97138 validation-aucpr:0.97558
[34] validation-logloss:0.46069 validation-auc:0.97144 validation-aucpr:0.97559
[35] validation-logloss:0.45608 validation-auc:0.97146 validation-aucpr:0.97558
[36] validation-logloss:0.45156 validation-auc:0.97155 validation-aucpr:0.97565
[37] validation-logloss:0.44731 validation-auc:0.97153 validation-aucpr:0.97562
[38] validation-logloss:0.44301 validation-auc:0.97161 validation-aucpr:0.97569
[39] validation-logloss:0.43881 validation-auc:0.97162 validation-aucpr:0.97571
[40] validation-logloss:0.43467 validation-auc:0.97162 validation-aucpr:0.97569
[41] validation-logloss:0.43068 validation-auc:0.97160 validation-aucpr:0.97566
[42] validation-logloss:0.42664 validation-auc:0.97167 validation-aucpr:0.97571
[43] validation-logloss:0.42275 validation-auc:0.97160 validation-aucpr:0.97566
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[45] validation-logloss:0.41504 validation-auc:0.97164 validation-aucpr:0.97568
[46] validation-logloss:0.41166 validation-auc:0.97156 validation-aucpr:0.97562
[47] validation-logloss:0.40802 validation-auc:0.97157 validation-aucpr:0.97563
[48] validation-logloss:0.40441 validation-auc:0.97170 validation-aucpr:0.97575
[49] validation-logloss:0.40088 validation-auc:0.97176 validation-aucpr:0.97579
[50] validation-logloss:0.39742 validation-auc:0.97177 validation-aucpr:0.97580
[51] validation-logloss:0.39408 validation-auc:0.97173 validation-aucpr:0.97576
[52] validation-logloss:0.39105 validation-auc:0.97177 validation-aucpr:0.97582
[53] validation-logloss:0.38784 validation-auc:0.97177 validation-aucpr:0.97582
[54] validation-logloss:0.38456 validation-auc:0.97178 validation-aucpr:0.97584
[55] validation-logloss:0.38140 validation-auc:0.97182 validation-aucpr:0.97587
[56] validation-logloss:0.37831 validation-auc:0.97180 validation-aucpr:0.97586
[57] validation-logloss:0.37561 validation-auc:0.97183 validation-aucpr:0.97588
[58] validation-logloss:0.37260 validation-auc:0.97179 validation-aucpr:0.97585
[59] validation-logloss:0.36966 validation-auc:0.97182 validation-aucpr:0.97587
[60] validation-logloss:0.36683 validation-auc:0.97177 validation-aucpr:0.97583
[61] validation-logloss:0.36405 validation-auc:0.97178 validation-aucpr:0.97584
[62] validation-logloss:0.36119 validation-auc:0.97183 validation-aucpr:0.97588
[63] validation-logloss:0.35843 validation-auc:0.97186 validation-aucpr:0.97590
[64] validation-logloss:0.35569 validation-auc:0.97185 validation-aucpr:0.97591
[65] validation-logloss:0.35301 validation-auc:0.97193 validation-aucpr:0.97595
[66] validation-logloss:0.35035 validation-auc:0.97197 validation-aucpr:0.97599
[67] validation-logloss:0.34778 validation-auc:0.97196 validation-aucpr:0.97597
[68] validation-logloss:0.34528 validation-auc:0.97195 validation-aucpr:0.97597
[69] validation-logloss:0.34276 validation-auc:0.97196 validation-aucpr:0.97599
[70] validation-logloss:0.34059 validation-auc:0.97197 validation-aucpr:0.97601
[71] validation-logloss:0.33819 validation-auc:0.97197 validation-aucpr:0.97600
[72] validation-logloss:0.33594 validation-auc:0.97199 validation-aucpr:0.97602
[73] validation-logloss:0.33366 validation-auc:0.97198 validation-aucpr:0.97600
[74] validation-logloss:0.33135 validation-auc:0.97199 validation-aucpr:0.97601
[75] validation-logloss:0.32909 validation-auc:0.97199 validation-aucpr:0.97600
[76] validation-logloss:0.32684 validation-auc:0.97202 validation-aucpr:0.97602
[77] validation-logloss:0.32465 validation-auc:0.97203 validation-aucpr:0.97603
[78] validation-logloss:0.32257 validation-auc:0.97202 validation-aucpr:0.97600
[79] validation-logloss:0.32047 validation-auc:0.97206 validation-aucpr:0.97602
[80] validation-logloss:0.31844 validation-auc:0.97207 validation-aucpr:0.97606
[81] validation-logloss:0.31642 validation-auc:0.97207 validation-aucpr:0.97605
[82] validation-logloss:0.31437 validation-auc:0.97212 validation-aucpr:0.97609
[83] validation-logloss:0.31240 validation-auc:0.97212 validation-aucpr:0.97608
[84] validation-logloss:0.31048 validation-auc:0.97213 validation-aucpr:0.97607
[85] validation-logloss:0.30870 validation-auc:0.97213 validation-aucpr:0.97607
[86] validation-logloss:0.30680 validation-auc:0.97210 validation-aucpr:0.97604
[87] validation-logloss:0.30495 validation-auc:0.97211 validation-aucpr:0.97605
[88] validation-logloss:0.30311 validation-auc:0.97213 validation-aucpr:0.97607
[89] validation-logloss:0.30130 validation-auc:0.97214 validation-aucpr:0.97608
[90] validation-logloss:0.29953 validation-auc:0.97214 validation-aucpr:0.97608
[91] validation-logloss:0.29802 validation-auc:0.97213 validation-aucpr:0.97607
[92] validation-logloss:0.29652 validation-auc:0.97217 validation-aucpr:0.97611
[93] validation-logloss:0.29481 validation-auc:0.97217 validation-aucpr:0.97611
[94] validation-logloss:0.29313 validation-auc:0.97219 validation-aucpr:0.97613
{'best_iteration': '94', 'best_score': '0.9761276928325958'}
Trial 33, Fold 3: Log loss = 0.29313219894809645, Average precision = 0.9761308604244551, ROC-AUC = 0.9721853450559081, Elapsed Time = 2.70938169999863 seconds
Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 33, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68351 validation-auc:0.96007 validation-aucpr:0.96605
[1] validation-logloss:0.67398 validation-auc:0.96567 validation-aucpr:0.97100
[2] validation-logloss:0.66490 validation-auc:0.96713 validation-aucpr:0.97232
[3] validation-logloss:0.65596 validation-auc:0.96765 validation-aucpr:0.97245
[4] validation-logloss:0.64728 validation-auc:0.96747 validation-aucpr:0.97243
[5] validation-logloss:0.63882 validation-auc:0.96748 validation-aucpr:0.97239
[6] validation-logloss:0.63045 validation-auc:0.96818 validation-aucpr:0.97301
[7] validation-logloss:0.62224 validation-auc:0.96833 validation-aucpr:0.97314
[8] validation-logloss:0.61430 validation-auc:0.96872 validation-aucpr:0.97340
[9] validation-logloss:0.60653 validation-auc:0.96893 validation-aucpr:0.97357
[10] validation-logloss:0.59906 validation-auc:0.96913 validation-aucpr:0.97374
[11] validation-logloss:0.59158 validation-auc:0.96956 validation-aucpr:0.97415
[12] validation-logloss:0.58455 validation-auc:0.96975 validation-aucpr:0.97434
[13] validation-logloss:0.57736 validation-auc:0.97008 validation-aucpr:0.97457
[14] validation-logloss:0.57060 validation-auc:0.97025 validation-aucpr:0.97477
[15] validation-logloss:0.56375 validation-auc:0.97052 validation-aucpr:0.97497
[16] validation-logloss:0.55709 validation-auc:0.97046 validation-aucpr:0.97495
[17] validation-logloss:0.55058 validation-auc:0.97053 validation-aucpr:0.97499
[18] validation-logloss:0.54423 validation-auc:0.97069 validation-aucpr:0.97511
[19] validation-logloss:0.53826 validation-auc:0.97056 validation-aucpr:0.97499
[20] validation-logloss:0.53217 validation-auc:0.97067 validation-aucpr:0.97506
[21] validation-logloss:0.52617 validation-auc:0.97077 validation-aucpr:0.97514
[22] validation-logloss:0.52039 validation-auc:0.97066 validation-aucpr:0.97507
[23] validation-logloss:0.51468 validation-auc:0.97074 validation-aucpr:0.97512
[24] validation-logloss:0.50909 validation-auc:0.97086 validation-aucpr:0.97519
[25] validation-logloss:0.50367 validation-auc:0.97091 validation-aucpr:0.97523
[26] validation-logloss:0.49841 validation-auc:0.97100 validation-aucpr:0.97529
[27] validation-logloss:0.49313 validation-auc:0.97104 validation-aucpr:0.97534
[28] validation-logloss:0.48871 validation-auc:0.97086 validation-aucpr:0.97534
[29] validation-logloss:0.48364 validation-auc:0.97085 validation-aucpr:0.97534
[30] validation-logloss:0.47871 validation-auc:0.97096 validation-aucpr:0.97542
[31] validation-logloss:0.47394 validation-auc:0.97090 validation-aucpr:0.97537
[32] validation-logloss:0.46924 validation-auc:0.97091 validation-aucpr:0.97538
[33] validation-logloss:0.46449 validation-auc:0.97099 validation-aucpr:0.97545
[34] validation-logloss:0.46021 validation-auc:0.97107 validation-aucpr:0.97556
[35] validation-logloss:0.45575 validation-auc:0.97101 validation-aucpr:0.97551
[36] validation-logloss:0.45131 validation-auc:0.97106 validation-aucpr:0.97555
[37] validation-logloss:0.44701 validation-auc:0.97107 validation-aucpr:0.97556
[38] validation-logloss:0.44273 validation-auc:0.97097 validation-aucpr:0.97548
[39] validation-logloss:0.43915 validation-auc:0.97086 validation-aucpr:0.97543
[40] validation-logloss:0.43552 validation-auc:0.97084 validation-aucpr:0.97543
[41] validation-logloss:0.43153 validation-auc:0.97081 validation-aucpr:0.97540
[42] validation-logloss:0.42761 validation-auc:0.97082 validation-aucpr:0.97541
[43] validation-logloss:0.42368 validation-auc:0.97088 validation-aucpr:0.97546
[44] validation-logloss:0.41989 validation-auc:0.97094 validation-aucpr:0.97549
[45] validation-logloss:0.41615 validation-auc:0.97089 validation-aucpr:0.97545
[46] validation-logloss:0.41293 validation-auc:0.97080 validation-aucpr:0.97540
[47] validation-logloss:0.40932 validation-auc:0.97090 validation-aucpr:0.97547
[48] validation-logloss:0.40573 validation-auc:0.97093 validation-aucpr:0.97550
[49] validation-logloss:0.40267 validation-auc:0.97089 validation-aucpr:0.97547
[50] validation-logloss:0.39917 validation-auc:0.97096 validation-aucpr:0.97552
[51] validation-logloss:0.39579 validation-auc:0.97095 validation-aucpr:0.97552
[52] validation-logloss:0.39245 validation-auc:0.97098 validation-aucpr:0.97553
[53] validation-logloss:0.38920 validation-auc:0.97110 validation-aucpr:0.97562
[54] validation-logloss:0.38599 validation-auc:0.97118 validation-aucpr:0.97568
[55] validation-logloss:0.38289 validation-auc:0.97119 validation-aucpr:0.97568
[56] validation-logloss:0.37976 validation-auc:0.97121 validation-aucpr:0.97570
[57] validation-logloss:0.37668 validation-auc:0.97129 validation-aucpr:0.97575
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[60] validation-logloss:0.36789 validation-auc:0.97138 validation-aucpr:0.97580
[61] validation-logloss:0.36504 validation-auc:0.97144 validation-aucpr:0.97585
[62] validation-logloss:0.36224 validation-auc:0.97152 validation-aucpr:0.97591
[63] validation-logloss:0.35951 validation-auc:0.97148 validation-aucpr:0.97588
[64] validation-logloss:0.35687 validation-auc:0.97147 validation-aucpr:0.97588
[65] validation-logloss:0.35411 validation-auc:0.97151 validation-aucpr:0.97592
[66] validation-logloss:0.35148 validation-auc:0.97152 validation-aucpr:0.97593
[67] validation-logloss:0.34893 validation-auc:0.97155 validation-aucpr:0.97595
[68] validation-logloss:0.34645 validation-auc:0.97156 validation-aucpr:0.97596
[69] validation-logloss:0.34401 validation-auc:0.97159 validation-aucpr:0.97598
[70] validation-logloss:0.34156 validation-auc:0.97161 validation-aucpr:0.97600
[71] validation-logloss:0.33909 validation-auc:0.97166 validation-aucpr:0.97603
[72] validation-logloss:0.33703 validation-auc:0.97165 validation-aucpr:0.97603
[73] validation-logloss:0.33470 validation-auc:0.97168 validation-aucpr:0.97605
[74] validation-logloss:0.33239 validation-auc:0.97174 validation-aucpr:0.97609
[75] validation-logloss:0.33015 validation-auc:0.97177 validation-aucpr:0.97612
[76] validation-logloss:0.32794 validation-auc:0.97185 validation-aucpr:0.97617
[77] validation-logloss:0.32574 validation-auc:0.97190 validation-aucpr:0.97621
[78] validation-logloss:0.32361 validation-auc:0.97192 validation-aucpr:0.97623
[79] validation-logloss:0.32151 validation-auc:0.97195 validation-aucpr:0.97625
[80] validation-logloss:0.31968 validation-auc:0.97195 validation-aucpr:0.97625
[81] validation-logloss:0.31758 validation-auc:0.97203 validation-aucpr:0.97631
[82] validation-logloss:0.31559 validation-auc:0.97205 validation-aucpr:0.97631
[83] validation-logloss:0.31363 validation-auc:0.97204 validation-aucpr:0.97632
[84] validation-logloss:0.31168 validation-auc:0.97209 validation-aucpr:0.97635
[85] validation-logloss:0.30977 validation-auc:0.97213 validation-aucpr:0.97638
[86] validation-logloss:0.30793 validation-auc:0.97211 validation-aucpr:0.97636
[87] validation-logloss:0.30606 validation-auc:0.97212 validation-aucpr:0.97637
[88] validation-logloss:0.30447 validation-auc:0.97209 validation-aucpr:0.97635
[89] validation-logloss:0.30288 validation-auc:0.97207 validation-aucpr:0.97634
[90] validation-logloss:0.30108 validation-auc:0.97212 validation-aucpr:0.97638
[91] validation-logloss:0.29930 validation-auc:0.97220 validation-aucpr:0.97643
[92] validation-logloss:0.29757 validation-auc:0.97223 validation-aucpr:0.97645
[93] validation-logloss:0.29584 validation-auc:0.97226 validation-aucpr:0.97648
[94] validation-logloss:0.29421 validation-auc:0.97225 validation-aucpr:0.97646
{'best_iteration': '93', 'best_score': '0.9764766823302508'}
Trial 33, Fold 4: Log loss = 0.2942099781841265, Average precision = 0.97645302945903, ROC-AUC = 0.9722454702175296, Elapsed Time = 2.9326142000027176 seconds
Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 33, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68343 validation-auc:0.95976 validation-aucpr:0.96214
[1] validation-logloss:0.67403 validation-auc:0.96579 validation-aucpr:0.96994
[2] validation-logloss:0.66571 validation-auc:0.96529 validation-aucpr:0.96930
[3] validation-logloss:0.65698 validation-auc:0.96665 validation-aucpr:0.97030
[4] validation-logloss:0.64817 validation-auc:0.96694 validation-aucpr:0.97075
[5] validation-logloss:0.63960 validation-auc:0.96740 validation-aucpr:0.97115
[6] validation-logloss:0.63132 validation-auc:0.96779 validation-aucpr:0.97176
[7] validation-logloss:0.62312 validation-auc:0.96820 validation-aucpr:0.97206
[8] validation-logloss:0.61508 validation-auc:0.96860 validation-aucpr:0.97224
[9] validation-logloss:0.60731 validation-auc:0.96879 validation-aucpr:0.97238
[10] validation-logloss:0.59967 validation-auc:0.96916 validation-aucpr:0.97264
[11] validation-logloss:0.59227 validation-auc:0.96939 validation-aucpr:0.97279
[12] validation-logloss:0.58516 validation-auc:0.96957 validation-aucpr:0.97289
[13] validation-logloss:0.57806 validation-auc:0.96978 validation-aucpr:0.97305
[14] validation-logloss:0.57131 validation-auc:0.96951 validation-aucpr:0.97299
[15] validation-logloss:0.56515 validation-auc:0.96946 validation-aucpr:0.97304
[16] validation-logloss:0.55846 validation-auc:0.96954 validation-aucpr:0.97311
[17] validation-logloss:0.55205 validation-auc:0.96956 validation-aucpr:0.97314
[18] validation-logloss:0.54573 validation-auc:0.96958 validation-aucpr:0.97317
[19] validation-logloss:0.53949 validation-auc:0.96961 validation-aucpr:0.97319
[20] validation-logloss:0.53346 validation-auc:0.96964 validation-aucpr:0.97315
[21] validation-logloss:0.52755 validation-auc:0.96976 validation-aucpr:0.97324
[22] validation-logloss:0.52168 validation-auc:0.96990 validation-aucpr:0.97334
[23] validation-logloss:0.51598 validation-auc:0.96985 validation-aucpr:0.97331
[24] validation-logloss:0.51038 validation-auc:0.96996 validation-aucpr:0.97337
[25] validation-logloss:0.50547 validation-auc:0.96981 validation-aucpr:0.97334
[26] validation-logloss:0.50011 validation-auc:0.96976 validation-aucpr:0.97330
[27] validation-logloss:0.49500 validation-auc:0.96982 validation-aucpr:0.97335
[28] validation-logloss:0.49040 validation-auc:0.96966 validation-aucpr:0.97327
[29] validation-logloss:0.48545 validation-auc:0.96964 validation-aucpr:0.97347
[30] validation-logloss:0.48056 validation-auc:0.96970 validation-aucpr:0.97350
[31] validation-logloss:0.47573 validation-auc:0.96967 validation-aucpr:0.97350
[32] validation-logloss:0.47092 validation-auc:0.96975 validation-aucpr:0.97356
[33] validation-logloss:0.46622 validation-auc:0.96984 validation-aucpr:0.97363
[34] validation-logloss:0.46165 validation-auc:0.96980 validation-aucpr:0.97360
[35] validation-logloss:0.45722 validation-auc:0.96986 validation-aucpr:0.97364
[36] validation-logloss:0.45288 validation-auc:0.96994 validation-aucpr:0.97404
[37] validation-logloss:0.44858 validation-auc:0.96994 validation-aucpr:0.97405
[38] validation-logloss:0.44438 validation-auc:0.97004 validation-aucpr:0.97411
[39] validation-logloss:0.44020 validation-auc:0.97015 validation-aucpr:0.97420
[40] validation-logloss:0.43615 validation-auc:0.97019 validation-aucpr:0.97423
[41] validation-logloss:0.43217 validation-auc:0.97031 validation-aucpr:0.97432
[42] validation-logloss:0.42826 validation-auc:0.97034 validation-aucpr:0.97433
[43] validation-logloss:0.42438 validation-auc:0.97039 validation-aucpr:0.97437
[44] validation-logloss:0.42058 validation-auc:0.97039 validation-aucpr:0.97438
[45] validation-logloss:0.41696 validation-auc:0.97039 validation-aucpr:0.97437
[46] validation-logloss:0.41333 validation-auc:0.97049 validation-aucpr:0.97450
[47] validation-logloss:0.40980 validation-auc:0.97052 validation-aucpr:0.97450
[48] validation-logloss:0.40665 validation-auc:0.97047 validation-aucpr:0.97449
[49] validation-logloss:0.40317 validation-auc:0.97058 validation-aucpr:0.97456
[50] validation-logloss:0.39981 validation-auc:0.97057 validation-aucpr:0.97454
[51] validation-logloss:0.39653 validation-auc:0.97058 validation-aucpr:0.97455
[52] validation-logloss:0.39333 validation-auc:0.97063 validation-aucpr:0.97456
[53] validation-logloss:0.39049 validation-auc:0.97055 validation-aucpr:0.97451
[54] validation-logloss:0.38737 validation-auc:0.97049 validation-aucpr:0.97447
[55] validation-logloss:0.38436 validation-auc:0.97046 validation-aucpr:0.97443
[56] validation-logloss:0.38130 validation-auc:0.97044 validation-aucpr:0.97443
[57] validation-logloss:0.37828 validation-auc:0.97051 validation-aucpr:0.97449
[58] validation-logloss:0.37539 validation-auc:0.97052 validation-aucpr:0.97453
[59] validation-logloss:0.37250 validation-auc:0.97060 validation-aucpr:0.97458
[60] validation-logloss:0.36969 validation-auc:0.97064 validation-aucpr:0.97459
[61] validation-logloss:0.36683 validation-auc:0.97072 validation-aucpr:0.97467
[62] validation-logloss:0.36410 validation-auc:0.97079 validation-aucpr:0.97471
[63] validation-logloss:0.36170 validation-auc:0.97084 validation-aucpr:0.97476
[64] validation-logloss:0.35902 validation-auc:0.97089 validation-aucpr:0.97480
[65] validation-logloss:0.35636 validation-auc:0.97100 validation-aucpr:0.97488
[66] validation-logloss:0.35377 validation-auc:0.97108 validation-aucpr:0.97493
[67] validation-logloss:0.35123 validation-auc:0.97112 validation-aucpr:0.97496
[68] validation-logloss:0.34877 validation-auc:0.97109 validation-aucpr:0.97493
[69] validation-logloss:0.34639 validation-auc:0.97109 validation-aucpr:0.97492
[70] validation-logloss:0.34398 validation-auc:0.97115 validation-aucpr:0.97496
[71] validation-logloss:0.34166 validation-auc:0.97116 validation-aucpr:0.97496
[72] validation-logloss:0.33933 validation-auc:0.97122 validation-aucpr:0.97501
[73] validation-logloss:0.33706 validation-auc:0.97125 validation-aucpr:0.97501
[74] validation-logloss:0.33487 validation-auc:0.97129 validation-aucpr:0.97505
[75] validation-logloss:0.33272 validation-auc:0.97133 validation-aucpr:0.97506
[76] validation-logloss:0.33052 validation-auc:0.97137 validation-aucpr:0.97508
[77] validation-logloss:0.32841 validation-auc:0.97137 validation-aucpr:0.97509
[78] validation-logloss:0.32638 validation-auc:0.97133 validation-aucpr:0.97506
[79] validation-logloss:0.32430 validation-auc:0.97135 validation-aucpr:0.97507
[80] validation-logloss:0.32242 validation-auc:0.97141 validation-aucpr:0.97513
[81] validation-logloss:0.32044 validation-auc:0.97141 validation-aucpr:0.97514
[82] validation-logloss:0.31847 validation-auc:0.97143 validation-aucpr:0.97516
[83] validation-logloss:0.31667 validation-auc:0.97142 validation-aucpr:0.97515
[84] validation-logloss:0.31470 validation-auc:0.97148 validation-aucpr:0.97518
[85] validation-logloss:0.31280 validation-auc:0.97155 validation-aucpr:0.97524
[86] validation-logloss:0.31092 validation-auc:0.97160 validation-aucpr:0.97527
[87] validation-logloss:0.30912 validation-auc:0.97162 validation-aucpr:0.97528
[88] validation-logloss:0.30735 validation-auc:0.97163 validation-aucpr:0.97528
[89] validation-logloss:0.30556 validation-auc:0.97166 validation-aucpr:0.97530
[90] validation-logloss:0.30381 validation-auc:0.97169 validation-aucpr:0.97531
[91] validation-logloss:0.30209 validation-auc:0.97173 validation-aucpr:0.97536
[92] validation-logloss:0.30039 validation-auc:0.97175 validation-aucpr:0.97537
[93] validation-logloss:0.29871 validation-auc:0.97177 validation-aucpr:0.97538
[94] validation-logloss:0.29705 validation-auc:0.97176 validation-aucpr:0.97537
{'best_iteration': '93', 'best_score': '0.9753777106220222'}
Trial 33, Fold 5: Log loss = 0.29705146661846155, Average precision = 0.9753714880967246, ROC-AUC = 0.9717643386656263, Elapsed Time = 2.967479999999341 seconds
Optimization Progress: 34%|###4 | 34/100 [2:21:33<21:33:26, 1175.85s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 34, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68198 validation-auc:0.95130 validation-aucpr:0.95540
[1] validation-logloss:0.67112 validation-auc:0.95892 validation-aucpr:0.96265
[2] validation-logloss:0.66069 validation-auc:0.96145 validation-aucpr:0.96424
[3] validation-logloss:0.65041 validation-auc:0.96213 validation-aucpr:0.96619
[4] validation-logloss:0.64063 validation-auc:0.96271 validation-aucpr:0.96646
[5] validation-logloss:0.63112 validation-auc:0.96362 validation-aucpr:0.96797
{'best_iteration': '5', 'best_score': '0.967973486067347'}
Trial 34, Fold 1: Log loss = 0.6311222487753312, Average precision = 0.9682110235622168, ROC-AUC = 0.9636233677702738, Elapsed Time = 0.641079200002423 seconds
Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 34, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68210 validation-auc:0.95181 validation-aucpr:0.95322
[1] validation-logloss:0.67115 validation-auc:0.95940 validation-aucpr:0.96118
[2] validation-logloss:0.66057 validation-auc:0.96273 validation-aucpr:0.96656
[3] validation-logloss:0.65033 validation-auc:0.96360 validation-aucpr:0.96698
[4] validation-logloss:0.64081 validation-auc:0.96418 validation-aucpr:0.96799
[5] validation-logloss:0.63121 validation-auc:0.96547 validation-aucpr:0.96980
{'best_iteration': '5', 'best_score': '0.9698008921278516'}
Trial 34, Fold 2: Log loss = 0.6312073776093977, Average precision = 0.969641383985065, ROC-AUC = 0.9654703984077407, Elapsed Time = 0.6488423999981023 seconds
Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 34, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68197 validation-auc:0.95564 validation-aucpr:0.95792
[1] validation-logloss:0.67107 validation-auc:0.96225 validation-aucpr:0.96621
[2] validation-logloss:0.66058 validation-auc:0.96390 validation-aucpr:0.96893
[3] validation-logloss:0.65033 validation-auc:0.96350 validation-aucpr:0.96671
[4] validation-logloss:0.64048 validation-auc:0.96404 validation-aucpr:0.96703
[5] validation-logloss:0.63090 validation-auc:0.96491 validation-aucpr:0.96755
{'best_iteration': '2', 'best_score': '0.968934987538646'}
Trial 34, Fold 3: Log loss = 0.6309028230443496, Average precision = 0.9679194572067682, ROC-AUC = 0.9649145155348595, Elapsed Time = 0.7778741000001901 seconds
Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 34, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68201 validation-auc:0.95272 validation-aucpr:0.95267
[1] validation-logloss:0.67121 validation-auc:0.95835 validation-aucpr:0.95833
[2] validation-logloss:0.66074 validation-auc:0.96139 validation-aucpr:0.96191
[3] validation-logloss:0.65168 validation-auc:0.96197 validation-aucpr:0.96553
[4] validation-logloss:0.64167 validation-auc:0.96350 validation-aucpr:0.96626
[5] validation-logloss:0.63191 validation-auc:0.96580 validation-aucpr:0.97126
{'best_iteration': '5', 'best_score': '0.9712628373370135'}
Trial 34, Fold 4: Log loss = 0.6319119314165961, Average precision = 0.971030496877912, ROC-AUC = 0.9657958653142293, Elapsed Time = 0.7649254999996629 seconds
Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 34, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68213 validation-auc:0.94976 validation-aucpr:0.95254
[1] validation-logloss:0.67129 validation-auc:0.95654 validation-aucpr:0.95866
[2] validation-logloss:0.66076 validation-auc:0.95844 validation-aucpr:0.95956
[3] validation-logloss:0.65057 validation-auc:0.95909 validation-aucpr:0.96011
[4] validation-logloss:0.64076 validation-auc:0.96032 validation-aucpr:0.96077
[5] validation-logloss:0.63112 validation-auc:0.96092 validation-aucpr:0.96102
{'best_iteration': '5', 'best_score': '0.9610187676182963'}
Trial 34, Fold 5: Log loss = 0.6311226248417754, Average precision = 0.9608989400500028, ROC-AUC = 0.9609236200137488, Elapsed Time = 0.7530755000007048 seconds
Optimization Progress: 35%|###5 | 35/100 [2:21:45<14:55:20, 826.47s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 35, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66299 validation-auc:0.94914 validation-aucpr:0.93790
[1] validation-logloss:0.63898 validation-auc:0.95939 validation-aucpr:0.95686
[2] validation-logloss:0.61353 validation-auc:0.96323 validation-aucpr:0.96870
[3] validation-logloss:0.59262 validation-auc:0.96437 validation-aucpr:0.96937
[4] validation-logloss:0.57084 validation-auc:0.96506 validation-aucpr:0.97017
[5] validation-logloss:0.55311 validation-auc:0.96458 validation-aucpr:0.96955
[6] validation-logloss:0.53632 validation-auc:0.96413 validation-aucpr:0.96899
[7] validation-logloss:0.52077 validation-auc:0.96405 validation-aucpr:0.96897
[8] validation-logloss:0.50527 validation-auc:0.96454 validation-aucpr:0.96942
[9] validation-logloss:0.49136 validation-auc:0.96459 validation-aucpr:0.96880
[10] validation-logloss:0.47835 validation-auc:0.96473 validation-aucpr:0.96880
[11] validation-logloss:0.46590 validation-auc:0.96515 validation-aucpr:0.97025
[12] validation-logloss:0.45412 validation-auc:0.96518 validation-aucpr:0.97015
[13] validation-logloss:0.44027 validation-auc:0.96578 validation-aucpr:0.97078
[14] validation-logloss:0.42795 validation-auc:0.96597 validation-aucpr:0.97101
[15] validation-logloss:0.41839 validation-auc:0.96600 validation-aucpr:0.97100
[16] validation-logloss:0.40945 validation-auc:0.96598 validation-aucpr:0.97097
[17] validation-logloss:0.40100 validation-auc:0.96604 validation-aucpr:0.97100
[18] validation-logloss:0.39091 validation-auc:0.96619 validation-aucpr:0.97119
[19] validation-logloss:0.38088 validation-auc:0.96644 validation-aucpr:0.97143
[20] validation-logloss:0.37389 validation-auc:0.96625 validation-aucpr:0.97126
[21] validation-logloss:0.36672 validation-auc:0.96642 validation-aucpr:0.97136
[22] validation-logloss:0.36027 validation-auc:0.96638 validation-aucpr:0.97131
[23] validation-logloss:0.35225 validation-auc:0.96640 validation-aucpr:0.97010
[24] validation-logloss:0.34465 validation-auc:0.96655 validation-aucpr:0.96938
[25] validation-logloss:0.33930 validation-auc:0.96643 validation-aucpr:0.96926
[26] validation-logloss:0.33384 validation-auc:0.96637 validation-aucpr:0.96920
[27] validation-logloss:0.32846 validation-auc:0.96654 validation-aucpr:0.97149
[28] validation-logloss:0.32191 validation-auc:0.96675 validation-aucpr:0.97172
[29] validation-logloss:0.31730 validation-auc:0.96677 validation-aucpr:0.97172
[30] validation-logloss:0.31262 validation-auc:0.96684 validation-aucpr:0.97181
[31] validation-logloss:0.30650 validation-auc:0.96725 validation-aucpr:0.97215
[32] validation-logloss:0.30122 validation-auc:0.96743 validation-aucpr:0.97222
[33] validation-logloss:0.29752 validation-auc:0.96742 validation-aucpr:0.97228
[34] validation-logloss:0.29412 validation-auc:0.96743 validation-aucpr:0.97225
[35] validation-logloss:0.28944 validation-auc:0.96760 validation-aucpr:0.97240
[36] validation-logloss:0.28606 validation-auc:0.96768 validation-aucpr:0.97252
[37] validation-logloss:0.28312 validation-auc:0.96760 validation-aucpr:0.97244
[38] validation-logloss:0.27888 validation-auc:0.96773 validation-aucpr:0.97229
[39] validation-logloss:0.27477 validation-auc:0.96780 validation-aucpr:0.97157
[40] validation-logloss:0.27189 validation-auc:0.96785 validation-aucpr:0.97158
[41] validation-logloss:0.26814 validation-auc:0.96821 validation-aucpr:0.97297
[42] validation-logloss:0.26437 validation-auc:0.96848 validation-aucpr:0.97320
[43] validation-logloss:0.26082 validation-auc:0.96871 validation-aucpr:0.97338
[44] validation-logloss:0.25867 validation-auc:0.96862 validation-aucpr:0.97327
[45] validation-logloss:0.25570 validation-auc:0.96879 validation-aucpr:0.97338
[46] validation-logloss:0.25341 validation-auc:0.96880 validation-aucpr:0.97338
[47] validation-logloss:0.25128 validation-auc:0.96887 validation-aucpr:0.97342
[48] validation-logloss:0.24854 validation-auc:0.96898 validation-aucpr:0.97351
[49] validation-logloss:0.24581 validation-auc:0.96909 validation-aucpr:0.97359
[50] validation-logloss:0.24395 validation-auc:0.96915 validation-aucpr:0.97361
[51] validation-logloss:0.24234 validation-auc:0.96914 validation-aucpr:0.97356
[52] validation-logloss:0.23986 validation-auc:0.96931 validation-aucpr:0.97374
[53] validation-logloss:0.23849 validation-auc:0.96930 validation-aucpr:0.97369
[54] validation-logloss:0.23644 validation-auc:0.96936 validation-aucpr:0.97373
[55] validation-logloss:0.23424 validation-auc:0.96956 validation-aucpr:0.97390
[56] validation-logloss:0.23228 validation-auc:0.96962 validation-aucpr:0.97378
[57] validation-logloss:0.23057 validation-auc:0.96972 validation-aucpr:0.97397
[58] validation-logloss:0.22912 validation-auc:0.96987 validation-aucpr:0.97407
[59] validation-logloss:0.22752 validation-auc:0.96996 validation-aucpr:0.97414
[60] validation-logloss:0.22577 validation-auc:0.97004 validation-aucpr:0.97396
{'best_iteration': '59', 'best_score': '0.9741431676411648'}
Trial 35, Fold 1: Log loss = 0.22577467774862892, Average precision = 0.9739730615777419, ROC-AUC = 0.9700445218737902, Elapsed Time = 1.6055976000025112 seconds
Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 35, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66261 validation-auc:0.94859 validation-aucpr:0.93978
[1] validation-logloss:0.63879 validation-auc:0.95982 validation-aucpr:0.95983
[2] validation-logloss:0.61305 validation-auc:0.96397 validation-aucpr:0.96862
[3] validation-logloss:0.58926 validation-auc:0.96611 validation-aucpr:0.97050
[4] validation-logloss:0.56954 validation-auc:0.96643 validation-aucpr:0.97023
[5] validation-logloss:0.55182 validation-auc:0.96710 validation-aucpr:0.97047
[6] validation-logloss:0.53210 validation-auc:0.96826 validation-aucpr:0.97156
[7] validation-logloss:0.51619 validation-auc:0.96785 validation-aucpr:0.97099
[8] validation-logloss:0.49923 validation-auc:0.96793 validation-aucpr:0.97126
[9] validation-logloss:0.48561 validation-auc:0.96722 validation-aucpr:0.97071
[10] validation-logloss:0.47272 validation-auc:0.96712 validation-aucpr:0.97061
[11] validation-logloss:0.46048 validation-auc:0.96695 validation-aucpr:0.97050
[12] validation-logloss:0.44716 validation-auc:0.96704 validation-aucpr:0.97059
[13] validation-logloss:0.43394 validation-auc:0.96738 validation-aucpr:0.97105
[14] validation-logloss:0.42353 validation-auc:0.96727 validation-aucpr:0.97090
[15] validation-logloss:0.41392 validation-auc:0.96723 validation-aucpr:0.97081
[16] validation-logloss:0.40440 validation-auc:0.96741 validation-aucpr:0.97100
[17] validation-logloss:0.39350 validation-auc:0.96788 validation-aucpr:0.97141
[18] validation-logloss:0.38477 validation-auc:0.96810 validation-aucpr:0.97158
[19] validation-logloss:0.37523 validation-auc:0.96844 validation-aucpr:0.97186
[20] validation-logloss:0.36598 validation-auc:0.96897 validation-aucpr:0.97231
[21] validation-logloss:0.35845 validation-auc:0.96919 validation-aucpr:0.97298
[22] validation-logloss:0.35027 validation-auc:0.96937 validation-aucpr:0.97314
[23] validation-logloss:0.34423 validation-auc:0.96939 validation-aucpr:0.97310
[24] validation-logloss:0.33807 validation-auc:0.96938 validation-aucpr:0.97308
[25] validation-logloss:0.33220 validation-auc:0.96923 validation-aucpr:0.97295
[26] validation-logloss:0.32661 validation-auc:0.96923 validation-aucpr:0.97292
[27] validation-logloss:0.32000 validation-auc:0.96944 validation-aucpr:0.97310
[28] validation-logloss:0.31496 validation-auc:0.96952 validation-aucpr:0.97315
[29] validation-logloss:0.31060 validation-auc:0.96941 validation-aucpr:0.97300
[30] validation-logloss:0.30565 validation-auc:0.96953 validation-aucpr:0.97309
[31] validation-logloss:0.30133 validation-auc:0.96959 validation-aucpr:0.97314
[32] validation-logloss:0.29609 validation-auc:0.96981 validation-aucpr:0.97333
[33] validation-logloss:0.29227 validation-auc:0.96976 validation-aucpr:0.97329
[34] validation-logloss:0.28763 validation-auc:0.96982 validation-aucpr:0.97333
[35] validation-logloss:0.28274 validation-auc:0.97004 validation-aucpr:0.97351
[36] validation-logloss:0.27824 validation-auc:0.97021 validation-aucpr:0.97363
[37] validation-logloss:0.27404 validation-auc:0.97030 validation-aucpr:0.97370
[38] validation-logloss:0.27089 validation-auc:0.97038 validation-aucpr:0.97376
[39] validation-logloss:0.26802 validation-auc:0.97033 validation-aucpr:0.97371
[40] validation-logloss:0.26510 validation-auc:0.97035 validation-aucpr:0.97369
[41] validation-logloss:0.26152 validation-auc:0.97045 validation-aucpr:0.97379
[42] validation-logloss:0.25807 validation-auc:0.97049 validation-aucpr:0.97385
[43] validation-logloss:0.25475 validation-auc:0.97067 validation-aucpr:0.97398
[44] validation-logloss:0.25226 validation-auc:0.97078 validation-aucpr:0.97409
[45] validation-logloss:0.25012 validation-auc:0.97069 validation-aucpr:0.97402
[46] validation-logloss:0.24785 validation-auc:0.97072 validation-aucpr:0.97404
[47] validation-logloss:0.24556 validation-auc:0.97085 validation-aucpr:0.97411
[48] validation-logloss:0.24263 validation-auc:0.97096 validation-aucpr:0.97419
[49] validation-logloss:0.24080 validation-auc:0.97083 validation-aucpr:0.97409
[50] validation-logloss:0.23812 validation-auc:0.97099 validation-aucpr:0.97421
[51] validation-logloss:0.23563 validation-auc:0.97109 validation-aucpr:0.97427
[52] validation-logloss:0.23325 validation-auc:0.97119 validation-aucpr:0.97437
[53] validation-logloss:0.23165 validation-auc:0.97121 validation-aucpr:0.97438
[54] validation-logloss:0.22932 validation-auc:0.97135 validation-aucpr:0.97449
[55] validation-logloss:0.22703 validation-auc:0.97149 validation-aucpr:0.97461
[56] validation-logloss:0.22506 validation-auc:0.97147 validation-aucpr:0.97452
[57] validation-logloss:0.22372 validation-auc:0.97154 validation-aucpr:0.97458
[58] validation-logloss:0.22178 validation-auc:0.97164 validation-aucpr:0.97463
[59] validation-logloss:0.22058 validation-auc:0.97161 validation-aucpr:0.97462
[60] validation-logloss:0.21927 validation-auc:0.97172 validation-aucpr:0.97503
{'best_iteration': '60', 'best_score': '0.9750271561781708'}
Trial 35, Fold 2: Log loss = 0.21927145707866819, Average precision = 0.9750233171169024, ROC-AUC = 0.9717249110075112, Elapsed Time = 1.8591325000015786 seconds
Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 35, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66260 validation-auc:0.95283 validation-aucpr:0.93270
[1] validation-logloss:0.63823 validation-auc:0.96152 validation-aucpr:0.96266
[2] validation-logloss:0.61253 validation-auc:0.96408 validation-aucpr:0.96896
[3] validation-logloss:0.59181 validation-auc:0.96459 validation-aucpr:0.96976
[4] validation-logloss:0.56957 validation-auc:0.96579 validation-aucpr:0.97085
[5] validation-logloss:0.54908 validation-auc:0.96653 validation-aucpr:0.97138
[6] validation-logloss:0.52994 validation-auc:0.96730 validation-aucpr:0.97207
[7] validation-logloss:0.51413 validation-auc:0.96684 validation-aucpr:0.97162
[8] validation-logloss:0.49927 validation-auc:0.96672 validation-aucpr:0.97147
[9] validation-logloss:0.48541 validation-auc:0.96688 validation-aucpr:0.97153
[10] validation-logloss:0.47233 validation-auc:0.96691 validation-aucpr:0.97154
[11] validation-logloss:0.45849 validation-auc:0.96728 validation-aucpr:0.97189
[12] validation-logloss:0.44407 validation-auc:0.96802 validation-aucpr:0.97252
[13] validation-logloss:0.43130 validation-auc:0.96804 validation-aucpr:0.97261
[14] validation-logloss:0.42121 validation-auc:0.96815 validation-aucpr:0.97274
[15] validation-logloss:0.40972 validation-auc:0.96853 validation-aucpr:0.97303
[16] validation-logloss:0.39902 validation-auc:0.96898 validation-aucpr:0.97343
[17] validation-logloss:0.39055 validation-auc:0.96862 validation-aucpr:0.97313
[18] validation-logloss:0.38225 validation-auc:0.96873 validation-aucpr:0.97313
[19] validation-logloss:0.37452 validation-auc:0.96863 validation-aucpr:0.97300
[20] validation-logloss:0.36668 validation-auc:0.96891 validation-aucpr:0.97326
[21] validation-logloss:0.35786 validation-auc:0.96914 validation-aucpr:0.97347
[22] validation-logloss:0.35073 validation-auc:0.96913 validation-aucpr:0.97346
[23] validation-logloss:0.34449 validation-auc:0.96912 validation-aucpr:0.97347
[24] validation-logloss:0.33722 validation-auc:0.96923 validation-aucpr:0.97356
[25] validation-logloss:0.33148 validation-auc:0.96930 validation-aucpr:0.97361
[26] validation-logloss:0.32607 validation-auc:0.96938 validation-aucpr:0.97365
[27] validation-logloss:0.32062 validation-auc:0.96956 validation-aucpr:0.97379
[28] validation-logloss:0.31548 validation-auc:0.96969 validation-aucpr:0.97389
[29] validation-logloss:0.31120 validation-auc:0.96952 validation-aucpr:0.97376
[30] validation-logloss:0.30652 validation-auc:0.96960 validation-aucpr:0.97379
[31] validation-logloss:0.30069 validation-auc:0.96985 validation-aucpr:0.97400
[32] validation-logloss:0.29542 validation-auc:0.96996 validation-aucpr:0.97413
[33] validation-logloss:0.28986 validation-auc:0.97030 validation-aucpr:0.97438
[34] validation-logloss:0.28615 validation-auc:0.97037 validation-aucpr:0.97443
[35] validation-logloss:0.28120 validation-auc:0.97064 validation-aucpr:0.97467
[36] validation-logloss:0.27658 validation-auc:0.97075 validation-aucpr:0.97478
[37] validation-logloss:0.27228 validation-auc:0.97084 validation-aucpr:0.97487
[38] validation-logloss:0.26845 validation-auc:0.97087 validation-aucpr:0.97492
[39] validation-logloss:0.26457 validation-auc:0.97100 validation-aucpr:0.97504
[40] validation-logloss:0.26170 validation-auc:0.97095 validation-aucpr:0.97501
[41] validation-logloss:0.25895 validation-auc:0.97102 validation-aucpr:0.97507
[42] validation-logloss:0.25632 validation-auc:0.97106 validation-aucpr:0.97509
[43] validation-logloss:0.25296 validation-auc:0.97115 validation-aucpr:0.97518
[44] validation-logloss:0.25074 validation-auc:0.97116 validation-aucpr:0.97523
[45] validation-logloss:0.24847 validation-auc:0.97115 validation-aucpr:0.97520
[46] validation-logloss:0.24541 validation-auc:0.97131 validation-aucpr:0.97534
[47] validation-logloss:0.24273 validation-auc:0.97134 validation-aucpr:0.97539
[48] validation-logloss:0.23982 validation-auc:0.97152 validation-aucpr:0.97553
[49] validation-logloss:0.23798 validation-auc:0.97152 validation-aucpr:0.97547
[50] validation-logloss:0.23599 validation-auc:0.97143 validation-aucpr:0.97539
[51] validation-logloss:0.23368 validation-auc:0.97152 validation-aucpr:0.97546
[52] validation-logloss:0.23136 validation-auc:0.97160 validation-aucpr:0.97553
[53] validation-logloss:0.22918 validation-auc:0.97174 validation-aucpr:0.97565
[54] validation-logloss:0.22728 validation-auc:0.97179 validation-aucpr:0.97573
[55] validation-logloss:0.22531 validation-auc:0.97181 validation-aucpr:0.97576
[56] validation-logloss:0.22389 validation-auc:0.97192 validation-aucpr:0.97587
[57] validation-logloss:0.22264 validation-auc:0.97193 validation-aucpr:0.97588
[58] validation-logloss:0.22086 validation-auc:0.97206 validation-aucpr:0.97599
[59] validation-logloss:0.21965 validation-auc:0.97204 validation-aucpr:0.97598
[60] validation-logloss:0.21796 validation-auc:0.97219 validation-aucpr:0.97610
{'best_iteration': '60', 'best_score': '0.9760955890250614'}
Trial 35, Fold 3: Log loss = 0.21795609703426969, Average precision = 0.976099486575763, ROC-AUC = 0.9721864736928277, Elapsed Time = 1.8033400999993319 seconds
Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 35, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66301 validation-auc:0.94282 validation-aucpr:0.92186
[1] validation-logloss:0.63903 validation-auc:0.95582 validation-aucpr:0.95447
[2] validation-logloss:0.61316 validation-auc:0.96062 validation-aucpr:0.96570
[3] validation-logloss:0.59228 validation-auc:0.96298 validation-aucpr:0.96858
[4] validation-logloss:0.57368 validation-auc:0.96267 validation-aucpr:0.96830
[5] validation-logloss:0.55249 validation-auc:0.96377 validation-aucpr:0.96944
[6] validation-logloss:0.53476 validation-auc:0.96462 validation-aucpr:0.97020
[7] validation-logloss:0.51660 validation-auc:0.96565 validation-aucpr:0.97095
[8] validation-logloss:0.50056 validation-auc:0.96608 validation-aucpr:0.97129
[9] validation-logloss:0.48715 validation-auc:0.96573 validation-aucpr:0.97089
[10] validation-logloss:0.47515 validation-auc:0.96500 validation-aucpr:0.97036
[11] validation-logloss:0.46019 validation-auc:0.96547 validation-aucpr:0.97085
[12] validation-logloss:0.44636 validation-auc:0.96597 validation-aucpr:0.97138
[13] validation-logloss:0.43538 validation-auc:0.96586 validation-aucpr:0.97128
[14] validation-logloss:0.42275 validation-auc:0.96628 validation-aucpr:0.97165
[15] validation-logloss:0.41109 validation-auc:0.96656 validation-aucpr:0.97191
[16] validation-logloss:0.40068 validation-auc:0.96705 validation-aucpr:0.97228
[17] validation-logloss:0.39193 validation-auc:0.96724 validation-aucpr:0.97245
[18] validation-logloss:0.38182 validation-auc:0.96751 validation-aucpr:0.97271
[19] validation-logloss:0.37227 validation-auc:0.96781 validation-aucpr:0.97297
[20] validation-logloss:0.36498 validation-auc:0.96778 validation-aucpr:0.97296
[21] validation-logloss:0.35633 validation-auc:0.96824 validation-aucpr:0.97328
[22] validation-logloss:0.34981 validation-auc:0.96829 validation-aucpr:0.97325
[23] validation-logloss:0.34193 validation-auc:0.96864 validation-aucpr:0.97354
[24] validation-logloss:0.33579 validation-auc:0.96868 validation-aucpr:0.97353
[25] validation-logloss:0.32875 validation-auc:0.96892 validation-aucpr:0.97376
[26] validation-logloss:0.32210 validation-auc:0.96894 validation-aucpr:0.97380
[27] validation-logloss:0.31715 validation-auc:0.96884 validation-aucpr:0.97372
[28] validation-logloss:0.31096 validation-auc:0.96905 validation-aucpr:0.97388
[29] validation-logloss:0.30498 validation-auc:0.96925 validation-aucpr:0.97406
[30] validation-logloss:0.30051 validation-auc:0.96929 validation-aucpr:0.97407
[31] validation-logloss:0.29631 validation-auc:0.96926 validation-aucpr:0.97400
[32] validation-logloss:0.29247 validation-auc:0.96916 validation-aucpr:0.97391
[33] validation-logloss:0.28859 validation-auc:0.96915 validation-aucpr:0.97390
[34] validation-logloss:0.28427 validation-auc:0.96936 validation-aucpr:0.97405
[35] validation-logloss:0.28084 validation-auc:0.96915 validation-aucpr:0.97390
[36] validation-logloss:0.27657 validation-auc:0.96933 validation-aucpr:0.97406
[37] validation-logloss:0.27340 validation-auc:0.96922 validation-aucpr:0.97397
[38] validation-logloss:0.26995 validation-auc:0.96925 validation-aucpr:0.97402
[39] validation-logloss:0.26634 validation-auc:0.96928 validation-aucpr:0.97406
[40] validation-logloss:0.26382 validation-auc:0.96925 validation-aucpr:0.97404
[41] validation-logloss:0.26121 validation-auc:0.96926 validation-aucpr:0.97401
[42] validation-logloss:0.25830 validation-auc:0.96928 validation-aucpr:0.97404
[43] validation-logloss:0.25512 validation-auc:0.96937 validation-aucpr:0.97413
[44] validation-logloss:0.25287 validation-auc:0.96937 validation-aucpr:0.97412
[45] validation-logloss:0.25029 validation-auc:0.96945 validation-aucpr:0.97421
[46] validation-logloss:0.24812 validation-auc:0.96950 validation-aucpr:0.97425
[47] validation-logloss:0.24534 validation-auc:0.96955 validation-aucpr:0.97429
[48] validation-logloss:0.24248 validation-auc:0.96972 validation-aucpr:0.97445
[49] validation-logloss:0.24045 validation-auc:0.96981 validation-aucpr:0.97453
[50] validation-logloss:0.23790 validation-auc:0.97003 validation-aucpr:0.97468
[51] validation-logloss:0.23557 validation-auc:0.97013 validation-aucpr:0.97479
[52] validation-logloss:0.23350 validation-auc:0.97004 validation-aucpr:0.97473
[53] validation-logloss:0.23181 validation-auc:0.97014 validation-aucpr:0.97478
[54] validation-logloss:0.23027 validation-auc:0.97018 validation-aucpr:0.97480
[55] validation-logloss:0.22864 validation-auc:0.97021 validation-aucpr:0.97482
[56] validation-logloss:0.22677 validation-auc:0.97021 validation-aucpr:0.97483
[57] validation-logloss:0.22553 validation-auc:0.97026 validation-aucpr:0.97485
[58] validation-logloss:0.22373 validation-auc:0.97043 validation-aucpr:0.97497
[59] validation-logloss:0.22182 validation-auc:0.97058 validation-aucpr:0.97510
[60] validation-logloss:0.22012 validation-auc:0.97073 validation-aucpr:0.97523
{'best_iteration': '60', 'best_score': '0.9752255955912826'}
Trial 35, Fold 4: Log loss = 0.22011990067822448, Average precision = 0.9752247920061735, ROC-AUC = 0.9707328602664338, Elapsed Time = 1.9176872999996704 seconds
Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 35, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66319 validation-auc:0.94499 validation-aucpr:0.93567
[1] validation-logloss:0.63932 validation-auc:0.95795 validation-aucpr:0.96326
[2] validation-logloss:0.61763 validation-auc:0.95886 validation-aucpr:0.96384
[3] validation-logloss:0.59363 validation-auc:0.96106 validation-aucpr:0.96636
[4] validation-logloss:0.57361 validation-auc:0.96176 validation-aucpr:0.96686
[5] validation-logloss:0.55533 validation-auc:0.96193 validation-aucpr:0.96692
[6] validation-logloss:0.53569 validation-auc:0.96279 validation-aucpr:0.96770
[7] validation-logloss:0.52028 validation-auc:0.96292 validation-aucpr:0.96761
[8] validation-logloss:0.50606 validation-auc:0.96280 validation-aucpr:0.96730
[9] validation-logloss:0.49035 validation-auc:0.96285 validation-aucpr:0.96737
[10] validation-logloss:0.47752 validation-auc:0.96294 validation-aucpr:0.96783
[11] validation-logloss:0.46509 validation-auc:0.96295 validation-aucpr:0.96784
[12] validation-logloss:0.45079 validation-auc:0.96386 validation-aucpr:0.96867
[13] validation-logloss:0.43756 validation-auc:0.96434 validation-aucpr:0.96906
[14] validation-logloss:0.42528 validation-auc:0.96485 validation-aucpr:0.96943
[15] validation-logloss:0.41590 validation-auc:0.96468 validation-aucpr:0.96926
[16] validation-logloss:0.40674 validation-auc:0.96483 validation-aucpr:0.96931
[17] validation-logloss:0.39652 validation-auc:0.96496 validation-aucpr:0.96938
[18] validation-logloss:0.38922 validation-auc:0.96470 validation-aucpr:0.96923
[19] validation-logloss:0.38148 validation-auc:0.96490 validation-aucpr:0.96931
[20] validation-logloss:0.37235 validation-auc:0.96524 validation-aucpr:0.96961
[21] validation-logloss:0.36377 validation-auc:0.96566 validation-aucpr:0.96991
[22] validation-logloss:0.35592 validation-auc:0.96576 validation-aucpr:0.97002
[23] validation-logloss:0.34805 validation-auc:0.96609 validation-aucpr:0.97036
[24] validation-logloss:0.34070 validation-auc:0.96642 validation-aucpr:0.97070
[25] validation-logloss:0.33506 validation-auc:0.96651 validation-aucpr:0.97075
[26] validation-logloss:0.32833 validation-auc:0.96683 validation-aucpr:0.97101
[27] validation-logloss:0.32313 validation-auc:0.96670 validation-aucpr:0.97078
[28] validation-logloss:0.31723 validation-auc:0.96687 validation-aucpr:0.97094
[29] validation-logloss:0.31288 validation-auc:0.96674 validation-aucpr:0.97078
[30] validation-logloss:0.30735 validation-auc:0.96694 validation-aucpr:0.97096
[31] validation-logloss:0.30260 validation-auc:0.96702 validation-aucpr:0.97096
[32] validation-logloss:0.29824 validation-auc:0.96730 validation-aucpr:0.97115
[33] validation-logloss:0.29336 validation-auc:0.96755 validation-aucpr:0.97134
[34] validation-logloss:0.28961 validation-auc:0.96759 validation-aucpr:0.97137
[35] validation-logloss:0.28632 validation-auc:0.96756 validation-aucpr:0.97129
[36] validation-logloss:0.28333 validation-auc:0.96745 validation-aucpr:0.97118
[37] validation-logloss:0.27999 validation-auc:0.96759 validation-aucpr:0.97128
[38] validation-logloss:0.27603 validation-auc:0.96777 validation-aucpr:0.97154
[39] validation-logloss:0.27261 validation-auc:0.96789 validation-aucpr:0.97165
[40] validation-logloss:0.26908 validation-auc:0.96808 validation-aucpr:0.97181
[41] validation-logloss:0.26554 validation-auc:0.96833 validation-aucpr:0.97200
[42] validation-logloss:0.26292 validation-auc:0.96836 validation-aucpr:0.97204
[43] validation-logloss:0.26004 validation-auc:0.96842 validation-aucpr:0.97207
[44] validation-logloss:0.25794 validation-auc:0.96831 validation-aucpr:0.97202
[45] validation-logloss:0.25498 validation-auc:0.96855 validation-aucpr:0.97224
[46] validation-logloss:0.25197 validation-auc:0.96882 validation-aucpr:0.97245
[47] validation-logloss:0.24991 validation-auc:0.96893 validation-aucpr:0.97248
[48] validation-logloss:0.24722 validation-auc:0.96916 validation-aucpr:0.97303
[49] validation-logloss:0.24475 validation-auc:0.96933 validation-aucpr:0.97310
[50] validation-logloss:0.24300 validation-auc:0.96933 validation-aucpr:0.97310
[51] validation-logloss:0.24116 validation-auc:0.96942 validation-aucpr:0.97316
[52] validation-logloss:0.23944 validation-auc:0.96944 validation-aucpr:0.97322
[53] validation-logloss:0.23726 validation-auc:0.96960 validation-aucpr:0.97338
[54] validation-logloss:0.23515 validation-auc:0.96985 validation-aucpr:0.97373
[55] validation-logloss:0.23370 validation-auc:0.96995 validation-aucpr:0.97380
[56] validation-logloss:0.23228 validation-auc:0.96998 validation-aucpr:0.97381
[57] validation-logloss:0.23042 validation-auc:0.97012 validation-aucpr:0.97393
[58] validation-logloss:0.22864 validation-auc:0.97019 validation-aucpr:0.97394
[59] validation-logloss:0.22669 validation-auc:0.97041 validation-aucpr:0.97414
[60] validation-logloss:0.22562 validation-auc:0.97026 validation-aucpr:0.97401
{'best_iteration': '59', 'best_score': '0.9741436480585423'}
Trial 35, Fold 5: Log loss = 0.22562292321361685, Average precision = 0.9740183756693583, ROC-AUC = 0.9702630473960947, Elapsed Time = 1.9858647999972163 seconds
Optimization Progress: 36%|###6 | 36/100 [2:22:02<10:22:32, 583.63s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 36, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.62882 validation-auc:0.93107 validation-aucpr:0.88995
[1] validation-logloss:0.57594 validation-auc:0.95804 validation-aucpr:0.94536
[2] validation-logloss:0.53013 validation-auc:0.96362 validation-aucpr:0.96144
[3] validation-logloss:0.49113 validation-auc:0.96601 validation-aucpr:0.96749
[4] validation-logloss:0.45755 validation-auc:0.96711 validation-aucpr:0.97002
[5] validation-logloss:0.42871 validation-auc:0.96667 validation-aucpr:0.96973
[6] validation-logloss:0.40344 validation-auc:0.96731 validation-aucpr:0.97032
[7] validation-logloss:0.38041 validation-auc:0.96826 validation-aucpr:0.97101
[8] validation-logloss:0.36071 validation-auc:0.96869 validation-aucpr:0.97158
[9] validation-logloss:0.34296 validation-auc:0.96910 validation-aucpr:0.97160
[10] validation-logloss:0.32781 validation-auc:0.96963 validation-aucpr:0.97423
[11] validation-logloss:0.31392 validation-auc:0.96985 validation-aucpr:0.97439
[12] validation-logloss:0.30155 validation-auc:0.96985 validation-aucpr:0.97434
[13] validation-logloss:0.29024 validation-auc:0.97019 validation-aucpr:0.97461
[14] validation-logloss:0.28052 validation-auc:0.97022 validation-aucpr:0.97461
[15] validation-logloss:0.27164 validation-auc:0.97014 validation-aucpr:0.97456
[16] validation-logloss:0.26314 validation-auc:0.97074 validation-aucpr:0.97493
[17] validation-logloss:0.25685 validation-auc:0.97086 validation-aucpr:0.97499
[18] validation-logloss:0.24995 validation-auc:0.97108 validation-aucpr:0.97519
[19] validation-logloss:0.24424 validation-auc:0.97108 validation-aucpr:0.97519
[20] validation-logloss:0.23914 validation-auc:0.97104 validation-aucpr:0.97512
[21] validation-logloss:0.23465 validation-auc:0.97108 validation-aucpr:0.97517
[22] validation-logloss:0.23038 validation-auc:0.97117 validation-aucpr:0.97521
[23] validation-logloss:0.22614 validation-auc:0.97137 validation-aucpr:0.97528
[24] validation-logloss:0.22267 validation-auc:0.97151 validation-aucpr:0.97535
[25] validation-logloss:0.21939 validation-auc:0.97155 validation-aucpr:0.97536
[26] validation-logloss:0.21622 validation-auc:0.97172 validation-aucpr:0.97543
[27] validation-logloss:0.21372 validation-auc:0.97176 validation-aucpr:0.97543
[28] validation-logloss:0.21157 validation-auc:0.97177 validation-aucpr:0.97542
[29] validation-logloss:0.20965 validation-auc:0.97172 validation-aucpr:0.97538
[30] validation-logloss:0.20771 validation-auc:0.97182 validation-aucpr:0.97548
[31] validation-logloss:0.20587 validation-auc:0.97189 validation-aucpr:0.97554
[32] validation-logloss:0.20471 validation-auc:0.97175 validation-aucpr:0.97536
[33] validation-logloss:0.20335 validation-auc:0.97179 validation-aucpr:0.97541
[34] validation-logloss:0.20202 validation-auc:0.97200 validation-aucpr:0.97578
[35] validation-logloss:0.20077 validation-auc:0.97205 validation-aucpr:0.97586
[36] validation-logloss:0.19983 validation-auc:0.97201 validation-aucpr:0.97582
[37] validation-logloss:0.19879 validation-auc:0.97223 validation-aucpr:0.97625
[38] validation-logloss:0.19818 validation-auc:0.97228 validation-aucpr:0.97627
[39] validation-logloss:0.19741 validation-auc:0.97232 validation-aucpr:0.97628
[40] validation-logloss:0.19680 validation-auc:0.97239 validation-aucpr:0.97638
[41] validation-logloss:0.19655 validation-auc:0.97233 validation-aucpr:0.97631
[42] validation-logloss:0.19579 validation-auc:0.97242 validation-aucpr:0.97640
[43] validation-logloss:0.19570 validation-auc:0.97227 validation-aucpr:0.97630
[44] validation-logloss:0.19512 validation-auc:0.97241 validation-aucpr:0.97640
[45] validation-logloss:0.19445 validation-auc:0.97254 validation-aucpr:0.97652
[46] validation-logloss:0.19409 validation-auc:0.97253 validation-aucpr:0.97652
[47] validation-logloss:0.19421 validation-auc:0.97248 validation-aucpr:0.97653
[48] validation-logloss:0.19414 validation-auc:0.97242 validation-aucpr:0.97646
[49] validation-logloss:0.19399 validation-auc:0.97239 validation-aucpr:0.97640
[50] validation-logloss:0.19410 validation-auc:0.97238 validation-aucpr:0.97638
[51] validation-logloss:0.19412 validation-auc:0.97235 validation-aucpr:0.97633
[52] validation-logloss:0.19433 validation-auc:0.97224 validation-aucpr:0.97624
[53] validation-logloss:0.19445 validation-auc:0.97225 validation-aucpr:0.97622
[54] validation-logloss:0.19440 validation-auc:0.97225 validation-aucpr:0.97621
[55] validation-logloss:0.19401 validation-auc:0.97239 validation-aucpr:0.97628
[56] validation-logloss:0.19375 validation-auc:0.97249 validation-aucpr:0.97638
[57] validation-logloss:0.19420 validation-auc:0.97239 validation-aucpr:0.97624
[58] validation-logloss:0.19450 validation-auc:0.97230 validation-aucpr:0.97621
[59] validation-logloss:0.19450 validation-auc:0.97239 validation-aucpr:0.97631
[60] validation-logloss:0.19462 validation-auc:0.97241 validation-aucpr:0.97632
{'best_iteration': '47', 'best_score': '0.9765331381787777'}
Trial 36, Fold 1: Log loss = 0.19461938434271853, Average precision = 0.9763247913170843, ROC-AUC = 0.9724131070675786, Elapsed Time = 18.183157800001936 seconds
Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 36, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.62898 validation-auc:0.93228 validation-aucpr:0.89005
[1] validation-logloss:0.57487 validation-auc:0.96346 validation-aucpr:0.95779
[2] validation-logloss:0.52876 validation-auc:0.96766 validation-aucpr:0.96886
[3] validation-logloss:0.48957 validation-auc:0.96903 validation-aucpr:0.97222
[4] validation-logloss:0.45558 validation-auc:0.96988 validation-aucpr:0.97319
[5] validation-logloss:0.42594 validation-auc:0.97049 validation-aucpr:0.97372
[6] validation-logloss:0.40042 validation-auc:0.97067 validation-aucpr:0.97366
[7] validation-logloss:0.37774 validation-auc:0.97056 validation-aucpr:0.97366
[8] validation-logloss:0.35764 validation-auc:0.97058 validation-aucpr:0.97373
[9] validation-logloss:0.33995 validation-auc:0.97072 validation-aucpr:0.97388
[10] validation-logloss:0.32435 validation-auc:0.97072 validation-aucpr:0.97397
[11] validation-logloss:0.31019 validation-auc:0.97094 validation-aucpr:0.97417
[12] validation-logloss:0.29750 validation-auc:0.97092 validation-aucpr:0.97415
[13] validation-logloss:0.28614 validation-auc:0.97111 validation-aucpr:0.97431
[14] validation-logloss:0.27602 validation-auc:0.97132 validation-aucpr:0.97448
[15] validation-logloss:0.26686 validation-auc:0.97143 validation-aucpr:0.97457
[16] validation-logloss:0.25854 validation-auc:0.97170 validation-aucpr:0.97473
[17] validation-logloss:0.25114 validation-auc:0.97165 validation-aucpr:0.97469
[18] validation-logloss:0.24463 validation-auc:0.97166 validation-aucpr:0.97462
[19] validation-logloss:0.23913 validation-auc:0.97150 validation-aucpr:0.97448
[20] validation-logloss:0.23364 validation-auc:0.97162 validation-aucpr:0.97449
[21] validation-logloss:0.22870 validation-auc:0.97175 validation-aucpr:0.97463
[22] validation-logloss:0.22466 validation-auc:0.97171 validation-aucpr:0.97453
[23] validation-logloss:0.22048 validation-auc:0.97193 validation-aucpr:0.97480
[24] validation-logloss:0.21650 validation-auc:0.97216 validation-aucpr:0.97498
[25] validation-logloss:0.21295 validation-auc:0.97230 validation-aucpr:0.97510
[26] validation-logloss:0.20982 validation-auc:0.97233 validation-aucpr:0.97499
[27] validation-logloss:0.20727 validation-auc:0.97226 validation-aucpr:0.97492
[28] validation-logloss:0.20469 validation-auc:0.97229 validation-aucpr:0.97496
[29] validation-logloss:0.20227 validation-auc:0.97250 validation-aucpr:0.97504
[30] validation-logloss:0.20029 validation-auc:0.97258 validation-aucpr:0.97501
[31] validation-logloss:0.19834 validation-auc:0.97273 validation-aucpr:0.97508
[32] validation-logloss:0.19651 validation-auc:0.97279 validation-aucpr:0.97512
[33] validation-logloss:0.19503 validation-auc:0.97273 validation-aucpr:0.97508
[34] validation-logloss:0.19346 validation-auc:0.97283 validation-aucpr:0.97515
[35] validation-logloss:0.19209 validation-auc:0.97288 validation-aucpr:0.97517
[36] validation-logloss:0.19092 validation-auc:0.97298 validation-aucpr:0.97537
[37] validation-logloss:0.19027 validation-auc:0.97285 validation-aucpr:0.97520
[38] validation-logloss:0.18949 validation-auc:0.97287 validation-aucpr:0.97527
[39] validation-logloss:0.18834 validation-auc:0.97303 validation-aucpr:0.97545
[40] validation-logloss:0.18734 validation-auc:0.97313 validation-aucpr:0.97552
[41] validation-logloss:0.18682 validation-auc:0.97300 validation-aucpr:0.97523
[42] validation-logloss:0.18600 validation-auc:0.97300 validation-aucpr:0.97527
[43] validation-logloss:0.18513 validation-auc:0.97312 validation-aucpr:0.97511
[44] validation-logloss:0.18442 validation-auc:0.97317 validation-aucpr:0.97514
[45] validation-logloss:0.18417 validation-auc:0.97312 validation-aucpr:0.97512
[46] validation-logloss:0.18365 validation-auc:0.97311 validation-aucpr:0.97518
[47] validation-logloss:0.18316 validation-auc:0.97310 validation-aucpr:0.97513
[48] validation-logloss:0.18263 validation-auc:0.97319 validation-aucpr:0.97527
[49] validation-logloss:0.18203 validation-auc:0.97330 validation-aucpr:0.97539
[50] validation-logloss:0.18156 validation-auc:0.97351 validation-aucpr:0.97596
[51] validation-logloss:0.18130 validation-auc:0.97356 validation-aucpr:0.97603
[52] validation-logloss:0.18101 validation-auc:0.97360 validation-aucpr:0.97599
[53] validation-logloss:0.18080 validation-auc:0.97366 validation-aucpr:0.97597
[54] validation-logloss:0.18062 validation-auc:0.97370 validation-aucpr:0.97594
[55] validation-logloss:0.18033 validation-auc:0.97374 validation-aucpr:0.97601
[56] validation-logloss:0.18008 validation-auc:0.97378 validation-aucpr:0.97660
[57] validation-logloss:0.17958 validation-auc:0.97388 validation-aucpr:0.97668
[58] validation-logloss:0.18019 validation-auc:0.97355 validation-aucpr:0.97621
[59] validation-logloss:0.18002 validation-auc:0.97355 validation-aucpr:0.97620
[60] validation-logloss:0.17990 validation-auc:0.97354 validation-aucpr:0.97615
{'best_iteration': '57', 'best_score': '0.9766846401509044'}
Trial 36, Fold 2: Log loss = 0.17990457088399403, Average precision = 0.9761546471904696, ROC-AUC = 0.9735385246655404, Elapsed Time = 18.41648890000215 seconds
Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 36, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.62844 validation-auc:0.93154 validation-aucpr:0.89432
[1] validation-logloss:0.57382 validation-auc:0.96068 validation-aucpr:0.95541
[2] validation-logloss:0.52780 validation-auc:0.96813 validation-aucpr:0.97256
[3] validation-logloss:0.48854 validation-auc:0.96884 validation-aucpr:0.97302
[4] validation-logloss:0.45469 validation-auc:0.96933 validation-aucpr:0.97345
[5] validation-logloss:0.42741 validation-auc:0.97040 validation-aucpr:0.97430
[6] validation-logloss:0.40123 validation-auc:0.97079 validation-aucpr:0.97480
[7] validation-logloss:0.37826 validation-auc:0.97124 validation-aucpr:0.97489
[8] validation-logloss:0.35859 validation-auc:0.97111 validation-aucpr:0.97500
[9] validation-logloss:0.34059 validation-auc:0.97112 validation-aucpr:0.97494
[10] validation-logloss:0.32423 validation-auc:0.97145 validation-aucpr:0.97517
[11] validation-logloss:0.30996 validation-auc:0.97150 validation-aucpr:0.97527
[12] validation-logloss:0.29720 validation-auc:0.97180 validation-aucpr:0.97552
[13] validation-logloss:0.28679 validation-auc:0.97161 validation-aucpr:0.97450
[14] validation-logloss:0.27664 validation-auc:0.97167 validation-aucpr:0.97471
[15] validation-logloss:0.26764 validation-auc:0.97175 validation-aucpr:0.97496
[16] validation-logloss:0.25914 validation-auc:0.97208 validation-aucpr:0.97522
[17] validation-logloss:0.25169 validation-auc:0.97210 validation-aucpr:0.97526
[18] validation-logloss:0.24488 validation-auc:0.97216 validation-aucpr:0.97512
[19] validation-logloss:0.23906 validation-auc:0.97217 validation-aucpr:0.97505
[20] validation-logloss:0.23373 validation-auc:0.97189 validation-aucpr:0.97315
[21] validation-logloss:0.22861 validation-auc:0.97207 validation-aucpr:0.97326
[22] validation-logloss:0.22423 validation-auc:0.97196 validation-aucpr:0.97310
[23] validation-logloss:0.22045 validation-auc:0.97197 validation-aucpr:0.97317
[24] validation-logloss:0.21691 validation-auc:0.97202 validation-aucpr:0.97319
[25] validation-logloss:0.21342 validation-auc:0.97227 validation-aucpr:0.97396
[26] validation-logloss:0.21050 validation-auc:0.97236 validation-aucpr:0.97406
[27] validation-logloss:0.20817 validation-auc:0.97228 validation-aucpr:0.97395
[28] validation-logloss:0.20590 validation-auc:0.97235 validation-aucpr:0.97431
[29] validation-logloss:0.20358 validation-auc:0.97249 validation-aucpr:0.97441
[30] validation-logloss:0.20130 validation-auc:0.97263 validation-aucpr:0.97454
[31] validation-logloss:0.19972 validation-auc:0.97261 validation-aucpr:0.97452
[32] validation-logloss:0.19800 validation-auc:0.97279 validation-aucpr:0.97463
[33] validation-logloss:0.19725 validation-auc:0.97258 validation-aucpr:0.97449
[34] validation-logloss:0.19583 validation-auc:0.97276 validation-aucpr:0.97495
[35] validation-logloss:0.19457 validation-auc:0.97283 validation-aucpr:0.97506
[36] validation-logloss:0.19368 validation-auc:0.97285 validation-aucpr:0.97503
[37] validation-logloss:0.19274 validation-auc:0.97293 validation-aucpr:0.97504
[38] validation-logloss:0.19166 validation-auc:0.97299 validation-aucpr:0.97507
[39] validation-logloss:0.19095 validation-auc:0.97301 validation-aucpr:0.97509
[40] validation-logloss:0.18987 validation-auc:0.97312 validation-aucpr:0.97531
[41] validation-logloss:0.18945 validation-auc:0.97303 validation-aucpr:0.97512
[42] validation-logloss:0.18894 validation-auc:0.97305 validation-aucpr:0.97495
[43] validation-logloss:0.18837 validation-auc:0.97302 validation-aucpr:0.97489
[44] validation-logloss:0.18801 validation-auc:0.97299 validation-aucpr:0.97490
[45] validation-logloss:0.18761 validation-auc:0.97310 validation-aucpr:0.97511
[46] validation-logloss:0.18711 validation-auc:0.97321 validation-aucpr:0.97525
[47] validation-logloss:0.18673 validation-auc:0.97330 validation-aucpr:0.97523
[48] validation-logloss:0.18641 validation-auc:0.97339 validation-aucpr:0.97572
[49] validation-logloss:0.18576 validation-auc:0.97354 validation-aucpr:0.97591
[50] validation-logloss:0.18567 validation-auc:0.97354 validation-aucpr:0.97591
[51] validation-logloss:0.18538 validation-auc:0.97364 validation-aucpr:0.97572
[52] validation-logloss:0.18547 validation-auc:0.97360 validation-aucpr:0.97567
[53] validation-logloss:0.18523 validation-auc:0.97369 validation-aucpr:0.97577
[54] validation-logloss:0.18514 validation-auc:0.97369 validation-aucpr:0.97578
[55] validation-logloss:0.18505 validation-auc:0.97380 validation-aucpr:0.97587
[56] validation-logloss:0.18471 validation-auc:0.97397 validation-aucpr:0.97609
[57] validation-logloss:0.18473 validation-auc:0.97393 validation-aucpr:0.97602
[58] validation-logloss:0.18511 validation-auc:0.97378 validation-aucpr:0.97558
[59] validation-logloss:0.18507 validation-auc:0.97396 validation-aucpr:0.97735
[60] validation-logloss:0.18504 validation-auc:0.97401 validation-aucpr:0.97734
{'best_iteration': '59', 'best_score': '0.9773455256355663'}
Trial 36, Fold 3: Log loss = 0.1850426326602382, Average precision = 0.9773415631367709, ROC-AUC = 0.9740058364072388, Elapsed Time = 17.800311499999225 seconds
Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 36, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.62889 validation-auc:0.92818 validation-aucpr:0.88664
[1] validation-logloss:0.57557 validation-auc:0.95670 validation-aucpr:0.94196
[2] validation-logloss:0.52984 validation-auc:0.96495 validation-aucpr:0.96483
[3] validation-logloss:0.49059 validation-auc:0.96722 validation-aucpr:0.96900
[4] validation-logloss:0.45661 validation-auc:0.96855 validation-aucpr:0.97389
[5] validation-logloss:0.42682 validation-auc:0.96938 validation-aucpr:0.97416
[6] validation-logloss:0.40070 validation-auc:0.97000 validation-aucpr:0.97468
[7] validation-logloss:0.38005 validation-auc:0.97005 validation-aucpr:0.97475
[8] validation-logloss:0.35980 validation-auc:0.97060 validation-aucpr:0.97517
[9] validation-logloss:0.34155 validation-auc:0.97098 validation-aucpr:0.97542
[10] validation-logloss:0.32525 validation-auc:0.97119 validation-aucpr:0.97562
[11] validation-logloss:0.31127 validation-auc:0.97120 validation-aucpr:0.97557
[12] validation-logloss:0.29845 validation-auc:0.97163 validation-aucpr:0.97585
[13] validation-logloss:0.28705 validation-auc:0.97167 validation-aucpr:0.97435
[14] validation-logloss:0.27739 validation-auc:0.97151 validation-aucpr:0.97415
[15] validation-logloss:0.26812 validation-auc:0.97156 validation-aucpr:0.97417
[16] validation-logloss:0.25988 validation-auc:0.97156 validation-aucpr:0.97405
[17] validation-logloss:0.25240 validation-auc:0.97166 validation-aucpr:0.97375
[18] validation-logloss:0.24577 validation-auc:0.97173 validation-aucpr:0.97380
[19] validation-logloss:0.23970 validation-auc:0.97188 validation-aucpr:0.97390
[20] validation-logloss:0.23487 validation-auc:0.97200 validation-aucpr:0.97398
[21] validation-logloss:0.23005 validation-auc:0.97186 validation-aucpr:0.97388
[22] validation-logloss:0.22576 validation-auc:0.97176 validation-aucpr:0.97353
[23] validation-logloss:0.22147 validation-auc:0.97199 validation-aucpr:0.97370
[24] validation-logloss:0.21736 validation-auc:0.97224 validation-aucpr:0.97387
[25] validation-logloss:0.21437 validation-auc:0.97204 validation-aucpr:0.97371
[26] validation-logloss:0.21144 validation-auc:0.97198 validation-aucpr:0.97367
[27] validation-logloss:0.20868 validation-auc:0.97205 validation-aucpr:0.97368
[28] validation-logloss:0.20614 validation-auc:0.97210 validation-aucpr:0.97372
[29] validation-logloss:0.20383 validation-auc:0.97222 validation-aucpr:0.97380
[30] validation-logloss:0.20181 validation-auc:0.97223 validation-aucpr:0.97380
[31] validation-logloss:0.19971 validation-auc:0.97237 validation-aucpr:0.97392
[32] validation-logloss:0.19797 validation-auc:0.97244 validation-aucpr:0.97425
[33] validation-logloss:0.19667 validation-auc:0.97242 validation-aucpr:0.97430
[34] validation-logloss:0.19558 validation-auc:0.97236 validation-aucpr:0.97434
[35] validation-logloss:0.19445 validation-auc:0.97242 validation-aucpr:0.97640
[36] validation-logloss:0.19343 validation-auc:0.97248 validation-aucpr:0.97652
[37] validation-logloss:0.19229 validation-auc:0.97253 validation-aucpr:0.97656
[38] validation-logloss:0.19126 validation-auc:0.97263 validation-aucpr:0.97665
[39] validation-logloss:0.19080 validation-auc:0.97242 validation-aucpr:0.97648
[40] validation-logloss:0.19028 validation-auc:0.97235 validation-aucpr:0.97640
[41] validation-logloss:0.18969 validation-auc:0.97239 validation-aucpr:0.97640
[42] validation-logloss:0.18913 validation-auc:0.97244 validation-aucpr:0.97642
[43] validation-logloss:0.18836 validation-auc:0.97256 validation-aucpr:0.97652
[44] validation-logloss:0.18776 validation-auc:0.97269 validation-aucpr:0.97659
[45] validation-logloss:0.18727 validation-auc:0.97274 validation-aucpr:0.97663
[46] validation-logloss:0.18678 validation-auc:0.97283 validation-aucpr:0.97670
[47] validation-logloss:0.18671 validation-auc:0.97279 validation-aucpr:0.97666
[48] validation-logloss:0.18654 validation-auc:0.97284 validation-aucpr:0.97667
[49] validation-logloss:0.18618 validation-auc:0.97294 validation-aucpr:0.97671
[50] validation-logloss:0.18629 validation-auc:0.97288 validation-aucpr:0.97665
[51] validation-logloss:0.18620 validation-auc:0.97290 validation-aucpr:0.97666
[52] validation-logloss:0.18590 validation-auc:0.97292 validation-aucpr:0.97668
[53] validation-logloss:0.18561 validation-auc:0.97298 validation-aucpr:0.97678
[54] validation-logloss:0.18563 validation-auc:0.97295 validation-aucpr:0.97675
[55] validation-logloss:0.18602 validation-auc:0.97286 validation-aucpr:0.97670
[56] validation-logloss:0.18585 validation-auc:0.97298 validation-aucpr:0.97678
[57] validation-logloss:0.18564 validation-auc:0.97307 validation-aucpr:0.97687
[58] validation-logloss:0.18598 validation-auc:0.97298 validation-aucpr:0.97682
[59] validation-logloss:0.18606 validation-auc:0.97296 validation-aucpr:0.97680
[60] validation-logloss:0.18627 validation-auc:0.97295 validation-aucpr:0.97680
{'best_iteration': '57', 'best_score': '0.9768669727658384'}
Trial 36, Fold 4: Log loss = 0.1862653102870968, Average precision = 0.976799427274251, ROC-AUC = 0.9729520751518272, Elapsed Time = 18.60203040000124 seconds
Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 36, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.62917 validation-auc:0.92813 validation-aucpr:0.88436
[1] validation-logloss:0.57570 validation-auc:0.95772 validation-aucpr:0.94855
[2] validation-logloss:0.53065 validation-auc:0.96252 validation-aucpr:0.96071
[3] validation-logloss:0.49165 validation-auc:0.96473 validation-aucpr:0.96366
[4] validation-logloss:0.45789 validation-auc:0.96504 validation-aucpr:0.96325
[5] validation-logloss:0.42881 validation-auc:0.96618 validation-aucpr:0.96591
[6] validation-logloss:0.40314 validation-auc:0.96712 validation-aucpr:0.96937
[7] validation-logloss:0.38033 validation-auc:0.96801 validation-aucpr:0.96974
[8] validation-logloss:0.36129 validation-auc:0.96796 validation-aucpr:0.96962
[9] validation-logloss:0.34395 validation-auc:0.96835 validation-aucpr:0.97219
[10] validation-logloss:0.32870 validation-auc:0.96836 validation-aucpr:0.97209
[11] validation-logloss:0.31462 validation-auc:0.96888 validation-aucpr:0.97246
[12] validation-logloss:0.30195 validation-auc:0.96961 validation-aucpr:0.97300
[13] validation-logloss:0.29093 validation-auc:0.97000 validation-aucpr:0.97331
[14] validation-logloss:0.28113 validation-auc:0.97029 validation-aucpr:0.97357
[15] validation-logloss:0.27252 validation-auc:0.97024 validation-aucpr:0.97352
[16] validation-logloss:0.26465 validation-auc:0.97028 validation-aucpr:0.97357
[17] validation-logloss:0.25799 validation-auc:0.97019 validation-aucpr:0.97349
[18] validation-logloss:0.25179 validation-auc:0.97000 validation-aucpr:0.97252
[19] validation-logloss:0.24679 validation-auc:0.97000 validation-aucpr:0.97260
[20] validation-logloss:0.24130 validation-auc:0.97029 validation-aucpr:0.97258
[21] validation-logloss:0.23693 validation-auc:0.97021 validation-aucpr:0.97252
[22] validation-logloss:0.23325 validation-auc:0.97013 validation-aucpr:0.97247
[23] validation-logloss:0.22951 validation-auc:0.97020 validation-aucpr:0.97251
[24] validation-logloss:0.22572 validation-auc:0.97045 validation-aucpr:0.97275
[25] validation-logloss:0.22259 validation-auc:0.97041 validation-aucpr:0.97277
[26] validation-logloss:0.21962 validation-auc:0.97073 validation-aucpr:0.97409
[27] validation-logloss:0.21706 validation-auc:0.97084 validation-aucpr:0.97419
[28] validation-logloss:0.21488 validation-auc:0.97077 validation-aucpr:0.97412
[29] validation-logloss:0.21306 validation-auc:0.97066 validation-aucpr:0.97405
[30] validation-logloss:0.21141 validation-auc:0.97053 validation-aucpr:0.97344
[31] validation-logloss:0.20985 validation-auc:0.97051 validation-aucpr:0.97341
[32] validation-logloss:0.20822 validation-auc:0.97067 validation-aucpr:0.97353
[33] validation-logloss:0.20678 validation-auc:0.97080 validation-aucpr:0.97370
[34] validation-logloss:0.20558 validation-auc:0.97089 validation-aucpr:0.97379
[35] validation-logloss:0.20438 validation-auc:0.97098 validation-aucpr:0.97347
[36] validation-logloss:0.20380 validation-auc:0.97084 validation-aucpr:0.97332
[37] validation-logloss:0.20259 validation-auc:0.97101 validation-aucpr:0.97333
[38] validation-logloss:0.20168 validation-auc:0.97101 validation-aucpr:0.97337
[39] validation-logloss:0.20099 validation-auc:0.97101 validation-aucpr:0.97325
[40] validation-logloss:0.20020 validation-auc:0.97102 validation-aucpr:0.97325
[41] validation-logloss:0.19923 validation-auc:0.97115 validation-aucpr:0.97317
[42] validation-logloss:0.19866 validation-auc:0.97124 validation-aucpr:0.97408
[43] validation-logloss:0.19784 validation-auc:0.97137 validation-aucpr:0.97409
[44] validation-logloss:0.19775 validation-auc:0.97125 validation-aucpr:0.97398
[45] validation-logloss:0.19728 validation-auc:0.97129 validation-aucpr:0.97399
[46] validation-logloss:0.19669 validation-auc:0.97150 validation-aucpr:0.97428
[47] validation-logloss:0.19636 validation-auc:0.97148 validation-aucpr:0.97428
[48] validation-logloss:0.19622 validation-auc:0.97153 validation-aucpr:0.97507
[49] validation-logloss:0.19650 validation-auc:0.97137 validation-aucpr:0.97492
[50] validation-logloss:0.19648 validation-auc:0.97135 validation-aucpr:0.97486
[51] validation-logloss:0.19648 validation-auc:0.97133 validation-aucpr:0.97480
[52] validation-logloss:0.19646 validation-auc:0.97132 validation-aucpr:0.97469
[53] validation-logloss:0.19640 validation-auc:0.97121 validation-aucpr:0.97454
[54] validation-logloss:0.19612 validation-auc:0.97132 validation-aucpr:0.97448
[55] validation-logloss:0.19625 validation-auc:0.97128 validation-aucpr:0.97444
[56] validation-logloss:0.19652 validation-auc:0.97122 validation-aucpr:0.97430
[57] validation-logloss:0.19664 validation-auc:0.97123 validation-aucpr:0.97405
[58] validation-logloss:0.19649 validation-auc:0.97129 validation-aucpr:0.97404
[59] validation-logloss:0.19664 validation-auc:0.97132 validation-aucpr:0.97415
[60] validation-logloss:0.19683 validation-auc:0.97129 validation-aucpr:0.97414
{'best_iteration': '48', 'best_score': '0.975065095160132'}
Trial 36, Fold 5: Log loss = 0.1968293727947802, Average precision = 0.9741463844987054, ROC-AUC = 0.9712943537149547, Elapsed Time = 19.465487499997835 seconds
Optimization Progress: 37%|###7 | 37/100 [2:23:42<7:40:34, 438.64s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 37, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65316 validation-auc:0.94886 validation-aucpr:0.95153
[1] validation-logloss:0.61796 validation-auc:0.95469 validation-aucpr:0.95909
[2] validation-logloss:0.58590 validation-auc:0.95620 validation-aucpr:0.96087
[3] validation-logloss:0.55682 validation-auc:0.95743 validation-aucpr:0.96198
[4] validation-logloss:0.53131 validation-auc:0.95800 validation-aucpr:0.96257
[5] validation-logloss:0.50323 validation-auc:0.96314 validation-aucpr:0.96681
[6] validation-logloss:0.48294 validation-auc:0.96276 validation-aucpr:0.96647
[7] validation-logloss:0.46407 validation-auc:0.96293 validation-aucpr:0.96638
[8] validation-logloss:0.44304 validation-auc:0.96503 validation-aucpr:0.96855
[9] validation-logloss:0.42751 validation-auc:0.96508 validation-aucpr:0.96846
[10] validation-logloss:0.40959 validation-auc:0.96634 validation-aucpr:0.96977
[11] validation-logloss:0.39313 validation-auc:0.96718 validation-aucpr:0.97054
[12] validation-logloss:0.37775 validation-auc:0.96762 validation-aucpr:0.97098
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[14] validation-logloss:0.35081 validation-auc:0.96847 validation-aucpr:0.97236
[15] validation-logloss:0.34183 validation-auc:0.96840 validation-aucpr:0.97217
[16] validation-logloss:0.33367 validation-auc:0.96803 validation-aucpr:0.97206
[17] validation-logloss:0.32332 validation-auc:0.96854 validation-aucpr:0.97373
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[22] validation-logloss:0.28734 validation-auc:0.96910 validation-aucpr:0.97421
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[24] validation-logloss:0.27623 validation-auc:0.96925 validation-aucpr:0.97427
[25] validation-logloss:0.27208 validation-auc:0.96917 validation-aucpr:0.97424
[26] validation-logloss:0.26751 validation-auc:0.96931 validation-aucpr:0.97434
[27] validation-logloss:0.26231 validation-auc:0.96947 validation-aucpr:0.97449
[28] validation-logloss:0.25727 validation-auc:0.96966 validation-aucpr:0.97469
[29] validation-logloss:0.25408 validation-auc:0.96966 validation-aucpr:0.97465
[30] validation-logloss:0.25026 validation-auc:0.96980 validation-aucpr:0.97476
[31] validation-logloss:0.24728 validation-auc:0.96987 validation-aucpr:0.97480
[32] validation-logloss:0.24335 validation-auc:0.97005 validation-aucpr:0.97494
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[34] validation-logloss:0.23906 validation-auc:0.96995 validation-aucpr:0.97486
[35] validation-logloss:0.23669 validation-auc:0.96999 validation-aucpr:0.97487
[36] validation-logloss:0.23455 validation-auc:0.97005 validation-aucpr:0.97490
[37] validation-logloss:0.23129 validation-auc:0.97024 validation-aucpr:0.97507
[38] validation-logloss:0.22958 validation-auc:0.97020 validation-aucpr:0.97502
[39] validation-logloss:0.22691 validation-auc:0.97034 validation-aucpr:0.97516
[40] validation-logloss:0.22449 validation-auc:0.97043 validation-aucpr:0.97523
[41] validation-logloss:0.22303 validation-auc:0.97039 validation-aucpr:0.97519
[42] validation-logloss:0.22078 validation-auc:0.97051 validation-aucpr:0.97528
[43] validation-logloss:0.21867 validation-auc:0.97061 validation-aucpr:0.97539
[44] validation-logloss:0.21678 validation-auc:0.97078 validation-aucpr:0.97555
[45] validation-logloss:0.21507 validation-auc:0.97090 validation-aucpr:0.97568
[46] validation-logloss:0.21394 validation-auc:0.97096 validation-aucpr:0.97570
[47] validation-logloss:0.21287 validation-auc:0.97096 validation-aucpr:0.97569
[48] validation-logloss:0.21220 validation-auc:0.97092 validation-aucpr:0.97565
[49] validation-logloss:0.21063 validation-auc:0.97104 validation-aucpr:0.97573
[50] validation-logloss:0.20909 validation-auc:0.97112 validation-aucpr:0.97581
[51] validation-logloss:0.20830 validation-auc:0.97112 validation-aucpr:0.97580
[52] validation-logloss:0.20754 validation-auc:0.97112 validation-aucpr:0.97578
[53] validation-logloss:0.20697 validation-auc:0.97113 validation-aucpr:0.97579
[54] validation-logloss:0.20648 validation-auc:0.97113 validation-aucpr:0.97577
[55] validation-logloss:0.20582 validation-auc:0.97117 validation-aucpr:0.97579
[56] validation-logloss:0.20521 validation-auc:0.97121 validation-aucpr:0.97579
[57] validation-logloss:0.20455 validation-auc:0.97127 validation-aucpr:0.97584
[58] validation-logloss:0.20388 validation-auc:0.97137 validation-aucpr:0.97591
[59] validation-logloss:0.20330 validation-auc:0.97140 validation-aucpr:0.97592
[60] validation-logloss:0.20250 validation-auc:0.97143 validation-aucpr:0.97597
[61] validation-logloss:0.20166 validation-auc:0.97152 validation-aucpr:0.97604
[62] validation-logloss:0.20138 validation-auc:0.97149 validation-aucpr:0.97600
[63] validation-logloss:0.20084 validation-auc:0.97160 validation-aucpr:0.97606
[64] validation-logloss:0.20045 validation-auc:0.97162 validation-aucpr:0.97607
[65] validation-logloss:0.20002 validation-auc:0.97167 validation-aucpr:0.97609
[66] validation-logloss:0.19963 validation-auc:0.97170 validation-aucpr:0.97611
[67] validation-logloss:0.19927 validation-auc:0.97172 validation-aucpr:0.97610
[68] validation-logloss:0.19852 validation-auc:0.97177 validation-aucpr:0.97614
[69] validation-logloss:0.19784 validation-auc:0.97186 validation-aucpr:0.97622
[70] validation-logloss:0.19714 validation-auc:0.97193 validation-aucpr:0.97628
[71] validation-logloss:0.19693 validation-auc:0.97194 validation-aucpr:0.97629
[72] validation-logloss:0.19627 validation-auc:0.97200 validation-aucpr:0.97633
[73] validation-logloss:0.19613 validation-auc:0.97204 validation-aucpr:0.97636
[74] validation-logloss:0.19555 validation-auc:0.97212 validation-aucpr:0.97644
[75] validation-logloss:0.19499 validation-auc:0.97220 validation-aucpr:0.97650
[76] validation-logloss:0.19451 validation-auc:0.97224 validation-aucpr:0.97655
[77] validation-logloss:0.19434 validation-auc:0.97226 validation-aucpr:0.97657
[78] validation-logloss:0.19393 validation-auc:0.97230 validation-aucpr:0.97662
[79] validation-logloss:0.19369 validation-auc:0.97232 validation-aucpr:0.97662
{'best_iteration': '79', 'best_score': '0.976617268810572'}
Trial 37, Fold 1: Log loss = 0.19369070806982336, Average precision = 0.9766212346890932, ROC-AUC = 0.9723205025260129, Elapsed Time = 2.6088954000006197 seconds
Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 37, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65269 validation-auc:0.94884 validation-aucpr:0.95081
[1] validation-logloss:0.61671 validation-auc:0.95616 validation-aucpr:0.95908
[2] validation-logloss:0.58483 validation-auc:0.95774 validation-aucpr:0.96032
[3] validation-logloss:0.55442 validation-auc:0.96198 validation-aucpr:0.96484
[4] validation-logloss:0.52515 validation-auc:0.96510 validation-aucpr:0.96840
[5] validation-logloss:0.50258 validation-auc:0.96459 validation-aucpr:0.96786
[6] validation-logloss:0.48280 validation-auc:0.96415 validation-aucpr:0.96738
[7] validation-logloss:0.46364 validation-auc:0.96450 validation-aucpr:0.96800
[8] validation-logloss:0.44581 validation-auc:0.96446 validation-aucpr:0.96775
[9] validation-logloss:0.42836 validation-auc:0.96536 validation-aucpr:0.96883
[10] validation-logloss:0.41042 validation-auc:0.96686 validation-aucpr:0.97040
[11] validation-logloss:0.39408 validation-auc:0.96753 validation-aucpr:0.97108
[12] validation-logloss:0.37884 validation-auc:0.96815 validation-aucpr:0.97168
[13] validation-logloss:0.36442 validation-auc:0.96865 validation-aucpr:0.97220
[14] validation-logloss:0.35229 validation-auc:0.96899 validation-aucpr:0.97246
[15] validation-logloss:0.34173 validation-auc:0.96890 validation-aucpr:0.97240
[16] validation-logloss:0.33220 validation-auc:0.96898 validation-aucpr:0.97242
[17] validation-logloss:0.32436 validation-auc:0.96889 validation-aucpr:0.97233
[18] validation-logloss:0.31710 validation-auc:0.96893 validation-aucpr:0.97228
[19] validation-logloss:0.30827 validation-auc:0.96933 validation-aucpr:0.97263
[20] validation-logloss:0.30015 validation-auc:0.96961 validation-aucpr:0.97283
[21] validation-logloss:0.29428 validation-auc:0.96960 validation-aucpr:0.97278
[22] validation-logloss:0.28852 validation-auc:0.96971 validation-aucpr:0.97285
[23] validation-logloss:0.28341 validation-auc:0.96957 validation-aucpr:0.97274
[24] validation-logloss:0.27684 validation-auc:0.96988 validation-aucpr:0.97303
[25] validation-logloss:0.27056 validation-auc:0.97009 validation-aucpr:0.97321
[26] validation-logloss:0.26590 validation-auc:0.97023 validation-aucpr:0.97329
[27] validation-logloss:0.26199 validation-auc:0.97022 validation-aucpr:0.97329
[28] validation-logloss:0.25691 validation-auc:0.97045 validation-aucpr:0.97345
[29] validation-logloss:0.25338 validation-auc:0.97046 validation-aucpr:0.97348
[30] validation-logloss:0.25018 validation-auc:0.97051 validation-aucpr:0.97350
[31] validation-logloss:0.24555 validation-auc:0.97080 validation-aucpr:0.97374
[32] validation-logloss:0.24275 validation-auc:0.97081 validation-aucpr:0.97372
[33] validation-logloss:0.24004 validation-auc:0.97085 validation-aucpr:0.97374
[34] validation-logloss:0.23790 validation-auc:0.97084 validation-aucpr:0.97373
[35] validation-logloss:0.23430 validation-auc:0.97102 validation-aucpr:0.97389
[36] validation-logloss:0.23130 validation-auc:0.97113 validation-aucpr:0.97243
[37] validation-logloss:0.22800 validation-auc:0.97141 validation-aucpr:0.97264
[38] validation-logloss:0.22620 validation-auc:0.97139 validation-aucpr:0.97260
[39] validation-logloss:0.22448 validation-auc:0.97149 validation-aucpr:0.97255
[40] validation-logloss:0.22293 validation-auc:0.97145 validation-aucpr:0.97251
[41] validation-logloss:0.22053 validation-auc:0.97158 validation-aucpr:0.97260
[42] validation-logloss:0.21903 validation-auc:0.97162 validation-aucpr:0.97462
[43] validation-logloss:0.21649 validation-auc:0.97178 validation-aucpr:0.97475
[44] validation-logloss:0.21429 validation-auc:0.97185 validation-aucpr:0.97482
[45] validation-logloss:0.21316 validation-auc:0.97183 validation-aucpr:0.97476
[46] validation-logloss:0.21103 validation-auc:0.97198 validation-aucpr:0.97488
[47] validation-logloss:0.20989 validation-auc:0.97198 validation-aucpr:0.97487
[48] validation-logloss:0.20877 validation-auc:0.97207 validation-aucpr:0.97491
[49] validation-logloss:0.20703 validation-auc:0.97215 validation-aucpr:0.97499
[50] validation-logloss:0.20558 validation-auc:0.97222 validation-aucpr:0.97492
[51] validation-logloss:0.20405 validation-auc:0.97235 validation-aucpr:0.97502
[52] validation-logloss:0.20265 validation-auc:0.97240 validation-aucpr:0.97506
[53] validation-logloss:0.20200 validation-auc:0.97240 validation-aucpr:0.97505
[54] validation-logloss:0.20119 validation-auc:0.97244 validation-aucpr:0.97508
[55] validation-logloss:0.19999 validation-auc:0.97253 validation-aucpr:0.97515
[56] validation-logloss:0.19909 validation-auc:0.97258 validation-aucpr:0.97519
[57] validation-logloss:0.19842 validation-auc:0.97264 validation-aucpr:0.97513
[58] validation-logloss:0.19732 validation-auc:0.97272 validation-aucpr:0.97518
[59] validation-logloss:0.19639 validation-auc:0.97275 validation-aucpr:0.97519
[60] validation-logloss:0.19557 validation-auc:0.97280 validation-aucpr:0.97522
[61] validation-logloss:0.19479 validation-auc:0.97276 validation-aucpr:0.97507
[62] validation-logloss:0.19421 validation-auc:0.97291 validation-aucpr:0.97551
[63] validation-logloss:0.19387 validation-auc:0.97290 validation-aucpr:0.97557
[64] validation-logloss:0.19326 validation-auc:0.97293 validation-aucpr:0.97559
[65] validation-logloss:0.19250 validation-auc:0.97298 validation-aucpr:0.97561
[66] validation-logloss:0.19180 validation-auc:0.97311 validation-aucpr:0.97591
[67] validation-logloss:0.19156 validation-auc:0.97310 validation-aucpr:0.97587
[68] validation-logloss:0.19102 validation-auc:0.97319 validation-aucpr:0.97600
[69] validation-logloss:0.19071 validation-auc:0.97322 validation-aucpr:0.97600
[70] validation-logloss:0.19019 validation-auc:0.97327 validation-aucpr:0.97608
[71] validation-logloss:0.18960 validation-auc:0.97334 validation-aucpr:0.97611
[72] validation-logloss:0.18935 validation-auc:0.97337 validation-aucpr:0.97610
[73] validation-logloss:0.18892 validation-auc:0.97339 validation-aucpr:0.97608
[74] validation-logloss:0.18867 validation-auc:0.97343 validation-aucpr:0.97610
[75] validation-logloss:0.18821 validation-auc:0.97347 validation-aucpr:0.97612
[76] validation-logloss:0.18780 validation-auc:0.97349 validation-aucpr:0.97612
[77] validation-logloss:0.18750 validation-auc:0.97356 validation-aucpr:0.97613
[78] validation-logloss:0.18719 validation-auc:0.97359 validation-aucpr:0.97613
[79] validation-logloss:0.18681 validation-auc:0.97360 validation-aucpr:0.97621
{'best_iteration': '79', 'best_score': '0.9762111427657041'}
Trial 37, Fold 2: Log loss = 0.1868082831728122, Average precision = 0.9762157308120819, ROC-AUC = 0.9736024863571153, Elapsed Time = 2.777645799997117 seconds
Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 37, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65310 validation-auc:0.94907 validation-aucpr:0.95224
[1] validation-logloss:0.61741 validation-auc:0.95518 validation-aucpr:0.95762
[2] validation-logloss:0.58641 validation-auc:0.95650 validation-aucpr:0.95934
[3] validation-logloss:0.55738 validation-auc:0.95841 validation-aucpr:0.96151
[4] validation-logloss:0.52935 validation-auc:0.96307 validation-aucpr:0.96726
[5] validation-logloss:0.50634 validation-auc:0.96323 validation-aucpr:0.96723
[6] validation-logloss:0.48031 validation-auc:0.96591 validation-aucpr:0.97008
[7] validation-logloss:0.45933 validation-auc:0.96649 validation-aucpr:0.97114
[8] validation-logloss:0.44152 validation-auc:0.96671 validation-aucpr:0.97127
[9] validation-logloss:0.42556 validation-auc:0.96673 validation-aucpr:0.97124
[10] validation-logloss:0.41120 validation-auc:0.96658 validation-aucpr:0.97108
[11] validation-logloss:0.39792 validation-auc:0.96661 validation-aucpr:0.97107
[12] validation-logloss:0.38564 validation-auc:0.96698 validation-aucpr:0.97118
[13] validation-logloss:0.37386 validation-auc:0.96698 validation-aucpr:0.97115
[14] validation-logloss:0.36398 validation-auc:0.96696 validation-aucpr:0.97101
[15] validation-logloss:0.35103 validation-auc:0.96784 validation-aucpr:0.97206
[16] validation-logloss:0.34179 validation-auc:0.96789 validation-aucpr:0.97208
[17] validation-logloss:0.33339 validation-auc:0.96794 validation-aucpr:0.97230
[18] validation-logloss:0.32395 validation-auc:0.96831 validation-aucpr:0.97264
[19] validation-logloss:0.31691 validation-auc:0.96827 validation-aucpr:0.97261
[20] validation-logloss:0.30721 validation-auc:0.96881 validation-aucpr:0.97315
[21] validation-logloss:0.29818 validation-auc:0.96919 validation-aucpr:0.97356
[22] validation-logloss:0.28990 validation-auc:0.96938 validation-aucpr:0.97377
[23] validation-logloss:0.28265 validation-auc:0.96962 validation-aucpr:0.97407
[24] validation-logloss:0.27700 validation-auc:0.96972 validation-aucpr:0.97414
[25] validation-logloss:0.27280 validation-auc:0.96976 validation-aucpr:0.97420
[26] validation-logloss:0.26661 validation-auc:0.96991 validation-aucpr:0.97437
[27] validation-logloss:0.26116 validation-auc:0.97011 validation-aucpr:0.97456
[28] validation-logloss:0.25748 validation-auc:0.97022 validation-aucpr:0.97460
[29] validation-logloss:0.25306 validation-auc:0.97031 validation-aucpr:0.97468
[30] validation-logloss:0.24859 validation-auc:0.97041 validation-aucpr:0.97474
[31] validation-logloss:0.24414 validation-auc:0.97056 validation-aucpr:0.97488
[32] validation-logloss:0.24142 validation-auc:0.97061 validation-aucpr:0.97490
[33] validation-logloss:0.23759 validation-auc:0.97077 validation-aucpr:0.97504
[34] validation-logloss:0.23566 validation-auc:0.97071 validation-aucpr:0.97500
[35] validation-logloss:0.23357 validation-auc:0.97065 validation-aucpr:0.97494
[36] validation-logloss:0.23137 validation-auc:0.97073 validation-aucpr:0.97497
[37] validation-logloss:0.22909 validation-auc:0.97089 validation-aucpr:0.97511
[38] validation-logloss:0.22632 validation-auc:0.97100 validation-aucpr:0.97519
[39] validation-logloss:0.22459 validation-auc:0.97109 validation-aucpr:0.97524
[40] validation-logloss:0.22192 validation-auc:0.97119 validation-aucpr:0.97534
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[44] validation-logloss:0.21435 validation-auc:0.97142 validation-aucpr:0.97549
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[50] validation-logloss:0.20657 validation-auc:0.97179 validation-aucpr:0.97574
[51] validation-logloss:0.20504 validation-auc:0.97190 validation-aucpr:0.97581
[52] validation-logloss:0.20432 validation-auc:0.97191 validation-aucpr:0.97582
[53] validation-logloss:0.20348 validation-auc:0.97198 validation-aucpr:0.97587
[54] validation-logloss:0.20226 validation-auc:0.97208 validation-aucpr:0.97598
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[58] validation-logloss:0.19899 validation-auc:0.97226 validation-aucpr:0.97599
[59] validation-logloss:0.19801 validation-auc:0.97236 validation-aucpr:0.97602
[60] validation-logloss:0.19695 validation-auc:0.97246 validation-aucpr:0.97624
[61] validation-logloss:0.19656 validation-auc:0.97251 validation-aucpr:0.97633
[62] validation-logloss:0.19602 validation-auc:0.97258 validation-aucpr:0.97638
[63] validation-logloss:0.19503 validation-auc:0.97272 validation-aucpr:0.97649
[64] validation-logloss:0.19453 validation-auc:0.97276 validation-aucpr:0.97653
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[66] validation-logloss:0.19338 validation-auc:0.97285 validation-aucpr:0.97658
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[68] validation-logloss:0.19222 validation-auc:0.97295 validation-aucpr:0.97663
[69] validation-logloss:0.19179 validation-auc:0.97296 validation-aucpr:0.97666
[70] validation-logloss:0.19130 validation-auc:0.97300 validation-aucpr:0.97669
[71] validation-logloss:0.19095 validation-auc:0.97306 validation-aucpr:0.97672
[72] validation-logloss:0.19068 validation-auc:0.97306 validation-aucpr:0.97670
[73] validation-logloss:0.19030 validation-auc:0.97312 validation-aucpr:0.97678
[74] validation-logloss:0.18990 validation-auc:0.97313 validation-aucpr:0.97677
[75] validation-logloss:0.18957 validation-auc:0.97321 validation-aucpr:0.97686
[76] validation-logloss:0.18928 validation-auc:0.97324 validation-aucpr:0.97688
[77] validation-logloss:0.18914 validation-auc:0.97326 validation-aucpr:0.97687
[78] validation-logloss:0.18896 validation-auc:0.97328 validation-aucpr:0.97687
[79] validation-logloss:0.18883 validation-auc:0.97329 validation-aucpr:0.97684
{'best_iteration': '76', 'best_score': '0.976880019972459'}
Trial 37, Fold 3: Log loss = 0.1888314692820942, Average precision = 0.9768399914184289, ROC-AUC = 0.9732865184771408, Elapsed Time = 2.7473267999994277 seconds
Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 37, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65303 validation-auc:0.94703 validation-aucpr:0.95109
[1] validation-logloss:0.61721 validation-auc:0.95355 validation-aucpr:0.95846
[2] validation-logloss:0.58656 validation-auc:0.95563 validation-aucpr:0.96027
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[5] validation-logloss:0.50782 validation-auc:0.96031 validation-aucpr:0.96557
[6] validation-logloss:0.48737 validation-auc:0.96048 validation-aucpr:0.96587
[7] validation-logloss:0.46371 validation-auc:0.96353 validation-aucpr:0.96925
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[15] validation-logloss:0.35063 validation-auc:0.96630 validation-aucpr:0.97180
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[50] validation-logloss:0.20863 validation-auc:0.97043 validation-aucpr:0.97523
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[59] validation-logloss:0.20249 validation-auc:0.97061 validation-aucpr:0.97536
[60] validation-logloss:0.20137 validation-auc:0.97072 validation-aucpr:0.97544
[61] validation-logloss:0.20036 validation-auc:0.97085 validation-aucpr:0.97555
[62] validation-logloss:0.19988 validation-auc:0.97088 validation-aucpr:0.97556
[63] validation-logloss:0.19908 validation-auc:0.97098 validation-aucpr:0.97564
[64] validation-logloss:0.19847 validation-auc:0.97109 validation-aucpr:0.97572
[65] validation-logloss:0.19757 validation-auc:0.97119 validation-aucpr:0.97579
[66] validation-logloss:0.19717 validation-auc:0.97120 validation-aucpr:0.97580
[67] validation-logloss:0.19628 validation-auc:0.97130 validation-aucpr:0.97587
[68] validation-logloss:0.19594 validation-auc:0.97129 validation-aucpr:0.97585
[69] validation-logloss:0.19516 validation-auc:0.97140 validation-aucpr:0.97594
[70] validation-logloss:0.19477 validation-auc:0.97145 validation-aucpr:0.97596
[71] validation-logloss:0.19402 validation-auc:0.97154 validation-aucpr:0.97603
[72] validation-logloss:0.19345 validation-auc:0.97162 validation-aucpr:0.97608
[73] validation-logloss:0.19302 validation-auc:0.97163 validation-aucpr:0.97609
[74] validation-logloss:0.19276 validation-auc:0.97167 validation-aucpr:0.97611
[75] validation-logloss:0.19229 validation-auc:0.97172 validation-aucpr:0.97614
[76] validation-logloss:0.19203 validation-auc:0.97175 validation-aucpr:0.97616
[77] validation-logloss:0.19176 validation-auc:0.97177 validation-aucpr:0.97617
[78] validation-logloss:0.19149 validation-auc:0.97181 validation-aucpr:0.97619
[79] validation-logloss:0.19112 validation-auc:0.97191 validation-aucpr:0.97626
{'best_iteration': '79', 'best_score': '0.9762577490225597'}
Trial 37, Fold 4: Log loss = 0.19112030204925323, Average precision = 0.9762616418832818, ROC-AUC = 0.9719088213624041, Elapsed Time = 2.7423531000022194 seconds
Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 37, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65387 validation-auc:0.94223 validation-aucpr:0.94643
[1] validation-logloss:0.61840 validation-auc:0.95093 validation-aucpr:0.95391
[2] validation-logloss:0.58759 validation-auc:0.95284 validation-aucpr:0.95590
[3] validation-logloss:0.55965 validation-auc:0.95482 validation-aucpr:0.95770
[4] validation-logloss:0.53436 validation-auc:0.95566 validation-aucpr:0.95856
[5] validation-logloss:0.51293 validation-auc:0.95564 validation-aucpr:0.95874
[6] validation-logloss:0.48753 validation-auc:0.96107 validation-aucpr:0.96501
[7] validation-logloss:0.46971 validation-auc:0.96100 validation-aucpr:0.96524
[8] validation-logloss:0.44757 validation-auc:0.96321 validation-aucpr:0.96772
[9] validation-logloss:0.42865 validation-auc:0.96397 validation-aucpr:0.96857
[10] validation-logloss:0.41510 validation-auc:0.96374 validation-aucpr:0.96847
[11] validation-logloss:0.40202 validation-auc:0.96379 validation-aucpr:0.96842
[12] validation-logloss:0.38965 validation-auc:0.96398 validation-aucpr:0.96883
[13] validation-logloss:0.37527 validation-auc:0.96452 validation-aucpr:0.96937
[14] validation-logloss:0.36189 validation-auc:0.96513 validation-aucpr:0.96997
[15] validation-logloss:0.35057 validation-auc:0.96543 validation-aucpr:0.97017
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[30] validation-logloss:0.25630 validation-auc:0.96787 validation-aucpr:0.97229
[31] validation-logloss:0.25349 validation-auc:0.96782 validation-aucpr:0.97224
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[61] validation-logloss:0.20651 validation-auc:0.97023 validation-aucpr:0.97369
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[63] validation-logloss:0.20568 validation-auc:0.97021 validation-aucpr:0.97359
[64] validation-logloss:0.20454 validation-auc:0.97038 validation-aucpr:0.97374
[65] validation-logloss:0.20413 validation-auc:0.97040 validation-aucpr:0.97374
[66] validation-logloss:0.20366 validation-auc:0.97045 validation-aucpr:0.97389
[67] validation-logloss:0.20330 validation-auc:0.97050 validation-aucpr:0.97390
[68] validation-logloss:0.20304 validation-auc:0.97051 validation-aucpr:0.97397
[69] validation-logloss:0.20220 validation-auc:0.97065 validation-aucpr:0.97400
[70] validation-logloss:0.20151 validation-auc:0.97075 validation-aucpr:0.97399
[71] validation-logloss:0.20081 validation-auc:0.97083 validation-aucpr:0.97406
[72] validation-logloss:0.20058 validation-auc:0.97085 validation-aucpr:0.97407
[73] validation-logloss:0.19996 validation-auc:0.97099 validation-aucpr:0.97414
[74] validation-logloss:0.19932 validation-auc:0.97110 validation-aucpr:0.97420
[75] validation-logloss:0.19900 validation-auc:0.97114 validation-aucpr:0.97423
[76] validation-logloss:0.19883 validation-auc:0.97116 validation-aucpr:0.97423
[77] validation-logloss:0.19831 validation-auc:0.97123 validation-aucpr:0.97416
[78] validation-logloss:0.19779 validation-auc:0.97133 validation-aucpr:0.97421
[79] validation-logloss:0.19763 validation-auc:0.97134 validation-aucpr:0.97420
{'best_iteration': '75', 'best_score': '0.9742342907888454'}
Trial 37, Fold 5: Log loss = 0.19762597901567752, Average precision = 0.9742038468289268, ROC-AUC = 0.9713423629303457, Elapsed Time = 3.3143954999977723 seconds
Optimization Progress: 38%|###8 | 38/100 [2:24:04<5:24:16, 313.82s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 38, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68047 validation-auc:0.91070 validation-aucpr:0.89779
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66627 validation-auc:0.93986 validation-aucpr:0.93921
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65357 validation-auc:0.94255 validation-aucpr:0.94553
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63943 validation-auc:0.95464 validation-aucpr:0.95920
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62532 validation-auc:0.95771 validation-aucpr:0.96321
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61454 validation-auc:0.95667 validation-aucpr:0.96179
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60442 validation-auc:0.95639 validation-aucpr:0.96159
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59377 validation-auc:0.95875 validation-aucpr:0.96465
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58136 validation-auc:0.96004 validation-aucpr:0.96635
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57201 validation-auc:0.96003 validation-aucpr:0.96629
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56250 validation-auc:0.95979 validation-aucpr:0.96599
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55420 validation-auc:0.95908 validation-aucpr:0.96513
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54528 validation-auc:0.95904 validation-aucpr:0.96511
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53712 validation-auc:0.95877 validation-aucpr:0.96484
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52924 validation-auc:0.95881 validation-aucpr:0.96484
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.52129 validation-auc:0.95893 validation-aucpr:0.96493
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.51374 validation-auc:0.95879 validation-aucpr:0.96480
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.50583 validation-auc:0.95890 validation-aucpr:0.96506
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49861 validation-auc:0.95907 validation-aucpr:0.96517
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.49061 validation-auc:0.95970 validation-aucpr:0.96592
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.48259 validation-auc:0.95984 validation-aucpr:0.96610
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47688 validation-auc:0.95943 validation-aucpr:0.96573
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.47153 validation-auc:0.95913 validation-aucpr:0.96540
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.46471 validation-auc:0.95910 validation-aucpr:0.96547
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.45931 validation-auc:0.95885 validation-aucpr:0.96517
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.45356 validation-auc:0.95911 validation-aucpr:0.96538
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.44761 validation-auc:0.95927 validation-aucpr:0.96555
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.44187 validation-auc:0.95935 validation-aucpr:0.96556
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43537 validation-auc:0.95955 validation-aucpr:0.96578
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.43072 validation-auc:0.95964 validation-aucpr:0.96587
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.42670 validation-auc:0.95926 validation-aucpr:0.96549
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.42200 validation-auc:0.95945 validation-aucpr:0.96561
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.41743 validation-auc:0.95952 validation-aucpr:0.96564
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.41275 validation-auc:0.95984 validation-aucpr:0.96595
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.40867 validation-auc:0.95973 validation-aucpr:0.96582
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.40456 validation-auc:0.95979 validation-aucpr:0.96588
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.40057 validation-auc:0.95981 validation-aucpr:0.96594
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.39640 validation-auc:0.95994 validation-aucpr:0.96604
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.39223 validation-auc:0.96027 validation-aucpr:0.96632
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.38729 validation-auc:0.96050 validation-aucpr:0.96656
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.38388 validation-auc:0.96046 validation-aucpr:0.96653
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.37950 validation-auc:0.96058 validation-aucpr:0.96670
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.37575 validation-auc:0.96085 validation-aucpr:0.96692
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.37287 validation-auc:0.96080 validation-aucpr:0.96685
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.36943 validation-auc:0.96092 validation-aucpr:0.96695
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.36624 validation-auc:0.96100 validation-aucpr:0.96696
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.36348 validation-auc:0.96098 validation-aucpr:0.96692
{'best_iteration': '45', 'best_score': '0.96695969606383'}
Trial 38, Fold 1: Log loss = 0.36347848486850604, Average precision = 0.9669244598053782, ROC-AUC = 0.9609798815139827, Elapsed Time = 3.9911858000014035 seconds
Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 38, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67991 validation-auc:0.91700 validation-aucpr:0.90474
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66559 validation-auc:0.94392 validation-aucpr:0.94367
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65247 validation-auc:0.94750 validation-aucpr:0.94769
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64102 validation-auc:0.94661 validation-aucpr:0.94698
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62646 validation-auc:0.95777 validation-aucpr:0.96131
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61526 validation-auc:0.95792 validation-aucpr:0.96174
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60483 validation-auc:0.95767 validation-aucpr:0.96140
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59424 validation-auc:0.95757 validation-aucpr:0.96126
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58437 validation-auc:0.95771 validation-aucpr:0.96131
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57301 validation-auc:0.95937 validation-aucpr:0.96349
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56199 validation-auc:0.95999 validation-aucpr:0.96423
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55212 validation-auc:0.96082 validation-aucpr:0.96498
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54278 validation-auc:0.96080 validation-aucpr:0.96502
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53517 validation-auc:0.96042 validation-aucpr:0.96461
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52775 validation-auc:0.96045 validation-aucpr:0.96472
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.52030 validation-auc:0.96059 validation-aucpr:0.96479
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.51314 validation-auc:0.96115 validation-aucpr:0.96548
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.50611 validation-auc:0.96084 validation-aucpr:0.96507
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49955 validation-auc:0.96053 validation-aucpr:0.96480
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.49278 validation-auc:0.96044 validation-aucpr:0.96468
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.48583 validation-auc:0.96048 validation-aucpr:0.96474
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47870 validation-auc:0.96057 validation-aucpr:0.96491
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.47333 validation-auc:0.96036 validation-aucpr:0.96471
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.46818 validation-auc:0.96025 validation-aucpr:0.96466
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.46213 validation-auc:0.96033 validation-aucpr:0.96471
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.45619 validation-auc:0.96062 validation-aucpr:0.96497
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.45052 validation-auc:0.96064 validation-aucpr:0.96498
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.44513 validation-auc:0.96064 validation-aucpr:0.96491
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43872 validation-auc:0.96113 validation-aucpr:0.96544
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.43388 validation-auc:0.96123 validation-aucpr:0.96551
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.42963 validation-auc:0.96118 validation-aucpr:0.96541
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.42376 validation-auc:0.96136 validation-aucpr:0.96565
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.41946 validation-auc:0.96145 validation-aucpr:0.96568
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.41429 validation-auc:0.96176 validation-aucpr:0.96599
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.40889 validation-auc:0.96198 validation-aucpr:0.96621
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.40497 validation-auc:0.96186 validation-aucpr:0.96609
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.39914 validation-auc:0.96212 validation-aucpr:0.96636
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.39472 validation-auc:0.96224 validation-aucpr:0.96646
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.39100 validation-auc:0.96226 validation-aucpr:0.96644
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.38873 validation-auc:0.96218 validation-aucpr:0.96624
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.38544 validation-auc:0.96214 validation-aucpr:0.96613
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.38067 validation-auc:0.96236 validation-aucpr:0.96638
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.37808 validation-auc:0.96238 validation-aucpr:0.96629
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.37478 validation-auc:0.96243 validation-aucpr:0.96632
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.37209 validation-auc:0.96237 validation-aucpr:0.96625
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.36884 validation-auc:0.96237 validation-aucpr:0.96626
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.36678 validation-auc:0.96238 validation-aucpr:0.96620
{'best_iteration': '37', 'best_score': '0.9664614916691174'}
Trial 38, Fold 2: Log loss = 0.36678439359062504, Average precision = 0.9661942089559218, ROC-AUC = 0.9623813446128473, Elapsed Time = 4.435023800000636 seconds
Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 38, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67990 validation-auc:0.91670 validation-aucpr:0.90637
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66606 validation-auc:0.93974 validation-aucpr:0.94017
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65833 validation-auc:0.93809 validation-aucpr:0.94062
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64593 validation-auc:0.94326 validation-aucpr:0.94557
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63434 validation-auc:0.94596 validation-aucpr:0.94895
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62376 validation-auc:0.94660 validation-aucpr:0.94965
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.61339 validation-auc:0.94600 validation-aucpr:0.94934
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.60377 validation-auc:0.94632 validation-aucpr:0.94958
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.59333 validation-auc:0.94843 validation-aucpr:0.95202
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.58330 validation-auc:0.94953 validation-aucpr:0.95288
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.57362 validation-auc:0.95136 validation-aucpr:0.95496
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.56403 validation-auc:0.95211 validation-aucpr:0.95568
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.55528 validation-auc:0.95223 validation-aucpr:0.95587
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.54731 validation-auc:0.95235 validation-aucpr:0.95601
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.53963 validation-auc:0.95233 validation-aucpr:0.95595
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.53109 validation-auc:0.95374 validation-aucpr:0.95744
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.52385 validation-auc:0.95377 validation-aucpr:0.95745
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.51606 validation-auc:0.95432 validation-aucpr:0.95798
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.50888 validation-auc:0.95406 validation-aucpr:0.95774
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.50197 validation-auc:0.95451 validation-aucpr:0.95816
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.49534 validation-auc:0.95449 validation-aucpr:0.95812
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.48733 validation-auc:0.95861 validation-aucpr:0.96279
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.48157 validation-auc:0.95843 validation-aucpr:0.96263
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.47534 validation-auc:0.95935 validation-aucpr:0.96362
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.46954 validation-auc:0.95919 validation-aucpr:0.96341
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.46341 validation-auc:0.95972 validation-aucpr:0.96403
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.45837 validation-auc:0.95946 validation-aucpr:0.96376
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.45358 validation-auc:0.95920 validation-aucpr:0.96349
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.44845 validation-auc:0.95923 validation-aucpr:0.96352
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.44362 validation-auc:0.95931 validation-aucpr:0.96352
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.43884 validation-auc:0.95931 validation-aucpr:0.96360
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.43456 validation-auc:0.95958 validation-aucpr:0.96389
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.42962 validation-auc:0.95970 validation-aucpr:0.96394
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.42423 validation-auc:0.96023 validation-aucpr:0.96449
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.41740 validation-auc:0.96159 validation-aucpr:0.96600
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.41321 validation-auc:0.96152 validation-aucpr:0.96590
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.40969 validation-auc:0.96137 validation-aucpr:0.96574
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.40516 validation-auc:0.96153 validation-aucpr:0.96582
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.40094 validation-auc:0.96164 validation-aucpr:0.96593
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.39723 validation-auc:0.96180 validation-aucpr:0.96607
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.39317 validation-auc:0.96192 validation-aucpr:0.96615
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.38947 validation-auc:0.96197 validation-aucpr:0.96616
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.38641 validation-auc:0.96184 validation-aucpr:0.96606
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.38126 validation-auc:0.96249 validation-aucpr:0.96686
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.37767 validation-auc:0.96267 validation-aucpr:0.96697
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.37454 validation-auc:0.96261 validation-aucpr:0.96687
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.37180 validation-auc:0.96255 validation-aucpr:0.96681
{'best_iteration': '44', 'best_score': '0.9669745897838138'}
Trial 38, Fold 3: Log loss = 0.37179536140253167, Average precision = 0.966817583403075, ROC-AUC = 0.9625506231279676, Elapsed Time = 4.31358569999793 seconds
Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 38, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67987 validation-auc:0.91555 validation-aucpr:0.90252
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66681 validation-auc:0.93186 validation-aucpr:0.93114
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65318 validation-auc:0.94676 validation-aucpr:0.94888
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64077 validation-auc:0.94892 validation-aucpr:0.95104
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62862 validation-auc:0.95050 validation-aucpr:0.95489
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61797 validation-auc:0.94978 validation-aucpr:0.95426
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60792 validation-auc:0.94967 validation-aucpr:0.95429
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59825 validation-auc:0.94948 validation-aucpr:0.95417
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58748 validation-auc:0.95159 validation-aucpr:0.95614
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57750 validation-auc:0.95210 validation-aucpr:0.95651
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56846 validation-auc:0.95197 validation-aucpr:0.95638
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55924 validation-auc:0.95239 validation-aucpr:0.95673
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.55056 validation-auc:0.95225 validation-aucpr:0.95667
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.54232 validation-auc:0.95224 validation-aucpr:0.95675
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.53515 validation-auc:0.95197 validation-aucpr:0.95643
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.52784 validation-auc:0.95205 validation-aucpr:0.95653
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.52119 validation-auc:0.95183 validation-aucpr:0.95636
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.51427 validation-auc:0.95169 validation-aucpr:0.95629
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.50712 validation-auc:0.95176 validation-aucpr:0.95628
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.50106 validation-auc:0.95172 validation-aucpr:0.95635
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.49408 validation-auc:0.95235 validation-aucpr:0.95702
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.48501 validation-auc:0.95667 validation-aucpr:0.96192
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.47918 validation-auc:0.95680 validation-aucpr:0.96203
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.47372 validation-auc:0.95661 validation-aucpr:0.96181
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.46891 validation-auc:0.95635 validation-aucpr:0.96158
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.46284 validation-auc:0.95633 validation-aucpr:0.96156
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.45758 validation-auc:0.95631 validation-aucpr:0.96148
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.45264 validation-auc:0.95617 validation-aucpr:0.96137
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.44717 validation-auc:0.95624 validation-aucpr:0.96152
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.44178 validation-auc:0.95638 validation-aucpr:0.96160
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.43737 validation-auc:0.95608 validation-aucpr:0.96128
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.43282 validation-auc:0.95604 validation-aucpr:0.96131
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.42890 validation-auc:0.95573 validation-aucpr:0.96100
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.42447 validation-auc:0.95582 validation-aucpr:0.96106
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.42014 validation-auc:0.95600 validation-aucpr:0.96122
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.41676 validation-auc:0.95572 validation-aucpr:0.96091
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.41308 validation-auc:0.95575 validation-aucpr:0.96095
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.40913 validation-auc:0.95580 validation-aucpr:0.96100
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.40364 validation-auc:0.95729 validation-aucpr:0.96271
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.40021 validation-auc:0.95724 validation-aucpr:0.96266
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.39668 validation-auc:0.95722 validation-aucpr:0.96261
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.39355 validation-auc:0.95725 validation-aucpr:0.96265
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.39022 validation-auc:0.95736 validation-aucpr:0.96273
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.38482 validation-auc:0.95855 validation-aucpr:0.96416
[20:23:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.37962 validation-auc:0.95947 validation-aucpr:0.96523
[20:23:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.37661 validation-auc:0.95948 validation-aucpr:0.96524
[20:23:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.37360 validation-auc:0.95957 validation-aucpr:0.96532
{'best_iteration': '46', 'best_score': '0.965317789327765'}
Trial 38, Fold 4: Log loss = 0.3736001626733337, Average precision = 0.9653230628179911, ROC-AUC = 0.9595715126218731, Elapsed Time = 4.244987300000503 seconds
Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 38, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68022 validation-auc:0.91274 validation-aucpr:0.90548
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66699 validation-auc:0.93385 validation-aucpr:0.93541
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65498 validation-auc:0.93962 validation-aucpr:0.94399
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64212 validation-auc:0.94736 validation-aucpr:0.95141
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63031 validation-auc:0.94783 validation-aucpr:0.95200
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61926 validation-auc:0.94828 validation-aucpr:0.95263
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60923 validation-auc:0.94794 validation-aucpr:0.95237
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59829 validation-auc:0.94880 validation-aucpr:0.95319
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58820 validation-auc:0.94991 validation-aucpr:0.95413
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57889 validation-auc:0.95053 validation-aucpr:0.95482
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56908 validation-auc:0.95076 validation-aucpr:0.95501
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.56091 validation-auc:0.95049 validation-aucpr:0.95488
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.55240 validation-auc:0.95070 validation-aucpr:0.95499
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.54541 validation-auc:0.95058 validation-aucpr:0.95487
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.53729 validation-auc:0.95096 validation-aucpr:0.95544
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.52967 validation-auc:0.95131 validation-aucpr:0.95573
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.52200 validation-auc:0.95163 validation-aucpr:0.95597
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.51475 validation-auc:0.95194 validation-aucpr:0.95640
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.50637 validation-auc:0.95498 validation-aucpr:0.96015
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.50118 validation-auc:0.95488 validation-aucpr:0.96009
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.49526 validation-auc:0.95476 validation-aucpr:0.95982
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.48938 validation-auc:0.95430 validation-aucpr:0.95942
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.48135 validation-auc:0.95583 validation-aucpr:0.96132
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.47587 validation-auc:0.95569 validation-aucpr:0.96112
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.47063 validation-auc:0.95557 validation-aucpr:0.96103
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.46535 validation-auc:0.95558 validation-aucpr:0.96102
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.45953 validation-auc:0.95600 validation-aucpr:0.96133
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.45418 validation-auc:0.95630 validation-aucpr:0.96150
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.44674 validation-auc:0.95741 validation-aucpr:0.96280
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.44186 validation-auc:0.95735 validation-aucpr:0.96273
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.43789 validation-auc:0.95724 validation-aucpr:0.96269
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.43354 validation-auc:0.95731 validation-aucpr:0.96271
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.42876 validation-auc:0.95736 validation-aucpr:0.96274
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.42533 validation-auc:0.95717 validation-aucpr:0.96254
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.41907 validation-auc:0.95782 validation-aucpr:0.96329
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.41473 validation-auc:0.95794 validation-aucpr:0.96337
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.41125 validation-auc:0.95783 validation-aucpr:0.96324
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.40745 validation-auc:0.95794 validation-aucpr:0.96332
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.40192 validation-auc:0.95830 validation-aucpr:0.96374
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.39675 validation-auc:0.95840 validation-aucpr:0.96388
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.39333 validation-auc:0.95845 validation-aucpr:0.96391
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.38985 validation-auc:0.95859 validation-aucpr:0.96402
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.38569 validation-auc:0.95890 validation-aucpr:0.96431
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.38316 validation-auc:0.95882 validation-aucpr:0.96424
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.38020 validation-auc:0.95886 validation-aucpr:0.96424
[20:23:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.37699 validation-auc:0.95910 validation-aucpr:0.96443
[20:23:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.37411 validation-auc:0.95900 validation-aucpr:0.96434
{'best_iteration': '45', 'best_score': '0.9644308472576195'}
Trial 38, Fold 5: Log loss = 0.37410844472793614, Average precision = 0.9643466108012383, ROC-AUC = 0.9589996841498989, Elapsed Time = 4.2861682000002475 seconds
Optimization Progress: 39%|###9 | 39/100 [2:24:34<3:52:14, 228.43s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 39, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.69226 validation-auc:0.63014 validation-aucpr:0.67987
[1] validation-logloss:0.68619 validation-auc:0.89844 validation-aucpr:0.87978
[2] validation-logloss:0.68015 validation-auc:0.92588 validation-aucpr:0.92816
[3] validation-logloss:0.67398 validation-auc:0.93739 validation-aucpr:0.94171
[4] validation-logloss:0.66769 validation-auc:0.94633 validation-aucpr:0.95256
[5] validation-logloss:0.66270 validation-auc:0.94563 validation-aucpr:0.95211
[6] validation-logloss:0.65710 validation-auc:0.94542 validation-aucpr:0.95179
[7] validation-logloss:0.65143 validation-auc:0.94734 validation-aucpr:0.95300
[8] validation-logloss:0.64719 validation-auc:0.94662 validation-aucpr:0.95258
[9] validation-logloss:0.64204 validation-auc:0.94688 validation-aucpr:0.95271
[10] validation-logloss:0.63700 validation-auc:0.94868 validation-aucpr:0.95488
[11] validation-logloss:0.63243 validation-auc:0.94884 validation-aucpr:0.95534
[12] validation-logloss:0.62740 validation-auc:0.94945 validation-aucpr:0.95623
[13] validation-logloss:0.62268 validation-auc:0.94926 validation-aucpr:0.95599
[14] validation-logloss:0.61683 validation-auc:0.95290 validation-aucpr:0.95988
[15] validation-logloss:0.61203 validation-auc:0.95280 validation-aucpr:0.95976
[16] validation-logloss:0.60998 validation-auc:0.95253 validation-aucpr:0.95944
[17] validation-logloss:0.60539 validation-auc:0.95241 validation-aucpr:0.95924
[18] validation-logloss:0.60083 validation-auc:0.95325 validation-aucpr:0.96025
[19] validation-logloss:0.59550 validation-auc:0.95373 validation-aucpr:0.96071
[20] validation-logloss:0.59187 validation-auc:0.95387 validation-aucpr:0.96089
[21] validation-logloss:0.58728 validation-auc:0.95381 validation-aucpr:0.96070
[22] validation-logloss:0.58309 validation-auc:0.95427 validation-aucpr:0.96106
[23] validation-logloss:0.57867 validation-auc:0.95426 validation-aucpr:0.96112
[24] validation-logloss:0.57463 validation-auc:0.95403 validation-aucpr:0.96094
[25] validation-logloss:0.57059 validation-auc:0.95388 validation-aucpr:0.96077
[26] validation-logloss:0.56706 validation-auc:0.95368 validation-aucpr:0.96059
[27] validation-logloss:0.56328 validation-auc:0.95333 validation-aucpr:0.96026
[28] validation-logloss:0.55978 validation-auc:0.95341 validation-aucpr:0.96029
[29] validation-logloss:0.55624 validation-auc:0.95323 validation-aucpr:0.96015
[30] validation-logloss:0.55136 validation-auc:0.95472 validation-aucpr:0.96176
[31] validation-logloss:0.54812 validation-auc:0.95459 validation-aucpr:0.96163
[32] validation-logloss:0.54459 validation-auc:0.95463 validation-aucpr:0.96168
[33] validation-logloss:0.53999 validation-auc:0.95551 validation-aucpr:0.96262
[34] validation-logloss:0.53558 validation-auc:0.95607 validation-aucpr:0.96315
{'best_iteration': '34', 'best_score': '0.9631540251948735'}
Trial 39, Fold 1: Log loss = 0.5355809136638554, Average precision = 0.9631595856202696, ROC-AUC = 0.9560693016542158, Elapsed Time = 2.151727899999969 seconds
Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 39, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.69183 validation-auc:0.62838 validation-aucpr:0.65085
[1] validation-logloss:0.68522 validation-auc:0.91819 validation-aucpr:0.88707
[2] validation-logloss:0.67930 validation-auc:0.93380 validation-aucpr:0.92814
[3] validation-logloss:0.67332 validation-auc:0.93813 validation-aucpr:0.93811
[4] validation-logloss:0.66756 validation-auc:0.94129 validation-aucpr:0.94215
[5] validation-logloss:0.66199 validation-auc:0.94103 validation-aucpr:0.94281
[6] validation-logloss:0.65615 validation-auc:0.94408 validation-aucpr:0.94565
[7] validation-logloss:0.65107 validation-auc:0.94330 validation-aucpr:0.94496
[8] validation-logloss:0.64544 validation-auc:0.94814 validation-aucpr:0.95118
[9] validation-logloss:0.64058 validation-auc:0.94744 validation-aucpr:0.95039
[10] validation-logloss:0.63569 validation-auc:0.94655 validation-aucpr:0.94948
[11] validation-logloss:0.63155 validation-auc:0.94802 validation-aucpr:0.95053
[12] validation-logloss:0.62661 validation-auc:0.94878 validation-aucpr:0.95132
[13] validation-logloss:0.62266 validation-auc:0.94955 validation-aucpr:0.95189
[14] validation-logloss:0.61744 validation-auc:0.95020 validation-aucpr:0.95256
[15] validation-logloss:0.61261 validation-auc:0.95021 validation-aucpr:0.95271
[16] validation-logloss:0.60739 validation-auc:0.95347 validation-aucpr:0.95646
[17] validation-logloss:0.60288 validation-auc:0.95307 validation-aucpr:0.95631
[18] validation-logloss:0.59799 validation-auc:0.95324 validation-aucpr:0.95624
[19] validation-logloss:0.59362 validation-auc:0.95318 validation-aucpr:0.95621
[20] validation-logloss:0.58867 validation-auc:0.95502 validation-aucpr:0.95846
[21] validation-logloss:0.58432 validation-auc:0.95498 validation-aucpr:0.95835
[22] validation-logloss:0.58034 validation-auc:0.95496 validation-aucpr:0.95829
[23] validation-logloss:0.57656 validation-auc:0.95457 validation-aucpr:0.95789
[24] validation-logloss:0.57325 validation-auc:0.95445 validation-aucpr:0.95795
[25] validation-logloss:0.56989 validation-auc:0.95414 validation-aucpr:0.95760
[26] validation-logloss:0.56605 validation-auc:0.95402 validation-aucpr:0.95744
[27] validation-logloss:0.56203 validation-auc:0.95412 validation-aucpr:0.95745
[28] validation-logloss:0.55888 validation-auc:0.95443 validation-aucpr:0.95764
[29] validation-logloss:0.55520 validation-auc:0.95454 validation-aucpr:0.95777
[30] validation-logloss:0.55145 validation-auc:0.95470 validation-aucpr:0.95789
[31] validation-logloss:0.54783 validation-auc:0.95467 validation-aucpr:0.95783
[32] validation-logloss:0.54450 validation-auc:0.95466 validation-aucpr:0.95759
[33] validation-logloss:0.54120 validation-auc:0.95454 validation-aucpr:0.95752
[34] validation-logloss:0.53651 validation-auc:0.95581 validation-aucpr:0.95902
{'best_iteration': '34', 'best_score': '0.9590245603636176'}
Trial 39, Fold 2: Log loss = 0.5365051738060637, Average precision = 0.9590721938204927, ROC-AUC = 0.9558121071060149, Elapsed Time = 2.1664930999977514 seconds
Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 39, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.69216 validation-auc:0.61740 validation-aucpr:0.65215
[1] validation-logloss:0.68566 validation-auc:0.86971 validation-aucpr:0.81245
[2] validation-logloss:0.68091 validation-auc:0.91964 validation-aucpr:0.91568
[3] validation-logloss:0.67510 validation-auc:0.92775 validation-aucpr:0.92560
[4] validation-logloss:0.66989 validation-auc:0.93403 validation-aucpr:0.93147
[5] validation-logloss:0.66449 validation-auc:0.93752 validation-aucpr:0.93762
[6] validation-logloss:0.65879 validation-auc:0.94044 validation-aucpr:0.94151
[7] validation-logloss:0.65311 validation-auc:0.94236 validation-aucpr:0.94400
[8] validation-logloss:0.64733 validation-auc:0.94527 validation-aucpr:0.94700
[9] validation-logloss:0.64229 validation-auc:0.94581 validation-aucpr:0.94753
[10] validation-logloss:0.63729 validation-auc:0.94573 validation-aucpr:0.94738
[11] validation-logloss:0.63265 validation-auc:0.94497 validation-aucpr:0.94660
[12] validation-logloss:0.62648 validation-auc:0.95397 validation-aucpr:0.95737
[13] validation-logloss:0.62120 validation-auc:0.95544 validation-aucpr:0.95867
[14] validation-logloss:0.61602 validation-auc:0.95613 validation-aucpr:0.95968
[15] validation-logloss:0.61144 validation-auc:0.95604 validation-aucpr:0.95994
[16] validation-logloss:0.60563 validation-auc:0.95853 validation-aucpr:0.96229
[17] validation-logloss:0.60105 validation-auc:0.95813 validation-aucpr:0.96182
[18] validation-logloss:0.59685 validation-auc:0.95805 validation-aucpr:0.96171
[19] validation-logloss:0.59245 validation-auc:0.95819 validation-aucpr:0.96223
[20] validation-logloss:0.58849 validation-auc:0.95778 validation-aucpr:0.96192
[21] validation-logloss:0.58433 validation-auc:0.95738 validation-aucpr:0.96139
[22] validation-logloss:0.58010 validation-auc:0.95719 validation-aucpr:0.96118
[23] validation-logloss:0.57543 validation-auc:0.95789 validation-aucpr:0.96197
[24] validation-logloss:0.57184 validation-auc:0.95796 validation-aucpr:0.96218
[25] validation-logloss:0.56730 validation-auc:0.95900 validation-aucpr:0.96386
[26] validation-logloss:0.56301 validation-auc:0.95894 validation-aucpr:0.96379
[27] validation-logloss:0.55920 validation-auc:0.95895 validation-aucpr:0.96379
[28] validation-logloss:0.55554 validation-auc:0.95877 validation-aucpr:0.96364
[29] validation-logloss:0.55210 validation-auc:0.95848 validation-aucpr:0.96330
[30] validation-logloss:0.54750 validation-auc:0.95914 validation-aucpr:0.96416
[31] validation-logloss:0.54393 validation-auc:0.95902 validation-aucpr:0.96397
[32] validation-logloss:0.54075 validation-auc:0.95900 validation-aucpr:0.96390
[33] validation-logloss:0.53771 validation-auc:0.95873 validation-aucpr:0.96357
[34] validation-logloss:0.53390 validation-auc:0.95867 validation-aucpr:0.96350
{'best_iteration': '30', 'best_score': '0.9641562408617427'}
Trial 39, Fold 3: Log loss = 0.5338974948694302, Average precision = 0.9635049136418115, ROC-AUC = 0.9586714227928076, Elapsed Time = 1.9661199000001943 seconds
Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 39, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.69196 validation-auc:0.62449 validation-aucpr:0.66023
[1] validation-logloss:0.68575 validation-auc:0.90475 validation-aucpr:0.88475
[2] validation-logloss:0.68009 validation-auc:0.92448 validation-aucpr:0.91614
[3] validation-logloss:0.67503 validation-auc:0.92851 validation-aucpr:0.92872
[4] validation-logloss:0.66948 validation-auc:0.93151 validation-aucpr:0.93417
[5] validation-logloss:0.66433 validation-auc:0.93189 validation-aucpr:0.93452
[6] validation-logloss:0.65829 validation-auc:0.93704 validation-aucpr:0.94134
[7] validation-logloss:0.65246 validation-auc:0.94025 validation-aucpr:0.94543
[8] validation-logloss:0.64834 validation-auc:0.94096 validation-aucpr:0.94616
[9] validation-logloss:0.64347 validation-auc:0.94169 validation-aucpr:0.94701
[10] validation-logloss:0.63824 validation-auc:0.94215 validation-aucpr:0.94760
[11] validation-logloss:0.63301 validation-auc:0.94292 validation-aucpr:0.94808
[12] validation-logloss:0.62816 validation-auc:0.94324 validation-aucpr:0.94850
[13] validation-logloss:0.62360 validation-auc:0.94278 validation-aucpr:0.94809
[14] validation-logloss:0.61860 validation-auc:0.94390 validation-aucpr:0.94929
[15] validation-logloss:0.61283 validation-auc:0.94953 validation-aucpr:0.95586
[16] validation-logloss:0.60798 validation-auc:0.95062 validation-aucpr:0.95677
[17] validation-logloss:0.60376 validation-auc:0.95029 validation-aucpr:0.95645
[18] validation-logloss:0.59899 validation-auc:0.95100 validation-aucpr:0.95702
[19] validation-logloss:0.59477 validation-auc:0.95127 validation-aucpr:0.95716
[20] validation-logloss:0.59084 validation-auc:0.95114 validation-aucpr:0.95692
[21] validation-logloss:0.58635 validation-auc:0.95153 validation-aucpr:0.95726
[22] validation-logloss:0.58247 validation-auc:0.95180 validation-aucpr:0.95739
[23] validation-logloss:0.57815 validation-auc:0.95215 validation-aucpr:0.95748
[24] validation-logloss:0.57444 validation-auc:0.95205 validation-aucpr:0.95738
[25] validation-logloss:0.57106 validation-auc:0.95199 validation-aucpr:0.95744
[26] validation-logloss:0.56674 validation-auc:0.95170 validation-aucpr:0.95698
[27] validation-logloss:0.56320 validation-auc:0.95158 validation-aucpr:0.95697
[28] validation-logloss:0.55932 validation-auc:0.95203 validation-aucpr:0.95747
[29] validation-logloss:0.55530 validation-auc:0.95201 validation-aucpr:0.95732
[30] validation-logloss:0.55240 validation-auc:0.95193 validation-aucpr:0.95721
[31] validation-logloss:0.54903 validation-auc:0.95176 validation-aucpr:0.95697
[32] validation-logloss:0.54522 validation-auc:0.95214 validation-aucpr:0.95734
[33] validation-logloss:0.54188 validation-auc:0.95239 validation-aucpr:0.95763
[34] validation-logloss:0.53804 validation-auc:0.95277 validation-aucpr:0.95800
{'best_iteration': '34', 'best_score': '0.9580042989195655'}
Trial 39, Fold 4: Log loss = 0.5380433135204978, Average precision = 0.9580086411722494, ROC-AUC = 0.9527683444566049, Elapsed Time = 1.9444853999993938 seconds
Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 39, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68732 validation-auc:0.89226 validation-aucpr:0.85523
[1] validation-logloss:0.68097 validation-auc:0.93162 validation-aucpr:0.93449
[2] validation-logloss:0.67451 validation-auc:0.94454 validation-aucpr:0.94874
[3] validation-logloss:0.66929 validation-auc:0.94362 validation-aucpr:0.94765
[4] validation-logloss:0.66235 validation-auc:0.95061 validation-aucpr:0.95586
[5] validation-logloss:0.65705 validation-auc:0.94966 validation-aucpr:0.95443
[6] validation-logloss:0.65287 validation-auc:0.94918 validation-aucpr:0.95490
[7] validation-logloss:0.64715 validation-auc:0.95098 validation-aucpr:0.95671
[8] validation-logloss:0.64217 validation-auc:0.95160 validation-aucpr:0.95717
[9] validation-logloss:0.63721 validation-auc:0.95198 validation-aucpr:0.95740
[10] validation-logloss:0.63220 validation-auc:0.95255 validation-aucpr:0.95795
[11] validation-logloss:0.62710 validation-auc:0.95267 validation-aucpr:0.95814
[12] validation-logloss:0.62116 validation-auc:0.95375 validation-aucpr:0.95939
[13] validation-logloss:0.61702 validation-auc:0.95357 validation-aucpr:0.95912
[14] validation-logloss:0.61229 validation-auc:0.95371 validation-aucpr:0.95920
[15] validation-logloss:0.60752 validation-auc:0.95408 validation-aucpr:0.95947
[16] validation-logloss:0.60282 validation-auc:0.95411 validation-aucpr:0.95929
[17] validation-logloss:0.59803 validation-auc:0.95475 validation-aucpr:0.95973
[18] validation-logloss:0.59392 validation-auc:0.95436 validation-aucpr:0.95932
[19] validation-logloss:0.58845 validation-auc:0.95475 validation-aucpr:0.95990
[20] validation-logloss:0.58439 validation-auc:0.95441 validation-aucpr:0.95961
[21] validation-logloss:0.57988 validation-auc:0.95471 validation-aucpr:0.95984
[22] validation-logloss:0.57582 validation-auc:0.95438 validation-aucpr:0.95944
[23] validation-logloss:0.57088 validation-auc:0.95479 validation-aucpr:0.95987
[24] validation-logloss:0.56769 validation-auc:0.95462 validation-aucpr:0.96014
[25] validation-logloss:0.56391 validation-auc:0.95457 validation-aucpr:0.96006
[26] validation-logloss:0.56038 validation-auc:0.95463 validation-aucpr:0.95986
[27] validation-logloss:0.55691 validation-auc:0.95456 validation-aucpr:0.95976
[28] validation-logloss:0.55317 validation-auc:0.95451 validation-aucpr:0.96033
[29] validation-logloss:0.54919 validation-auc:0.95472 validation-aucpr:0.96047
[30] validation-logloss:0.54521 validation-auc:0.95509 validation-aucpr:0.96065
[31] validation-logloss:0.54210 validation-auc:0.95479 validation-aucpr:0.96037
[32] validation-logloss:0.53876 validation-auc:0.95480 validation-aucpr:0.96033
[33] validation-logloss:0.53487 validation-auc:0.95501 validation-aucpr:0.96040
[34] validation-logloss:0.53123 validation-auc:0.95518 validation-aucpr:0.96049
{'best_iteration': '30', 'best_score': '0.9606467506066156'}
Trial 39, Fold 5: Log loss = 0.53122527454656, Average precision = 0.9604999865216366, ROC-AUC = 0.9551781208776917, Elapsed Time = 1.9197061000013491 seconds
Optimization Progress: 40%|#### | 40/100 [2:24:52<2:45:16, 165.27s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 40, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63086 validation-auc:0.92500 validation-aucpr:0.89044
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58262 validation-auc:0.95148 validation-aucpr:0.93704
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53810 validation-auc:0.95822 validation-aucpr:0.95171
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49924 validation-auc:0.96113 validation-aucpr:0.95731
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.46479 validation-auc:0.96298 validation-aucpr:0.95741
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.43537 validation-auc:0.96380 validation-aucpr:0.96024
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40962 validation-auc:0.96541 validation-aucpr:0.96554
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38675 validation-auc:0.96655 validation-aucpr:0.97040
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36661 validation-auc:0.96709 validation-aucpr:0.97055
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34893 validation-auc:0.96723 validation-aucpr:0.97031
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.33281 validation-auc:0.96752 validation-aucpr:0.97008
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31876 validation-auc:0.96801 validation-aucpr:0.97031
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30626 validation-auc:0.96853 validation-aucpr:0.97081
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29482 validation-auc:0.96873 validation-aucpr:0.97098
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28462 validation-auc:0.96877 validation-aucpr:0.97108
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27554 validation-auc:0.96882 validation-aucpr:0.97108
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26827 validation-auc:0.96887 validation-aucpr:0.97061
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.26081 validation-auc:0.96929 validation-aucpr:0.97242
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.25427 validation-auc:0.96958 validation-aucpr:0.97268
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24872 validation-auc:0.96971 validation-aucpr:0.97294
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.24336 validation-auc:0.96967 validation-aucpr:0.97285
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23875 validation-auc:0.96958 validation-aucpr:0.97293
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.23440 validation-auc:0.96957 validation-aucpr:0.97291
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.23050 validation-auc:0.96972 validation-aucpr:0.97297
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22743 validation-auc:0.96960 validation-aucpr:0.97235
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.22443 validation-auc:0.96979 validation-aucpr:0.97322
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.22213 validation-auc:0.96969 validation-aucpr:0.97311
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21927 validation-auc:0.97009 validation-aucpr:0.97401
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21714 validation-auc:0.97031 validation-aucpr:0.97417
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.21412 validation-auc:0.97078 validation-aucpr:0.97455
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21255 validation-auc:0.97087 validation-aucpr:0.97457
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21084 validation-auc:0.97087 validation-aucpr:0.97457
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20880 validation-auc:0.97104 validation-aucpr:0.97470
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20764 validation-auc:0.97097 validation-aucpr:0.97464
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20657 validation-auc:0.97095 validation-aucpr:0.97480
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20569 validation-auc:0.97096 validation-aucpr:0.97471
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20429 validation-auc:0.97109 validation-aucpr:0.97478
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20324 validation-auc:0.97115 validation-aucpr:0.97476
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20260 validation-auc:0.97114 validation-aucpr:0.97459
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20206 validation-auc:0.97119 validation-aucpr:0.97455
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20156 validation-auc:0.97118 validation-aucpr:0.97451
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20090 validation-auc:0.97117 validation-aucpr:0.97434
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20026 validation-auc:0.97142 validation-aucpr:0.97565
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19967 validation-auc:0.97146 validation-aucpr:0.97565
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19959 validation-auc:0.97144 validation-aucpr:0.97563
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19928 validation-auc:0.97149 validation-aucpr:0.97568
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19988 validation-auc:0.97127 validation-aucpr:0.97548
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19996 validation-auc:0.97125 validation-aucpr:0.97542
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19972 validation-auc:0.97137 validation-aucpr:0.97557
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20021 validation-auc:0.97123 validation-aucpr:0.97545
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20051 validation-auc:0.97113 validation-aucpr:0.97529
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20041 validation-auc:0.97122 validation-aucpr:0.97535
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20049 validation-auc:0.97120 validation-aucpr:0.97530
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20077 validation-auc:0.97124 validation-aucpr:0.97537
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20081 validation-auc:0.97125 validation-aucpr:0.97533
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20059 validation-auc:0.97143 validation-aucpr:0.97552
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20097 validation-auc:0.97139 validation-aucpr:0.97547
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20147 validation-auc:0.97130 validation-aucpr:0.97533
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20180 validation-auc:0.97125 validation-aucpr:0.97525
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20167 validation-auc:0.97149 validation-aucpr:0.97556
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20178 validation-auc:0.97149 validation-aucpr:0.97552
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20223 validation-auc:0.97151 validation-aucpr:0.97549
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20266 validation-auc:0.97155 validation-aucpr:0.97556
[20:23:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20272 validation-auc:0.97159 validation-aucpr:0.97550
[20:23:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20292 validation-auc:0.97164 validation-aucpr:0.97561
[20:23:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20348 validation-auc:0.97160 validation-aucpr:0.97567
{'best_iteration': '45', 'best_score': '0.9756752977752717'}
Trial 40, Fold 1: Log loss = 0.20347556267412154, Average precision = 0.9756785766613285, ROC-AUC = 0.9715972162477355, Elapsed Time = 10.003865800001222 seconds
Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 40, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62933 validation-auc:0.93299 validation-aucpr:0.89951
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57635 validation-auc:0.95740 validation-aucpr:0.95010
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53071 validation-auc:0.96401 validation-aucpr:0.95772
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49117 validation-auc:0.96651 validation-aucpr:0.96493
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.45771 validation-auc:0.96900 validation-aucpr:0.96967
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.42846 validation-auc:0.96988 validation-aucpr:0.97048
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40255 validation-auc:0.97074 validation-aucpr:0.97400
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.37990 validation-auc:0.97084 validation-aucpr:0.97404
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.35973 validation-auc:0.97108 validation-aucpr:0.97430
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34202 validation-auc:0.97155 validation-aucpr:0.97474
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.32611 validation-auc:0.97216 validation-aucpr:0.97520
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31223 validation-auc:0.97221 validation-aucpr:0.97525
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.29923 validation-auc:0.97264 validation-aucpr:0.97556
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.28799 validation-auc:0.97276 validation-aucpr:0.97569
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.27745 validation-auc:0.97295 validation-aucpr:0.97581
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.26811 validation-auc:0.97308 validation-aucpr:0.97597
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.25979 validation-auc:0.97321 validation-aucpr:0.97608
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25219 validation-auc:0.97341 validation-aucpr:0.97624
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.24536 validation-auc:0.97338 validation-aucpr:0.97620
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.23901 validation-auc:0.97341 validation-aucpr:0.97624
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.23343 validation-auc:0.97348 validation-aucpr:0.97628
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.22848 validation-auc:0.97342 validation-aucpr:0.97592
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.22433 validation-auc:0.97349 validation-aucpr:0.97595
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22092 validation-auc:0.97339 validation-aucpr:0.97589
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.21711 validation-auc:0.97353 validation-aucpr:0.97597
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.21399 validation-auc:0.97327 validation-aucpr:0.97578
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21080 validation-auc:0.97343 validation-aucpr:0.97592
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.20806 validation-auc:0.97339 validation-aucpr:0.97592
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.20592 validation-auc:0.97331 validation-aucpr:0.97582
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.20447 validation-auc:0.97303 validation-aucpr:0.97564
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.20214 validation-auc:0.97325 validation-aucpr:0.97565
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.19967 validation-auc:0.97343 validation-aucpr:0.97581
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.19832 validation-auc:0.97339 validation-aucpr:0.97576
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.19675 validation-auc:0.97343 validation-aucpr:0.97577
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.19511 validation-auc:0.97362 validation-aucpr:0.97602
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.19399 validation-auc:0.97358 validation-aucpr:0.97600
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.19249 validation-auc:0.97365 validation-aucpr:0.97607
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.19150 validation-auc:0.97367 validation-aucpr:0.97608
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.18998 validation-auc:0.97389 validation-aucpr:0.97626
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.18927 validation-auc:0.97370 validation-aucpr:0.97602
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.18866 validation-auc:0.97367 validation-aucpr:0.97592
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.18808 validation-auc:0.97368 validation-aucpr:0.97597
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.18730 validation-auc:0.97377 validation-aucpr:0.97603
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.18624 validation-auc:0.97389 validation-aucpr:0.97612
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.18553 validation-auc:0.97384 validation-aucpr:0.97593
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.18510 validation-auc:0.97384 validation-aucpr:0.97585
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.18454 validation-auc:0.97395 validation-aucpr:0.97593
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.18430 validation-auc:0.97390 validation-aucpr:0.97586
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.18375 validation-auc:0.97393 validation-aucpr:0.97592
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.18336 validation-auc:0.97401 validation-aucpr:0.97589
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.18318 validation-auc:0.97401 validation-aucpr:0.97580
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.18312 validation-auc:0.97416 validation-aucpr:0.97649
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.18314 validation-auc:0.97414 validation-aucpr:0.97645
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.18305 validation-auc:0.97405 validation-aucpr:0.97637
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.18301 validation-auc:0.97394 validation-aucpr:0.97641
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.18250 validation-auc:0.97420 validation-aucpr:0.97640
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.18288 validation-auc:0.97422 validation-aucpr:0.97701
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.18335 validation-auc:0.97407 validation-aucpr:0.97681
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.18342 validation-auc:0.97409 validation-aucpr:0.97677
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.18362 validation-auc:0.97402 validation-aucpr:0.97661
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.18394 validation-auc:0.97388 validation-aucpr:0.97657
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.18416 validation-auc:0.97381 validation-aucpr:0.97653
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.18443 validation-auc:0.97375 validation-aucpr:0.97648
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.18457 validation-auc:0.97370 validation-aucpr:0.97646
[20:24:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.18491 validation-auc:0.97366 validation-aucpr:0.97626
[20:24:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.18488 validation-auc:0.97371 validation-aucpr:0.97642
{'best_iteration': '56', 'best_score': '0.9770064115831377'}
Trial 40, Fold 2: Log loss = 0.18488184562218168, Average precision = 0.9764291748176318, ROC-AUC = 0.9737075716985524, Elapsed Time = 10.05679930000042 seconds
Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 40, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62968 validation-auc:0.92847 validation-aucpr:0.88851
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57559 validation-auc:0.95534 validation-aucpr:0.92830
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53082 validation-auc:0.96108 validation-aucpr:0.94587
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49208 validation-auc:0.96333 validation-aucpr:0.95202
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.45802 validation-auc:0.96630 validation-aucpr:0.96477
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.42909 validation-auc:0.96659 validation-aucpr:0.96669
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40297 validation-auc:0.96826 validation-aucpr:0.97081
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38010 validation-auc:0.96913 validation-aucpr:0.97153
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36031 validation-auc:0.96964 validation-aucpr:0.97258
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34277 validation-auc:0.96987 validation-aucpr:0.97347
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.32704 validation-auc:0.96995 validation-aucpr:0.97257
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31287 validation-auc:0.97034 validation-aucpr:0.97299
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30041 validation-auc:0.97057 validation-aucpr:0.97432
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.28884 validation-auc:0.97076 validation-aucpr:0.97447
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.27891 validation-auc:0.97067 validation-aucpr:0.97403
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27020 validation-auc:0.97053 validation-aucpr:0.97397
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26189 validation-auc:0.97048 validation-aucpr:0.97396
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25574 validation-auc:0.97030 validation-aucpr:0.97375
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.24918 validation-auc:0.97039 validation-aucpr:0.97379
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24247 validation-auc:0.97063 validation-aucpr:0.97363
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.23732 validation-auc:0.97066 validation-aucpr:0.97362
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23235 validation-auc:0.97082 validation-aucpr:0.97385
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.22836 validation-auc:0.97068 validation-aucpr:0.97372
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22460 validation-auc:0.97064 validation-aucpr:0.97356
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22082 validation-auc:0.97085 validation-aucpr:0.97371
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.21764 validation-auc:0.97089 validation-aucpr:0.97371
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21469 validation-auc:0.97097 validation-aucpr:0.97379
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21218 validation-auc:0.97114 validation-aucpr:0.97386
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.20991 validation-auc:0.97135 validation-aucpr:0.97407
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.20801 validation-auc:0.97128 validation-aucpr:0.97400
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.20642 validation-auc:0.97116 validation-aucpr:0.97394
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.20502 validation-auc:0.97119 validation-aucpr:0.97411
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20418 validation-auc:0.97090 validation-aucpr:0.97403
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20262 validation-auc:0.97097 validation-aucpr:0.97407
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20138 validation-auc:0.97107 validation-aucpr:0.97441
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20005 validation-auc:0.97127 validation-aucpr:0.97472
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.19936 validation-auc:0.97120 validation-aucpr:0.97458
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.19834 validation-auc:0.97131 validation-aucpr:0.97458
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19748 validation-auc:0.97142 validation-aucpr:0.97461
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19682 validation-auc:0.97142 validation-aucpr:0.97437
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19620 validation-auc:0.97147 validation-aucpr:0.97452
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19542 validation-auc:0.97158 validation-aucpr:0.97465
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19514 validation-auc:0.97156 validation-aucpr:0.97469
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19438 validation-auc:0.97169 validation-aucpr:0.97482
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19381 validation-auc:0.97180 validation-aucpr:0.97483
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19365 validation-auc:0.97192 validation-aucpr:0.97514
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19326 validation-auc:0.97204 validation-aucpr:0.97521
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19287 validation-auc:0.97212 validation-aucpr:0.97524
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19252 validation-auc:0.97218 validation-aucpr:0.97544
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19272 validation-auc:0.97224 validation-aucpr:0.97587
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19284 validation-auc:0.97218 validation-aucpr:0.97577
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19275 validation-auc:0.97219 validation-aucpr:0.97570
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19298 validation-auc:0.97213 validation-aucpr:0.97566
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19312 validation-auc:0.97212 validation-aucpr:0.97560
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19317 validation-auc:0.97208 validation-aucpr:0.97554
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19329 validation-auc:0.97209 validation-aucpr:0.97555
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19391 validation-auc:0.97190 validation-aucpr:0.97515
[20:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19401 validation-auc:0.97205 validation-aucpr:0.97550
[20:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19429 validation-auc:0.97203 validation-aucpr:0.97545
[20:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19472 validation-auc:0.97192 validation-aucpr:0.97526
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19485 validation-auc:0.97195 validation-aucpr:0.97534
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.19518 validation-auc:0.97198 validation-aucpr:0.97548
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.19588 validation-auc:0.97180 validation-aucpr:0.97537
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.19550 validation-auc:0.97199 validation-aucpr:0.97541
[20:24:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.19632 validation-auc:0.97184 validation-aucpr:0.97521
[20:24:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.19636 validation-auc:0.97199 validation-aucpr:0.97540
{'best_iteration': '49', 'best_score': '0.9758689164852605'}
Trial 40, Fold 3: Log loss = 0.1963552571288972, Average precision = 0.9754003522438767, ROC-AUC = 0.9719943796891102, Elapsed Time = 10.401604899998347 seconds
Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 40, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62979 validation-auc:0.92368 validation-aucpr:0.88066
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57616 validation-auc:0.95172 validation-aucpr:0.93060
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53068 validation-auc:0.96166 validation-aucpr:0.94972
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49535 validation-auc:0.96485 validation-aucpr:0.96724
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.46143 validation-auc:0.96570 validation-aucpr:0.96976
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.43121 validation-auc:0.96641 validation-aucpr:0.96948
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40529 validation-auc:0.96674 validation-aucpr:0.97005
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38467 validation-auc:0.96644 validation-aucpr:0.97005
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36431 validation-auc:0.96686 validation-aucpr:0.97039
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34664 validation-auc:0.96729 validation-aucpr:0.97045
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.33041 validation-auc:0.96759 validation-aucpr:0.97054
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31703 validation-auc:0.96865 validation-aucpr:0.97089
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30367 validation-auc:0.96933 validation-aucpr:0.97101
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29249 validation-auc:0.96944 validation-aucpr:0.97073
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28241 validation-auc:0.96939 validation-aucpr:0.96966
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27276 validation-auc:0.96989 validation-aucpr:0.97023
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26428 validation-auc:0.96987 validation-aucpr:0.97029
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25633 validation-auc:0.97044 validation-aucpr:0.97060
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.24953 validation-auc:0.97050 validation-aucpr:0.97066
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24389 validation-auc:0.97061 validation-aucpr:0.97073
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.23843 validation-auc:0.97060 validation-aucpr:0.97075
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23385 validation-auc:0.97044 validation-aucpr:0.97065
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.22903 validation-auc:0.97076 validation-aucpr:0.97089
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22443 validation-auc:0.97106 validation-aucpr:0.97111
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22085 validation-auc:0.97117 validation-aucpr:0.97109
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.21764 validation-auc:0.97137 validation-aucpr:0.97167
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21432 validation-auc:0.97143 validation-aucpr:0.97169
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21143 validation-auc:0.97150 validation-aucpr:0.97194
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.20944 validation-auc:0.97131 validation-aucpr:0.97181
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.20740 validation-auc:0.97129 validation-aucpr:0.97179
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.20496 validation-auc:0.97151 validation-aucpr:0.97179
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.20366 validation-auc:0.97132 validation-aucpr:0.97188
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20189 validation-auc:0.97167 validation-aucpr:0.97355
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20018 validation-auc:0.97186 validation-aucpr:0.97366
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.19918 validation-auc:0.97181 validation-aucpr:0.97393
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.19726 validation-auc:0.97214 validation-aucpr:0.97419
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.19609 validation-auc:0.97215 validation-aucpr:0.97392
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.19531 validation-auc:0.97216 validation-aucpr:0.97410
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19406 validation-auc:0.97237 validation-aucpr:0.97408
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19343 validation-auc:0.97241 validation-aucpr:0.97410
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19290 validation-auc:0.97238 validation-aucpr:0.97456
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19252 validation-auc:0.97233 validation-aucpr:0.97439
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19170 validation-auc:0.97267 validation-aucpr:0.97656
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19197 validation-auc:0.97251 validation-aucpr:0.97649
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19176 validation-auc:0.97251 validation-aucpr:0.97645
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19156 validation-auc:0.97247 validation-aucpr:0.97638
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19144 validation-auc:0.97253 validation-aucpr:0.97641
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19106 validation-auc:0.97253 validation-aucpr:0.97641
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19144 validation-auc:0.97244 validation-aucpr:0.97643
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19094 validation-auc:0.97255 validation-aucpr:0.97652
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19094 validation-auc:0.97258 validation-aucpr:0.97664
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19141 validation-auc:0.97246 validation-aucpr:0.97653
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19122 validation-auc:0.97247 validation-aucpr:0.97656
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19111 validation-auc:0.97246 validation-aucpr:0.97657
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19121 validation-auc:0.97246 validation-aucpr:0.97657
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19154 validation-auc:0.97240 validation-aucpr:0.97652
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19184 validation-auc:0.97234 validation-aucpr:0.97647
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19200 validation-auc:0.97231 validation-aucpr:0.97646
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19232 validation-auc:0.97227 validation-aucpr:0.97644
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19242 validation-auc:0.97230 validation-aucpr:0.97644
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19260 validation-auc:0.97233 validation-aucpr:0.97645
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.19250 validation-auc:0.97232 validation-aucpr:0.97645
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.19268 validation-auc:0.97230 validation-aucpr:0.97645
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.19290 validation-auc:0.97236 validation-aucpr:0.97653
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.19347 validation-auc:0.97233 validation-aucpr:0.97649
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.19370 validation-auc:0.97231 validation-aucpr:0.97643
{'best_iteration': '50', 'best_score': '0.9766366978311805'}
Trial 40, Fold 4: Log loss = 0.19370183926702098, Average precision = 0.9764367725265992, ROC-AUC = 0.9723107434351237, Elapsed Time = 10.073183600001357 seconds
Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 40, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63070 validation-auc:0.91775 validation-aucpr:0.88462
[20:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57687 validation-auc:0.95126 validation-aucpr:0.92715
[20:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53179 validation-auc:0.96085 validation-aucpr:0.95436
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49368 validation-auc:0.96162 validation-aucpr:0.95762
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.45990 validation-auc:0.96465 validation-aucpr:0.96513
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.43151 validation-auc:0.96584 validation-aucpr:0.96723
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40641 validation-auc:0.96743 validation-aucpr:0.97168
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38427 validation-auc:0.96770 validation-aucpr:0.97184
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36531 validation-auc:0.96746 validation-aucpr:0.97163
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34792 validation-auc:0.96732 validation-aucpr:0.97018
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.33225 validation-auc:0.96775 validation-aucpr:0.97056
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31820 validation-auc:0.96843 validation-aucpr:0.97095
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30570 validation-auc:0.96908 validation-aucpr:0.97274
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29523 validation-auc:0.96941 validation-aucpr:0.97297
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28562 validation-auc:0.96956 validation-aucpr:0.97314
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27628 validation-auc:0.96959 validation-aucpr:0.97322
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26786 validation-auc:0.97016 validation-aucpr:0.97375
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.26049 validation-auc:0.97005 validation-aucpr:0.97331
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.25419 validation-auc:0.96992 validation-aucpr:0.97318
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24805 validation-auc:0.97006 validation-aucpr:0.97332
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.24329 validation-auc:0.97002 validation-aucpr:0.97332
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23852 validation-auc:0.97006 validation-aucpr:0.97339
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.23398 validation-auc:0.97003 validation-aucpr:0.97341
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.23034 validation-auc:0.97003 validation-aucpr:0.97337
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22673 validation-auc:0.97034 validation-aucpr:0.97352
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.22371 validation-auc:0.97046 validation-aucpr:0.97350
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.22108 validation-auc:0.97055 validation-aucpr:0.97346
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21870 validation-auc:0.97071 validation-aucpr:0.97361
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21629 validation-auc:0.97073 validation-aucpr:0.97366
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.21376 validation-auc:0.97093 validation-aucpr:0.97380
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21172 validation-auc:0.97110 validation-aucpr:0.97394
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21004 validation-auc:0.97112 validation-aucpr:0.97398
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20861 validation-auc:0.97105 validation-aucpr:0.97385
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20687 validation-auc:0.97120 validation-aucpr:0.97401
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20634 validation-auc:0.97091 validation-aucpr:0.97377
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20532 validation-auc:0.97099 validation-aucpr:0.97393
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20447 validation-auc:0.97098 validation-aucpr:0.97431
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20382 validation-auc:0.97093 validation-aucpr:0.97429
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20278 validation-auc:0.97099 validation-aucpr:0.97436
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20214 validation-auc:0.97095 validation-aucpr:0.97441
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20184 validation-auc:0.97096 validation-aucpr:0.97464
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20114 validation-auc:0.97102 validation-aucpr:0.97404
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20094 validation-auc:0.97086 validation-aucpr:0.97391
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20071 validation-auc:0.97078 validation-aucpr:0.97380
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20060 validation-auc:0.97073 validation-aucpr:0.97376
[20:24:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20071 validation-auc:0.97066 validation-aucpr:0.97386
[20:24:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20065 validation-auc:0.97057 validation-aucpr:0.97381
[20:24:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20006 validation-auc:0.97071 validation-aucpr:0.97388
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19982 validation-auc:0.97083 validation-aucpr:0.97462
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19978 validation-auc:0.97082 validation-aucpr:0.97459
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20035 validation-auc:0.97060 validation-aucpr:0.97443
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19996 validation-auc:0.97072 validation-aucpr:0.97452
[20:24:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20049 validation-auc:0.97051 validation-aucpr:0.97431
[20:24:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20053 validation-auc:0.97068 validation-aucpr:0.97460
[20:24:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20073 validation-auc:0.97060 validation-aucpr:0.97446
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20136 validation-auc:0.97047 validation-aucpr:0.97442
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20190 validation-auc:0.97035 validation-aucpr:0.97430
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20211 validation-auc:0.97035 validation-aucpr:0.97424
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20246 validation-auc:0.97038 validation-aucpr:0.97427
[20:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20281 validation-auc:0.97041 validation-aucpr:0.97428
[20:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20269 validation-auc:0.97049 validation-aucpr:0.97428
[20:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20249 validation-auc:0.97060 validation-aucpr:0.97435
[20:24:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20295 validation-auc:0.97063 validation-aucpr:0.97442
[20:24:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20341 validation-auc:0.97058 validation-aucpr:0.97431
[20:24:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20377 validation-auc:0.97061 validation-aucpr:0.97424
[20:24:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20425 validation-auc:0.97057 validation-aucpr:0.97414
{'best_iteration': '40', 'best_score': '0.9746363737266491'}
Trial 40, Fold 5: Log loss = 0.20424930220962945, Average precision = 0.9741473211463513, ROC-AUC = 0.9705699050591755, Elapsed Time = 13.895724099998915 seconds
Optimization Progress: 41%|####1 | 41/100 [2:25:54<2:12:09, 134.40s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 41, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68331 validation-auc:0.95047 validation-aucpr:0.95393
[1] validation-logloss:0.67501 validation-auc:0.95415 validation-aucpr:0.95984
[2] validation-logloss:0.66635 validation-auc:0.95465 validation-aucpr:0.96120
[3] validation-logloss:0.65800 validation-auc:0.95560 validation-aucpr:0.96189
[4] validation-logloss:0.65015 validation-auc:0.95545 validation-aucpr:0.96146
[5] validation-logloss:0.64237 validation-auc:0.95490 validation-aucpr:0.96079
[6] validation-logloss:0.63338 validation-auc:0.95775 validation-aucpr:0.96387
[7] validation-logloss:0.62638 validation-auc:0.95698 validation-aucpr:0.96304
[8] validation-logloss:0.61959 validation-auc:0.95631 validation-aucpr:0.96217
[9] validation-logloss:0.61143 validation-auc:0.95731 validation-aucpr:0.96141
[10] validation-logloss:0.60467 validation-auc:0.95773 validation-aucpr:0.96159
[11] validation-logloss:0.59794 validation-auc:0.95751 validation-aucpr:0.96142
[12] validation-logloss:0.59150 validation-auc:0.95714 validation-aucpr:0.96107
[13] validation-logloss:0.58518 validation-auc:0.95685 validation-aucpr:0.96086
[14] validation-logloss:0.57757 validation-auc:0.95794 validation-aucpr:0.96208
[15] validation-logloss:0.57045 validation-auc:0.95840 validation-aucpr:0.96264
[16] validation-logloss:0.56431 validation-auc:0.95861 validation-aucpr:0.96285
[17] validation-logloss:0.55851 validation-auc:0.95866 validation-aucpr:0.96288
[18] validation-logloss:0.55287 validation-auc:0.95854 validation-aucpr:0.96279
[19] validation-logloss:0.54760 validation-auc:0.95810 validation-aucpr:0.96242
[20] validation-logloss:0.54115 validation-auc:0.95854 validation-aucpr:0.96284
[21] validation-logloss:0.53599 validation-auc:0.95821 validation-aucpr:0.96253
[22] validation-logloss:0.52952 validation-auc:0.95895 validation-aucpr:0.96315
[23] validation-logloss:0.52320 validation-auc:0.95932 validation-aucpr:0.96358
[24] validation-logloss:0.51706 validation-auc:0.95968 validation-aucpr:0.96391
[25] validation-logloss:0.51138 validation-auc:0.95966 validation-aucpr:0.96395
[26] validation-logloss:0.50640 validation-auc:0.95978 validation-aucpr:0.96405
[27] validation-logloss:0.50172 validation-auc:0.95990 validation-aucpr:0.96411
[28] validation-logloss:0.49741 validation-auc:0.95991 validation-aucpr:0.96404
[29] validation-logloss:0.49232 validation-auc:0.96003 validation-aucpr:0.96425
[30] validation-logloss:0.48809 validation-auc:0.96007 validation-aucpr:0.96424
[31] validation-logloss:0.48318 validation-auc:0.96020 validation-aucpr:0.96490
[32] validation-logloss:0.47804 validation-auc:0.96044 validation-aucpr:0.96511
[33] validation-logloss:0.47418 validation-auc:0.96033 validation-aucpr:0.96501
[34] validation-logloss:0.46926 validation-auc:0.96059 validation-aucpr:0.96528
[35] validation-logloss:0.46435 validation-auc:0.96088 validation-aucpr:0.96559
[36] validation-logloss:0.45970 validation-auc:0.96086 validation-aucpr:0.96559
[37] validation-logloss:0.45593 validation-auc:0.96089 validation-aucpr:0.96559
[38] validation-logloss:0.45131 validation-auc:0.96104 validation-aucpr:0.96574
[39] validation-logloss:0.44726 validation-auc:0.96107 validation-aucpr:0.96579
[40] validation-logloss:0.44277 validation-auc:0.96125 validation-aucpr:0.96602
[41] validation-logloss:0.43859 validation-auc:0.96130 validation-aucpr:0.96609
[42] validation-logloss:0.43545 validation-auc:0.96132 validation-aucpr:0.96611
[43] validation-logloss:0.43150 validation-auc:0.96136 validation-aucpr:0.96617
[44] validation-logloss:0.42759 validation-auc:0.96152 validation-aucpr:0.96822
[45] validation-logloss:0.42366 validation-auc:0.96164 validation-aucpr:0.96832
[46] validation-logloss:0.41974 validation-auc:0.96166 validation-aucpr:0.96838
[47] validation-logloss:0.41686 validation-auc:0.96159 validation-aucpr:0.96831
[48] validation-logloss:0.41391 validation-auc:0.96163 validation-aucpr:0.96832
{'best_iteration': '46', 'best_score': '0.9683846721459073'}
Trial 41, Fold 1: Log loss = 0.4139050734398529, Average precision = 0.9682114877618576, ROC-AUC = 0.9616322954455294, Elapsed Time = 4.119934400001512 seconds
Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 41, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68371 validation-auc:0.94526 validation-aucpr:0.94741
[1] validation-logloss:0.67530 validation-auc:0.95126 validation-aucpr:0.95521
[2] validation-logloss:0.66527 validation-auc:0.95994 validation-aucpr:0.96478
[3] validation-logloss:0.65699 validation-auc:0.95987 validation-aucpr:0.96462
[4] validation-logloss:0.64865 validation-auc:0.96036 validation-aucpr:0.96485
[5] validation-logloss:0.63981 validation-auc:0.96165 validation-aucpr:0.96635
[6] validation-logloss:0.63116 validation-auc:0.96223 validation-aucpr:0.96684
[7] validation-logloss:0.62251 validation-auc:0.96268 validation-aucpr:0.96721
[8] validation-logloss:0.61413 validation-auc:0.96318 validation-aucpr:0.96766
[9] validation-logloss:0.60700 validation-auc:0.96329 validation-aucpr:0.96768
[10] validation-logloss:0.59867 validation-auc:0.96395 validation-aucpr:0.96826
[11] validation-logloss:0.59178 validation-auc:0.96351 validation-aucpr:0.96787
[12] validation-logloss:0.58515 validation-auc:0.96352 validation-aucpr:0.96780
[13] validation-logloss:0.57791 validation-auc:0.96353 validation-aucpr:0.96789
[14] validation-logloss:0.57170 validation-auc:0.96322 validation-aucpr:0.96765
[15] validation-logloss:0.56460 validation-auc:0.96329 validation-aucpr:0.96768
[16] validation-logloss:0.55746 validation-auc:0.96352 validation-aucpr:0.96788
[17] validation-logloss:0.55055 validation-auc:0.96372 validation-aucpr:0.96801
[18] validation-logloss:0.54412 validation-auc:0.96359 validation-aucpr:0.96793
[19] validation-logloss:0.53864 validation-auc:0.96363 validation-aucpr:0.96786
[20] validation-logloss:0.53364 validation-auc:0.96322 validation-aucpr:0.96752
[21] validation-logloss:0.52860 validation-auc:0.96300 validation-aucpr:0.96725
[22] validation-logloss:0.52228 validation-auc:0.96335 validation-aucpr:0.96755
[23] validation-logloss:0.51648 validation-auc:0.96336 validation-aucpr:0.96756
[24] validation-logloss:0.51122 validation-auc:0.96327 validation-aucpr:0.96748
[25] validation-logloss:0.50565 validation-auc:0.96325 validation-aucpr:0.96750
[26] validation-logloss:0.50024 validation-auc:0.96327 validation-aucpr:0.96755
[27] validation-logloss:0.49564 validation-auc:0.96315 validation-aucpr:0.96753
[28] validation-logloss:0.49108 validation-auc:0.96323 validation-aucpr:0.96754
[29] validation-logloss:0.48579 validation-auc:0.96339 validation-aucpr:0.96763
[30] validation-logloss:0.48156 validation-auc:0.96343 validation-aucpr:0.96763
[31] validation-logloss:0.47673 validation-auc:0.96349 validation-aucpr:0.96764
[32] validation-logloss:0.47228 validation-auc:0.96346 validation-aucpr:0.96761
[33] validation-logloss:0.46826 validation-auc:0.96346 validation-aucpr:0.96755
[34] validation-logloss:0.46334 validation-auc:0.96364 validation-aucpr:0.96769
[35] validation-logloss:0.45901 validation-auc:0.96358 validation-aucpr:0.96769
[36] validation-logloss:0.45518 validation-auc:0.96368 validation-aucpr:0.96803
[37] validation-logloss:0.45090 validation-auc:0.96357 validation-aucpr:0.96793
[38] validation-logloss:0.44655 validation-auc:0.96361 validation-aucpr:0.96791
[39] validation-logloss:0.44311 validation-auc:0.96358 validation-aucpr:0.96792
[40] validation-logloss:0.43960 validation-auc:0.96348 validation-aucpr:0.96779
[41] validation-logloss:0.43520 validation-auc:0.96355 validation-aucpr:0.96786
[42] validation-logloss:0.43172 validation-auc:0.96367 validation-aucpr:0.96791
[43] validation-logloss:0.42754 validation-auc:0.96370 validation-aucpr:0.96797
[44] validation-logloss:0.42439 validation-auc:0.96379 validation-aucpr:0.96802
[45] validation-logloss:0.42052 validation-auc:0.96388 validation-aucpr:0.96813
[46] validation-logloss:0.41743 validation-auc:0.96393 validation-aucpr:0.96815
[47] validation-logloss:0.41383 validation-auc:0.96398 validation-aucpr:0.96821
[48] validation-logloss:0.41082 validation-auc:0.96399 validation-aucpr:0.96820
{'best_iteration': '10', 'best_score': '0.9682580044293139'}
Trial 41, Fold 2: Log loss = 0.41081987104648127, Average precision = 0.9681047990157229, ROC-AUC = 0.9639932095900958, Elapsed Time = 4.259771599998203 seconds
Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 41, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68306 validation-auc:0.95170 validation-aucpr:0.95260
[1] validation-logloss:0.67316 validation-auc:0.95748 validation-aucpr:0.96148
[2] validation-logloss:0.66321 validation-auc:0.96296 validation-aucpr:0.96563
[3] validation-logloss:0.65478 validation-auc:0.96364 validation-aucpr:0.96816
[4] validation-logloss:0.64658 validation-auc:0.96294 validation-aucpr:0.96766
[5] validation-logloss:0.63740 validation-auc:0.96314 validation-aucpr:0.96794
[6] validation-logloss:0.62997 validation-auc:0.96312 validation-aucpr:0.96784
[7] validation-logloss:0.62234 validation-auc:0.96269 validation-aucpr:0.96606
[8] validation-logloss:0.61551 validation-auc:0.96253 validation-aucpr:0.96568
[9] validation-logloss:0.60844 validation-auc:0.96245 validation-aucpr:0.96742
[10] validation-logloss:0.60185 validation-auc:0.96209 validation-aucpr:0.96701
[11] validation-logloss:0.59528 validation-auc:0.96211 validation-aucpr:0.96693
[12] validation-logloss:0.58888 validation-auc:0.96195 validation-aucpr:0.96674
[13] validation-logloss:0.58141 validation-auc:0.96205 validation-aucpr:0.96691
[14] validation-logloss:0.57413 validation-auc:0.96239 validation-aucpr:0.96716
[15] validation-logloss:0.56678 validation-auc:0.96287 validation-aucpr:0.96762
[16] validation-logloss:0.55953 validation-auc:0.96336 validation-aucpr:0.96810
[17] validation-logloss:0.55258 validation-auc:0.96357 validation-aucpr:0.96830
[18] validation-logloss:0.54665 validation-auc:0.96387 validation-aucpr:0.96862
[19] validation-logloss:0.54129 validation-auc:0.96371 validation-aucpr:0.96843
[20] validation-logloss:0.53478 validation-auc:0.96400 validation-aucpr:0.96873
[21] validation-logloss:0.52968 validation-auc:0.96379 validation-aucpr:0.96853
[22] validation-logloss:0.52431 validation-auc:0.96372 validation-aucpr:0.96848
[23] validation-logloss:0.51844 validation-auc:0.96382 validation-aucpr:0.96844
[24] validation-logloss:0.51257 validation-auc:0.96384 validation-aucpr:0.96854
[25] validation-logloss:0.50783 validation-auc:0.96364 validation-aucpr:0.96836
[26] validation-logloss:0.50311 validation-auc:0.96366 validation-aucpr:0.96832
[27] validation-logloss:0.49863 validation-auc:0.96349 validation-aucpr:0.96813
[28] validation-logloss:0.49382 validation-auc:0.96356 validation-aucpr:0.96817
[29] validation-logloss:0.48855 validation-auc:0.96346 validation-aucpr:0.96812
[30] validation-logloss:0.48418 validation-auc:0.96361 validation-aucpr:0.96889
[31] validation-logloss:0.47986 validation-auc:0.96379 validation-aucpr:0.96901
[32] validation-logloss:0.47486 validation-auc:0.96398 validation-aucpr:0.96916
[33] validation-logloss:0.46987 validation-auc:0.96406 validation-aucpr:0.96931
[34] validation-logloss:0.46588 validation-auc:0.96409 validation-aucpr:0.96928
[35] validation-logloss:0.46177 validation-auc:0.96410 validation-aucpr:0.96928
[36] validation-logloss:0.45746 validation-auc:0.96414 validation-aucpr:0.96933
[37] validation-logloss:0.45290 validation-auc:0.96426 validation-aucpr:0.96946
[38] validation-logloss:0.44836 validation-auc:0.96439 validation-aucpr:0.96960
[39] validation-logloss:0.44411 validation-auc:0.96436 validation-aucpr:0.96956
[40] validation-logloss:0.44051 validation-auc:0.96441 validation-aucpr:0.96958
[41] validation-logloss:0.43630 validation-auc:0.96454 validation-aucpr:0.96966
[42] validation-logloss:0.43266 validation-auc:0.96454 validation-aucpr:0.96967
[43] validation-logloss:0.42844 validation-auc:0.96456 validation-aucpr:0.96972
[44] validation-logloss:0.42503 validation-auc:0.96459 validation-aucpr:0.96974
[45] validation-logloss:0.42173 validation-auc:0.96464 validation-aucpr:0.96977
[46] validation-logloss:0.41833 validation-auc:0.96480 validation-aucpr:0.96989
[47] validation-logloss:0.41458 validation-auc:0.96484 validation-aucpr:0.96992
[48] validation-logloss:0.41149 validation-auc:0.96487 validation-aucpr:0.96990
{'best_iteration': '47', 'best_score': '0.9699236434103997'}
Trial 41, Fold 3: Log loss = 0.4114919188101245, Average precision = 0.9698635641105825, ROC-AUC = 0.9648704986949949, Elapsed Time = 4.189911100002064 seconds
Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 41, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68323 validation-auc:0.94846 validation-aucpr:0.95009
[1] validation-logloss:0.67334 validation-auc:0.95575 validation-aucpr:0.95982
[2] validation-logloss:0.66363 validation-auc:0.95780 validation-aucpr:0.96448
[3] validation-logloss:0.65565 validation-auc:0.95568 validation-aucpr:0.96329
[4] validation-logloss:0.64788 validation-auc:0.95672 validation-aucpr:0.96365
[5] validation-logloss:0.63867 validation-auc:0.95851 validation-aucpr:0.96501
[6] validation-logloss:0.63133 validation-auc:0.95796 validation-aucpr:0.96455
[7] validation-logloss:0.62366 validation-auc:0.95799 validation-aucpr:0.96498
[8] validation-logloss:0.61696 validation-auc:0.95686 validation-aucpr:0.96420
[9] validation-logloss:0.60978 validation-auc:0.95775 validation-aucpr:0.96483
[10] validation-logloss:0.60245 validation-auc:0.95793 validation-aucpr:0.96499
[11] validation-logloss:0.59574 validation-auc:0.95807 validation-aucpr:0.96497
[12] validation-logloss:0.58888 validation-auc:0.95834 validation-aucpr:0.96513
[13] validation-logloss:0.58268 validation-auc:0.95800 validation-aucpr:0.96483
[14] validation-logloss:0.57520 validation-auc:0.95887 validation-aucpr:0.96564
[15] validation-logloss:0.56899 validation-auc:0.95944 validation-aucpr:0.96592
[16] validation-logloss:0.56254 validation-auc:0.95948 validation-aucpr:0.96600
[17] validation-logloss:0.55692 validation-auc:0.95948 validation-aucpr:0.96591
[18] validation-logloss:0.55025 validation-auc:0.96031 validation-aucpr:0.96666
[19] validation-logloss:0.54397 validation-auc:0.96035 validation-aucpr:0.96678
[20] validation-logloss:0.53832 validation-auc:0.96047 validation-aucpr:0.96683
[21] validation-logloss:0.53304 validation-auc:0.96039 validation-aucpr:0.96678
[22] validation-logloss:0.52691 validation-auc:0.96083 validation-aucpr:0.96712
[23] validation-logloss:0.52184 validation-auc:0.96053 validation-aucpr:0.96687
[24] validation-logloss:0.51689 validation-auc:0.96055 validation-aucpr:0.96685
[25] validation-logloss:0.51114 validation-auc:0.96082 validation-aucpr:0.96711
[26] validation-logloss:0.50614 validation-auc:0.96095 validation-aucpr:0.96721
[27] validation-logloss:0.50164 validation-auc:0.96074 validation-aucpr:0.96701
[28] validation-logloss:0.49612 validation-auc:0.96094 validation-aucpr:0.96722
[29] validation-logloss:0.49051 validation-auc:0.96138 validation-aucpr:0.96765
[30] validation-logloss:0.48618 validation-auc:0.96132 validation-aucpr:0.96758
[31] validation-logloss:0.48213 validation-auc:0.96127 validation-aucpr:0.96754
[32] validation-logloss:0.47715 validation-auc:0.96140 validation-aucpr:0.96767
[33] validation-logloss:0.47226 validation-auc:0.96156 validation-aucpr:0.96782
[34] validation-logloss:0.46760 validation-auc:0.96157 validation-aucpr:0.96785
[35] validation-logloss:0.46381 validation-auc:0.96155 validation-aucpr:0.96781
[36] validation-logloss:0.45912 validation-auc:0.96158 validation-aucpr:0.96791
[37] validation-logloss:0.45454 validation-auc:0.96169 validation-aucpr:0.96804
[38] validation-logloss:0.44998 validation-auc:0.96181 validation-aucpr:0.96818
[39] validation-logloss:0.44650 validation-auc:0.96180 validation-aucpr:0.96815
[40] validation-logloss:0.44300 validation-auc:0.96188 validation-aucpr:0.96818
[41] validation-logloss:0.43950 validation-auc:0.96185 validation-aucpr:0.96815
[42] validation-logloss:0.43635 validation-auc:0.96180 validation-aucpr:0.96809
[43] validation-logloss:0.43234 validation-auc:0.96184 validation-aucpr:0.96818
[44] validation-logloss:0.42835 validation-auc:0.96196 validation-aucpr:0.96827
[45] validation-logloss:0.42533 validation-auc:0.96192 validation-aucpr:0.96823
[46] validation-logloss:0.42230 validation-auc:0.96195 validation-aucpr:0.96822
[47] validation-logloss:0.41868 validation-auc:0.96199 validation-aucpr:0.96829
[48] validation-logloss:0.41574 validation-auc:0.96199 validation-aucpr:0.96827
{'best_iteration': '47', 'best_score': '0.968289622878098'}
Trial 41, Fold 4: Log loss = 0.4157433809493005, Average precision = 0.9680932874566976, ROC-AUC = 0.9619874413137145, Elapsed Time = 4.13615320000099 seconds
Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 41, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68440 validation-auc:0.92976 validation-aucpr:0.92645
[1] validation-logloss:0.67556 validation-auc:0.94104 validation-aucpr:0.94659
[2] validation-logloss:0.66566 validation-auc:0.95372 validation-aucpr:0.96037
[3] validation-logloss:0.65798 validation-auc:0.95399 validation-aucpr:0.96125
[4] validation-logloss:0.64974 validation-auc:0.95305 validation-aucpr:0.96017
[5] validation-logloss:0.64186 validation-auc:0.95301 validation-aucpr:0.96000
[6] validation-logloss:0.63323 validation-auc:0.95498 validation-aucpr:0.96207
[7] validation-logloss:0.62626 validation-auc:0.95426 validation-aucpr:0.96035
[8] validation-logloss:0.61917 validation-auc:0.95547 validation-aucpr:0.96114
[9] validation-logloss:0.61248 validation-auc:0.95466 validation-aucpr:0.96039
[10] validation-logloss:0.60430 validation-auc:0.95563 validation-aucpr:0.96153
[11] validation-logloss:0.59715 validation-auc:0.95595 validation-aucpr:0.96166
[12] validation-logloss:0.59094 validation-auc:0.95508 validation-aucpr:0.96094
[13] validation-logloss:0.58471 validation-auc:0.95447 validation-aucpr:0.96041
[14] validation-logloss:0.57758 validation-auc:0.95541 validation-aucpr:0.96167
[15] validation-logloss:0.57130 validation-auc:0.95643 validation-aucpr:0.96241
[16] validation-logloss:0.56515 validation-auc:0.95653 validation-aucpr:0.96256
[17] validation-logloss:0.55844 validation-auc:0.95650 validation-aucpr:0.96262
[18] validation-logloss:0.55182 validation-auc:0.95666 validation-aucpr:0.96287
[19] validation-logloss:0.54651 validation-auc:0.95675 validation-aucpr:0.96280
[20] validation-logloss:0.54133 validation-auc:0.95653 validation-aucpr:0.96228
[21] validation-logloss:0.53643 validation-auc:0.95630 validation-aucpr:0.96195
[22] validation-logloss:0.53106 validation-auc:0.95645 validation-aucpr:0.96208
[23] validation-logloss:0.52491 validation-auc:0.95656 validation-aucpr:0.96237
[24] validation-logloss:0.51874 validation-auc:0.95748 validation-aucpr:0.96312
[25] validation-logloss:0.51294 validation-auc:0.95767 validation-aucpr:0.96334
[26] validation-logloss:0.50801 validation-auc:0.95796 validation-aucpr:0.96352
[27] validation-logloss:0.50348 validation-auc:0.95815 validation-aucpr:0.96461
[28] validation-logloss:0.49829 validation-auc:0.95824 validation-aucpr:0.96473
[29] validation-logloss:0.49297 validation-auc:0.95851 validation-aucpr:0.96497
[30] validation-logloss:0.48900 validation-auc:0.95821 validation-aucpr:0.96462
[31] validation-logloss:0.48430 validation-auc:0.95829 validation-aucpr:0.96467
[32] validation-logloss:0.47924 validation-auc:0.95850 validation-aucpr:0.96486
[33] validation-logloss:0.47440 validation-auc:0.95862 validation-aucpr:0.96500
[34] validation-logloss:0.47048 validation-auc:0.95869 validation-aucpr:0.96505
[35] validation-logloss:0.46634 validation-auc:0.95873 validation-aucpr:0.96510
[36] validation-logloss:0.46242 validation-auc:0.95935 validation-aucpr:0.96543
[37] validation-logloss:0.45775 validation-auc:0.95957 validation-aucpr:0.96562
[38] validation-logloss:0.45424 validation-auc:0.95942 validation-aucpr:0.96547
[39] validation-logloss:0.44959 validation-auc:0.95985 validation-aucpr:0.96581
[40] validation-logloss:0.44624 validation-auc:0.95998 validation-aucpr:0.96590
[41] validation-logloss:0.44178 validation-auc:0.96003 validation-aucpr:0.96599
[42] validation-logloss:0.43828 validation-auc:0.96014 validation-aucpr:0.96605
[43] validation-logloss:0.43408 validation-auc:0.96034 validation-aucpr:0.96624
[44] validation-logloss:0.43106 validation-auc:0.96029 validation-aucpr:0.96618
[45] validation-logloss:0.42792 validation-auc:0.96031 validation-aucpr:0.96615
[46] validation-logloss:0.42403 validation-auc:0.96038 validation-aucpr:0.96624
[47] validation-logloss:0.42112 validation-auc:0.96046 validation-aucpr:0.96629
[48] validation-logloss:0.41818 validation-auc:0.96047 validation-aucpr:0.96630
{'best_iteration': '48', 'best_score': '0.9662968778042819'}
Trial 41, Fold 5: Log loss = 0.4181768956842521, Average precision = 0.9661507923621591, ROC-AUC = 0.9604730319751779, Elapsed Time = 4.168298500000674 seconds
Optimization Progress: 42%|####2 | 42/100 [2:26:24<1:39:34, 103.01s/it]
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 42, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68113 validation-auc:0.94891 validation-aucpr:0.92942
[1] validation-logloss:0.66946 validation-auc:0.96345 validation-aucpr:0.96057
[2] validation-logloss:0.65818 validation-auc:0.96507 validation-aucpr:0.96730
[3] validation-logloss:0.64900 validation-auc:0.96546 validation-aucpr:0.96958
[4] validation-logloss:0.63959 validation-auc:0.96565 validation-aucpr:0.96935
[5] validation-logloss:0.63061 validation-auc:0.96558 validation-aucpr:0.96940
[6] validation-logloss:0.62165 validation-auc:0.96546 validation-aucpr:0.96913
[7] validation-logloss:0.61220 validation-auc:0.96615 validation-aucpr:0.97013
[8] validation-logloss:0.60262 validation-auc:0.96637 validation-aucpr:0.97038
[9] validation-logloss:0.59342 validation-auc:0.96671 validation-aucpr:0.97070
[10] validation-logloss:0.58558 validation-auc:0.96699 validation-aucpr:0.97215
[11] validation-logloss:0.57772 validation-auc:0.96691 validation-aucpr:0.97202
[12] validation-logloss:0.57015 validation-auc:0.96691 validation-aucpr:0.97199
[13] validation-logloss:0.56185 validation-auc:0.96710 validation-aucpr:0.97216
[14] validation-logloss:0.55398 validation-auc:0.96715 validation-aucpr:0.97220
[15] validation-logloss:0.54724 validation-auc:0.96708 validation-aucpr:0.97206
[16] validation-logloss:0.53935 validation-auc:0.96748 validation-aucpr:0.97242
[17] validation-logloss:0.53190 validation-auc:0.96776 validation-aucpr:0.97268
[18] validation-logloss:0.52546 validation-auc:0.96766 validation-aucpr:0.97252
[19] validation-logloss:0.51907 validation-auc:0.96767 validation-aucpr:0.97249
[20] validation-logloss:0.51231 validation-auc:0.96776 validation-aucpr:0.97264
[21] validation-logloss:0.50565 validation-auc:0.96789 validation-aucpr:0.97287
[22] validation-logloss:0.49986 validation-auc:0.96792 validation-aucpr:0.97284
[23] validation-logloss:0.49349 validation-auc:0.96809 validation-aucpr:0.97298
[24] validation-logloss:0.48810 validation-auc:0.96805 validation-aucpr:0.97291
[25] validation-logloss:0.48268 validation-auc:0.96789 validation-aucpr:0.97275
[26] validation-logloss:0.47651 validation-auc:0.96814 validation-aucpr:0.97293
[27] validation-logloss:0.47143 validation-auc:0.96801 validation-aucpr:0.97283
[28] validation-logloss:0.46570 validation-auc:0.96819 validation-aucpr:0.97299
[29] validation-logloss:0.46075 validation-auc:0.96821 validation-aucpr:0.97297
[30] validation-logloss:0.45532 validation-auc:0.96830 validation-aucpr:0.97307
[31] validation-logloss:0.44999 validation-auc:0.96841 validation-aucpr:0.97313
[32] validation-logloss:0.44556 validation-auc:0.96828 validation-aucpr:0.97300
[33] validation-logloss:0.44033 validation-auc:0.96845 validation-aucpr:0.97314
[34] validation-logloss:0.43525 validation-auc:0.96856 validation-aucpr:0.97326
[35] validation-logloss:0.43037 validation-auc:0.96871 validation-aucpr:0.97347
[36] validation-logloss:0.42546 validation-auc:0.96883 validation-aucpr:0.97360
[37] validation-logloss:0.42128 validation-auc:0.96888 validation-aucpr:0.97359
[38] validation-logloss:0.41739 validation-auc:0.96878 validation-aucpr:0.97348
[39] validation-logloss:0.41274 validation-auc:0.96901 validation-aucpr:0.97366
[40] validation-logloss:0.40883 validation-auc:0.96901 validation-aucpr:0.97364
[41] validation-logloss:0.40532 validation-auc:0.96898 validation-aucpr:0.97358
[42] validation-logloss:0.40110 validation-auc:0.96901 validation-aucpr:0.97363
[43] validation-logloss:0.39686 validation-auc:0.96911 validation-aucpr:0.97372
[44] validation-logloss:0.39354 validation-auc:0.96904 validation-aucpr:0.97363
[45] validation-logloss:0.38949 validation-auc:0.96918 validation-aucpr:0.97375
[46] validation-logloss:0.38628 validation-auc:0.96901 validation-aucpr:0.97361
[47] validation-logloss:0.38298 validation-auc:0.96900 validation-aucpr:0.97360
[48] validation-logloss:0.37915 validation-auc:0.96918 validation-aucpr:0.97388
[49] validation-logloss:0.37602 validation-auc:0.96921 validation-aucpr:0.97387
[50] validation-logloss:0.37293 validation-auc:0.96924 validation-aucpr:0.97388
[51] validation-logloss:0.36928 validation-auc:0.96936 validation-aucpr:0.97400
[52] validation-logloss:0.36576 validation-auc:0.96946 validation-aucpr:0.97409
[53] validation-logloss:0.36232 validation-auc:0.96952 validation-aucpr:0.97415
[54] validation-logloss:0.35955 validation-auc:0.96948 validation-aucpr:0.97412
[55] validation-logloss:0.35615 validation-auc:0.96965 validation-aucpr:0.97426
[56] validation-logloss:0.35294 validation-auc:0.96969 validation-aucpr:0.97431
[57] validation-logloss:0.35037 validation-auc:0.96964 validation-aucpr:0.97425
[58] validation-logloss:0.34730 validation-auc:0.96974 validation-aucpr:0.97432
[59] validation-logloss:0.34485 validation-auc:0.96969 validation-aucpr:0.97428
[60] validation-logloss:0.34238 validation-auc:0.96965 validation-aucpr:0.97423
[61] validation-logloss:0.34002 validation-auc:0.96960 validation-aucpr:0.97416
[62] validation-logloss:0.33716 validation-auc:0.96964 validation-aucpr:0.97421
[63] validation-logloss:0.33428 validation-auc:0.96972 validation-aucpr:0.97428
[64] validation-logloss:0.33204 validation-auc:0.96970 validation-aucpr:0.97426
[65] validation-logloss:0.32990 validation-auc:0.96962 validation-aucpr:0.97418
[66] validation-logloss:0.32727 validation-auc:0.96965 validation-aucpr:0.97421
[67] validation-logloss:0.32518 validation-auc:0.96954 validation-aucpr:0.97411
[68] validation-logloss:0.32298 validation-auc:0.96957 validation-aucpr:0.97413
[69] validation-logloss:0.32039 validation-auc:0.96961 validation-aucpr:0.97416
[70] validation-logloss:0.31792 validation-auc:0.96969 validation-aucpr:0.97421
[71] validation-logloss:0.31601 validation-auc:0.96967 validation-aucpr:0.97417
[72] validation-logloss:0.31367 validation-auc:0.96972 validation-aucpr:0.97422
{'best_iteration': '58', 'best_score': '0.9743230383233791'}
Trial 42, Fold 1: Log loss = 0.3136689057542144, Average precision = 0.9742187899161252, ROC-AUC = 0.969719808529655, Elapsed Time = 1.9366607000010845 seconds
Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 42, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68136 validation-auc:0.94753 validation-aucpr:0.90845
[1] validation-logloss:0.67107 validation-auc:0.96181 validation-aucpr:0.95788
[2] validation-logloss:0.66099 validation-auc:0.96363 validation-aucpr:0.96454
[3] validation-logloss:0.65146 validation-auc:0.96404 validation-aucpr:0.96720
[4] validation-logloss:0.64192 validation-auc:0.96476 validation-aucpr:0.96778
[5] validation-logloss:0.63260 validation-auc:0.96443 validation-aucpr:0.96743
[6] validation-logloss:0.62251 validation-auc:0.96641 validation-aucpr:0.96968
[7] validation-logloss:0.61280 validation-auc:0.96754 validation-aucpr:0.97090
[8] validation-logloss:0.60463 validation-auc:0.96730 validation-aucpr:0.97064
[9] validation-logloss:0.59676 validation-auc:0.96671 validation-aucpr:0.97034
[10] validation-logloss:0.58901 validation-auc:0.96640 validation-aucpr:0.97000
[11] validation-logloss:0.58023 validation-auc:0.96723 validation-aucpr:0.97075
[12] validation-logloss:0.57163 validation-auc:0.96762 validation-aucpr:0.97113
[13] validation-logloss:0.56320 validation-auc:0.96809 validation-aucpr:0.97159
[14] validation-logloss:0.55529 validation-auc:0.96818 validation-aucpr:0.97170
[15] validation-logloss:0.54840 validation-auc:0.96824 validation-aucpr:0.97170
[16] validation-logloss:0.54170 validation-auc:0.96811 validation-aucpr:0.97155
[17] validation-logloss:0.53506 validation-auc:0.96815 validation-aucpr:0.97155
[18] validation-logloss:0.52786 validation-auc:0.96826 validation-aucpr:0.97169
[19] validation-logloss:0.52079 validation-auc:0.96839 validation-aucpr:0.97179
[20] validation-logloss:0.51377 validation-auc:0.96858 validation-aucpr:0.97195
[21] validation-logloss:0.50773 validation-auc:0.96873 validation-aucpr:0.97207
[22] validation-logloss:0.50206 validation-auc:0.96872 validation-aucpr:0.97205
[23] validation-logloss:0.49631 validation-auc:0.96871 validation-aucpr:0.97204
[24] validation-logloss:0.49081 validation-auc:0.96876 validation-aucpr:0.97186
[25] validation-logloss:0.48452 validation-auc:0.96889 validation-aucpr:0.97198
[26] validation-logloss:0.47831 validation-auc:0.96912 validation-aucpr:0.97219
[27] validation-logloss:0.47318 validation-auc:0.96906 validation-aucpr:0.97215
[28] validation-logloss:0.46725 validation-auc:0.96918 validation-aucpr:0.97225
[29] validation-logloss:0.46147 validation-auc:0.96928 validation-aucpr:0.97236
[30] validation-logloss:0.45580 validation-auc:0.96936 validation-aucpr:0.97242
[31] validation-logloss:0.45047 validation-auc:0.96951 validation-aucpr:0.97253
[32] validation-logloss:0.44574 validation-auc:0.96951 validation-aucpr:0.97255
[33] validation-logloss:0.44137 validation-auc:0.96946 validation-aucpr:0.97251
[34] validation-logloss:0.43622 validation-auc:0.96960 validation-aucpr:0.97263
[35] validation-logloss:0.43128 validation-auc:0.96968 validation-aucpr:0.97271
[36] validation-logloss:0.42712 validation-auc:0.96960 validation-aucpr:0.97266
[37] validation-logloss:0.42225 validation-auc:0.96968 validation-aucpr:0.97273
[38] validation-logloss:0.41757 validation-auc:0.96974 validation-aucpr:0.97279
[39] validation-logloss:0.41296 validation-auc:0.96976 validation-aucpr:0.97284
[40] validation-logloss:0.40843 validation-auc:0.96991 validation-aucpr:0.97296
[41] validation-logloss:0.40416 validation-auc:0.96994 validation-aucpr:0.97300
[42] validation-logloss:0.40045 validation-auc:0.96987 validation-aucpr:0.97295
[43] validation-logloss:0.39631 validation-auc:0.96988 validation-aucpr:0.97298
[44] validation-logloss:0.39278 validation-auc:0.96982 validation-aucpr:0.97294
[45] validation-logloss:0.38931 validation-auc:0.96983 validation-aucpr:0.97293
[46] validation-logloss:0.38597 validation-auc:0.96982 validation-aucpr:0.97292
[47] validation-logloss:0.38208 validation-auc:0.96987 validation-aucpr:0.97296
[48] validation-logloss:0.37822 validation-auc:0.96989 validation-aucpr:0.97300
[49] validation-logloss:0.37511 validation-auc:0.96979 validation-aucpr:0.97292
[50] validation-logloss:0.37202 validation-auc:0.96980 validation-aucpr:0.97292
[51] validation-logloss:0.36832 validation-auc:0.96992 validation-aucpr:0.97302
[52] validation-logloss:0.36484 validation-auc:0.96996 validation-aucpr:0.97306
[53] validation-logloss:0.36186 validation-auc:0.96993 validation-aucpr:0.97304
[54] validation-logloss:0.35842 validation-auc:0.96999 validation-aucpr:0.97310
[55] validation-logloss:0.35504 validation-auc:0.97013 validation-aucpr:0.97321
[56] validation-logloss:0.35179 validation-auc:0.97016 validation-aucpr:0.97324
[57] validation-logloss:0.34865 validation-auc:0.97026 validation-aucpr:0.97331
[58] validation-logloss:0.34605 validation-auc:0.97023 validation-aucpr:0.97329
[59] validation-logloss:0.34291 validation-auc:0.97035 validation-aucpr:0.97335
[60] validation-logloss:0.33996 validation-auc:0.97034 validation-aucpr:0.97336
[61] validation-logloss:0.33699 validation-auc:0.97047 validation-aucpr:0.97345
[62] validation-logloss:0.33452 validation-auc:0.97047 validation-aucpr:0.97342
[63] validation-logloss:0.33216 validation-auc:0.97046 validation-aucpr:0.97342
[64] validation-logloss:0.32945 validation-auc:0.97053 validation-aucpr:0.97346
[65] validation-logloss:0.32718 validation-auc:0.97050 validation-aucpr:0.97344
[66] validation-logloss:0.32442 validation-auc:0.97060 validation-aucpr:0.97348
[67] validation-logloss:0.32186 validation-auc:0.97063 validation-aucpr:0.97351
[68] validation-logloss:0.31925 validation-auc:0.97071 validation-aucpr:0.97356
[69] validation-logloss:0.31706 validation-auc:0.97078 validation-aucpr:0.97360
[70] validation-logloss:0.31451 validation-auc:0.97084 validation-aucpr:0.97367
[71] validation-logloss:0.31244 validation-auc:0.97085 validation-aucpr:0.97367
[72] validation-logloss:0.31005 validation-auc:0.97089 validation-aucpr:0.97369
{'best_iteration': '72', 'best_score': '0.973692450730977'}
Trial 42, Fold 2: Log loss = 0.3100492448380225, Average precision = 0.9736645921803798, ROC-AUC = 0.9708852146366203, Elapsed Time = 2.2306970999998157 seconds
Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 42, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68110 validation-auc:0.94677 validation-aucpr:0.91224
[1] validation-logloss:0.67096 validation-auc:0.96183 validation-aucpr:0.95749
[2] validation-logloss:0.65956 validation-auc:0.96645 validation-aucpr:0.96741
[3] validation-logloss:0.64856 validation-auc:0.96805 validation-aucpr:0.97135
[4] validation-logloss:0.63795 validation-auc:0.96863 validation-aucpr:0.97192
[5] validation-logloss:0.62753 validation-auc:0.96904 validation-aucpr:0.97229
[6] validation-logloss:0.61771 validation-auc:0.96928 validation-aucpr:0.97283
[7] validation-logloss:0.60954 validation-auc:0.96891 validation-aucpr:0.97234
[8] validation-logloss:0.59999 validation-auc:0.96962 validation-aucpr:0.97292
[9] validation-logloss:0.59160 validation-auc:0.96949 validation-aucpr:0.97285
[10] validation-logloss:0.58350 validation-auc:0.96911 validation-aucpr:0.97249
[11] validation-logloss:0.57585 validation-auc:0.96850 validation-aucpr:0.97197
[12] validation-logloss:0.56734 validation-auc:0.96860 validation-aucpr:0.97213
[13] validation-logloss:0.56000 validation-auc:0.96837 validation-aucpr:0.97192
[14] validation-logloss:0.55316 validation-auc:0.96833 validation-aucpr:0.97225
[15] validation-logloss:0.54635 validation-auc:0.96834 validation-aucpr:0.97226
[16] validation-logloss:0.53954 validation-auc:0.96854 validation-aucpr:0.97247
[17] validation-logloss:0.53208 validation-auc:0.96873 validation-aucpr:0.97262
[18] validation-logloss:0.52474 validation-auc:0.96884 validation-aucpr:0.97271
[19] validation-logloss:0.51824 validation-auc:0.96905 validation-aucpr:0.97288
[20] validation-logloss:0.51109 validation-auc:0.96936 validation-aucpr:0.97313
[21] validation-logloss:0.50495 validation-auc:0.96925 validation-aucpr:0.97303
[22] validation-logloss:0.49913 validation-auc:0.96911 validation-aucpr:0.97293
[23] validation-logloss:0.49257 validation-auc:0.96921 validation-aucpr:0.97301
[24] validation-logloss:0.48703 validation-auc:0.96924 validation-aucpr:0.97291
[25] validation-logloss:0.48081 validation-auc:0.96952 validation-aucpr:0.97162
[26] validation-logloss:0.47489 validation-auc:0.96976 validation-aucpr:0.97403
[27] validation-logloss:0.46903 validation-auc:0.96987 validation-aucpr:0.97412
[28] validation-logloss:0.46402 validation-auc:0.96983 validation-aucpr:0.97407
[29] validation-logloss:0.45826 validation-auc:0.96995 validation-aucpr:0.97419
[30] validation-logloss:0.45275 validation-auc:0.97011 validation-aucpr:0.97433
[31] validation-logloss:0.44815 validation-auc:0.97007 validation-aucpr:0.97426
[32] validation-logloss:0.44284 validation-auc:0.97012 validation-aucpr:0.97432
[33] validation-logloss:0.43784 validation-auc:0.97007 validation-aucpr:0.97426
[34] validation-logloss:0.43297 validation-auc:0.97011 validation-aucpr:0.97431
[35] validation-logloss:0.42818 validation-auc:0.97007 validation-aucpr:0.97429
[36] validation-logloss:0.42337 validation-auc:0.97022 validation-aucpr:0.97445
[37] validation-logloss:0.41866 validation-auc:0.97025 validation-aucpr:0.97447
[38] validation-logloss:0.41400 validation-auc:0.97033 validation-aucpr:0.97454
[39] validation-logloss:0.41025 validation-auc:0.97042 validation-aucpr:0.97466
[40] validation-logloss:0.40624 validation-auc:0.97044 validation-aucpr:0.97466
[41] validation-logloss:0.40250 validation-auc:0.97040 validation-aucpr:0.97462
[42] validation-logloss:0.39880 validation-auc:0.97042 validation-aucpr:0.97462
[43] validation-logloss:0.39518 validation-auc:0.97037 validation-aucpr:0.97458
[44] validation-logloss:0.39099 validation-auc:0.97046 validation-aucpr:0.97468
[45] validation-logloss:0.38699 validation-auc:0.97054 validation-aucpr:0.97475
[46] validation-logloss:0.38306 validation-auc:0.97068 validation-aucpr:0.97490
[47] validation-logloss:0.37929 validation-auc:0.97074 validation-aucpr:0.97495
[48] validation-logloss:0.37543 validation-auc:0.97079 validation-aucpr:0.97499
[49] validation-logloss:0.37226 validation-auc:0.97082 validation-aucpr:0.97503
[50] validation-logloss:0.36912 validation-auc:0.97079 validation-aucpr:0.97501
[51] validation-logloss:0.36553 validation-auc:0.97092 validation-aucpr:0.97511
[52] validation-logloss:0.36246 validation-auc:0.97098 validation-aucpr:0.97513
[53] validation-logloss:0.35964 validation-auc:0.97096 validation-aucpr:0.97509
[54] validation-logloss:0.35681 validation-auc:0.97099 validation-aucpr:0.97510
[55] validation-logloss:0.35399 validation-auc:0.97101 validation-aucpr:0.97512
[56] validation-logloss:0.35126 validation-auc:0.97093 validation-aucpr:0.97507
[57] validation-logloss:0.34813 validation-auc:0.97101 validation-aucpr:0.97514
[58] validation-logloss:0.34512 validation-auc:0.97106 validation-aucpr:0.97521
[59] validation-logloss:0.34200 validation-auc:0.97115 validation-aucpr:0.97528
[60] validation-logloss:0.33905 validation-auc:0.97111 validation-aucpr:0.97527
[61] validation-logloss:0.33614 validation-auc:0.97111 validation-aucpr:0.97526
[62] validation-logloss:0.33335 validation-auc:0.97113 validation-aucpr:0.97528
[63] validation-logloss:0.33105 validation-auc:0.97111 validation-aucpr:0.97528
[64] validation-logloss:0.32873 validation-auc:0.97108 validation-aucpr:0.97530
[65] validation-logloss:0.32640 validation-auc:0.97112 validation-aucpr:0.97533
[66] validation-logloss:0.32371 validation-auc:0.97115 validation-aucpr:0.97535
[67] validation-logloss:0.32151 validation-auc:0.97107 validation-aucpr:0.97528
[68] validation-logloss:0.31938 validation-auc:0.97110 validation-aucpr:0.97541
[69] validation-logloss:0.31726 validation-auc:0.97116 validation-aucpr:0.97545
[70] validation-logloss:0.31472 validation-auc:0.97119 validation-aucpr:0.97548
[71] validation-logloss:0.31275 validation-auc:0.97114 validation-aucpr:0.97543
[72] validation-logloss:0.31030 validation-auc:0.97119 validation-aucpr:0.97549
{'best_iteration': '72', 'best_score': '0.9754871959768607'}
Trial 42, Fold 3: Log loss = 0.3102979970624399, Average precision = 0.9754888562852468, ROC-AUC = 0.9711920693241902, Elapsed Time = 2.2501545999984955 seconds
Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 42, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68115 validation-auc:0.94672 validation-aucpr:0.93040
[1] validation-logloss:0.67083 validation-auc:0.95995 validation-aucpr:0.96448
[2] validation-logloss:0.66061 validation-auc:0.96409 validation-aucpr:0.96962
[3] validation-logloss:0.64959 validation-auc:0.96598 validation-aucpr:0.97121
[4] validation-logloss:0.64015 validation-auc:0.96565 validation-aucpr:0.97111
[5] validation-logloss:0.63110 validation-auc:0.96524 validation-aucpr:0.97060
[6] validation-logloss:0.62102 validation-auc:0.96618 validation-aucpr:0.97146
[7] validation-logloss:0.61125 validation-auc:0.96686 validation-aucpr:0.97194
[8] validation-logloss:0.60289 validation-auc:0.96669 validation-aucpr:0.97173
[9] validation-logloss:0.59483 validation-auc:0.96658 validation-aucpr:0.97153
[10] validation-logloss:0.58681 validation-auc:0.96644 validation-aucpr:0.97140
[11] validation-logloss:0.57930 validation-auc:0.96629 validation-aucpr:0.97130
[12] validation-logloss:0.57186 validation-auc:0.96621 validation-aucpr:0.97128
[13] validation-logloss:0.56450 validation-auc:0.96634 validation-aucpr:0.97133
[14] validation-logloss:0.55647 validation-auc:0.96664 validation-aucpr:0.97168
[15] validation-logloss:0.54983 validation-auc:0.96637 validation-aucpr:0.97146
[16] validation-logloss:0.54209 validation-auc:0.96645 validation-aucpr:0.97160
[17] validation-logloss:0.53460 validation-auc:0.96669 validation-aucpr:0.97185
[18] validation-logloss:0.52717 validation-auc:0.96696 validation-aucpr:0.97211
[19] validation-logloss:0.52092 validation-auc:0.96676 validation-aucpr:0.97189
[20] validation-logloss:0.51468 validation-auc:0.96689 validation-aucpr:0.97199
[21] validation-logloss:0.50879 validation-auc:0.96700 validation-aucpr:0.97202
[22] validation-logloss:0.50207 validation-auc:0.96719 validation-aucpr:0.97222
[23] validation-logloss:0.49562 validation-auc:0.96726 validation-aucpr:0.97233
[24] validation-logloss:0.49019 validation-auc:0.96723 validation-aucpr:0.97227
[25] validation-logloss:0.48391 validation-auc:0.96730 validation-aucpr:0.97236
[26] validation-logloss:0.47768 validation-auc:0.96739 validation-aucpr:0.97248
[27] validation-logloss:0.47247 validation-auc:0.96736 validation-aucpr:0.97244
[28] validation-logloss:0.46746 validation-auc:0.96749 validation-aucpr:0.97251
[29] validation-logloss:0.46258 validation-auc:0.96749 validation-aucpr:0.97248
[30] validation-logloss:0.45698 validation-auc:0.96758 validation-aucpr:0.97258
[31] validation-logloss:0.45232 validation-auc:0.96761 validation-aucpr:0.97259
[32] validation-logloss:0.44776 validation-auc:0.96758 validation-aucpr:0.97255
[33] validation-logloss:0.44268 validation-auc:0.96768 validation-aucpr:0.97266
[34] validation-logloss:0.43752 validation-auc:0.96782 validation-aucpr:0.97280
[35] validation-logloss:0.43263 validation-auc:0.96785 validation-aucpr:0.97286
[36] validation-logloss:0.42772 validation-auc:0.96805 validation-aucpr:0.97301
[37] validation-logloss:0.42365 validation-auc:0.96798 validation-aucpr:0.97294
[38] validation-logloss:0.41966 validation-auc:0.96788 validation-aucpr:0.97285
[39] validation-logloss:0.41582 validation-auc:0.96789 validation-aucpr:0.97285
[40] validation-logloss:0.41125 validation-auc:0.96802 validation-aucpr:0.97295
[41] validation-logloss:0.40760 validation-auc:0.96804 validation-aucpr:0.97295
[42] validation-logloss:0.40323 validation-auc:0.96811 validation-aucpr:0.97304
[43] validation-logloss:0.39895 validation-auc:0.96815 validation-aucpr:0.97308
[44] validation-logloss:0.39477 validation-auc:0.96828 validation-aucpr:0.97321
[45] validation-logloss:0.39065 validation-auc:0.96840 validation-aucpr:0.97332
[46] validation-logloss:0.38721 validation-auc:0.96845 validation-aucpr:0.97334
[47] validation-logloss:0.38329 validation-auc:0.96853 validation-aucpr:0.97342
[48] validation-logloss:0.37944 validation-auc:0.96866 validation-aucpr:0.97354
[49] validation-logloss:0.37563 validation-auc:0.96875 validation-aucpr:0.97362
[50] validation-logloss:0.37190 validation-auc:0.96887 validation-aucpr:0.97372
[51] validation-logloss:0.36835 validation-auc:0.96894 validation-aucpr:0.97378
[52] validation-logloss:0.36537 validation-auc:0.96901 validation-aucpr:0.97381
[53] validation-logloss:0.36251 validation-auc:0.96897 validation-aucpr:0.97376
[54] validation-logloss:0.35966 validation-auc:0.96894 validation-aucpr:0.97375
[55] validation-logloss:0.35628 validation-auc:0.96905 validation-aucpr:0.97384
[56] validation-logloss:0.35350 validation-auc:0.96904 validation-aucpr:0.97382
[57] validation-logloss:0.35029 validation-auc:0.96905 validation-aucpr:0.97386
[58] validation-logloss:0.34781 validation-auc:0.96895 validation-aucpr:0.97379
[59] validation-logloss:0.34464 validation-auc:0.96911 validation-aucpr:0.97394
[60] validation-logloss:0.34155 validation-auc:0.96925 validation-aucpr:0.97405
[61] validation-logloss:0.33858 validation-auc:0.96929 validation-aucpr:0.97410
[62] validation-logloss:0.33617 validation-auc:0.96932 validation-aucpr:0.97411
[63] validation-logloss:0.33338 validation-auc:0.96936 validation-aucpr:0.97415
[64] validation-logloss:0.33052 validation-auc:0.96945 validation-aucpr:0.97422
[65] validation-logloss:0.32845 validation-auc:0.96936 validation-aucpr:0.97416
[66] validation-logloss:0.32628 validation-auc:0.96931 validation-aucpr:0.97410
[67] validation-logloss:0.32409 validation-auc:0.96932 validation-aucpr:0.97410
[68] validation-logloss:0.32146 validation-auc:0.96936 validation-aucpr:0.97415
[69] validation-logloss:0.31887 validation-auc:0.96948 validation-aucpr:0.97425
[70] validation-logloss:0.31683 validation-auc:0.96947 validation-aucpr:0.97423
[71] validation-logloss:0.31490 validation-auc:0.96943 validation-aucpr:0.97420
[72] validation-logloss:0.31291 validation-auc:0.96942 validation-aucpr:0.97418
{'best_iteration': '69', 'best_score': '0.9742472920910086'}
Trial 42, Fold 4: Log loss = 0.3129062231328123, Average precision = 0.9741810472294776, ROC-AUC = 0.9694151013016492, Elapsed Time = 2.174041499998566 seconds
Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 42, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68107 validation-auc:0.95236 validation-aucpr:0.94188
[1] validation-logloss:0.66936 validation-auc:0.96419 validation-aucpr:0.96656
[2] validation-logloss:0.65987 validation-auc:0.96348 validation-aucpr:0.96789
[3] validation-logloss:0.64997 validation-auc:0.96523 validation-aucpr:0.96913
[4] validation-logloss:0.64050 validation-auc:0.96514 validation-aucpr:0.96886
[5] validation-logloss:0.63016 validation-auc:0.96583 validation-aucpr:0.96954
[6] validation-logloss:0.62177 validation-auc:0.96545 validation-aucpr:0.96914
[7] validation-logloss:0.61182 validation-auc:0.96661 validation-aucpr:0.97020
[8] validation-logloss:0.60338 validation-auc:0.96661 validation-aucpr:0.97008
[9] validation-logloss:0.59418 validation-auc:0.96700 validation-aucpr:0.97075
[10] validation-logloss:0.58521 validation-auc:0.96728 validation-aucpr:0.97095
[11] validation-logloss:0.57658 validation-auc:0.96764 validation-aucpr:0.97121
[12] validation-logloss:0.56951 validation-auc:0.96722 validation-aucpr:0.97089
[13] validation-logloss:0.56125 validation-auc:0.96745 validation-aucpr:0.97106
[14] validation-logloss:0.55343 validation-auc:0.96740 validation-aucpr:0.97112
[15] validation-logloss:0.54564 validation-auc:0.96751 validation-aucpr:0.97119
[16] validation-logloss:0.53801 validation-auc:0.96765 validation-aucpr:0.97132
[17] validation-logloss:0.53125 validation-auc:0.96762 validation-aucpr:0.97134
[18] validation-logloss:0.52475 validation-auc:0.96760 validation-aucpr:0.97127
[19] validation-logloss:0.51776 validation-auc:0.96781 validation-aucpr:0.97146
[20] validation-logloss:0.51073 validation-auc:0.96806 validation-aucpr:0.97171
[21] validation-logloss:0.50410 validation-auc:0.96813 validation-aucpr:0.97179
[22] validation-logloss:0.49836 validation-auc:0.96804 validation-aucpr:0.97169
[23] validation-logloss:0.49223 validation-auc:0.96803 validation-aucpr:0.97172
[24] validation-logloss:0.48686 validation-auc:0.96796 validation-aucpr:0.97161
[25] validation-logloss:0.48154 validation-auc:0.96785 validation-aucpr:0.97151
[26] validation-logloss:0.47635 validation-auc:0.96768 validation-aucpr:0.97134
[27] validation-logloss:0.47032 validation-auc:0.96790 validation-aucpr:0.97154
[28] validation-logloss:0.46449 validation-auc:0.96821 validation-aucpr:0.97177
[29] validation-logloss:0.45888 validation-auc:0.96828 validation-aucpr:0.97186
[30] validation-logloss:0.45402 validation-auc:0.96822 validation-aucpr:0.97180
[31] validation-logloss:0.44865 validation-auc:0.96825 validation-aucpr:0.97185
[32] validation-logloss:0.44361 validation-auc:0.96837 validation-aucpr:0.97200
[33] validation-logloss:0.43918 validation-auc:0.96836 validation-aucpr:0.97198
[34] validation-logloss:0.43520 validation-auc:0.96834 validation-aucpr:0.97235
[35] validation-logloss:0.43101 validation-auc:0.96834 validation-aucpr:0.97234
[36] validation-logloss:0.42621 validation-auc:0.96841 validation-aucpr:0.97241
[37] validation-logloss:0.42152 validation-auc:0.96849 validation-aucpr:0.97248
[38] validation-logloss:0.41764 validation-auc:0.96840 validation-aucpr:0.97241
[39] validation-logloss:0.41305 validation-auc:0.96862 validation-aucpr:0.97260
[40] validation-logloss:0.40867 validation-auc:0.96865 validation-aucpr:0.97262
[41] validation-logloss:0.40445 validation-auc:0.96874 validation-aucpr:0.97271
[42] validation-logloss:0.40086 validation-auc:0.96860 validation-aucpr:0.97261
[43] validation-logloss:0.39727 validation-auc:0.96851 validation-aucpr:0.97253
[44] validation-logloss:0.39325 validation-auc:0.96864 validation-aucpr:0.97263
[45] validation-logloss:0.38929 validation-auc:0.96874 validation-aucpr:0.97273
[46] validation-logloss:0.38553 validation-auc:0.96885 validation-aucpr:0.97282
[47] validation-logloss:0.38237 validation-auc:0.96871 validation-aucpr:0.97270
[48] validation-logloss:0.37869 validation-auc:0.96879 validation-aucpr:0.97278
[49] validation-logloss:0.37506 validation-auc:0.96891 validation-aucpr:0.97286
[50] validation-logloss:0.37199 validation-auc:0.96888 validation-aucpr:0.97281
[51] validation-logloss:0.36922 validation-auc:0.96878 validation-aucpr:0.97271
[52] validation-logloss:0.36576 validation-auc:0.96892 validation-aucpr:0.97284
[53] validation-logloss:0.36240 validation-auc:0.96896 validation-aucpr:0.97288
[54] validation-logloss:0.35906 validation-auc:0.96909 validation-aucpr:0.97297
[55] validation-logloss:0.35579 validation-auc:0.96923 validation-aucpr:0.97309
[56] validation-logloss:0.35271 validation-auc:0.96928 validation-aucpr:0.97315
[57] validation-logloss:0.35008 validation-auc:0.96921 validation-aucpr:0.97307
[58] validation-logloss:0.34699 validation-auc:0.96931 validation-aucpr:0.97315
[59] validation-logloss:0.34447 validation-auc:0.96934 validation-aucpr:0.97317
[60] validation-logloss:0.34200 validation-auc:0.96934 validation-aucpr:0.97315
[61] validation-logloss:0.33909 validation-auc:0.96945 validation-aucpr:0.97324
[62] validation-logloss:0.33630 validation-auc:0.96952 validation-aucpr:0.97330
[63] validation-logloss:0.33347 validation-auc:0.96967 validation-aucpr:0.97341
[64] validation-logloss:0.33082 validation-auc:0.96975 validation-aucpr:0.97348
[65] validation-logloss:0.32813 validation-auc:0.96983 validation-aucpr:0.97353
[66] validation-logloss:0.32592 validation-auc:0.96981 validation-aucpr:0.97355
[67] validation-logloss:0.32335 validation-auc:0.96990 validation-aucpr:0.97363
[68] validation-logloss:0.32088 validation-auc:0.96992 validation-aucpr:0.97366
[69] validation-logloss:0.31838 validation-auc:0.96998 validation-aucpr:0.97372
[70] validation-logloss:0.31637 validation-auc:0.96990 validation-aucpr:0.97364
[71] validation-logloss:0.31395 validation-auc:0.96999 validation-aucpr:0.97372
[72] validation-logloss:0.31165 validation-auc:0.97001 validation-aucpr:0.97374
{'best_iteration': '72', 'best_score': '0.9737411445974385'}
Trial 42, Fold 5: Log loss = 0.3116536302078631, Average precision = 0.9737242713120442, ROC-AUC = 0.9700108132211136, Elapsed Time = 2.2429255999995803 seconds
Optimization Progress: 43%|####3 | 43/100 [2:26:42<1:13:49, 77.72s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 43, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67385 validation-auc:0.94143 validation-aucpr:0.94742
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65498 validation-auc:0.95671 validation-aucpr:0.96049
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63800 validation-auc:0.95647 validation-aucpr:0.95995
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.62230 validation-auc:0.95708 validation-aucpr:0.96084
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60781 validation-auc:0.95682 validation-aucpr:0.96138
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.59385 validation-auc:0.95659 validation-aucpr:0.96136
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.57868 validation-auc:0.95931 validation-aucpr:0.96438
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.56312 validation-auc:0.96151 validation-aucpr:0.96677
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.55050 validation-auc:0.96175 validation-aucpr:0.96706
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.53826 validation-auc:0.96186 validation-aucpr:0.96713
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.52539 validation-auc:0.96263 validation-aucpr:0.96795
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.51261 validation-auc:0.96324 validation-aucpr:0.96890
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.50210 validation-auc:0.96361 validation-aucpr:0.96916
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.49218 validation-auc:0.96384 validation-aucpr:0.96935
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.48074 validation-auc:0.96445 validation-aucpr:0.96984
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.47129 validation-auc:0.96483 validation-aucpr:0.97009
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.46290 validation-auc:0.96465 validation-aucpr:0.96996
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.45273 validation-auc:0.96500 validation-aucpr:0.97034
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.44265 validation-auc:0.96554 validation-aucpr:0.97084
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.43481 validation-auc:0.96553 validation-aucpr:0.97078
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.42595 validation-auc:0.96580 validation-aucpr:0.97105
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.41885 validation-auc:0.96592 validation-aucpr:0.97106
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.41081 validation-auc:0.96601 validation-aucpr:0.97118
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.40276 validation-auc:0.96633 validation-aucpr:0.97152
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.39522 validation-auc:0.96666 validation-aucpr:0.97186
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.38782 validation-auc:0.96693 validation-aucpr:0.97209
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.38213 validation-auc:0.96705 validation-aucpr:0.97216
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.37615 validation-auc:0.96706 validation-aucpr:0.97216
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.36947 validation-auc:0.96729 validation-aucpr:0.97241
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.36354 validation-auc:0.96742 validation-aucpr:0.97253
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.35877 validation-auc:0.96738 validation-aucpr:0.97246
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.35410 validation-auc:0.96729 validation-aucpr:0.97239
[20:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.34979 validation-auc:0.96724 validation-aucpr:0.97232
{'best_iteration': '29', 'best_score': '0.9725345395665433'}
Trial 43, Fold 1: Log loss = 0.3497921846878542, Average precision = 0.972323647074988, ROC-AUC = 0.9672356170210733, Elapsed Time = 2.4518600999981572 seconds
Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 43, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67176 validation-auc:0.95300 validation-aucpr:0.95682
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65324 validation-auc:0.96051 validation-aucpr:0.96394
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63595 validation-auc:0.96163 validation-aucpr:0.96546
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61979 validation-auc:0.96285 validation-aucpr:0.96620
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60494 validation-auc:0.96213 validation-aucpr:0.96530
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.59031 validation-auc:0.96224 validation-aucpr:0.96529
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.57627 validation-auc:0.96246 validation-aucpr:0.96533
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.56302 validation-auc:0.96253 validation-aucpr:0.96525
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.55059 validation-auc:0.96256 validation-aucpr:0.96517
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.53914 validation-auc:0.96235 validation-aucpr:0.96488
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.52789 validation-auc:0.96254 validation-aucpr:0.96513
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.51472 validation-auc:0.96476 validation-aucpr:0.96765
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.50443 validation-auc:0.96453 validation-aucpr:0.96733
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.49465 validation-auc:0.96431 validation-aucpr:0.96703
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.48296 validation-auc:0.96552 validation-aucpr:0.96843
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.47463 validation-auc:0.96530 validation-aucpr:0.96819
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.46602 validation-auc:0.96511 validation-aucpr:0.96782
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.45579 validation-auc:0.96585 validation-aucpr:0.96909
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.44767 validation-auc:0.96589 validation-aucpr:0.96910
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.43946 validation-auc:0.96600 validation-aucpr:0.96919
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.43205 validation-auc:0.96600 validation-aucpr:0.96913
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.42504 validation-auc:0.96606 validation-aucpr:0.96914
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.41813 validation-auc:0.96600 validation-aucpr:0.96905
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.41036 validation-auc:0.96645 validation-aucpr:0.96956
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.40417 validation-auc:0.96641 validation-aucpr:0.96954
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.39650 validation-auc:0.96692 validation-aucpr:0.97011
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.38941 validation-auc:0.96724 validation-aucpr:0.97045
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.38340 validation-auc:0.96734 validation-aucpr:0.97057
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.37810 validation-auc:0.96732 validation-aucpr:0.97067
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.37114 validation-auc:0.96769 validation-aucpr:0.97104
[20:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.36624 validation-auc:0.96782 validation-aucpr:0.97121
[20:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.36149 validation-auc:0.96773 validation-aucpr:0.97116
[20:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.35701 validation-auc:0.96767 validation-aucpr:0.97112
{'best_iteration': '30', 'best_score': '0.9712114953884028'}
Trial 43, Fold 2: Log loss = 0.35701467220388006, Average precision = 0.9710963596730398, ROC-AUC = 0.9676705895492514, Elapsed Time = 2.6748434000001 seconds
Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 43, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67382 validation-auc:0.94070 validation-aucpr:0.94001
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65604 validation-auc:0.95100 validation-aucpr:0.95679
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63855 validation-auc:0.95879 validation-aucpr:0.96296
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.62278 validation-auc:0.95898 validation-aucpr:0.96299
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60629 validation-auc:0.96177 validation-aucpr:0.96583
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.59131 validation-auc:0.96180 validation-aucpr:0.96575
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.57491 validation-auc:0.96501 validation-aucpr:0.96936
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.56225 validation-auc:0.96471 validation-aucpr:0.96908
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.54733 validation-auc:0.96631 validation-aucpr:0.97065
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.53580 validation-auc:0.96619 validation-aucpr:0.97036
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.52216 validation-auc:0.96698 validation-aucpr:0.97118
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.50873 validation-auc:0.96790 validation-aucpr:0.97198
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.49808 validation-auc:0.96792 validation-aucpr:0.97201
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.48663 validation-auc:0.96808 validation-aucpr:0.97226
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.47519 validation-auc:0.96843 validation-aucpr:0.97256
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.46613 validation-auc:0.96842 validation-aucpr:0.97248
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.45656 validation-auc:0.96843 validation-aucpr:0.97237
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.44822 validation-auc:0.96833 validation-aucpr:0.97225
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.43848 validation-auc:0.96848 validation-aucpr:0.97267
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.42922 validation-auc:0.96867 validation-aucpr:0.97286
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.42026 validation-auc:0.96900 validation-aucpr:0.97317
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.41331 validation-auc:0.96903 validation-aucpr:0.97313
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.40523 validation-auc:0.96916 validation-aucpr:0.97326
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.39886 validation-auc:0.96910 validation-aucpr:0.97324
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.39269 validation-auc:0.96909 validation-aucpr:0.97323
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.38665 validation-auc:0.96909 validation-aucpr:0.97321
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.38110 validation-auc:0.96892 validation-aucpr:0.97302
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.37410 validation-auc:0.96911 validation-aucpr:0.97323
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.36727 validation-auc:0.96925 validation-aucpr:0.97340
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.36214 validation-auc:0.96916 validation-aucpr:0.97331
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.35716 validation-auc:0.96916 validation-aucpr:0.97329
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.35248 validation-auc:0.96919 validation-aucpr:0.97330
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.34807 validation-auc:0.96917 validation-aucpr:0.97326
{'best_iteration': '28', 'best_score': '0.9734004595640693'}
Trial 43, Fold 3: Log loss = 0.3480675914193448, Average precision = 0.973268520424059, ROC-AUC = 0.9691660908110293, Elapsed Time = 2.6797023999970406 seconds
Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 43, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67080 validation-auc:0.95951 validation-aucpr:0.95794
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65173 validation-auc:0.96170 validation-aucpr:0.96288
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63229 validation-auc:0.96368 validation-aucpr:0.96512
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61794 validation-auc:0.96164 validation-aucpr:0.96793
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60059 validation-auc:0.96336 validation-aucpr:0.96925
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.58404 validation-auc:0.96371 validation-aucpr:0.96966
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.57067 validation-auc:0.96348 validation-aucpr:0.96956
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.55840 validation-auc:0.96286 validation-aucpr:0.96895
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.54428 validation-auc:0.96392 validation-aucpr:0.96957
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.53052 validation-auc:0.96444 validation-aucpr:0.97017
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.51729 validation-auc:0.96500 validation-aucpr:0.97072
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.50510 validation-auc:0.96511 validation-aucpr:0.97089
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.49417 validation-auc:0.96554 validation-aucpr:0.97127
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.48409 validation-auc:0.96554 validation-aucpr:0.97131
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.47354 validation-auc:0.96575 validation-aucpr:0.97153
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.46279 validation-auc:0.96605 validation-aucpr:0.97182
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.45428 validation-auc:0.96597 validation-aucpr:0.97174
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.44603 validation-auc:0.96586 validation-aucpr:0.97161
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.43800 validation-auc:0.96604 validation-aucpr:0.97173
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.42877 validation-auc:0.96639 validation-aucpr:0.97199
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.42200 validation-auc:0.96606 validation-aucpr:0.97173
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.41340 validation-auc:0.96633 validation-aucpr:0.97199
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.40636 validation-auc:0.96637 validation-aucpr:0.97200
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.39860 validation-auc:0.96666 validation-aucpr:0.97226
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.39217 validation-auc:0.96662 validation-aucpr:0.97226
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.38671 validation-auc:0.96636 validation-aucpr:0.97206
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.38118 validation-auc:0.96629 validation-aucpr:0.97196
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.37453 validation-auc:0.96637 validation-aucpr:0.97203
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.36924 validation-auc:0.96648 validation-aucpr:0.97207
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.36295 validation-auc:0.96673 validation-aucpr:0.97228
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.35834 validation-auc:0.96665 validation-aucpr:0.97221
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.35346 validation-auc:0.96671 validation-aucpr:0.97226
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.34885 validation-auc:0.96674 validation-aucpr:0.97228
{'best_iteration': '32', 'best_score': '0.972280019775681'}
Trial 43, Fold 4: Log loss = 0.3488531874288383, Average precision = 0.9722768432375872, ROC-AUC = 0.966742848558982, Elapsed Time = 2.7160441000014544 seconds
Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 43, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67104 validation-auc:0.95828 validation-aucpr:0.95734
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65267 validation-auc:0.95999 validation-aucpr:0.96523
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.63352 validation-auc:0.96188 validation-aucpr:0.96721
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.61552 validation-auc:0.96312 validation-aucpr:0.96567
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.60066 validation-auc:0.96277 validation-aucpr:0.96801
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.58378 validation-auc:0.96366 validation-aucpr:0.96886
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.56842 validation-auc:0.96455 validation-aucpr:0.96952
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.55610 validation-auc:0.96377 validation-aucpr:0.96883
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.54162 validation-auc:0.96411 validation-aucpr:0.96938
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.52970 validation-auc:0.96415 validation-aucpr:0.96936
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.51665 validation-auc:0.96470 validation-aucpr:0.96970
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.50393 validation-auc:0.96545 validation-aucpr:0.97034
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.49413 validation-auc:0.96491 validation-aucpr:0.97004
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.48452 validation-auc:0.96458 validation-aucpr:0.96967
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.47564 validation-auc:0.96439 validation-aucpr:0.96937
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.46488 validation-auc:0.96487 validation-aucpr:0.96966
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.45544 validation-auc:0.96509 validation-aucpr:0.96984
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.44590 validation-auc:0.96516 validation-aucpr:0.96980
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.43792 validation-auc:0.96524 validation-aucpr:0.96981
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.43070 validation-auc:0.96497 validation-aucpr:0.96958
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.42208 validation-auc:0.96522 validation-aucpr:0.96982
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.41548 validation-auc:0.96506 validation-aucpr:0.96963
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.40868 validation-auc:0.96514 validation-aucpr:0.96965
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.40121 validation-auc:0.96535 validation-aucpr:0.96988
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.39362 validation-auc:0.96578 validation-aucpr:0.97024
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.38656 validation-auc:0.96600 validation-aucpr:0.97043
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.38093 validation-auc:0.96602 validation-aucpr:0.97044
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.37468 validation-auc:0.96602 validation-aucpr:0.97042
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.36800 validation-auc:0.96647 validation-aucpr:0.97079
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.36191 validation-auc:0.96671 validation-aucpr:0.97097
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.35665 validation-auc:0.96673 validation-aucpr:0.97097
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.35208 validation-auc:0.96670 validation-aucpr:0.97089
[20:25:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.34650 validation-auc:0.96692 validation-aucpr:0.97107
{'best_iteration': '32', 'best_score': '0.9710684845858037'}
Trial 43, Fold 5: Log loss = 0.34649803740362317, Average precision = 0.9710592798843336, ROC-AUC = 0.9669182319826096, Elapsed Time = 2.757409000001644 seconds
Optimization Progress: 44%|####4 | 44/100 [2:27:03<56:40, 60.72s/it]
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 44, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65514 validation-auc:0.87809 validation-aucpr:0.88222
[1] validation-logloss:0.60224 validation-auc:0.94543 validation-aucpr:0.95331
[2] validation-logloss:0.56799 validation-auc:0.94776 validation-aucpr:0.95521
[3] validation-logloss:0.53744 validation-auc:0.94993 validation-aucpr:0.95683
[4] validation-logloss:0.51274 validation-auc:0.95064 validation-aucpr:0.95757
[5] validation-logloss:0.48533 validation-auc:0.95375 validation-aucpr:0.95984
[6] validation-logloss:0.46379 validation-auc:0.95533 validation-aucpr:0.96080
[7] validation-logloss:0.44329 validation-auc:0.95685 validation-aucpr:0.96192
[8] validation-logloss:0.42770 validation-auc:0.95626 validation-aucpr:0.96119
[9] validation-logloss:0.41215 validation-auc:0.95657 validation-aucpr:0.96147
[10] validation-logloss:0.40000 validation-auc:0.95614 validation-aucpr:0.96111
[11] validation-logloss:0.39012 validation-auc:0.95590 validation-aucpr:0.96112
[12] validation-logloss:0.37851 validation-auc:0.95607 validation-aucpr:0.96137
[13] validation-logloss:0.36718 validation-auc:0.95651 validation-aucpr:0.96176
[14] validation-logloss:0.35940 validation-auc:0.95671 validation-aucpr:0.96199
[15] validation-logloss:0.35371 validation-auc:0.95651 validation-aucpr:0.96170
[16] validation-logloss:0.34584 validation-auc:0.95643 validation-aucpr:0.96159
[17] validation-logloss:0.34032 validation-auc:0.95600 validation-aucpr:0.96112
[18] validation-logloss:0.33289 validation-auc:0.95639 validation-aucpr:0.96159
[19] validation-logloss:0.32676 validation-auc:0.95654 validation-aucpr:0.96176
[20] validation-logloss:0.32132 validation-auc:0.95697 validation-aucpr:0.96216
[21] validation-logloss:0.31601 validation-auc:0.95704 validation-aucpr:0.96237
[22] validation-logloss:0.30578 validation-auc:0.95890 validation-aucpr:0.96452
[23] validation-logloss:0.30272 validation-auc:0.95888 validation-aucpr:0.96454
[24] validation-logloss:0.29897 validation-auc:0.95901 validation-aucpr:0.96454
[25] validation-logloss:0.29444 validation-auc:0.95938 validation-aucpr:0.96489
[26] validation-logloss:0.29176 validation-auc:0.95927 validation-aucpr:0.96479
[27] validation-logloss:0.28948 validation-auc:0.95927 validation-aucpr:0.96480
[28] validation-logloss:0.28755 validation-auc:0.95930 validation-aucpr:0.96483
[29] validation-logloss:0.28515 validation-auc:0.95939 validation-aucpr:0.96491
[30] validation-logloss:0.28262 validation-auc:0.95965 validation-aucpr:0.96513
[31] validation-logloss:0.27951 validation-auc:0.95994 validation-aucpr:0.96538
[32] validation-logloss:0.27777 validation-auc:0.95987 validation-aucpr:0.96537
[33] validation-logloss:0.27504 validation-auc:0.96012 validation-aucpr:0.96555
[34] validation-logloss:0.27266 validation-auc:0.96042 validation-aucpr:0.96575
[35] validation-logloss:0.27056 validation-auc:0.96057 validation-aucpr:0.96588
[36] validation-logloss:0.27007 validation-auc:0.96028 validation-aucpr:0.96565
[37] validation-logloss:0.26836 validation-auc:0.96044 validation-aucpr:0.96582
[38] validation-logloss:0.26680 validation-auc:0.96056 validation-aucpr:0.96594
[39] validation-logloss:0.26384 validation-auc:0.96112 validation-aucpr:0.96636
[40] validation-logloss:0.26152 validation-auc:0.96136 validation-aucpr:0.96654
[41] validation-logloss:0.26021 validation-auc:0.96138 validation-aucpr:0.96652
[42] validation-logloss:0.25329 validation-auc:0.96281 validation-aucpr:0.96807
[43] validation-logloss:0.25231 validation-auc:0.96287 validation-aucpr:0.96812
[44] validation-logloss:0.25102 validation-auc:0.96296 validation-aucpr:0.96821
[45] validation-logloss:0.25024 validation-auc:0.96307 validation-aucpr:0.96830
[46] validation-logloss:0.24901 validation-auc:0.96344 validation-aucpr:0.96858
[47] validation-logloss:0.24842 validation-auc:0.96339 validation-aucpr:0.96853
[48] validation-logloss:0.24651 validation-auc:0.96377 validation-aucpr:0.96885
[49] validation-logloss:0.24573 validation-auc:0.96376 validation-aucpr:0.96888
[50] validation-logloss:0.24435 validation-auc:0.96404 validation-aucpr:0.96907
[51] validation-logloss:0.24356 validation-auc:0.96421 validation-aucpr:0.96914
[52] validation-logloss:0.24305 validation-auc:0.96417 validation-aucpr:0.96909
[53] validation-logloss:0.24296 validation-auc:0.96410 validation-aucpr:0.96904
[54] validation-logloss:0.23998 validation-auc:0.96458 validation-aucpr:0.96954
[55] validation-logloss:0.23959 validation-auc:0.96468 validation-aucpr:0.96961
[56] validation-logloss:0.23537 validation-auc:0.96527 validation-aucpr:0.97022
[57] validation-logloss:0.23459 validation-auc:0.96540 validation-aucpr:0.97032
[58] validation-logloss:0.23420 validation-auc:0.96547 validation-aucpr:0.97031
[59] validation-logloss:0.23391 validation-auc:0.96545 validation-aucpr:0.97027
[60] validation-logloss:0.23326 validation-auc:0.96554 validation-aucpr:0.97034
[61] validation-logloss:0.23270 validation-auc:0.96562 validation-aucpr:0.97041
[62] validation-logloss:0.22946 validation-auc:0.96605 validation-aucpr:0.97088
[63] validation-logloss:0.22924 validation-auc:0.96605 validation-aucpr:0.97088
[64] validation-logloss:0.22885 validation-auc:0.96604 validation-aucpr:0.97088
[65] validation-logloss:0.22901 validation-auc:0.96595 validation-aucpr:0.97077
[66] validation-logloss:0.22846 validation-auc:0.96601 validation-aucpr:0.97082
[67] validation-logloss:0.22830 validation-auc:0.96601 validation-aucpr:0.97079
[68] validation-logloss:0.22786 validation-auc:0.96614 validation-aucpr:0.97088
[69] validation-logloss:0.22539 validation-auc:0.96643 validation-aucpr:0.97120
[70] validation-logloss:0.22514 validation-auc:0.96645 validation-aucpr:0.97123
[71] validation-logloss:0.22442 validation-auc:0.96666 validation-aucpr:0.97137
[72] validation-logloss:0.22189 validation-auc:0.96694 validation-aucpr:0.97168
[73] validation-logloss:0.22122 validation-auc:0.96712 validation-aucpr:0.97185
[74] validation-logloss:0.22103 validation-auc:0.96715 validation-aucpr:0.97190
[75] validation-logloss:0.22108 validation-auc:0.96710 validation-aucpr:0.97187
[76] validation-logloss:0.22095 validation-auc:0.96707 validation-aucpr:0.97182
[77] validation-logloss:0.22053 validation-auc:0.96720 validation-aucpr:0.97193
[78] validation-logloss:0.21776 validation-auc:0.96760 validation-aucpr:0.97231
[79] validation-logloss:0.21621 validation-auc:0.96785 validation-aucpr:0.97253
[80] validation-logloss:0.21596 validation-auc:0.96789 validation-aucpr:0.97253
[81] validation-logloss:0.21594 validation-auc:0.96790 validation-aucpr:0.97251
[82] validation-logloss:0.21572 validation-auc:0.96797 validation-aucpr:0.97254
[83] validation-logloss:0.21560 validation-auc:0.96794 validation-aucpr:0.97254
[84] validation-logloss:0.21534 validation-auc:0.96797 validation-aucpr:0.97257
[85] validation-logloss:0.21526 validation-auc:0.96797 validation-aucpr:0.97257
[86] validation-logloss:0.21492 validation-auc:0.96806 validation-aucpr:0.97264
[87] validation-logloss:0.21471 validation-auc:0.96813 validation-aucpr:0.97266
[88] validation-logloss:0.21443 validation-auc:0.96816 validation-aucpr:0.97267
[89] validation-logloss:0.21427 validation-auc:0.96817 validation-aucpr:0.97270
[90] validation-logloss:0.21416 validation-auc:0.96820 validation-aucpr:0.97274
[91] validation-logloss:0.21407 validation-auc:0.96823 validation-aucpr:0.97272
[92] validation-logloss:0.21435 validation-auc:0.96810 validation-aucpr:0.97264
[93] validation-logloss:0.21269 validation-auc:0.96827 validation-aucpr:0.97283
[94] validation-logloss:0.21240 validation-auc:0.96834 validation-aucpr:0.97288
[95] validation-logloss:0.21201 validation-auc:0.96842 validation-aucpr:0.97292
[96] validation-logloss:0.21179 validation-auc:0.96849 validation-aucpr:0.97299
{'best_iteration': '96', 'best_score': '0.9729902603003616'}
Trial 44, Fold 1: Log loss = 0.2117866213049766, Average precision = 0.9729949916357183, ROC-AUC = 0.9684905579214522, Elapsed Time = 68.23660120000204 seconds
Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 44, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65052 validation-auc:0.89657 validation-aucpr:0.89769
[1] validation-logloss:0.61009 validation-auc:0.92863 validation-aucpr:0.92567
[2] validation-logloss:0.57328 validation-auc:0.93758 validation-aucpr:0.93752
[3] validation-logloss:0.54145 validation-auc:0.94245 validation-aucpr:0.94347
[4] validation-logloss:0.51154 validation-auc:0.94911 validation-aucpr:0.95099
[5] validation-logloss:0.48645 validation-auc:0.95101 validation-aucpr:0.95320
[6] validation-logloss:0.46522 validation-auc:0.95169 validation-aucpr:0.95353
[7] validation-logloss:0.44730 validation-auc:0.95194 validation-aucpr:0.95392
[8] validation-logloss:0.43012 validation-auc:0.95228 validation-aucpr:0.95396
[9] validation-logloss:0.41893 validation-auc:0.95276 validation-aucpr:0.95468
[10] validation-logloss:0.40492 validation-auc:0.95306 validation-aucpr:0.95491
[11] validation-logloss:0.39368 validation-auc:0.95307 validation-aucpr:0.95479
[12] validation-logloss:0.38240 validation-auc:0.95373 validation-aucpr:0.95511
[13] validation-logloss:0.37219 validation-auc:0.95378 validation-aucpr:0.95508
[14] validation-logloss:0.36350 validation-auc:0.95368 validation-aucpr:0.95487
[15] validation-logloss:0.35781 validation-auc:0.95387 validation-aucpr:0.95493
[16] validation-logloss:0.34934 validation-auc:0.95430 validation-aucpr:0.95523
[17] validation-logloss:0.34139 validation-auc:0.95457 validation-aucpr:0.95566
[18] validation-logloss:0.33652 validation-auc:0.95441 validation-aucpr:0.95588
[19] validation-logloss:0.32959 validation-auc:0.95502 validation-aucpr:0.95661
[20] validation-logloss:0.32387 validation-auc:0.95525 validation-aucpr:0.95679
[21] validation-logloss:0.31831 validation-auc:0.95572 validation-aucpr:0.95726
[22] validation-logloss:0.31491 validation-auc:0.95581 validation-aucpr:0.95734
[23] validation-logloss:0.30258 validation-auc:0.96004 validation-aucpr:0.96250
[24] validation-logloss:0.30155 validation-auc:0.95995 validation-aucpr:0.96241
[25] validation-logloss:0.29197 validation-auc:0.96190 validation-aucpr:0.96494
[26] validation-logloss:0.28547 validation-auc:0.96282 validation-aucpr:0.96650
[27] validation-logloss:0.28354 validation-auc:0.96318 validation-aucpr:0.96668
[28] validation-logloss:0.28221 validation-auc:0.96316 validation-aucpr:0.96655
[29] validation-logloss:0.27826 validation-auc:0.96352 validation-aucpr:0.96681
[30] validation-logloss:0.27040 validation-auc:0.96444 validation-aucpr:0.96789
[31] validation-logloss:0.26728 validation-auc:0.96481 validation-aucpr:0.96813
[32] validation-logloss:0.26525 validation-auc:0.96471 validation-aucpr:0.96801
[33] validation-logloss:0.26265 validation-auc:0.96494 validation-aucpr:0.96818
[34] validation-logloss:0.25617 validation-auc:0.96566 validation-aucpr:0.96895
[35] validation-logloss:0.25311 validation-auc:0.96570 validation-aucpr:0.96908
[36] validation-logloss:0.25016 validation-auc:0.96598 validation-aucpr:0.96933
[37] validation-logloss:0.24877 validation-auc:0.96598 validation-aucpr:0.96931
[38] validation-logloss:0.24738 validation-auc:0.96598 validation-aucpr:0.96937
[39] validation-logloss:0.24651 validation-auc:0.96595 validation-aucpr:0.96922
[40] validation-logloss:0.24490 validation-auc:0.96609 validation-aucpr:0.96937
[41] validation-logloss:0.24358 validation-auc:0.96622 validation-aucpr:0.96945
[42] validation-logloss:0.24188 validation-auc:0.96642 validation-aucpr:0.96958
[43] validation-logloss:0.23940 validation-auc:0.96673 validation-aucpr:0.96986
[44] validation-logloss:0.23812 validation-auc:0.96690 validation-aucpr:0.96997
[45] validation-logloss:0.23711 validation-auc:0.96698 validation-aucpr:0.97002
[46] validation-logloss:0.23607 validation-auc:0.96712 validation-aucpr:0.97009
[47] validation-logloss:0.23553 validation-auc:0.96712 validation-aucpr:0.97017
[48] validation-logloss:0.23410 validation-auc:0.96735 validation-aucpr:0.97038
[49] validation-logloss:0.23371 validation-auc:0.96730 validation-aucpr:0.97023
[50] validation-logloss:0.23299 validation-auc:0.96736 validation-aucpr:0.97047
[51] validation-logloss:0.23232 validation-auc:0.96750 validation-aucpr:0.97061
[52] validation-logloss:0.23173 validation-auc:0.96754 validation-aucpr:0.97060
[53] validation-logloss:0.23103 validation-auc:0.96760 validation-aucpr:0.97057
[54] validation-logloss:0.22990 validation-auc:0.96780 validation-aucpr:0.97073
[55] validation-logloss:0.22903 validation-auc:0.96790 validation-aucpr:0.97081
[56] validation-logloss:0.22843 validation-auc:0.96787 validation-aucpr:0.97080
[57] validation-logloss:0.22799 validation-auc:0.96792 validation-aucpr:0.97088
[58] validation-logloss:0.22772 validation-auc:0.96798 validation-aucpr:0.97089
[59] validation-logloss:0.22417 validation-auc:0.96847 validation-aucpr:0.97144
[60] validation-logloss:0.22374 validation-auc:0.96845 validation-aucpr:0.97141
[61] validation-logloss:0.22272 validation-auc:0.96867 validation-aucpr:0.97158
[62] validation-logloss:0.22210 validation-auc:0.96872 validation-aucpr:0.97154
[63] validation-logloss:0.22122 validation-auc:0.96882 validation-aucpr:0.97190
[64] validation-logloss:0.22056 validation-auc:0.96885 validation-aucpr:0.97190
[65] validation-logloss:0.21971 validation-auc:0.96894 validation-aucpr:0.97206
[66] validation-logloss:0.21906 validation-auc:0.96903 validation-aucpr:0.97218
[67] validation-logloss:0.21858 validation-auc:0.96908 validation-aucpr:0.97223
[68] validation-logloss:0.21762 validation-auc:0.96918 validation-aucpr:0.97233
[69] validation-logloss:0.21476 validation-auc:0.96943 validation-aucpr:0.97261
[70] validation-logloss:0.21445 validation-auc:0.96950 validation-aucpr:0.97266
[71] validation-logloss:0.21413 validation-auc:0.96952 validation-aucpr:0.97263
[72] validation-logloss:0.21369 validation-auc:0.96964 validation-aucpr:0.97273
[73] validation-logloss:0.21104 validation-auc:0.96990 validation-aucpr:0.97299
[74] validation-logloss:0.21059 validation-auc:0.97000 validation-aucpr:0.97300
[75] validation-logloss:0.21055 validation-auc:0.97001 validation-aucpr:0.97300
[76] validation-logloss:0.21051 validation-auc:0.97003 validation-aucpr:0.97301
[77] validation-logloss:0.21019 validation-auc:0.97013 validation-aucpr:0.97309
[78] validation-logloss:0.20753 validation-auc:0.97048 validation-aucpr:0.97338
[79] validation-logloss:0.20706 validation-auc:0.97055 validation-aucpr:0.97344
[80] validation-logloss:0.20674 validation-auc:0.97058 validation-aucpr:0.97350
[81] validation-logloss:0.20615 validation-auc:0.97069 validation-aucpr:0.97365
[82] validation-logloss:0.20602 validation-auc:0.97069 validation-aucpr:0.97360
[83] validation-logloss:0.20568 validation-auc:0.97075 validation-aucpr:0.97363
[84] validation-logloss:0.20525 validation-auc:0.97081 validation-aucpr:0.97371
[85] validation-logloss:0.20505 validation-auc:0.97089 validation-aucpr:0.97378
[86] validation-logloss:0.20496 validation-auc:0.97082 validation-aucpr:0.97368
[87] validation-logloss:0.20454 validation-auc:0.97091 validation-aucpr:0.97374
[88] validation-logloss:0.20261 validation-auc:0.97111 validation-aucpr:0.97393
[89] validation-logloss:0.20247 validation-auc:0.97112 validation-aucpr:0.97392
[90] validation-logloss:0.20234 validation-auc:0.97112 validation-aucpr:0.97394
[91] validation-logloss:0.20202 validation-auc:0.97115 validation-aucpr:0.97397
[92] validation-logloss:0.20174 validation-auc:0.97117 validation-aucpr:0.97398
[93] validation-logloss:0.20136 validation-auc:0.97123 validation-aucpr:0.97402
[94] validation-logloss:0.20097 validation-auc:0.97127 validation-aucpr:0.97407
[95] validation-logloss:0.20092 validation-auc:0.97128 validation-aucpr:0.97415
[96] validation-logloss:0.20096 validation-auc:0.97125 validation-aucpr:0.97411
{'best_iteration': '95', 'best_score': '0.9741451280839953'}
Trial 44, Fold 2: Log loss = 0.20095771260405446, Average precision = 0.9741141990732255, ROC-AUC = 0.9712527477730253, Elapsed Time = 68.58425809999972 seconds
Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 44, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65141 validation-auc:0.90045 validation-aucpr:0.90638
[1] validation-logloss:0.60926 validation-auc:0.93363 validation-aucpr:0.93785
[2] validation-logloss:0.57088 validation-auc:0.94340 validation-aucpr:0.94753
[3] validation-logloss:0.53191 validation-auc:0.95832 validation-aucpr:0.96383
[4] validation-logloss:0.50519 validation-auc:0.95929 validation-aucpr:0.96419
[5] validation-logloss:0.48058 validation-auc:0.95968 validation-aucpr:0.96452
[6] validation-logloss:0.45990 validation-auc:0.95980 validation-aucpr:0.96452
[7] validation-logloss:0.43786 validation-auc:0.96147 validation-aucpr:0.96608
[8] validation-logloss:0.42200 validation-auc:0.96118 validation-aucpr:0.96589
[9] validation-logloss:0.40742 validation-auc:0.96073 validation-aucpr:0.96531
[10] validation-logloss:0.39350 validation-auc:0.96084 validation-aucpr:0.96550
[11] validation-logloss:0.37598 validation-auc:0.96156 validation-aucpr:0.96658
[12] validation-logloss:0.36719 validation-auc:0.96088 validation-aucpr:0.96597
[13] validation-logloss:0.35543 validation-auc:0.96150 validation-aucpr:0.96665
[14] validation-logloss:0.33987 validation-auc:0.96241 validation-aucpr:0.96764
[15] validation-logloss:0.33203 validation-auc:0.96235 validation-aucpr:0.96759
[16] validation-logloss:0.32498 validation-auc:0.96285 validation-aucpr:0.96799
[17] validation-logloss:0.31915 validation-auc:0.96273 validation-aucpr:0.96780
[18] validation-logloss:0.31341 validation-auc:0.96268 validation-aucpr:0.96778
[19] validation-logloss:0.30974 validation-auc:0.96266 validation-aucpr:0.96775
[20] validation-logloss:0.30339 validation-auc:0.96327 validation-aucpr:0.96819
[21] validation-logloss:0.29886 validation-auc:0.96335 validation-aucpr:0.96821
[22] validation-logloss:0.29468 validation-auc:0.96347 validation-aucpr:0.96826
[23] validation-logloss:0.29108 validation-auc:0.96354 validation-aucpr:0.96832
[24] validation-logloss:0.28797 validation-auc:0.96352 validation-aucpr:0.96829
[25] validation-logloss:0.28393 validation-auc:0.96379 validation-aucpr:0.96849
[26] validation-logloss:0.27760 validation-auc:0.96419 validation-aucpr:0.96886
[27] validation-logloss:0.27454 validation-auc:0.96415 validation-aucpr:0.96882
[28] validation-logloss:0.26806 validation-auc:0.96452 validation-aucpr:0.96926
[29] validation-logloss:0.26614 validation-auc:0.96450 validation-aucpr:0.96925
[30] validation-logloss:0.26350 validation-auc:0.96473 validation-aucpr:0.96945
[31] validation-logloss:0.26146 validation-auc:0.96473 validation-aucpr:0.96941
[32] validation-logloss:0.25934 validation-auc:0.96475 validation-aucpr:0.96939
[33] validation-logloss:0.25649 validation-auc:0.96489 validation-aucpr:0.96949
[34] validation-logloss:0.25342 validation-auc:0.96521 validation-aucpr:0.96969
[35] validation-logloss:0.25250 validation-auc:0.96522 validation-aucpr:0.96965
[36] validation-logloss:0.25040 validation-auc:0.96545 validation-aucpr:0.96982
[37] validation-logloss:0.24526 validation-auc:0.96586 validation-aucpr:0.97024
[38] validation-logloss:0.24075 validation-auc:0.96619 validation-aucpr:0.97055
[39] validation-logloss:0.23944 validation-auc:0.96631 validation-aucpr:0.97063
[40] validation-logloss:0.23797 validation-auc:0.96652 validation-aucpr:0.97086
[41] validation-logloss:0.23682 validation-auc:0.96657 validation-aucpr:0.97095
[42] validation-logloss:0.23556 validation-auc:0.96666 validation-aucpr:0.97105
[43] validation-logloss:0.23417 validation-auc:0.96698 validation-aucpr:0.97139
[44] validation-logloss:0.23339 validation-auc:0.96704 validation-aucpr:0.97143
[45] validation-logloss:0.23243 validation-auc:0.96732 validation-aucpr:0.97164
[46] validation-logloss:0.23181 validation-auc:0.96739 validation-aucpr:0.97169
[47] validation-logloss:0.23118 validation-auc:0.96740 validation-aucpr:0.97177
[48] validation-logloss:0.23027 validation-auc:0.96749 validation-aucpr:0.97187
[49] validation-logloss:0.22915 validation-auc:0.96755 validation-aucpr:0.97193
[50] validation-logloss:0.22866 validation-auc:0.96753 validation-aucpr:0.97190
[51] validation-logloss:0.22558 validation-auc:0.96781 validation-aucpr:0.97216
[52] validation-logloss:0.22515 validation-auc:0.96783 validation-aucpr:0.97220
[53] validation-logloss:0.22458 validation-auc:0.96791 validation-aucpr:0.97222
[54] validation-logloss:0.22327 validation-auc:0.96815 validation-aucpr:0.97246
[55] validation-logloss:0.22230 validation-auc:0.96832 validation-aucpr:0.97259
[56] validation-logloss:0.22103 validation-auc:0.96854 validation-aucpr:0.97276
[57] validation-logloss:0.22036 validation-auc:0.96861 validation-aucpr:0.97284
[58] validation-logloss:0.21950 validation-auc:0.96880 validation-aucpr:0.97300
[59] validation-logloss:0.21865 validation-auc:0.96892 validation-aucpr:0.97307
[60] validation-logloss:0.21855 validation-auc:0.96881 validation-aucpr:0.97298
[61] validation-logloss:0.21769 validation-auc:0.96900 validation-aucpr:0.97317
[62] validation-logloss:0.21694 validation-auc:0.96909 validation-aucpr:0.97327
[63] validation-logloss:0.21646 validation-auc:0.96919 validation-aucpr:0.97336
[64] validation-logloss:0.21585 validation-auc:0.96926 validation-aucpr:0.97338
[65] validation-logloss:0.21549 validation-auc:0.96926 validation-aucpr:0.97337
[66] validation-logloss:0.21495 validation-auc:0.96931 validation-aucpr:0.97340
[67] validation-logloss:0.21436 validation-auc:0.96940 validation-aucpr:0.97347
[68] validation-logloss:0.21391 validation-auc:0.96946 validation-aucpr:0.97350
[69] validation-logloss:0.21372 validation-auc:0.96943 validation-aucpr:0.97348
[70] validation-logloss:0.21254 validation-auc:0.96965 validation-aucpr:0.97367
[71] validation-logloss:0.21203 validation-auc:0.96970 validation-aucpr:0.97370
[72] validation-logloss:0.21154 validation-auc:0.96982 validation-aucpr:0.97378
[73] validation-logloss:0.21114 validation-auc:0.96989 validation-aucpr:0.97383
[74] validation-logloss:0.21081 validation-auc:0.96991 validation-aucpr:0.97386
[75] validation-logloss:0.21047 validation-auc:0.96997 validation-aucpr:0.97394
[76] validation-logloss:0.21039 validation-auc:0.96994 validation-aucpr:0.97390
[77] validation-logloss:0.21049 validation-auc:0.96992 validation-aucpr:0.97390
[78] validation-logloss:0.20989 validation-auc:0.97006 validation-aucpr:0.97403
[79] validation-logloss:0.20895 validation-auc:0.97019 validation-aucpr:0.97414
[80] validation-logloss:0.20907 validation-auc:0.97008 validation-aucpr:0.97402
[81] validation-logloss:0.20879 validation-auc:0.97011 validation-aucpr:0.97405
[82] validation-logloss:0.20671 validation-auc:0.97029 validation-aucpr:0.97423
[83] validation-logloss:0.20487 validation-auc:0.97043 validation-aucpr:0.97440
[84] validation-logloss:0.20461 validation-auc:0.97049 validation-aucpr:0.97444
[85] validation-logloss:0.20300 validation-auc:0.97061 validation-aucpr:0.97456
[86] validation-logloss:0.20293 validation-auc:0.97059 validation-aucpr:0.97453
[87] validation-logloss:0.20288 validation-auc:0.97058 validation-aucpr:0.97451
[88] validation-logloss:0.20275 validation-auc:0.97057 validation-aucpr:0.97454
[89] validation-logloss:0.20245 validation-auc:0.97060 validation-aucpr:0.97456
[90] validation-logloss:0.20212 validation-auc:0.97071 validation-aucpr:0.97463
[91] validation-logloss:0.20158 validation-auc:0.97075 validation-aucpr:0.97468
[92] validation-logloss:0.20156 validation-auc:0.97076 validation-aucpr:0.97466
[93] validation-logloss:0.20156 validation-auc:0.97072 validation-aucpr:0.97466
[94] validation-logloss:0.20127 validation-auc:0.97079 validation-aucpr:0.97474
[95] validation-logloss:0.20122 validation-auc:0.97076 validation-aucpr:0.97471
[96] validation-logloss:0.19974 validation-auc:0.97089 validation-aucpr:0.97486
{'best_iteration': '96', 'best_score': '0.9748559120999989'}
Trial 44, Fold 3: Log loss = 0.19974281941264244, Average precision = 0.974860322917372, ROC-AUC = 0.9708878640531249, Elapsed Time = 66.81937679999828 seconds
Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 44, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65307 validation-auc:0.88711 validation-aucpr:0.88252
[1] validation-logloss:0.61193 validation-auc:0.92548 validation-aucpr:0.92554
[2] validation-logloss:0.57837 validation-auc:0.93327 validation-aucpr:0.93622
[3] validation-logloss:0.54799 validation-auc:0.93860 validation-aucpr:0.94305
[4] validation-logloss:0.52443 validation-auc:0.93920 validation-aucpr:0.94342
[5] validation-logloss:0.49923 validation-auc:0.94169 validation-aucpr:0.94706
[6] validation-logloss:0.47716 validation-auc:0.94517 validation-aucpr:0.95059
[7] validation-logloss:0.45608 validation-auc:0.94744 validation-aucpr:0.95274
[8] validation-logloss:0.44067 validation-auc:0.94700 validation-aucpr:0.95228
[9] validation-logloss:0.42538 validation-auc:0.94756 validation-aucpr:0.95311
[10] validation-logloss:0.41327 validation-auc:0.94839 validation-aucpr:0.95368
[11] validation-logloss:0.40183 validation-auc:0.94905 validation-aucpr:0.95425
[12] validation-logloss:0.39174 validation-auc:0.94901 validation-aucpr:0.95411
[13] validation-logloss:0.38195 validation-auc:0.94947 validation-aucpr:0.95446
[14] validation-logloss:0.37091 validation-auc:0.95057 validation-aucpr:0.95547
[15] validation-logloss:0.36336 validation-auc:0.95041 validation-aucpr:0.95528
[16] validation-logloss:0.35689 validation-auc:0.95050 validation-aucpr:0.95546
[17] validation-logloss:0.35039 validation-auc:0.95098 validation-aucpr:0.95602
[18] validation-logloss:0.34235 validation-auc:0.95224 validation-aucpr:0.95728
[19] validation-logloss:0.33656 validation-auc:0.95254 validation-aucpr:0.95749
[20] validation-logloss:0.33244 validation-auc:0.95267 validation-aucpr:0.95753
[21] validation-logloss:0.32856 validation-auc:0.95254 validation-aucpr:0.95759
[22] validation-logloss:0.32320 validation-auc:0.95282 validation-aucpr:0.95812
[23] validation-logloss:0.31843 validation-auc:0.95320 validation-aucpr:0.95840
[24] validation-logloss:0.31385 validation-auc:0.95359 validation-aucpr:0.95870
[25] validation-logloss:0.30343 validation-auc:0.95698 validation-aucpr:0.96260
[26] validation-logloss:0.30109 validation-auc:0.95707 validation-aucpr:0.96265
[27] validation-logloss:0.29804 validation-auc:0.95732 validation-aucpr:0.96276
[28] validation-logloss:0.29463 validation-auc:0.95762 validation-aucpr:0.96296
[29] validation-logloss:0.29148 validation-auc:0.95792 validation-aucpr:0.96329
[30] validation-logloss:0.28306 validation-auc:0.95964 validation-aucpr:0.96522
[31] validation-logloss:0.27929 validation-auc:0.95997 validation-aucpr:0.96554
[32] validation-logloss:0.27696 validation-auc:0.96014 validation-aucpr:0.96568
[33] validation-logloss:0.27467 validation-auc:0.96027 validation-aucpr:0.96581
[34] validation-logloss:0.27397 validation-auc:0.96010 validation-aucpr:0.96567
[35] validation-logloss:0.27224 validation-auc:0.96016 validation-aucpr:0.96572
[36] validation-logloss:0.27075 validation-auc:0.96024 validation-aucpr:0.96577
[37] validation-logloss:0.26906 validation-auc:0.96027 validation-aucpr:0.96590
[38] validation-logloss:0.26795 validation-auc:0.96014 validation-aucpr:0.96575
[39] validation-logloss:0.26734 validation-auc:0.96014 validation-aucpr:0.96578
[40] validation-logloss:0.26411 validation-auc:0.96058 validation-aucpr:0.96624
[41] validation-logloss:0.26204 validation-auc:0.96075 validation-aucpr:0.96637
[42] validation-logloss:0.26077 validation-auc:0.96080 validation-aucpr:0.96640
[43] validation-logloss:0.25890 validation-auc:0.96096 validation-aucpr:0.96653
[44] validation-logloss:0.25672 validation-auc:0.96130 validation-aucpr:0.96682
[45] validation-logloss:0.25578 validation-auc:0.96138 validation-aucpr:0.96685
[46] validation-logloss:0.25468 validation-auc:0.96137 validation-aucpr:0.96688
[47] validation-logloss:0.25356 validation-auc:0.96149 validation-aucpr:0.96702
[48] validation-logloss:0.25298 validation-auc:0.96163 validation-aucpr:0.96710
[49] validation-logloss:0.25210 validation-auc:0.96171 validation-aucpr:0.96713
[50] validation-logloss:0.25137 validation-auc:0.96183 validation-aucpr:0.96724
[51] validation-logloss:0.24612 validation-auc:0.96278 validation-aucpr:0.96836
[52] validation-logloss:0.24557 validation-auc:0.96272 validation-aucpr:0.96832
[53] validation-logloss:0.24042 validation-auc:0.96354 validation-aucpr:0.96919
[54] validation-logloss:0.24004 validation-auc:0.96359 validation-aucpr:0.96925
[55] validation-logloss:0.23961 validation-auc:0.96361 validation-aucpr:0.96928
[56] validation-logloss:0.23906 validation-auc:0.96361 validation-aucpr:0.96932
[57] validation-logloss:0.23824 validation-auc:0.96373 validation-aucpr:0.96941
[58] validation-logloss:0.23734 validation-auc:0.96388 validation-aucpr:0.96951
[59] validation-logloss:0.23634 validation-auc:0.96397 validation-aucpr:0.96957
[60] validation-logloss:0.23208 validation-auc:0.96456 validation-aucpr:0.97019
[61] validation-logloss:0.23122 validation-auc:0.96467 validation-aucpr:0.97024
[62] validation-logloss:0.23014 validation-auc:0.96487 validation-aucpr:0.97040
[63] validation-logloss:0.22988 validation-auc:0.96495 validation-aucpr:0.97044
[64] validation-logloss:0.22606 validation-auc:0.96543 validation-aucpr:0.97093
[65] validation-logloss:0.22592 validation-auc:0.96547 validation-aucpr:0.97097
[66] validation-logloss:0.22306 validation-auc:0.96577 validation-aucpr:0.97129
[67] validation-logloss:0.22288 validation-auc:0.96576 validation-aucpr:0.97129
[68] validation-logloss:0.22196 validation-auc:0.96581 validation-aucpr:0.97134
[69] validation-logloss:0.22145 validation-auc:0.96592 validation-aucpr:0.97143
[70] validation-logloss:0.22093 validation-auc:0.96599 validation-aucpr:0.97149
[71] validation-logloss:0.22071 validation-auc:0.96603 validation-aucpr:0.97150
[72] validation-logloss:0.22048 validation-auc:0.96612 validation-aucpr:0.97157
[73] validation-logloss:0.21798 validation-auc:0.96638 validation-aucpr:0.97185
[74] validation-logloss:0.21710 validation-auc:0.96653 validation-aucpr:0.97196
[75] validation-logloss:0.21644 validation-auc:0.96663 validation-aucpr:0.97204
[76] validation-logloss:0.21424 validation-auc:0.96680 validation-aucpr:0.97224
[77] validation-logloss:0.21408 validation-auc:0.96680 validation-aucpr:0.97223
[78] validation-logloss:0.21390 validation-auc:0.96687 validation-aucpr:0.97229
[79] validation-logloss:0.21360 validation-auc:0.96698 validation-aucpr:0.97236
[80] validation-logloss:0.21151 validation-auc:0.96728 validation-aucpr:0.97263
[81] validation-logloss:0.21110 validation-auc:0.96737 validation-aucpr:0.97270
[82] validation-logloss:0.21094 validation-auc:0.96736 validation-aucpr:0.97269
[83] validation-logloss:0.21082 validation-auc:0.96732 validation-aucpr:0.97265
[84] validation-logloss:0.21045 validation-auc:0.96743 validation-aucpr:0.97272
[85] validation-logloss:0.20909 validation-auc:0.96761 validation-aucpr:0.97288
[86] validation-logloss:0.20886 validation-auc:0.96759 validation-aucpr:0.97287
[87] validation-logloss:0.20862 validation-auc:0.96768 validation-aucpr:0.97293
[88] validation-logloss:0.20844 validation-auc:0.96767 validation-aucpr:0.97295
[89] validation-logloss:0.20683 validation-auc:0.96783 validation-aucpr:0.97311
[90] validation-logloss:0.20697 validation-auc:0.96778 validation-aucpr:0.97308
[91] validation-logloss:0.20565 validation-auc:0.96791 validation-aucpr:0.97322
[92] validation-logloss:0.20560 validation-auc:0.96791 validation-aucpr:0.97321
[93] validation-logloss:0.20453 validation-auc:0.96807 validation-aucpr:0.97336
[94] validation-logloss:0.20419 validation-auc:0.96817 validation-aucpr:0.97341
[95] validation-logloss:0.20392 validation-auc:0.96819 validation-aucpr:0.97344
[96] validation-logloss:0.20383 validation-auc:0.96818 validation-aucpr:0.97345
{'best_iteration': '96', 'best_score': '0.9734476786511739'}
Trial 44, Fold 4: Log loss = 0.20383334708858364, Average precision = 0.9734519949775687, ROC-AUC = 0.9681788593460516, Elapsed Time = 64.23538910000207 seconds
Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 44, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65330 validation-auc:0.88770 validation-aucpr:0.88733
[1] validation-logloss:0.61476 validation-auc:0.92004 validation-aucpr:0.92299
[2] validation-logloss:0.58016 validation-auc:0.93163 validation-aucpr:0.93327
[3] validation-logloss:0.53890 validation-auc:0.95149 validation-aucpr:0.95585
[4] validation-logloss:0.51155 validation-auc:0.95213 validation-aucpr:0.95581
[5] validation-logloss:0.48701 validation-auc:0.95344 validation-aucpr:0.95857
[6] validation-logloss:0.46591 validation-auc:0.95347 validation-aucpr:0.95918
[7] validation-logloss:0.43846 validation-auc:0.95647 validation-aucpr:0.96209
[8] validation-logloss:0.42255 validation-auc:0.95610 validation-aucpr:0.96161
[9] validation-logloss:0.40789 validation-auc:0.95691 validation-aucpr:0.96231
[10] validation-logloss:0.39005 validation-auc:0.95718 validation-aucpr:0.96266
[11] validation-logloss:0.37306 validation-auc:0.95856 validation-aucpr:0.96401
[12] validation-logloss:0.36427 validation-auc:0.95808 validation-aucpr:0.96355
[13] validation-logloss:0.35356 validation-auc:0.95885 validation-aucpr:0.96414
[14] validation-logloss:0.34307 validation-auc:0.95939 validation-aucpr:0.96452
[15] validation-logloss:0.33481 validation-auc:0.95946 validation-aucpr:0.96457
[16] validation-logloss:0.32709 validation-auc:0.95962 validation-aucpr:0.96473
[17] validation-logloss:0.32001 validation-auc:0.96010 validation-aucpr:0.96512
[18] validation-logloss:0.31459 validation-auc:0.96013 validation-aucpr:0.96533
[19] validation-logloss:0.30917 validation-auc:0.96026 validation-aucpr:0.96539
[20] validation-logloss:0.30309 validation-auc:0.96039 validation-aucpr:0.96549
[21] validation-logloss:0.29766 validation-auc:0.96083 validation-aucpr:0.96585
[22] validation-logloss:0.29466 validation-auc:0.96074 validation-aucpr:0.96569
[23] validation-logloss:0.29177 validation-auc:0.96075 validation-aucpr:0.96566
[24] validation-logloss:0.28757 validation-auc:0.96113 validation-aucpr:0.96595
[25] validation-logloss:0.28038 validation-auc:0.96151 validation-aucpr:0.96637
[26] validation-logloss:0.27774 validation-auc:0.96149 validation-aucpr:0.96635
[27] validation-logloss:0.27535 validation-auc:0.96161 validation-aucpr:0.96653
[28] validation-logloss:0.27353 validation-auc:0.96163 validation-aucpr:0.96653
[29] validation-logloss:0.27082 validation-auc:0.96177 validation-aucpr:0.96667
[30] validation-logloss:0.26823 validation-auc:0.96196 validation-aucpr:0.96685
[31] validation-logloss:0.26588 validation-auc:0.96222 validation-aucpr:0.96697
[32] validation-logloss:0.26441 validation-auc:0.96235 validation-aucpr:0.96700
[33] validation-logloss:0.26283 validation-auc:0.96249 validation-aucpr:0.96712
[34] validation-logloss:0.26085 validation-auc:0.96292 validation-aucpr:0.96744
[35] validation-logloss:0.25799 validation-auc:0.96335 validation-aucpr:0.96784
[36] validation-logloss:0.25601 validation-auc:0.96344 validation-aucpr:0.96792
[37] validation-logloss:0.25373 validation-auc:0.96379 validation-aucpr:0.96817
[38] validation-logloss:0.25195 validation-auc:0.96386 validation-aucpr:0.96825
[39] validation-logloss:0.25126 validation-auc:0.96369 validation-aucpr:0.96819
[40] validation-logloss:0.24873 validation-auc:0.96384 validation-aucpr:0.96836
[41] validation-logloss:0.24748 validation-auc:0.96398 validation-aucpr:0.96843
[42] validation-logloss:0.24624 validation-auc:0.96399 validation-aucpr:0.96846
[43] validation-logloss:0.24459 validation-auc:0.96416 validation-aucpr:0.96859
[44] validation-logloss:0.24323 validation-auc:0.96432 validation-aucpr:0.96877
[45] validation-logloss:0.24233 validation-auc:0.96441 validation-aucpr:0.96884
[46] validation-logloss:0.24102 validation-auc:0.96446 validation-aucpr:0.96888
[47] validation-logloss:0.24022 validation-auc:0.96462 validation-aucpr:0.96905
[48] validation-logloss:0.23971 validation-auc:0.96459 validation-aucpr:0.96907
[49] validation-logloss:0.23883 validation-auc:0.96468 validation-aucpr:0.96915
[50] validation-logloss:0.23744 validation-auc:0.96491 validation-aucpr:0.96932
[51] validation-logloss:0.23457 validation-auc:0.96513 validation-aucpr:0.96959
[52] validation-logloss:0.23355 validation-auc:0.96539 validation-aucpr:0.96977
[53] validation-logloss:0.23278 validation-auc:0.96544 validation-aucpr:0.96982
[54] validation-logloss:0.23168 validation-auc:0.96560 validation-aucpr:0.96992
[55] validation-logloss:0.23106 validation-auc:0.96565 validation-aucpr:0.96996
[56] validation-logloss:0.23004 validation-auc:0.96585 validation-aucpr:0.97008
[57] validation-logloss:0.22906 validation-auc:0.96596 validation-aucpr:0.97019
[58] validation-logloss:0.22865 validation-auc:0.96597 validation-aucpr:0.97020
[59] validation-logloss:0.22783 validation-auc:0.96617 validation-aucpr:0.97035
[60] validation-logloss:0.22423 validation-auc:0.96663 validation-aucpr:0.97078
[61] validation-logloss:0.22373 validation-auc:0.96675 validation-aucpr:0.97084
[62] validation-logloss:0.22317 validation-auc:0.96683 validation-aucpr:0.97089
[63] validation-logloss:0.22276 validation-auc:0.96696 validation-aucpr:0.97101
[64] validation-logloss:0.22232 validation-auc:0.96704 validation-aucpr:0.97105
[65] validation-logloss:0.22116 validation-auc:0.96721 validation-aucpr:0.97119
[66] validation-logloss:0.22107 validation-auc:0.96713 validation-aucpr:0.97116
[67] validation-logloss:0.22082 validation-auc:0.96714 validation-aucpr:0.97119
[68] validation-logloss:0.22089 validation-auc:0.96711 validation-aucpr:0.97115
[69] validation-logloss:0.21832 validation-auc:0.96739 validation-aucpr:0.97142
[70] validation-logloss:0.21820 validation-auc:0.96737 validation-aucpr:0.97142
[71] validation-logloss:0.21806 validation-auc:0.96736 validation-aucpr:0.97143
[72] validation-logloss:0.21758 validation-auc:0.96738 validation-aucpr:0.97145
[73] validation-logloss:0.21670 validation-auc:0.96764 validation-aucpr:0.97163
[74] validation-logloss:0.21660 validation-auc:0.96767 validation-aucpr:0.97167
[75] validation-logloss:0.21614 validation-auc:0.96773 validation-aucpr:0.97172
[76] validation-logloss:0.21377 validation-auc:0.96808 validation-aucpr:0.97206
[77] validation-logloss:0.21362 validation-auc:0.96805 validation-aucpr:0.97201
[78] validation-logloss:0.21350 validation-auc:0.96810 validation-aucpr:0.97202
[79] validation-logloss:0.21325 validation-auc:0.96816 validation-aucpr:0.97206
[80] validation-logloss:0.21283 validation-auc:0.96826 validation-aucpr:0.97214
[81] validation-logloss:0.21241 validation-auc:0.96834 validation-aucpr:0.97227
[82] validation-logloss:0.21197 validation-auc:0.96848 validation-aucpr:0.97241
[83] validation-logloss:0.21166 validation-auc:0.96852 validation-aucpr:0.97245
[84] validation-logloss:0.21136 validation-auc:0.96862 validation-aucpr:0.97251
[85] validation-logloss:0.21109 validation-auc:0.96872 validation-aucpr:0.97259
[86] validation-logloss:0.21100 validation-auc:0.96871 validation-aucpr:0.97260
[87] validation-logloss:0.21092 validation-auc:0.96871 validation-aucpr:0.97261
[88] validation-logloss:0.20893 validation-auc:0.96904 validation-aucpr:0.97288
[89] validation-logloss:0.20884 validation-auc:0.96899 validation-aucpr:0.97283
[90] validation-logloss:0.20869 validation-auc:0.96905 validation-aucpr:0.97287
[91] validation-logloss:0.20880 validation-auc:0.96900 validation-aucpr:0.97285
[92] validation-logloss:0.20741 validation-auc:0.96917 validation-aucpr:0.97303
[93] validation-logloss:0.20573 validation-auc:0.96951 validation-aucpr:0.97333
[94] validation-logloss:0.20570 validation-auc:0.96948 validation-aucpr:0.97332
[95] validation-logloss:0.20552 validation-auc:0.96948 validation-aucpr:0.97332
[96] validation-logloss:0.20552 validation-auc:0.96945 validation-aucpr:0.97329
{'best_iteration': '93', 'best_score': '0.9733290849490748'}
Trial 44, Fold 5: Log loss = 0.20551938913953613, Average precision = 0.9732923313730766, ROC-AUC = 0.969452910465786, Elapsed Time = 65.30914010000197 seconds
Optimization Progress: 45%|####5 | 45/100 [2:32:45<2:12:49, 144.91s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 45, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67020 validation-auc:0.93525 validation-aucpr:0.92509
[1] validation-logloss:0.64890 validation-auc:0.94891 validation-aucpr:0.95024
[2] validation-logloss:0.62574 validation-auc:0.96047 validation-aucpr:0.96201
[3] validation-logloss:0.60522 validation-auc:0.96166 validation-aucpr:0.96670
[4] validation-logloss:0.58834 validation-auc:0.96130 validation-aucpr:0.96647
[5] validation-logloss:0.57257 validation-auc:0.96252 validation-aucpr:0.96778
[6] validation-logloss:0.55801 validation-auc:0.96231 validation-aucpr:0.96771
[7] validation-logloss:0.54446 validation-auc:0.96181 validation-aucpr:0.96734
[8] validation-logloss:0.53092 validation-auc:0.96239 validation-aucpr:0.96754
[9] validation-logloss:0.51747 validation-auc:0.96278 validation-aucpr:0.96804
[10] validation-logloss:0.50541 validation-auc:0.96268 validation-aucpr:0.96677
[11] validation-logloss:0.49394 validation-auc:0.96286 validation-aucpr:0.96687
[12] validation-logloss:0.48301 validation-auc:0.96275 validation-aucpr:0.96681
[13] validation-logloss:0.46951 validation-auc:0.96341 validation-aucpr:0.96766
[14] validation-logloss:0.45861 validation-auc:0.96354 validation-aucpr:0.96788
[15] validation-logloss:0.44905 validation-auc:0.96362 validation-aucpr:0.96787
[16] validation-logloss:0.44024 validation-auc:0.96366 validation-aucpr:0.96771
[17] validation-logloss:0.43190 validation-auc:0.96368 validation-aucpr:0.96899
[18] validation-logloss:0.42381 validation-auc:0.96371 validation-aucpr:0.96903
[19] validation-logloss:0.41327 validation-auc:0.96422 validation-aucpr:0.96947
[20] validation-logloss:0.40348 validation-auc:0.96464 validation-aucpr:0.96986
[21] validation-logloss:0.39450 validation-auc:0.96480 validation-aucpr:0.97009
[22] validation-logloss:0.38759 validation-auc:0.96487 validation-aucpr:0.97012
[23] validation-logloss:0.37931 validation-auc:0.96516 validation-aucpr:0.97045
[24] validation-logloss:0.37362 validation-auc:0.96516 validation-aucpr:0.97042
[25] validation-logloss:0.36794 validation-auc:0.96515 validation-aucpr:0.97042
[26] validation-logloss:0.36058 validation-auc:0.96552 validation-aucpr:0.97078
[27] validation-logloss:0.35558 validation-auc:0.96537 validation-aucpr:0.97062
[28] validation-logloss:0.34877 validation-auc:0.96564 validation-aucpr:0.97095
[29] validation-logloss:0.34394 validation-auc:0.96560 validation-aucpr:0.97090
[30] validation-logloss:0.33938 validation-auc:0.96543 validation-aucpr:0.97071
[31] validation-logloss:0.33510 validation-auc:0.96545 validation-aucpr:0.97072
[32] validation-logloss:0.33073 validation-auc:0.96548 validation-aucpr:0.97074
[33] validation-logloss:0.32505 validation-auc:0.96566 validation-aucpr:0.97090
[34] validation-logloss:0.31949 validation-auc:0.96586 validation-aucpr:0.97106
[35] validation-logloss:0.31457 validation-auc:0.96591 validation-aucpr:0.97123
[36] validation-logloss:0.30962 validation-auc:0.96606 validation-aucpr:0.97142
[37] validation-logloss:0.30635 validation-auc:0.96609 validation-aucpr:0.97142
[38] validation-logloss:0.30181 validation-auc:0.96616 validation-aucpr:0.97150
[39] validation-logloss:0.29718 validation-auc:0.96631 validation-aucpr:0.97165
[40] validation-logloss:0.29408 validation-auc:0.96633 validation-aucpr:0.97165
[41] validation-logloss:0.29102 validation-auc:0.96640 validation-aucpr:0.97167
[42] validation-logloss:0.28729 validation-auc:0.96656 validation-aucpr:0.97183
[43] validation-logloss:0.28366 validation-auc:0.96661 validation-aucpr:0.97188
[44] validation-logloss:0.28029 validation-auc:0.96670 validation-aucpr:0.97201
{'best_iteration': '44', 'best_score': '0.9720059714082437'}
Trial 45, Fold 1: Log loss = 0.2802868031056375, Average precision = 0.972009096896103, ROC-AUC = 0.966703439631398, Elapsed Time = 0.9623223999988113 seconds
Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 45, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67120 validation-auc:0.92551 validation-aucpr:0.90220
[1] validation-logloss:0.65013 validation-auc:0.94577 validation-aucpr:0.93924
[2] validation-logloss:0.62772 validation-auc:0.95785 validation-aucpr:0.95836
[3] validation-logloss:0.60896 validation-auc:0.95986 validation-aucpr:0.96349
[4] validation-logloss:0.58978 validation-auc:0.96129 validation-aucpr:0.96573
[5] validation-logloss:0.57354 validation-auc:0.96166 validation-aucpr:0.96584
[6] validation-logloss:0.55841 validation-auc:0.96182 validation-aucpr:0.96615
[7] validation-logloss:0.54384 validation-auc:0.96260 validation-aucpr:0.96672
[8] validation-logloss:0.53068 validation-auc:0.96251 validation-aucpr:0.96635
[9] validation-logloss:0.51821 validation-auc:0.96181 validation-aucpr:0.96542
[10] validation-logloss:0.50572 validation-auc:0.96173 validation-aucpr:0.96542
[11] validation-logloss:0.49384 validation-auc:0.96165 validation-aucpr:0.96526
[12] validation-logloss:0.48007 validation-auc:0.96377 validation-aucpr:0.96774
[13] validation-logloss:0.46869 validation-auc:0.96384 validation-aucpr:0.96790
[14] validation-logloss:0.45694 validation-auc:0.96446 validation-aucpr:0.96851
[15] validation-logloss:0.44759 validation-auc:0.96452 validation-aucpr:0.96851
[16] validation-logloss:0.43819 validation-auc:0.96469 validation-aucpr:0.96864
[17] validation-logloss:0.42772 validation-auc:0.96503 validation-aucpr:0.96880
[18] validation-logloss:0.41973 validation-auc:0.96499 validation-aucpr:0.96877
[19] validation-logloss:0.41073 validation-auc:0.96548 validation-aucpr:0.96922
[20] validation-logloss:0.40113 validation-auc:0.96572 validation-aucpr:0.96943
[21] validation-logloss:0.39249 validation-auc:0.96593 validation-aucpr:0.96965
[22] validation-logloss:0.38644 validation-auc:0.96569 validation-aucpr:0.96941
[23] validation-logloss:0.37810 validation-auc:0.96584 validation-aucpr:0.96961
[24] validation-logloss:0.37187 validation-auc:0.96605 validation-aucpr:0.96978
[25] validation-logloss:0.36653 validation-auc:0.96593 validation-aucpr:0.96995
[26] validation-logloss:0.35877 validation-auc:0.96624 validation-aucpr:0.97023
[27] validation-logloss:0.35337 validation-auc:0.96644 validation-aucpr:0.97041
[28] validation-logloss:0.34821 validation-auc:0.96639 validation-aucpr:0.97032
[29] validation-logloss:0.34229 validation-auc:0.96645 validation-aucpr:0.97056
[30] validation-logloss:0.33685 validation-auc:0.96649 validation-aucpr:0.97059
[31] validation-logloss:0.33111 validation-auc:0.96664 validation-aucpr:0.97073
[32] validation-logloss:0.32660 validation-auc:0.96678 validation-aucpr:0.97087
[33] validation-logloss:0.32262 validation-auc:0.96681 validation-aucpr:0.97087
[34] validation-logloss:0.31837 validation-auc:0.96692 validation-aucpr:0.97089
[35] validation-logloss:0.31465 validation-auc:0.96697 validation-aucpr:0.97091
[36] validation-logloss:0.31079 validation-auc:0.96716 validation-aucpr:0.97102
[37] validation-logloss:0.30724 validation-auc:0.96706 validation-aucpr:0.97092
[38] validation-logloss:0.30404 validation-auc:0.96713 validation-aucpr:0.97097
[39] validation-logloss:0.29927 validation-auc:0.96735 validation-aucpr:0.97109
[40] validation-logloss:0.29478 validation-auc:0.96752 validation-aucpr:0.97123
[41] validation-logloss:0.29058 validation-auc:0.96759 validation-aucpr:0.97132
[42] validation-logloss:0.28669 validation-auc:0.96765 validation-aucpr:0.97135
[43] validation-logloss:0.28378 validation-auc:0.96779 validation-aucpr:0.97141
[44] validation-logloss:0.28087 validation-auc:0.96790 validation-aucpr:0.97148
{'best_iteration': '44', 'best_score': '0.9714752468667572'}
Trial 45, Fold 2: Log loss = 0.2808740290736027, Average precision = 0.9714337620041505, ROC-AUC = 0.9678955556412089, Elapsed Time = 1.1687717999993765 seconds
Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 45, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67066 validation-auc:0.93383 validation-aucpr:0.93355
[1] validation-logloss:0.64961 validation-auc:0.94795 validation-aucpr:0.94906
[2] validation-logloss:0.63024 validation-auc:0.95066 validation-aucpr:0.95428
[3] validation-logloss:0.61199 validation-auc:0.95437 validation-aucpr:0.95822
[4] validation-logloss:0.59271 validation-auc:0.96009 validation-aucpr:0.96488
[5] validation-logloss:0.57738 validation-auc:0.96006 validation-aucpr:0.96502
[6] validation-logloss:0.56230 validation-auc:0.96039 validation-aucpr:0.96532
[7] validation-logloss:0.54728 validation-auc:0.96137 validation-aucpr:0.96604
[8] validation-logloss:0.53354 validation-auc:0.96152 validation-aucpr:0.96613
[9] validation-logloss:0.52071 validation-auc:0.96188 validation-aucpr:0.96639
[10] validation-logloss:0.50877 validation-auc:0.96184 validation-aucpr:0.96685
[11] validation-logloss:0.49462 validation-auc:0.96298 validation-aucpr:0.96799
[12] validation-logloss:0.48383 validation-auc:0.96290 validation-aucpr:0.96783
[13] validation-logloss:0.47382 validation-auc:0.96301 validation-aucpr:0.96807
[14] validation-logloss:0.46076 validation-auc:0.96418 validation-aucpr:0.96921
[15] validation-logloss:0.44905 validation-auc:0.96465 validation-aucpr:0.96967
[16] validation-logloss:0.43784 validation-auc:0.96502 validation-aucpr:0.97002
[17] validation-logloss:0.42992 validation-auc:0.96474 validation-aucpr:0.96988
[18] validation-logloss:0.42194 validation-auc:0.96493 validation-aucpr:0.96999
[19] validation-logloss:0.41404 validation-auc:0.96517 validation-aucpr:0.97013
[20] validation-logloss:0.40541 validation-auc:0.96555 validation-aucpr:0.97042
[21] validation-logloss:0.39608 validation-auc:0.96585 validation-aucpr:0.97076
[22] validation-logloss:0.38740 validation-auc:0.96601 validation-aucpr:0.97090
[23] validation-logloss:0.38071 validation-auc:0.96612 validation-aucpr:0.97102
[24] validation-logloss:0.37481 validation-auc:0.96585 validation-aucpr:0.97079
[25] validation-logloss:0.36868 validation-auc:0.96594 validation-aucpr:0.97086
[26] validation-logloss:0.36332 validation-auc:0.96602 validation-aucpr:0.97093
[27] validation-logloss:0.35840 validation-auc:0.96593 validation-aucpr:0.97082
[28] validation-logloss:0.35333 validation-auc:0.96595 validation-aucpr:0.97082
[29] validation-logloss:0.34877 validation-auc:0.96586 validation-aucpr:0.97072
[30] validation-logloss:0.34366 validation-auc:0.96592 validation-aucpr:0.97076
[31] validation-logloss:0.33903 validation-auc:0.96602 validation-aucpr:0.97082
[32] validation-logloss:0.33293 validation-auc:0.96616 validation-aucpr:0.97099
[33] validation-logloss:0.32746 validation-auc:0.96625 validation-aucpr:0.97117
[34] validation-logloss:0.32185 validation-auc:0.96631 validation-aucpr:0.97126
[35] validation-logloss:0.31656 validation-auc:0.96643 validation-aucpr:0.97135
[36] validation-logloss:0.31280 validation-auc:0.96645 validation-aucpr:0.97135
[37] validation-logloss:0.30812 validation-auc:0.96665 validation-aucpr:0.97152
[38] validation-logloss:0.30497 validation-auc:0.96667 validation-aucpr:0.97154
[39] validation-logloss:0.30146 validation-auc:0.96678 validation-aucpr:0.97164
[40] validation-logloss:0.29820 validation-auc:0.96683 validation-aucpr:0.97169
[41] validation-logloss:0.29515 validation-auc:0.96694 validation-aucpr:0.97175
[42] validation-logloss:0.29065 validation-auc:0.96715 validation-aucpr:0.97192
[43] validation-logloss:0.28642 validation-auc:0.96734 validation-aucpr:0.97209
[44] validation-logloss:0.28359 validation-auc:0.96745 validation-aucpr:0.97217
{'best_iteration': '44', 'best_score': '0.972170186636452'}
Trial 45, Fold 3: Log loss = 0.28359285391712913, Average precision = 0.9721740803263408, ROC-AUC = 0.9674528199670618, Elapsed Time = 1.1913502999996126 seconds
Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 45, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67095 validation-auc:0.92539 validation-aucpr:0.92800
[1] validation-logloss:0.64635 validation-auc:0.95647 validation-aucpr:0.96293
[2] validation-logloss:0.62755 validation-auc:0.95729 validation-aucpr:0.96357
[3] validation-logloss:0.61132 validation-auc:0.95610 validation-aucpr:0.96227
[4] validation-logloss:0.59410 validation-auc:0.95678 validation-aucpr:0.96299
[5] validation-logloss:0.57847 validation-auc:0.95727 validation-aucpr:0.96324
[6] validation-logloss:0.56368 validation-auc:0.95713 validation-aucpr:0.96299
[7] validation-logloss:0.54591 validation-auc:0.96012 validation-aucpr:0.96585
[8] validation-logloss:0.53017 validation-auc:0.96098 validation-aucpr:0.96673
[9] validation-logloss:0.51480 validation-auc:0.96181 validation-aucpr:0.96761
[10] validation-logloss:0.50292 validation-auc:0.96198 validation-aucpr:0.96776
[11] validation-logloss:0.49151 validation-auc:0.96186 validation-aucpr:0.96757
[12] validation-logloss:0.47862 validation-auc:0.96248 validation-aucpr:0.96818
[13] validation-logloss:0.46816 validation-auc:0.96272 validation-aucpr:0.96838
[14] validation-logloss:0.45841 validation-auc:0.96269 validation-aucpr:0.96834
[15] validation-logloss:0.44648 validation-auc:0.96319 validation-aucpr:0.96888
[16] validation-logloss:0.43797 validation-auc:0.96296 validation-aucpr:0.96868
[17] validation-logloss:0.42744 validation-auc:0.96355 validation-aucpr:0.96927
[18] validation-logloss:0.41951 validation-auc:0.96369 validation-aucpr:0.96928
[19] validation-logloss:0.40988 validation-auc:0.96406 validation-aucpr:0.96972
[20] validation-logloss:0.40058 validation-auc:0.96437 validation-aucpr:0.97006
[21] validation-logloss:0.39199 validation-auc:0.96456 validation-aucpr:0.97033
[22] validation-logloss:0.38535 validation-auc:0.96445 validation-aucpr:0.97026
[23] validation-logloss:0.37912 validation-auc:0.96447 validation-aucpr:0.97030
[24] validation-logloss:0.37130 validation-auc:0.96468 validation-aucpr:0.97046
[25] validation-logloss:0.36608 validation-auc:0.96452 validation-aucpr:0.97040
[26] validation-logloss:0.36027 validation-auc:0.96464 validation-aucpr:0.97043
[27] validation-logloss:0.35325 validation-auc:0.96480 validation-aucpr:0.97063
[28] validation-logloss:0.34832 validation-auc:0.96467 validation-aucpr:0.97049
[29] validation-logloss:0.34190 validation-auc:0.96479 validation-aucpr:0.97066
[30] validation-logloss:0.33736 validation-auc:0.96480 validation-aucpr:0.97063
[31] validation-logloss:0.33247 validation-auc:0.96498 validation-aucpr:0.97072
[32] validation-logloss:0.32649 validation-auc:0.96544 validation-aucpr:0.97106
[33] validation-logloss:0.32104 validation-auc:0.96576 validation-aucpr:0.97129
[34] validation-logloss:0.31617 validation-auc:0.96588 validation-aucpr:0.97142
[35] validation-logloss:0.31271 validation-auc:0.96578 validation-aucpr:0.97134
[36] validation-logloss:0.30906 validation-auc:0.96584 validation-aucpr:0.97137
[37] validation-logloss:0.30541 validation-auc:0.96599 validation-aucpr:0.97146
[38] validation-logloss:0.30187 validation-auc:0.96613 validation-aucpr:0.97161
[39] validation-logloss:0.29898 validation-auc:0.96615 validation-aucpr:0.97159
[40] validation-logloss:0.29469 validation-auc:0.96630 validation-aucpr:0.97177
[41] validation-logloss:0.29206 validation-auc:0.96616 validation-aucpr:0.97167
[42] validation-logloss:0.28781 validation-auc:0.96636 validation-aucpr:0.97186
[43] validation-logloss:0.28481 validation-auc:0.96649 validation-aucpr:0.97195
[44] validation-logloss:0.28226 validation-auc:0.96658 validation-aucpr:0.97202
{'best_iteration': '44', 'best_score': '0.9720178657714623'}
Trial 45, Fold 4: Log loss = 0.28226231217644576, Average precision = 0.9720161104064858, ROC-AUC = 0.9665810812583008, Elapsed Time = 1.1915703999984544 seconds
Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 45, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67074 validation-auc:0.93172 validation-aucpr:0.93217
[1] validation-logloss:0.64738 validation-auc:0.95505 validation-aucpr:0.96156
[2] validation-logloss:0.62848 validation-auc:0.95691 validation-aucpr:0.96271
[3] validation-logloss:0.61110 validation-auc:0.95738 validation-aucpr:0.96289
[4] validation-logloss:0.59377 validation-auc:0.95839 validation-aucpr:0.96339
[5] validation-logloss:0.57738 validation-auc:0.95814 validation-aucpr:0.96248
[6] validation-logloss:0.55930 validation-auc:0.96079 validation-aucpr:0.96557
[7] validation-logloss:0.54477 validation-auc:0.96096 validation-aucpr:0.96582
[8] validation-logloss:0.52923 validation-auc:0.96120 validation-aucpr:0.96612
[9] validation-logloss:0.51663 validation-auc:0.96147 validation-aucpr:0.96653
[10] validation-logloss:0.50514 validation-auc:0.96139 validation-aucpr:0.96640
[11] validation-logloss:0.49352 validation-auc:0.96158 validation-aucpr:0.96658
[12] validation-logloss:0.48050 validation-auc:0.96185 validation-aucpr:0.96682
[13] validation-logloss:0.47081 validation-auc:0.96159 validation-aucpr:0.96659
[14] validation-logloss:0.45957 validation-auc:0.96133 validation-aucpr:0.96646
[15] validation-logloss:0.44817 validation-auc:0.96174 validation-aucpr:0.96712
[16] validation-logloss:0.43897 validation-auc:0.96192 validation-aucpr:0.96726
[17] validation-logloss:0.42869 validation-auc:0.96204 validation-aucpr:0.96742
[18] validation-logloss:0.42061 validation-auc:0.96223 validation-aucpr:0.96751
[19] validation-logloss:0.41209 validation-auc:0.96231 validation-aucpr:0.96756
[20] validation-logloss:0.40513 validation-auc:0.96254 validation-aucpr:0.96770
[21] validation-logloss:0.39835 validation-auc:0.96270 validation-aucpr:0.96781
[22] validation-logloss:0.39211 validation-auc:0.96270 validation-aucpr:0.96775
[23] validation-logloss:0.38608 validation-auc:0.96288 validation-aucpr:0.96793
[24] validation-logloss:0.38035 validation-auc:0.96286 validation-aucpr:0.96793
[25] validation-logloss:0.37272 validation-auc:0.96335 validation-aucpr:0.96845
[26] validation-logloss:0.36503 validation-auc:0.96364 validation-aucpr:0.96871
[27] validation-logloss:0.35780 validation-auc:0.96378 validation-aucpr:0.96885
[28] validation-logloss:0.35277 validation-auc:0.96379 validation-aucpr:0.96883
[29] validation-logloss:0.34634 validation-auc:0.96394 validation-aucpr:0.96896
[30] validation-logloss:0.34001 validation-auc:0.96417 validation-aucpr:0.96921
[31] validation-logloss:0.33597 validation-auc:0.96415 validation-aucpr:0.96925
[32] validation-logloss:0.32987 validation-auc:0.96441 validation-aucpr:0.96951
[33] validation-logloss:0.32435 validation-auc:0.96462 validation-aucpr:0.96970
[34] validation-logloss:0.31901 validation-auc:0.96482 validation-aucpr:0.96989
[35] validation-logloss:0.31552 validation-auc:0.96469 validation-aucpr:0.96975
[36] validation-logloss:0.31089 validation-auc:0.96485 validation-aucpr:0.96985
[37] validation-logloss:0.30631 validation-auc:0.96493 validation-aucpr:0.96997
[38] validation-logloss:0.30311 validation-auc:0.96494 validation-aucpr:0.96993
[39] validation-logloss:0.29898 validation-auc:0.96502 validation-aucpr:0.96999
[40] validation-logloss:0.29579 validation-auc:0.96499 validation-aucpr:0.96995
[41] validation-logloss:0.29176 validation-auc:0.96518 validation-aucpr:0.97011
[42] validation-logloss:0.28916 validation-auc:0.96517 validation-aucpr:0.97008
[43] validation-logloss:0.28549 validation-auc:0.96526 validation-aucpr:0.97021
[44] validation-logloss:0.28291 validation-auc:0.96528 validation-aucpr:0.97024
{'best_iteration': '44', 'best_score': '0.9702411674009223'}
Trial 45, Fold 5: Log loss = 0.2829139369086157, Average precision = 0.9702407906841284, ROC-AUC = 0.9652755141853854, Elapsed Time = 1.2029946000002383 seconds
Optimization Progress: 46%|####6 | 46/100 [2:32:58<1:34:55, 105.48s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 46, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:31:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64076 validation-auc:0.93869 validation-aucpr:0.93708
[20:32:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60029 validation-auc:0.95067 validation-aucpr:0.94280
[20:32:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56030 validation-auc:0.95506 validation-aucpr:0.95160
[20:32:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53027 validation-auc:0.95681 validation-aucpr:0.95480
[20:32:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49789 validation-auc:0.96018 validation-aucpr:0.96071
[20:32:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.47328 validation-auc:0.96102 validation-aucpr:0.96032
[20:32:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44763 validation-auc:0.96347 validation-aucpr:0.96383
[20:32:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42532 validation-auc:0.96390 validation-aucpr:0.96473
[20:32:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40458 validation-auc:0.96433 validation-aucpr:0.96536
[20:32:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38660 validation-auc:0.96466 validation-aucpr:0.96732
[20:32:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37016 validation-auc:0.96508 validation-aucpr:0.96784
[20:32:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35489 validation-auc:0.96557 validation-aucpr:0.96902
[20:32:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.34342 validation-auc:0.96582 validation-aucpr:0.96914
[20:32:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.33001 validation-auc:0.96666 validation-aucpr:0.96975
[20:32:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.31871 validation-auc:0.96695 validation-aucpr:0.96999
[20:32:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.30794 validation-auc:0.96728 validation-aucpr:0.97033
[20:32:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.29834 validation-auc:0.96733 validation-aucpr:0.97032
[20:32:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.28978 validation-auc:0.96753 validation-aucpr:0.97046
[20:33:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28196 validation-auc:0.96785 validation-aucpr:0.97066
[20:33:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27476 validation-auc:0.96808 validation-aucpr:0.96989
[20:33:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.26881 validation-auc:0.96804 validation-aucpr:0.97005
[20:33:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.26402 validation-auc:0.96782 validation-aucpr:0.96950
[20:33:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.25898 validation-auc:0.96819 validation-aucpr:0.97107
[20:33:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25395 validation-auc:0.96831 validation-aucpr:0.97135
[20:33:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24992 validation-auc:0.96829 validation-aucpr:0.97190
[20:33:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.24638 validation-auc:0.96804 validation-aucpr:0.97170
[20:33:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.24278 validation-auc:0.96821 validation-aucpr:0.97175
[20:33:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23971 validation-auc:0.96849 validation-aucpr:0.97269
[20:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23655 validation-auc:0.96847 validation-aucpr:0.97272
[20:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.23392 validation-auc:0.96854 validation-aucpr:0.97277
[20:33:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.23152 validation-auc:0.96869 validation-aucpr:0.97284
[20:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22930 validation-auc:0.96867 validation-aucpr:0.97268
[20:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.22672 validation-auc:0.96867 validation-aucpr:0.97288
[20:33:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22411 validation-auc:0.96881 validation-aucpr:0.97280
[20:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22201 validation-auc:0.96879 validation-aucpr:0.97284
[20:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22027 validation-auc:0.96875 validation-aucpr:0.97290
[20:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.21837 validation-auc:0.96890 validation-aucpr:0.97326
[20:33:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21717 validation-auc:0.96899 validation-aucpr:0.97409
[20:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21562 validation-auc:0.96918 validation-aucpr:0.97427
[20:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21412 validation-auc:0.96935 validation-aucpr:0.97437
[20:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21328 validation-auc:0.96935 validation-aucpr:0.97441
[20:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21210 validation-auc:0.96949 validation-aucpr:0.97450
[20:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21082 validation-auc:0.96957 validation-aucpr:0.97456
[20:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20935 validation-auc:0.96982 validation-aucpr:0.97474
[20:34:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20814 validation-auc:0.97003 validation-aucpr:0.97491
[20:34:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20765 validation-auc:0.96996 validation-aucpr:0.97483
[20:34:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20746 validation-auc:0.96991 validation-aucpr:0.97476
[20:34:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20698 validation-auc:0.96998 validation-aucpr:0.97478
[20:34:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20669 validation-auc:0.96991 validation-aucpr:0.97471
[20:34:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20629 validation-auc:0.96990 validation-aucpr:0.97475
[20:34:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20609 validation-auc:0.96986 validation-aucpr:0.97469
[20:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20626 validation-auc:0.96972 validation-aucpr:0.97459
[20:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20545 validation-auc:0.96984 validation-aucpr:0.97462
[20:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20534 validation-auc:0.96973 validation-aucpr:0.97461
[20:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20525 validation-auc:0.96974 validation-aucpr:0.97457
[20:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20504 validation-auc:0.96975 validation-aucpr:0.97454
[20:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20480 validation-auc:0.96963 validation-aucpr:0.97446
[20:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20434 validation-auc:0.96968 validation-aucpr:0.97449
[20:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20389 validation-auc:0.96981 validation-aucpr:0.97455
[20:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20355 validation-auc:0.96992 validation-aucpr:0.97461
[20:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20304 validation-auc:0.97009 validation-aucpr:0.97491
[20:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20314 validation-auc:0.97001 validation-aucpr:0.97488
[20:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20279 validation-auc:0.97005 validation-aucpr:0.97492
[20:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20264 validation-auc:0.97006 validation-aucpr:0.97495
[20:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20293 validation-auc:0.96995 validation-aucpr:0.97489
[20:34:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20292 validation-auc:0.96999 validation-aucpr:0.97489
[20:34:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20284 validation-auc:0.97001 validation-aucpr:0.97490
[20:34:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20285 validation-auc:0.97003 validation-aucpr:0.97489
[20:34:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20308 validation-auc:0.96998 validation-aucpr:0.97488
[20:34:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20314 validation-auc:0.96999 validation-aucpr:0.97484
[20:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20319 validation-auc:0.97004 validation-aucpr:0.97492
[20:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20310 validation-auc:0.97012 validation-aucpr:0.97498
[20:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20294 validation-auc:0.97020 validation-aucpr:0.97501
[20:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20297 validation-auc:0.97025 validation-aucpr:0.97501
{'best_iteration': '72', 'best_score': '0.9750078737434187'}
Trial 46, Fold 1: Log loss = 0.20296792392887414, Average precision = 0.9750121228461307, ROC-AUC = 0.9702506416598559, Elapsed Time = 173.2442964999973 seconds
Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 46, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64530 validation-auc:0.92763 validation-aucpr:0.91331
[20:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59987 validation-auc:0.95017 validation-aucpr:0.94426
[20:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56506 validation-auc:0.95467 validation-aucpr:0.95053
[20:35:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53379 validation-auc:0.95752 validation-aucpr:0.96177
[20:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50317 validation-auc:0.96085 validation-aucpr:0.96533
[20:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.47577 validation-auc:0.96249 validation-aucpr:0.96685
[20:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.45064 validation-auc:0.96373 validation-aucpr:0.96797
[20:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.43124 validation-auc:0.96391 validation-aucpr:0.96800
[20:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.41027 validation-auc:0.96509 validation-aucpr:0.96904
[20:35:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.39076 validation-auc:0.96650 validation-aucpr:0.97012
[20:35:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37329 validation-auc:0.96726 validation-aucpr:0.97089
[20:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35690 validation-auc:0.96831 validation-aucpr:0.97173
[20:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.34278 validation-auc:0.96884 validation-aucpr:0.97255
[20:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.32986 validation-auc:0.96911 validation-aucpr:0.97285
[20:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.32078 validation-auc:0.96899 validation-aucpr:0.97258
[20:35:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.31055 validation-auc:0.96885 validation-aucpr:0.97248
[20:35:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.30204 validation-auc:0.96888 validation-aucpr:0.97237
[20:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.29528 validation-auc:0.96852 validation-aucpr:0.97090
[20:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28725 validation-auc:0.96844 validation-aucpr:0.97097
[20:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27942 validation-auc:0.96904 validation-aucpr:0.97276
[20:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.27311 validation-auc:0.96918 validation-aucpr:0.97279
[20:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.26682 validation-auc:0.96922 validation-aucpr:0.97281
[20:36:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.26013 validation-auc:0.96966 validation-aucpr:0.97233
[20:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25512 validation-auc:0.97008 validation-aucpr:0.97375
[20:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24913 validation-auc:0.97060 validation-aucpr:0.97416
[20:36:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.24414 validation-auc:0.97055 validation-aucpr:0.97419
[20:36:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23976 validation-auc:0.97085 validation-aucpr:0.97441
[20:36:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23633 validation-auc:0.97094 validation-aucpr:0.97448
[20:36:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23270 validation-auc:0.97092 validation-aucpr:0.97445
[20:36:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22912 validation-auc:0.97128 validation-aucpr:0.97472
[20:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22581 validation-auc:0.97129 validation-aucpr:0.97473
[20:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22264 validation-auc:0.97138 validation-aucpr:0.97480
[20:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21926 validation-auc:0.97160 validation-aucpr:0.97507
[20:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21690 validation-auc:0.97169 validation-aucpr:0.97508
[20:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21436 validation-auc:0.97200 validation-aucpr:0.97532
[20:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21231 validation-auc:0.97184 validation-aucpr:0.97525
[20:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.21087 validation-auc:0.97170 validation-aucpr:0.97519
[20:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20879 validation-auc:0.97181 validation-aucpr:0.97527
[20:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20737 validation-auc:0.97193 validation-aucpr:0.97533
[20:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20593 validation-auc:0.97188 validation-aucpr:0.97529
[20:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20459 validation-auc:0.97188 validation-aucpr:0.97529
[20:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20355 validation-auc:0.97188 validation-aucpr:0.97523
[20:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20274 validation-auc:0.97178 validation-aucpr:0.97511
[20:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20221 validation-auc:0.97158 validation-aucpr:0.97477
[20:37:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20085 validation-auc:0.97174 validation-aucpr:0.97497
[20:37:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19980 validation-auc:0.97177 validation-aucpr:0.97501
[20:37:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19873 validation-auc:0.97188 validation-aucpr:0.97520
[20:37:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19776 validation-auc:0.97204 validation-aucpr:0.97529
[20:37:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19678 validation-auc:0.97221 validation-aucpr:0.97534
[20:37:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19596 validation-auc:0.97231 validation-aucpr:0.97541
[20:37:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19549 validation-auc:0.97226 validation-aucpr:0.97520
[20:37:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19499 validation-auc:0.97227 validation-aucpr:0.97502
[20:37:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19379 validation-auc:0.97246 validation-aucpr:0.97513
[20:37:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19348 validation-auc:0.97242 validation-aucpr:0.97502
[20:37:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19293 validation-auc:0.97246 validation-aucpr:0.97508
[20:37:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19233 validation-auc:0.97253 validation-aucpr:0.97504
[20:37:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19182 validation-auc:0.97260 validation-aucpr:0.97508
[20:37:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19149 validation-auc:0.97259 validation-aucpr:0.97490
[20:37:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19056 validation-auc:0.97277 validation-aucpr:0.97499
[20:37:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19033 validation-auc:0.97272 validation-aucpr:0.97473
[20:37:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.18994 validation-auc:0.97281 validation-aucpr:0.97445
[20:37:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.18946 validation-auc:0.97291 validation-aucpr:0.97488
[20:37:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.18955 validation-auc:0.97286 validation-aucpr:0.97481
[20:37:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.18912 validation-auc:0.97297 validation-aucpr:0.97430
[20:37:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.18896 validation-auc:0.97299 validation-aucpr:0.97452
[20:37:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.18846 validation-auc:0.97315 validation-aucpr:0.97447
[20:37:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.18846 validation-auc:0.97314 validation-aucpr:0.97469
[20:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.18827 validation-auc:0.97317 validation-aucpr:0.97435
[20:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.18866 validation-auc:0.97304 validation-aucpr:0.97393
[20:37:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.18839 validation-auc:0.97311 validation-aucpr:0.97416
[20:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.18845 validation-auc:0.97306 validation-aucpr:0.97396
[20:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.18825 validation-auc:0.97312 validation-aucpr:0.97427
[20:37:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.18809 validation-auc:0.97320 validation-aucpr:0.97449
[20:37:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.18801 validation-auc:0.97327 validation-aucpr:0.97446
{'best_iteration': '49', 'best_score': '0.9754094178962217'}
Trial 46, Fold 2: Log loss = 0.1880081891241934, Average precision = 0.9745046069561031, ROC-AUC = 0.9732694758419043, Elapsed Time = 168.89245759999903 seconds
Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 46, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:37:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64015 validation-auc:0.94881 validation-aucpr:0.95418
[20:37:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59977 validation-auc:0.95293 validation-aucpr:0.94017
[20:37:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56469 validation-auc:0.95817 validation-aucpr:0.95175
[20:37:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53286 validation-auc:0.96073 validation-aucpr:0.96001
[20:37:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50034 validation-auc:0.96359 validation-aucpr:0.96391
[20:37:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.47538 validation-auc:0.96345 validation-aucpr:0.96154
[20:38:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.45279 validation-auc:0.96437 validation-aucpr:0.96486
[20:38:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42941 validation-auc:0.96566 validation-aucpr:0.96793
[20:38:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40882 validation-auc:0.96647 validation-aucpr:0.96871
[20:38:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.39185 validation-auc:0.96710 validation-aucpr:0.96990
[20:38:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37413 validation-auc:0.96786 validation-aucpr:0.97070
[20:38:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.36034 validation-auc:0.96843 validation-aucpr:0.97064
[20:38:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.34531 validation-auc:0.96876 validation-aucpr:0.97029
[20:38:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.33452 validation-auc:0.96855 validation-aucpr:0.97022
[20:38:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.32420 validation-auc:0.96910 validation-aucpr:0.97121
[20:38:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.31556 validation-auc:0.96891 validation-aucpr:0.97098
[20:38:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.30545 validation-auc:0.96907 validation-aucpr:0.97119
[20:38:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.29540 validation-auc:0.96967 validation-aucpr:0.97154
[20:38:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28594 validation-auc:0.97012 validation-aucpr:0.97209
[20:38:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27938 validation-auc:0.97005 validation-aucpr:0.97208
[20:38:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.27369 validation-auc:0.97003 validation-aucpr:0.97217
[20:38:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.26886 validation-auc:0.96957 validation-aucpr:0.97014
[20:38:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.26349 validation-auc:0.96971 validation-aucpr:0.97073
[20:39:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25954 validation-auc:0.96968 validation-aucpr:0.97049
[20:39:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.25570 validation-auc:0.96954 validation-aucpr:0.97038
[20:39:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.25105 validation-auc:0.96924 validation-aucpr:0.97015
[20:39:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.24661 validation-auc:0.96921 validation-aucpr:0.97030
[20:39:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.24222 validation-auc:0.96924 validation-aucpr:0.97005
[20:39:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23821 validation-auc:0.96936 validation-aucpr:0.97119
[20:39:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.23455 validation-auc:0.96946 validation-aucpr:0.97130
[20:39:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.23209 validation-auc:0.96950 validation-aucpr:0.97099
[20:39:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22917 validation-auc:0.96952 validation-aucpr:0.97093
[20:39:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.22587 validation-auc:0.96968 validation-aucpr:0.97098
[20:39:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22377 validation-auc:0.96961 validation-aucpr:0.97030
[20:39:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22120 validation-auc:0.96963 validation-aucpr:0.97057
[20:39:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21861 validation-auc:0.96989 validation-aucpr:0.97091
[20:39:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.21659 validation-auc:0.97000 validation-aucpr:0.97153
[20:39:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21427 validation-auc:0.97031 validation-aucpr:0.97178
[20:39:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21225 validation-auc:0.97058 validation-aucpr:0.97201
[20:39:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21127 validation-auc:0.97052 validation-aucpr:0.97195
[20:39:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20980 validation-auc:0.97073 validation-aucpr:0.97271
[20:39:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20801 validation-auc:0.97094 validation-aucpr:0.97286
[20:39:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20659 validation-auc:0.97101 validation-aucpr:0.97449
[20:39:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20589 validation-auc:0.97113 validation-aucpr:0.97506
[20:39:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20486 validation-auc:0.97125 validation-aucpr:0.97524
[20:39:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20417 validation-auc:0.97121 validation-aucpr:0.97525
[20:39:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20347 validation-auc:0.97124 validation-aucpr:0.97527
[20:40:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20292 validation-auc:0.97125 validation-aucpr:0.97523
[20:40:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20223 validation-auc:0.97127 validation-aucpr:0.97523
[20:40:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20142 validation-auc:0.97132 validation-aucpr:0.97528
[20:40:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20047 validation-auc:0.97147 validation-aucpr:0.97539
[20:40:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19951 validation-auc:0.97170 validation-aucpr:0.97563
[20:40:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19904 validation-auc:0.97170 validation-aucpr:0.97557
[20:40:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19862 validation-auc:0.97192 validation-aucpr:0.97572
[20:40:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19795 validation-auc:0.97204 validation-aucpr:0.97564
[20:40:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19772 validation-auc:0.97210 validation-aucpr:0.97574
[20:40:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19823 validation-auc:0.97185 validation-aucpr:0.97563
[20:40:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19753 validation-auc:0.97205 validation-aucpr:0.97573
[20:40:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19714 validation-auc:0.97210 validation-aucpr:0.97574
[20:40:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19676 validation-auc:0.97217 validation-aucpr:0.97582
[20:40:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19674 validation-auc:0.97208 validation-aucpr:0.97547
[20:40:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.19676 validation-auc:0.97211 validation-aucpr:0.97558
[20:40:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.19740 validation-auc:0.97180 validation-aucpr:0.97515
[20:40:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.19699 validation-auc:0.97192 validation-aucpr:0.97497
[20:40:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.19663 validation-auc:0.97202 validation-aucpr:0.97504
[20:40:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.19680 validation-auc:0.97200 validation-aucpr:0.97542
[20:40:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.19671 validation-auc:0.97207 validation-aucpr:0.97565
[20:40:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.19708 validation-auc:0.97192 validation-aucpr:0.97547
[20:40:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.19685 validation-auc:0.97197 validation-aucpr:0.97546
[20:40:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.19651 validation-auc:0.97209 validation-aucpr:0.97548
[20:40:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.19653 validation-auc:0.97211 validation-aucpr:0.97533
[20:40:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.19678 validation-auc:0.97207 validation-aucpr:0.97539
[20:40:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.19691 validation-auc:0.97210 validation-aucpr:0.97562
[20:40:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.19689 validation-auc:0.97210 validation-aucpr:0.97545
{'best_iteration': '59', 'best_score': '0.9758164074598226'}
Trial 46, Fold 3: Log loss = 0.19688780501448533, Average precision = 0.9754565903667709, ROC-AUC = 0.9721035565004672, Elapsed Time = 172.1257302999984 seconds
Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 46, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:40:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64068 validation-auc:0.94491 validation-aucpr:0.95001
[20:40:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59933 validation-auc:0.95102 validation-aucpr:0.94377
[20:40:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.55913 validation-auc:0.95819 validation-aucpr:0.95572
[20:40:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.52414 validation-auc:0.96134 validation-aucpr:0.96267
[20:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49257 validation-auc:0.96277 validation-aucpr:0.96354
[20:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.46471 validation-auc:0.96381 validation-aucpr:0.96777
[20:40:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.43999 validation-auc:0.96407 validation-aucpr:0.97089
[20:40:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.41826 validation-auc:0.96471 validation-aucpr:0.97126
[20:40:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40089 validation-auc:0.96550 validation-aucpr:0.97169
[20:41:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38277 validation-auc:0.96595 validation-aucpr:0.97206
[20:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.36836 validation-auc:0.96560 validation-aucpr:0.97193
[20:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35533 validation-auc:0.96565 validation-aucpr:0.97187
[20:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.34177 validation-auc:0.96552 validation-aucpr:0.97187
[20:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.33001 validation-auc:0.96565 validation-aucpr:0.97186
[20:41:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.32085 validation-auc:0.96577 validation-aucpr:0.97197
[20:41:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.31007 validation-auc:0.96623 validation-aucpr:0.97228
[20:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.30043 validation-auc:0.96667 validation-aucpr:0.97255
[20:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.29244 validation-auc:0.96723 validation-aucpr:0.97278
[20:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28510 validation-auc:0.96693 validation-aucpr:0.97254
[20:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27865 validation-auc:0.96748 validation-aucpr:0.97297
[20:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.27151 validation-auc:0.96796 validation-aucpr:0.97326
[20:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.26551 validation-auc:0.96801 validation-aucpr:0.97332
[20:41:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.26032 validation-auc:0.96824 validation-aucpr:0.97347
[20:41:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25524 validation-auc:0.96813 validation-aucpr:0.97343
[20:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.25017 validation-auc:0.96847 validation-aucpr:0.97366
[20:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.24620 validation-auc:0.96823 validation-aucpr:0.97347
[20:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.24141 validation-auc:0.96856 validation-aucpr:0.97376
[20:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23745 validation-auc:0.96881 validation-aucpr:0.97392
[20:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23466 validation-auc:0.96866 validation-aucpr:0.97378
[20:42:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.23143 validation-auc:0.96882 validation-aucpr:0.97388
[20:42:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22848 validation-auc:0.96898 validation-aucpr:0.97397
[20:42:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22675 validation-auc:0.96881 validation-aucpr:0.97386
[20:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.22512 validation-auc:0.96855 validation-aucpr:0.97362
[20:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22291 validation-auc:0.96850 validation-aucpr:0.97358
[20:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22065 validation-auc:0.96868 validation-aucpr:0.97372
[20:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21871 validation-auc:0.96874 validation-aucpr:0.97379
[20:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.21751 validation-auc:0.96865 validation-aucpr:0.97372
[20:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21534 validation-auc:0.96895 validation-aucpr:0.97391
[20:42:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21412 validation-auc:0.96899 validation-aucpr:0.97392
[20:42:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21309 validation-auc:0.96903 validation-aucpr:0.97394
[20:42:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21181 validation-auc:0.96897 validation-aucpr:0.97391
[20:42:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21112 validation-auc:0.96891 validation-aucpr:0.97395
[20:42:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21006 validation-auc:0.96900 validation-aucpr:0.97401
[20:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20858 validation-auc:0.96924 validation-aucpr:0.97417
[20:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20823 validation-auc:0.96918 validation-aucpr:0.97410
[20:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20789 validation-auc:0.96916 validation-aucpr:0.97412
[20:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20689 validation-auc:0.96925 validation-aucpr:0.97420
[20:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20608 validation-auc:0.96930 validation-aucpr:0.97422
[20:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20534 validation-auc:0.96934 validation-aucpr:0.97429
[20:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20515 validation-auc:0.96924 validation-aucpr:0.97422
[20:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20486 validation-auc:0.96919 validation-aucpr:0.97419
[20:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20453 validation-auc:0.96917 validation-aucpr:0.97416
[20:42:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20432 validation-auc:0.96919 validation-aucpr:0.97417
[20:42:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20364 validation-auc:0.96936 validation-aucpr:0.97428
[20:42:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20361 validation-auc:0.96930 validation-aucpr:0.97425
[20:43:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20292 validation-auc:0.96949 validation-aucpr:0.97436
[20:43:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20261 validation-auc:0.96944 validation-aucpr:0.97435
[20:43:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20233 validation-auc:0.96950 validation-aucpr:0.97441
[20:43:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20216 validation-auc:0.96960 validation-aucpr:0.97448
[20:43:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20197 validation-auc:0.96958 validation-aucpr:0.97447
[20:43:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20186 validation-auc:0.96955 validation-aucpr:0.97447
[20:43:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20170 validation-auc:0.96953 validation-aucpr:0.97445
[20:43:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20162 validation-auc:0.96957 validation-aucpr:0.97445
[20:43:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20171 validation-auc:0.96954 validation-aucpr:0.97443
[20:43:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20123 validation-auc:0.96966 validation-aucpr:0.97453
[20:43:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20144 validation-auc:0.96963 validation-aucpr:0.97449
[20:43:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20147 validation-auc:0.96962 validation-aucpr:0.97448
[20:43:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20166 validation-auc:0.96959 validation-aucpr:0.97446
[20:43:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20177 validation-auc:0.96958 validation-aucpr:0.97441
[20:43:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20188 validation-auc:0.96963 validation-aucpr:0.97443
[20:43:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20178 validation-auc:0.96965 validation-aucpr:0.97445
[20:43:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20162 validation-auc:0.96971 validation-aucpr:0.97450
[20:43:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20162 validation-auc:0.96977 validation-aucpr:0.97454
[20:43:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20176 validation-auc:0.96973 validation-aucpr:0.97450
{'best_iteration': '72', 'best_score': '0.974535678305386'}
Trial 46, Fold 4: Log loss = 0.20176173603682535, Average precision = 0.9745087340972265, ROC-AUC = 0.9697287256998838, Elapsed Time = 167.63923059999797 seconds
Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 46, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:43:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64095 validation-auc:0.94365 validation-aucpr:0.95270
[20:43:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60106 validation-auc:0.94824 validation-aucpr:0.93268
[20:43:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56080 validation-auc:0.95714 validation-aucpr:0.95764
[20:43:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.53025 validation-auc:0.95956 validation-aucpr:0.96236
[20:43:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.50057 validation-auc:0.96036 validation-aucpr:0.96652
[20:43:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.47220 validation-auc:0.96245 validation-aucpr:0.96836
[20:43:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44705 validation-auc:0.96355 validation-aucpr:0.96933
[20:43:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42671 validation-auc:0.96425 validation-aucpr:0.96971
[20:43:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40976 validation-auc:0.96418 validation-aucpr:0.96938
[20:43:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.39223 validation-auc:0.96447 validation-aucpr:0.96969
[20:43:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37842 validation-auc:0.96468 validation-aucpr:0.96979
[20:43:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.36427 validation-auc:0.96431 validation-aucpr:0.96953
[20:44:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.35133 validation-auc:0.96507 validation-aucpr:0.97002
[20:44:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.34098 validation-auc:0.96486 validation-aucpr:0.96966
[20:44:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.33112 validation-auc:0.96476 validation-aucpr:0.96954
[20:44:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.32005 validation-auc:0.96486 validation-aucpr:0.96962
[20:44:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.31020 validation-auc:0.96497 validation-aucpr:0.96989
[20:44:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.30115 validation-auc:0.96523 validation-aucpr:0.97010
[20:44:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.29316 validation-auc:0.96527 validation-aucpr:0.97018
[20:44:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.28683 validation-auc:0.96546 validation-aucpr:0.97038
[20:44:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.27941 validation-auc:0.96576 validation-aucpr:0.97062
[20:44:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.27290 validation-auc:0.96592 validation-aucpr:0.97082
[20:44:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.26819 validation-auc:0.96583 validation-aucpr:0.97061
[20:44:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.26251 validation-auc:0.96603 validation-aucpr:0.97082
[20:44:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.25721 validation-auc:0.96638 validation-aucpr:0.97110
[20:44:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.25214 validation-auc:0.96667 validation-aucpr:0.97130
[20:44:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.24734 validation-auc:0.96707 validation-aucpr:0.97181
[20:44:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.24338 validation-auc:0.96733 validation-aucpr:0.97203
[20:44:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.24089 validation-auc:0.96726 validation-aucpr:0.97196
[20:45:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.23739 validation-auc:0.96751 validation-aucpr:0.97246
[20:45:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.23433 validation-auc:0.96770 validation-aucpr:0.97272
[20:45:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.23142 validation-auc:0.96788 validation-aucpr:0.97285
[20:45:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.22975 validation-auc:0.96775 validation-aucpr:0.97269
[20:45:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22759 validation-auc:0.96774 validation-aucpr:0.97269
[20:45:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22535 validation-auc:0.96789 validation-aucpr:0.97277
[20:45:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22429 validation-auc:0.96757 validation-aucpr:0.97254
[20:45:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22317 validation-auc:0.96743 validation-aucpr:0.97238
[20:45:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.22150 validation-auc:0.96740 validation-aucpr:0.97240
[20:45:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.22027 validation-auc:0.96744 validation-aucpr:0.97241
[20:45:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21889 validation-auc:0.96751 validation-aucpr:0.97247
[20:45:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21713 validation-auc:0.96773 validation-aucpr:0.97252
[20:45:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21589 validation-auc:0.96786 validation-aucpr:0.97250
[20:45:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21502 validation-auc:0.96787 validation-aucpr:0.97250
[20:45:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.21363 validation-auc:0.96800 validation-aucpr:0.97260
[20:45:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.21269 validation-auc:0.96818 validation-aucpr:0.97270
[20:45:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.21192 validation-auc:0.96821 validation-aucpr:0.97268
[20:45:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.21177 validation-auc:0.96800 validation-aucpr:0.97250
[20:45:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.21135 validation-auc:0.96796 validation-aucpr:0.97244
[20:45:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.21064 validation-auc:0.96802 validation-aucpr:0.97254
[20:45:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.21044 validation-auc:0.96793 validation-aucpr:0.97246
[20:45:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.21026 validation-auc:0.96789 validation-aucpr:0.97237
[20:45:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20991 validation-auc:0.96790 validation-aucpr:0.97240
[20:45:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20947 validation-auc:0.96794 validation-aucpr:0.97232
[20:45:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20873 validation-auc:0.96811 validation-aucpr:0.97236
[20:45:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20815 validation-auc:0.96826 validation-aucpr:0.97252
[20:45:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20810 validation-auc:0.96830 validation-aucpr:0.97274
[20:45:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20774 validation-auc:0.96835 validation-aucpr:0.97275
[20:45:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20711 validation-auc:0.96848 validation-aucpr:0.97279
[20:45:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20706 validation-auc:0.96840 validation-aucpr:0.97270
[20:45:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20705 validation-auc:0.96846 validation-aucpr:0.97294
[20:46:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20714 validation-auc:0.96844 validation-aucpr:0.97287
[20:46:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20705 validation-auc:0.96854 validation-aucpr:0.97293
[20:46:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20691 validation-auc:0.96854 validation-aucpr:0.97288
[20:46:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20695 validation-auc:0.96852 validation-aucpr:0.97278
[20:46:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20666 validation-auc:0.96865 validation-aucpr:0.97295
[20:46:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20673 validation-auc:0.96860 validation-aucpr:0.97283
[20:46:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20700 validation-auc:0.96851 validation-aucpr:0.97272
[20:46:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20678 validation-auc:0.96859 validation-aucpr:0.97262
[20:46:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20698 validation-auc:0.96853 validation-aucpr:0.97255
[20:46:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20728 validation-auc:0.96848 validation-aucpr:0.97250
[20:46:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20717 validation-auc:0.96851 validation-aucpr:0.97241
[20:46:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20694 validation-auc:0.96862 validation-aucpr:0.97248
[20:46:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20677 validation-auc:0.96869 validation-aucpr:0.97249
[20:46:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20641 validation-auc:0.96886 validation-aucpr:0.97271
{'best_iteration': '64', 'best_score': '0.9729466973423092'}
Trial 46, Fold 5: Log loss = 0.20640855234259178, Average precision = 0.9727156435063508, ROC-AUC = 0.9688557679802315, Elapsed Time = 176.15985360000195 seconds
Optimization Progress: 47%|####6 | 47/100 [2:47:24<4:54:45, 333.68s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 47, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68214 validation-auc:0.93801 validation-aucpr:0.91337
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67009 validation-auc:0.95823 validation-aucpr:0.95300
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65805 validation-auc:0.96329 validation-aucpr:0.96345
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64657 validation-auc:0.96524 validation-aucpr:0.96771
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63605 validation-auc:0.96552 validation-aucpr:0.97081
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62582 validation-auc:0.96558 validation-aucpr:0.97099
{'best_iteration': '5', 'best_score': '0.9709855396319533'}
Trial 47, Fold 1: Log loss = 0.6258185969607232, Average precision = 0.9708432228852684, ROC-AUC = 0.965581356375733, Elapsed Time = 0.40443649999724585 seconds
Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 47, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68157 validation-auc:0.94237 validation-aucpr:0.93199
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67085 validation-auc:0.95490 validation-aucpr:0.95445
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66064 validation-auc:0.95773 validation-aucpr:0.95933
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64926 validation-auc:0.96375 validation-aucpr:0.96673
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63908 validation-auc:0.96489 validation-aucpr:0.96765
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62971 validation-auc:0.96467 validation-aucpr:0.96709
{'best_iteration': '4', 'best_score': '0.967654452445068'}
Trial 47, Fold 2: Log loss = 0.6297064625081882, Average precision = 0.9674295405735265, ROC-AUC = 0.9646678802442896, Elapsed Time = 0.47734399999899324 seconds
Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 47, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68186 validation-auc:0.94238 validation-aucpr:0.94465
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67043 validation-auc:0.95718 validation-aucpr:0.95483
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65883 validation-auc:0.96507 validation-aucpr:0.96715
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64858 validation-auc:0.96542 validation-aucpr:0.96956
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63734 validation-auc:0.96742 validation-aucpr:0.97159
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62655 validation-auc:0.96825 validation-aucpr:0.97230
{'best_iteration': '5', 'best_score': '0.9722959147201717'}
Trial 47, Fold 3: Log loss = 0.6265516179895246, Average precision = 0.9721495798330522, ROC-AUC = 0.9682459507517023, Elapsed Time = 0.4703719999997702 seconds
Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 47, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68187 validation-auc:0.93803 validation-aucpr:0.92342
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67100 validation-auc:0.95101 validation-aucpr:0.94634
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65905 validation-auc:0.96171 validation-aucpr:0.96040
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64763 validation-auc:0.96442 validation-aucpr:0.96716
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.63786 validation-auc:0.96423 validation-aucpr:0.96973
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.62843 validation-auc:0.96482 validation-aucpr:0.96998
{'best_iteration': '5', 'best_score': '0.9699807104101339'}
Trial 47, Fold 4: Log loss = 0.6284295527765685, Average precision = 0.9698986077541827, ROC-AUC = 0.9648172141271512, Elapsed Time = 0.46956090000094264 seconds
Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 47, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68206 validation-auc:0.93532 validation-aucpr:0.91851
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67146 validation-auc:0.94912 validation-aucpr:0.94584
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66166 validation-auc:0.95150 validation-aucpr:0.95554
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65157 validation-auc:0.95365 validation-aucpr:0.95748
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64238 validation-auc:0.95407 validation-aucpr:0.95810
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63275 validation-auc:0.95544 validation-aucpr:0.95924
{'best_iteration': '5', 'best_score': '0.959242797316479'}
Trial 47, Fold 5: Log loss = 0.6327488828734219, Average precision = 0.9587947031588553, ROC-AUC = 0.9554357059249763, Elapsed Time = 0.46724950000134413 seconds
Optimization Progress: 48%|####8 | 48/100 [2:47:34<3:25:00, 236.55s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 48, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65265 validation-auc:0.92815 validation-aucpr:0.91366
[1] validation-logloss:0.61911 validation-auc:0.95030 validation-aucpr:0.94360
[2] validation-logloss:0.58864 validation-auc:0.95679 validation-aucpr:0.95677
[3] validation-logloss:0.56412 validation-auc:0.95688 validation-aucpr:0.95950
[4] validation-logloss:0.54044 validation-auc:0.95838 validation-aucpr:0.96405
[5] validation-logloss:0.52041 validation-auc:0.95850 validation-aucpr:0.96382
[6] validation-logloss:0.50156 validation-auc:0.95872 validation-aucpr:0.96450
[7] validation-logloss:0.48352 validation-auc:0.95926 validation-aucpr:0.96478
[8] validation-logloss:0.46713 validation-auc:0.95954 validation-aucpr:0.96497
{'best_iteration': '8', 'best_score': '0.9649673171907855'}
Trial 48, Fold 1: Log loss = 0.4671281350911924, Average precision = 0.9649679365351161, ROC-AUC = 0.9595362215196226, Elapsed Time = 5.979565199999342 seconds
Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 48, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65169 validation-auc:0.93400 validation-aucpr:0.89597
[1] validation-logloss:0.61991 validation-auc:0.95580 validation-aucpr:0.94681
[2] validation-logloss:0.58676 validation-auc:0.96162 validation-aucpr:0.96618
[3] validation-logloss:0.55719 validation-auc:0.96371 validation-aucpr:0.96789
[4] validation-logloss:0.53294 validation-auc:0.96504 validation-aucpr:0.96922
[5] validation-logloss:0.51166 validation-auc:0.96557 validation-aucpr:0.96951
[6] validation-logloss:0.49281 validation-auc:0.96552 validation-aucpr:0.96931
[7] validation-logloss:0.47469 validation-auc:0.96570 validation-aucpr:0.96899
[8] validation-logloss:0.45966 validation-auc:0.96478 validation-aucpr:0.96808
{'best_iteration': '5', 'best_score': '0.9695143141055345'}
Trial 48, Fold 2: Log loss = 0.4596563851648362, Average precision = 0.9680996079646447, ROC-AUC = 0.9647844984174224, Elapsed Time = 6.274692999999388 seconds
Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 48, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65214 validation-auc:0.93166 validation-aucpr:0.91066
[1] validation-logloss:0.62242 validation-auc:0.94954 validation-aucpr:0.94639
[2] validation-logloss:0.59343 validation-auc:0.95684 validation-aucpr:0.95985
[3] validation-logloss:0.56865 validation-auc:0.95702 validation-aucpr:0.96193
[4] validation-logloss:0.54572 validation-auc:0.95796 validation-aucpr:0.96284
[5] validation-logloss:0.52381 validation-auc:0.95933 validation-aucpr:0.96349
[6] validation-logloss:0.49920 validation-auc:0.96212 validation-aucpr:0.96605
[7] validation-logloss:0.48134 validation-auc:0.96264 validation-aucpr:0.96614
[8] validation-logloss:0.46541 validation-auc:0.96263 validation-aucpr:0.96602
{'best_iteration': '7', 'best_score': '0.9661439859958221'}
Trial 48, Fold 3: Log loss = 0.4654080460313595, Average precision = 0.9660221162005403, ROC-AUC = 0.9626300039246468, Elapsed Time = 6.240940799998498 seconds
Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 48, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65142 validation-auc:0.93416 validation-aucpr:0.90515
[1] validation-logloss:0.62067 validation-auc:0.95532 validation-aucpr:0.95617
[2] validation-logloss:0.58966 validation-auc:0.95845 validation-aucpr:0.96334
[3] validation-logloss:0.56525 validation-auc:0.95871 validation-aucpr:0.96323
[4] validation-logloss:0.54214 validation-auc:0.95918 validation-aucpr:0.96428
[5] validation-logloss:0.52043 validation-auc:0.95991 validation-aucpr:0.96552
[6] validation-logloss:0.50021 validation-auc:0.96001 validation-aucpr:0.96560
[7] validation-logloss:0.48187 validation-auc:0.96050 validation-aucpr:0.96581
[8] validation-logloss:0.46433 validation-auc:0.96139 validation-aucpr:0.96660
{'best_iteration': '8', 'best_score': '0.9666011794865117'}
Trial 48, Fold 4: Log loss = 0.4643289259501296, Average precision = 0.9665960273611527, ROC-AUC = 0.9613865700503825, Elapsed Time = 5.843712499998219 seconds
Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 48, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65256 validation-auc:0.92726 validation-aucpr:0.90348
[1] validation-logloss:0.62107 validation-auc:0.95214 validation-aucpr:0.95102
[2] validation-logloss:0.59356 validation-auc:0.95333 validation-aucpr:0.95624
[3] validation-logloss:0.56813 validation-auc:0.95497 validation-aucpr:0.95773
[4] validation-logloss:0.54440 validation-auc:0.95656 validation-aucpr:0.96067
[5] validation-logloss:0.52202 validation-auc:0.95745 validation-aucpr:0.96157
[6] validation-logloss:0.50376 validation-auc:0.95800 validation-aucpr:0.96312
[7] validation-logloss:0.48722 validation-auc:0.95832 validation-aucpr:0.96341
[8] validation-logloss:0.47188 validation-auc:0.95810 validation-aucpr:0.96326
{'best_iteration': '7', 'best_score': '0.9634119132513211'}
Trial 48, Fold 5: Log loss = 0.4718818396789914, Average precision = 0.9632099260839972, ROC-AUC = 0.9580976905783771, Elapsed Time = 6.20024960000228 seconds
Optimization Progress: 49%|####9 | 49/100 [2:48:13<2:30:30, 177.07s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 49, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66453 validation-auc:0.94548 validation-aucpr:0.90619
[1] validation-logloss:0.63846 validation-auc:0.96347 validation-aucpr:0.96426
[2] validation-logloss:0.61384 validation-auc:0.96495 validation-aucpr:0.96393
[3] validation-logloss:0.59333 validation-auc:0.96549 validation-aucpr:0.96700
[4] validation-logloss:0.57190 validation-auc:0.96666 validation-aucpr:0.96992
[5] validation-logloss:0.55194 validation-auc:0.96736 validation-aucpr:0.97049
[6] validation-logloss:0.53343 validation-auc:0.96833 validation-aucpr:0.97261
[7] validation-logloss:0.51641 validation-auc:0.96822 validation-aucpr:0.97019
[8] validation-logloss:0.50181 validation-auc:0.96823 validation-aucpr:0.97071
[9] validation-logloss:0.48792 validation-auc:0.96822 validation-aucpr:0.97064
[10] validation-logloss:0.47476 validation-auc:0.96831 validation-aucpr:0.97069
[11] validation-logloss:0.46081 validation-auc:0.96830 validation-aucpr:0.96846
[12] validation-logloss:0.44897 validation-auc:0.96835 validation-aucpr:0.96840
[13] validation-logloss:0.43635 validation-auc:0.96838 validation-aucpr:0.96849
[14] validation-logloss:0.42460 validation-auc:0.96836 validation-aucpr:0.96852
[15] validation-logloss:0.41479 validation-auc:0.96824 validation-aucpr:0.96834
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[20] validation-logloss:0.36716 validation-auc:0.96878 validation-aucpr:0.97138
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[24] validation-logloss:0.33804 validation-auc:0.96935 validation-aucpr:0.97176
[25] validation-logloss:0.33202 validation-auc:0.96953 validation-aucpr:0.97183
[26] validation-logloss:0.32584 validation-auc:0.96964 validation-aucpr:0.97226
[27] validation-logloss:0.31963 validation-auc:0.96987 validation-aucpr:0.97235
[28] validation-logloss:0.31398 validation-auc:0.96979 validation-aucpr:0.97237
[29] validation-logloss:0.30868 validation-auc:0.96990 validation-aucpr:0.97232
[30] validation-logloss:0.30407 validation-auc:0.96997 validation-aucpr:0.97233
[31] validation-logloss:0.29888 validation-auc:0.97009 validation-aucpr:0.97243
[32] validation-logloss:0.29415 validation-auc:0.97022 validation-aucpr:0.97256
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[34] validation-logloss:0.28535 validation-auc:0.97049 validation-aucpr:0.97264
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[41] validation-logloss:0.25987 validation-auc:0.97124 validation-aucpr:0.97332
[42] validation-logloss:0.25688 validation-auc:0.97134 validation-aucpr:0.97376
[43] validation-logloss:0.25389 validation-auc:0.97142 validation-aucpr:0.97385
[44] validation-logloss:0.25148 validation-auc:0.97139 validation-aucpr:0.97383
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[47] validation-logloss:0.24432 validation-auc:0.97144 validation-aucpr:0.97412
[48] validation-logloss:0.24187 validation-auc:0.97150 validation-aucpr:0.97417
[49] validation-logloss:0.23993 validation-auc:0.97152 validation-aucpr:0.97423
[50] validation-logloss:0.23788 validation-auc:0.97155 validation-aucpr:0.97482
[51] validation-logloss:0.23574 validation-auc:0.97160 validation-aucpr:0.97484
[52] validation-logloss:0.23416 validation-auc:0.97156 validation-aucpr:0.97481
[53] validation-logloss:0.23247 validation-auc:0.97167 validation-aucpr:0.97487
[54] validation-logloss:0.23067 validation-auc:0.97169 validation-aucpr:0.97487
[55] validation-logloss:0.22873 validation-auc:0.97180 validation-aucpr:0.97498
[56] validation-logloss:0.22675 validation-auc:0.97196 validation-aucpr:0.97511
[57] validation-logloss:0.22537 validation-auc:0.97193 validation-aucpr:0.97509
[58] validation-logloss:0.22397 validation-auc:0.97194 validation-aucpr:0.97509
[59] validation-logloss:0.22242 validation-auc:0.97196 validation-aucpr:0.97507
[60] validation-logloss:0.22126 validation-auc:0.97204 validation-aucpr:0.97590
[61] validation-logloss:0.21994 validation-auc:0.97203 validation-aucpr:0.97589
[62] validation-logloss:0.21884 validation-auc:0.97206 validation-aucpr:0.97591
[63] validation-logloss:0.21750 validation-auc:0.97211 validation-aucpr:0.97595
[64] validation-logloss:0.21645 validation-auc:0.97221 validation-aucpr:0.97600
[65] validation-logloss:0.21559 validation-auc:0.97219 validation-aucpr:0.97595
[66] validation-logloss:0.21449 validation-auc:0.97221 validation-aucpr:0.97589
[67] validation-logloss:0.21330 validation-auc:0.97230 validation-aucpr:0.97596
[68] validation-logloss:0.21223 validation-auc:0.97233 validation-aucpr:0.97599
[69] validation-logloss:0.21095 validation-auc:0.97251 validation-aucpr:0.97611
[70] validation-logloss:0.21009 validation-auc:0.97254 validation-aucpr:0.97612
[71] validation-logloss:0.20926 validation-auc:0.97255 validation-aucpr:0.97610
[72] validation-logloss:0.20827 validation-auc:0.97261 validation-aucpr:0.97614
[73] validation-logloss:0.20744 validation-auc:0.97265 validation-aucpr:0.97617
[74] validation-logloss:0.20664 validation-auc:0.97267 validation-aucpr:0.97620
[75] validation-logloss:0.20606 validation-auc:0.97264 validation-aucpr:0.97614
[76] validation-logloss:0.20532 validation-auc:0.97265 validation-aucpr:0.97615
[77] validation-logloss:0.20469 validation-auc:0.97269 validation-aucpr:0.97617
[78] validation-logloss:0.20417 validation-auc:0.97268 validation-aucpr:0.97617
[79] validation-logloss:0.20354 validation-auc:0.97267 validation-aucpr:0.97616
[80] validation-logloss:0.20307 validation-auc:0.97264 validation-aucpr:0.97617
[81] validation-logloss:0.20250 validation-auc:0.97267 validation-aucpr:0.97617
[82] validation-logloss:0.20198 validation-auc:0.97270 validation-aucpr:0.97576
[83] validation-logloss:0.20150 validation-auc:0.97269 validation-aucpr:0.97578
{'best_iteration': '74', 'best_score': '0.9761984583995469'}
Trial 49, Fold 1: Log loss = 0.20149546090569612, Average precision = 0.9759896129523458, ROC-AUC = 0.972685767697624, Elapsed Time = 7.393411099998048 seconds
Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 49, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66469 validation-auc:0.94495 validation-aucpr:0.91806
[1] validation-logloss:0.64048 validation-auc:0.96350 validation-aucpr:0.96717
[2] validation-logloss:0.61606 validation-auc:0.96530 validation-aucpr:0.96965
[3] validation-logloss:0.59336 validation-auc:0.96765 validation-aucpr:0.97149
[4] validation-logloss:0.57192 validation-auc:0.96836 validation-aucpr:0.97218
[5] validation-logloss:0.55201 validation-auc:0.96915 validation-aucpr:0.97281
[6] validation-logloss:0.53392 validation-auc:0.97004 validation-aucpr:0.97336
[7] validation-logloss:0.51795 validation-auc:0.97064 validation-aucpr:0.97374
[8] validation-logloss:0.50353 validation-auc:0.97049 validation-aucpr:0.97369
[9] validation-logloss:0.48934 validation-auc:0.97049 validation-aucpr:0.97373
[10] validation-logloss:0.47447 validation-auc:0.97074 validation-aucpr:0.97395
[11] validation-logloss:0.46054 validation-auc:0.97078 validation-aucpr:0.97402
[12] validation-logloss:0.44724 validation-auc:0.97098 validation-aucpr:0.97418
[13] validation-logloss:0.43486 validation-auc:0.97107 validation-aucpr:0.97427
[14] validation-logloss:0.42315 validation-auc:0.97115 validation-aucpr:0.97430
[15] validation-logloss:0.41165 validation-auc:0.97136 validation-aucpr:0.97448
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[25] validation-logloss:0.32974 validation-auc:0.97219 validation-aucpr:0.97506
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[27] validation-logloss:0.31810 validation-auc:0.97216 validation-aucpr:0.97501
[28] validation-logloss:0.31207 validation-auc:0.97215 validation-aucpr:0.97512
[29] validation-logloss:0.30722 validation-auc:0.97206 validation-aucpr:0.97491
[30] validation-logloss:0.30186 validation-auc:0.97202 validation-aucpr:0.97490
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[34] validation-logloss:0.28337 validation-auc:0.97213 validation-aucpr:0.97508
[35] validation-logloss:0.27893 validation-auc:0.97232 validation-aucpr:0.97522
[36] validation-logloss:0.27576 validation-auc:0.97204 validation-aucpr:0.97490
[37] validation-logloss:0.27232 validation-auc:0.97201 validation-aucpr:0.97486
[38] validation-logloss:0.26826 validation-auc:0.97235 validation-aucpr:0.97509
[39] validation-logloss:0.26477 validation-auc:0.97229 validation-aucpr:0.97505
[40] validation-logloss:0.26137 validation-auc:0.97225 validation-aucpr:0.97501
[41] validation-logloss:0.25850 validation-auc:0.97230 validation-aucpr:0.97533
[42] validation-logloss:0.25534 validation-auc:0.97222 validation-aucpr:0.97525
[43] validation-logloss:0.25261 validation-auc:0.97244 validation-aucpr:0.97531
[44] validation-logloss:0.24964 validation-auc:0.97252 validation-aucpr:0.97535
[45] validation-logloss:0.24715 validation-auc:0.97260 validation-aucpr:0.97539
[46] validation-logloss:0.24437 validation-auc:0.97266 validation-aucpr:0.97542
[47] validation-logloss:0.24170 validation-auc:0.97271 validation-aucpr:0.97546
[48] validation-logloss:0.23923 validation-auc:0.97277 validation-aucpr:0.97550
[49] validation-logloss:0.23661 validation-auc:0.97287 validation-aucpr:0.97558
[50] validation-logloss:0.23423 validation-auc:0.97295 validation-aucpr:0.97565
[51] validation-logloss:0.23200 validation-auc:0.97296 validation-aucpr:0.97566
[52] validation-logloss:0.22986 validation-auc:0.97298 validation-aucpr:0.97569
[53] validation-logloss:0.22772 validation-auc:0.97311 validation-aucpr:0.97579
[54] validation-logloss:0.22592 validation-auc:0.97291 validation-aucpr:0.97439
[55] validation-logloss:0.22410 validation-auc:0.97291 validation-aucpr:0.97440
[56] validation-logloss:0.22216 validation-auc:0.97301 validation-aucpr:0.97446
[57] validation-logloss:0.22034 validation-auc:0.97308 validation-aucpr:0.97456
[58] validation-logloss:0.21911 validation-auc:0.97297 validation-aucpr:0.97459
[59] validation-logloss:0.21766 validation-auc:0.97294 validation-aucpr:0.97455
[60] validation-logloss:0.21610 validation-auc:0.97299 validation-aucpr:0.97459
[61] validation-logloss:0.21459 validation-auc:0.97302 validation-aucpr:0.97461
[62] validation-logloss:0.21329 validation-auc:0.97300 validation-aucpr:0.97458
[63] validation-logloss:0.21215 validation-auc:0.97298 validation-aucpr:0.97460
[64] validation-logloss:0.21070 validation-auc:0.97299 validation-aucpr:0.97460
[65] validation-logloss:0.20968 validation-auc:0.97287 validation-aucpr:0.97451
[66] validation-logloss:0.20846 validation-auc:0.97294 validation-aucpr:0.97452
[67] validation-logloss:0.20720 validation-auc:0.97310 validation-aucpr:0.97464
[68] validation-logloss:0.20651 validation-auc:0.97303 validation-aucpr:0.97456
[69] validation-logloss:0.20533 validation-auc:0.97317 validation-aucpr:0.97468
[70] validation-logloss:0.20462 validation-auc:0.97303 validation-aucpr:0.97456
[71] validation-logloss:0.20364 validation-auc:0.97302 validation-aucpr:0.97467
[72] validation-logloss:0.20257 validation-auc:0.97305 validation-aucpr:0.97470
[73] validation-logloss:0.20159 validation-auc:0.97308 validation-aucpr:0.97474
[74] validation-logloss:0.20088 validation-auc:0.97320 validation-aucpr:0.97576
[75] validation-logloss:0.20011 validation-auc:0.97315 validation-aucpr:0.97574
[76] validation-logloss:0.19959 validation-auc:0.97305 validation-aucpr:0.97567
[77] validation-logloss:0.19863 validation-auc:0.97317 validation-aucpr:0.97578
[78] validation-logloss:0.19782 validation-auc:0.97322 validation-aucpr:0.97585
[79] validation-logloss:0.19710 validation-auc:0.97322 validation-aucpr:0.97585
[80] validation-logloss:0.19641 validation-auc:0.97325 validation-aucpr:0.97588
[81] validation-logloss:0.19587 validation-auc:0.97324 validation-aucpr:0.97586
[82] validation-logloss:0.19528 validation-auc:0.97319 validation-aucpr:0.97464
[83] validation-logloss:0.19465 validation-auc:0.97325 validation-aucpr:0.97475
{'best_iteration': '80', 'best_score': '0.9758752127330358'}
Trial 49, Fold 2: Log loss = 0.19464862815883344, Average precision = 0.9751688581640191, ROC-AUC = 0.9732532388288591, Elapsed Time = 7.5820448999984364 seconds
Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 49, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66473 validation-auc:0.94799 validation-aucpr:0.91996
[1] validation-logloss:0.64044 validation-auc:0.96164 validation-aucpr:0.96550
[2] validation-logloss:0.61552 validation-auc:0.96729 validation-aucpr:0.97098
[3] validation-logloss:0.59323 validation-auc:0.96741 validation-aucpr:0.97069
[4] validation-logloss:0.57184 validation-auc:0.96851 validation-aucpr:0.97192
[5] validation-logloss:0.55153 validation-auc:0.96931 validation-aucpr:0.97253
[6] validation-logloss:0.53282 validation-auc:0.96956 validation-aucpr:0.97266
[7] validation-logloss:0.51507 validation-auc:0.97025 validation-aucpr:0.97338
[8] validation-logloss:0.49851 validation-auc:0.97034 validation-aucpr:0.97347
[9] validation-logloss:0.48326 validation-auc:0.97059 validation-aucpr:0.97357
[10] validation-logloss:0.46862 validation-auc:0.97051 validation-aucpr:0.97349
[11] validation-logloss:0.45619 validation-auc:0.97081 validation-aucpr:0.97432
[12] validation-logloss:0.44435 validation-auc:0.97097 validation-aucpr:0.97446
[13] validation-logloss:0.43209 validation-auc:0.97087 validation-aucpr:0.97436
[14] validation-logloss:0.42036 validation-auc:0.97080 validation-aucpr:0.97431
[15] validation-logloss:0.40910 validation-auc:0.97107 validation-aucpr:0.97471
[16] validation-logloss:0.39861 validation-auc:0.97104 validation-aucpr:0.97456
[17] validation-logloss:0.38888 validation-auc:0.97090 validation-aucpr:0.97449
[18] validation-logloss:0.37944 validation-auc:0.97097 validation-aucpr:0.97373
[19] validation-logloss:0.37030 validation-auc:0.97136 validation-aucpr:0.97508
[20] validation-logloss:0.36171 validation-auc:0.97164 validation-aucpr:0.97524
[21] validation-logloss:0.35391 validation-auc:0.97156 validation-aucpr:0.97520
[22] validation-logloss:0.34620 validation-auc:0.97154 validation-aucpr:0.97519
[23] validation-logloss:0.33934 validation-auc:0.97155 validation-aucpr:0.97519
[24] validation-logloss:0.33324 validation-auc:0.97150 validation-aucpr:0.97514
[25] validation-logloss:0.32647 validation-auc:0.97155 validation-aucpr:0.97518
[26] validation-logloss:0.32064 validation-auc:0.97159 validation-aucpr:0.97520
[27] validation-logloss:0.31523 validation-auc:0.97156 validation-aucpr:0.97520
[28] validation-logloss:0.30954 validation-auc:0.97175 validation-aucpr:0.97537
[29] validation-logloss:0.30419 validation-auc:0.97176 validation-aucpr:0.97538
[30] validation-logloss:0.29969 validation-auc:0.97174 validation-aucpr:0.97536
[31] validation-logloss:0.29535 validation-auc:0.97159 validation-aucpr:0.97528
[32] validation-logloss:0.29055 validation-auc:0.97152 validation-aucpr:0.97523
[33] validation-logloss:0.28586 validation-auc:0.97168 validation-aucpr:0.97531
[34] validation-logloss:0.28148 validation-auc:0.97175 validation-aucpr:0.97536
[35] validation-logloss:0.27728 validation-auc:0.97190 validation-aucpr:0.97545
[36] validation-logloss:0.27319 validation-auc:0.97200 validation-aucpr:0.97552
[37] validation-logloss:0.26951 validation-auc:0.97186 validation-aucpr:0.97541
[38] validation-logloss:0.26591 validation-auc:0.97179 validation-aucpr:0.97538
[39] validation-logloss:0.26255 validation-auc:0.97180 validation-aucpr:0.97538
[40] validation-logloss:0.25936 validation-auc:0.97183 validation-aucpr:0.97541
[41] validation-logloss:0.25663 validation-auc:0.97173 validation-aucpr:0.97534
[42] validation-logloss:0.25353 validation-auc:0.97177 validation-aucpr:0.97537
[43] validation-logloss:0.25076 validation-auc:0.97192 validation-aucpr:0.97553
[44] validation-logloss:0.24793 validation-auc:0.97198 validation-aucpr:0.97556
[45] validation-logloss:0.24514 validation-auc:0.97206 validation-aucpr:0.97564
[46] validation-logloss:0.24297 validation-auc:0.97201 validation-aucpr:0.97560
[47] validation-logloss:0.24092 validation-auc:0.97202 validation-aucpr:0.97590
[48] validation-logloss:0.23839 validation-auc:0.97212 validation-aucpr:0.97595
[49] validation-logloss:0.23595 validation-auc:0.97220 validation-aucpr:0.97601
[50] validation-logloss:0.23414 validation-auc:0.97214 validation-aucpr:0.97595
[51] validation-logloss:0.23191 validation-auc:0.97229 validation-aucpr:0.97607
[52] validation-logloss:0.22990 validation-auc:0.97231 validation-aucpr:0.97609
[53] validation-logloss:0.22810 validation-auc:0.97241 validation-aucpr:0.97616
[54] validation-logloss:0.22626 validation-auc:0.97242 validation-aucpr:0.97617
[55] validation-logloss:0.22476 validation-auc:0.97245 validation-aucpr:0.97617
[56] validation-logloss:0.22322 validation-auc:0.97235 validation-aucpr:0.97611
[57] validation-logloss:0.22155 validation-auc:0.97235 validation-aucpr:0.97608
[58] validation-logloss:0.21989 validation-auc:0.97241 validation-aucpr:0.97611
[59] validation-logloss:0.21833 validation-auc:0.97246 validation-aucpr:0.97616
[60] validation-logloss:0.21703 validation-auc:0.97246 validation-aucpr:0.97616
[61] validation-logloss:0.21587 validation-auc:0.97239 validation-aucpr:0.97611
[62] validation-logloss:0.21443 validation-auc:0.97249 validation-aucpr:0.97618
[63] validation-logloss:0.21324 validation-auc:0.97248 validation-aucpr:0.97618
[64] validation-logloss:0.21222 validation-auc:0.97244 validation-aucpr:0.97615
[65] validation-logloss:0.21109 validation-auc:0.97242 validation-aucpr:0.97613
[66] validation-logloss:0.20992 validation-auc:0.97245 validation-aucpr:0.97614
[67] validation-logloss:0.20904 validation-auc:0.97248 validation-aucpr:0.97616
[68] validation-logloss:0.20811 validation-auc:0.97252 validation-aucpr:0.97620
[69] validation-logloss:0.20722 validation-auc:0.97228 validation-aucpr:0.97391
[70] validation-logloss:0.20659 validation-auc:0.97220 validation-aucpr:0.97385
[71] validation-logloss:0.20563 validation-auc:0.97224 validation-aucpr:0.97387
[72] validation-logloss:0.20485 validation-auc:0.97217 validation-aucpr:0.97384
[73] validation-logloss:0.20396 validation-auc:0.97224 validation-aucpr:0.97378
[74] validation-logloss:0.20321 validation-auc:0.97225 validation-aucpr:0.97376
[75] validation-logloss:0.20227 validation-auc:0.97231 validation-aucpr:0.97356
[76] validation-logloss:0.20151 validation-auc:0.97232 validation-aucpr:0.97353
[77] validation-logloss:0.20097 validation-auc:0.97228 validation-aucpr:0.97344
[78] validation-logloss:0.20040 validation-auc:0.97230 validation-aucpr:0.97335
[79] validation-logloss:0.19978 validation-auc:0.97238 validation-aucpr:0.97335
[80] validation-logloss:0.19924 validation-auc:0.97240 validation-aucpr:0.97319
[81] validation-logloss:0.19861 validation-auc:0.97247 validation-aucpr:0.97325
[82] validation-logloss:0.19802 validation-auc:0.97250 validation-aucpr:0.97313
[83] validation-logloss:0.19761 validation-auc:0.97245 validation-aucpr:0.97292
{'best_iteration': '68', 'best_score': '0.9762039347909636'}
Trial 49, Fold 3: Log loss = 0.1976074858911532, Average precision = 0.9739148147201059, ROC-AUC = 0.9724530577332395, Elapsed Time = 8.235687200001848 seconds
Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 49, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66450 validation-auc:0.94787 validation-aucpr:0.92504
[1] validation-logloss:0.64030 validation-auc:0.96410 validation-aucpr:0.96521
[2] validation-logloss:0.61847 validation-auc:0.96429 validation-aucpr:0.96687
[3] validation-logloss:0.59758 validation-auc:0.96413 validation-aucpr:0.96693
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[10] validation-logloss:0.47649 validation-auc:0.96867 validation-aucpr:0.97366
[11] validation-logloss:0.46245 validation-auc:0.96901 validation-aucpr:0.97399
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[15] validation-logloss:0.41287 validation-auc:0.97028 validation-aucpr:0.97481
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[20] validation-logloss:0.36746 validation-auc:0.97038 validation-aucpr:0.97499
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[22] validation-logloss:0.35120 validation-auc:0.97065 validation-aucpr:0.97524
[23] validation-logloss:0.34383 validation-auc:0.97055 validation-aucpr:0.97516
[24] validation-logloss:0.33664 validation-auc:0.97070 validation-aucpr:0.97529
[25] validation-logloss:0.32991 validation-auc:0.97062 validation-aucpr:0.97525
[26] validation-logloss:0.32332 validation-auc:0.97084 validation-aucpr:0.97541
[27] validation-logloss:0.31794 validation-auc:0.97084 validation-aucpr:0.97540
[28] validation-logloss:0.31266 validation-auc:0.97100 validation-aucpr:0.97550
[29] validation-logloss:0.30693 validation-auc:0.97122 validation-aucpr:0.97571
[30] validation-logloss:0.30216 validation-auc:0.97128 validation-aucpr:0.97573
[31] validation-logloss:0.29692 validation-auc:0.97142 validation-aucpr:0.97584
[32] validation-logloss:0.29218 validation-auc:0.97133 validation-aucpr:0.97579
[33] validation-logloss:0.28736 validation-auc:0.97155 validation-aucpr:0.97594
[34] validation-logloss:0.28389 validation-auc:0.97134 validation-aucpr:0.97578
[35] validation-logloss:0.27964 validation-auc:0.97131 validation-aucpr:0.97575
[36] validation-logloss:0.27595 validation-auc:0.97141 validation-aucpr:0.97578
[37] validation-logloss:0.27177 validation-auc:0.97166 validation-aucpr:0.97596
[38] validation-logloss:0.26848 validation-auc:0.97164 validation-aucpr:0.97594
[39] validation-logloss:0.26539 validation-auc:0.97163 validation-aucpr:0.97592
[40] validation-logloss:0.26187 validation-auc:0.97174 validation-aucpr:0.97601
[41] validation-logloss:0.25872 validation-auc:0.97183 validation-aucpr:0.97608
[42] validation-logloss:0.25598 validation-auc:0.97186 validation-aucpr:0.97608
[43] validation-logloss:0.25280 validation-auc:0.97194 validation-aucpr:0.97614
[44] validation-logloss:0.24978 validation-auc:0.97213 validation-aucpr:0.97632
[45] validation-logloss:0.24708 validation-auc:0.97206 validation-aucpr:0.97626
[46] validation-logloss:0.24506 validation-auc:0.97194 validation-aucpr:0.97618
[47] validation-logloss:0.24307 validation-auc:0.97183 validation-aucpr:0.97611
[48] validation-logloss:0.24100 validation-auc:0.97189 validation-aucpr:0.97615
[49] validation-logloss:0.23856 validation-auc:0.97193 validation-aucpr:0.97619
[50] validation-logloss:0.23615 validation-auc:0.97206 validation-aucpr:0.97630
[51] validation-logloss:0.23406 validation-auc:0.97202 validation-aucpr:0.97628
[52] validation-logloss:0.23197 validation-auc:0.97204 validation-aucpr:0.97630
[53] validation-logloss:0.23027 validation-auc:0.97196 validation-aucpr:0.97625
[54] validation-logloss:0.22830 validation-auc:0.97199 validation-aucpr:0.97629
[55] validation-logloss:0.22682 validation-auc:0.97198 validation-aucpr:0.97627
[56] validation-logloss:0.22536 validation-auc:0.97201 validation-aucpr:0.97628
[57] validation-logloss:0.22346 validation-auc:0.97208 validation-aucpr:0.97634
[58] validation-logloss:0.22200 validation-auc:0.97203 validation-aucpr:0.97631
[59] validation-logloss:0.22063 validation-auc:0.97216 validation-aucpr:0.97639
[60] validation-logloss:0.21925 validation-auc:0.97209 validation-aucpr:0.97635
[61] validation-logloss:0.21776 validation-auc:0.97211 validation-aucpr:0.97637
[62] validation-logloss:0.21623 validation-auc:0.97217 validation-aucpr:0.97642
[63] validation-logloss:0.21505 validation-auc:0.97215 validation-aucpr:0.97638
[64] validation-logloss:0.21339 validation-auc:0.97236 validation-aucpr:0.97653
[65] validation-logloss:0.21206 validation-auc:0.97236 validation-aucpr:0.97654
[66] validation-logloss:0.21094 validation-auc:0.97230 validation-aucpr:0.97652
[67] validation-logloss:0.21018 validation-auc:0.97223 validation-aucpr:0.97645
[68] validation-logloss:0.20932 validation-auc:0.97222 validation-aucpr:0.97644
[69] validation-logloss:0.20809 validation-auc:0.97231 validation-aucpr:0.97652
[70] validation-logloss:0.20704 validation-auc:0.97233 validation-aucpr:0.97655
[71] validation-logloss:0.20588 validation-auc:0.97242 validation-aucpr:0.97663
[72] validation-logloss:0.20499 validation-auc:0.97242 validation-aucpr:0.97663
[73] validation-logloss:0.20408 validation-auc:0.97249 validation-aucpr:0.97668
[74] validation-logloss:0.20309 validation-auc:0.97256 validation-aucpr:0.97673
[75] validation-logloss:0.20230 validation-auc:0.97255 validation-aucpr:0.97672
[76] validation-logloss:0.20167 validation-auc:0.97255 validation-aucpr:0.97671
[77] validation-logloss:0.20097 validation-auc:0.97255 validation-aucpr:0.97671
[78] validation-logloss:0.20038 validation-auc:0.97247 validation-aucpr:0.97667
[79] validation-logloss:0.19976 validation-auc:0.97254 validation-aucpr:0.97671
[80] validation-logloss:0.19908 validation-auc:0.97254 validation-aucpr:0.97672
[81] validation-logloss:0.19843 validation-auc:0.97257 validation-aucpr:0.97673
[82] validation-logloss:0.19805 validation-auc:0.97244 validation-aucpr:0.97667
[83] validation-logloss:0.19745 validation-auc:0.97252 validation-aucpr:0.97671
{'best_iteration': '81', 'best_score': '0.9767312128066848'}
Trial 49, Fold 4: Log loss = 0.19745498544430348, Average precision = 0.9767147911153203, ROC-AUC = 0.9725193792904473, Elapsed Time = 8.099118099999032 seconds
Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 49, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66484 validation-auc:0.94332 validation-aucpr:0.91325
[1] validation-logloss:0.63930 validation-auc:0.95927 validation-aucpr:0.96318
[2] validation-logloss:0.61462 validation-auc:0.96318 validation-aucpr:0.96803
[3] validation-logloss:0.59367 validation-auc:0.96464 validation-aucpr:0.96923
[4] validation-logloss:0.57256 validation-auc:0.96532 validation-aucpr:0.96981
[5] validation-logloss:0.55438 validation-auc:0.96604 validation-aucpr:0.97025
[6] validation-logloss:0.53571 validation-auc:0.96682 validation-aucpr:0.97086
[7] validation-logloss:0.51811 validation-auc:0.96739 validation-aucpr:0.97119
[8] validation-logloss:0.50145 validation-auc:0.96787 validation-aucpr:0.97186
[9] validation-logloss:0.48783 validation-auc:0.96762 validation-aucpr:0.97145
[10] validation-logloss:0.47329 validation-auc:0.96773 validation-aucpr:0.97156
[11] validation-logloss:0.45964 validation-auc:0.96757 validation-aucpr:0.97145
[12] validation-logloss:0.44821 validation-auc:0.96761 validation-aucpr:0.97135
[13] validation-logloss:0.43732 validation-auc:0.96783 validation-aucpr:0.97144
[14] validation-logloss:0.42563 validation-auc:0.96808 validation-aucpr:0.97167
[15] validation-logloss:0.41466 validation-auc:0.96823 validation-aucpr:0.97181
[16] validation-logloss:0.40545 validation-auc:0.96825 validation-aucpr:0.97180
[17] validation-logloss:0.39554 validation-auc:0.96871 validation-aucpr:0.97213
[18] validation-logloss:0.38599 validation-auc:0.96895 validation-aucpr:0.97231
[19] validation-logloss:0.37701 validation-auc:0.96897 validation-aucpr:0.97248
[20] validation-logloss:0.36948 validation-auc:0.96908 validation-aucpr:0.97252
[21] validation-logloss:0.36152 validation-auc:0.96906 validation-aucpr:0.97249
[22] validation-logloss:0.35370 validation-auc:0.96919 validation-aucpr:0.97262
[23] validation-logloss:0.34637 validation-auc:0.96925 validation-aucpr:0.97303
[24] validation-logloss:0.33926 validation-auc:0.96946 validation-aucpr:0.97326
[25] validation-logloss:0.33366 validation-auc:0.96929 validation-aucpr:0.97284
[26] validation-logloss:0.32724 validation-auc:0.96945 validation-aucpr:0.97297
[27] validation-logloss:0.32095 validation-auc:0.96956 validation-aucpr:0.97307
[28] validation-logloss:0.31532 validation-auc:0.96953 validation-aucpr:0.97324
[29] validation-logloss:0.31058 validation-auc:0.96935 validation-aucpr:0.97304
[30] validation-logloss:0.30617 validation-auc:0.96924 validation-aucpr:0.97293
[31] validation-logloss:0.30100 validation-auc:0.96937 validation-aucpr:0.97300
[32] validation-logloss:0.29605 validation-auc:0.96958 validation-aucpr:0.97320
[33] validation-logloss:0.29211 validation-auc:0.96940 validation-aucpr:0.97284
[34] validation-logloss:0.28756 validation-auc:0.96952 validation-aucpr:0.97295
[35] validation-logloss:0.28323 validation-auc:0.96963 validation-aucpr:0.97304
[36] validation-logloss:0.27916 validation-auc:0.96970 validation-aucpr:0.97307
[37] validation-logloss:0.27527 validation-auc:0.96973 validation-aucpr:0.97305
[38] validation-logloss:0.27152 validation-auc:0.96991 validation-aucpr:0.97318
[39] validation-logloss:0.26807 validation-auc:0.96988 validation-aucpr:0.97321
[40] validation-logloss:0.26474 validation-auc:0.96998 validation-aucpr:0.97327
[41] validation-logloss:0.26199 validation-auc:0.97015 validation-aucpr:0.97410
[42] validation-logloss:0.25931 validation-auc:0.97018 validation-aucpr:0.97408
[43] validation-logloss:0.25634 validation-auc:0.97023 validation-aucpr:0.97418
[44] validation-logloss:0.25392 validation-auc:0.97021 validation-aucpr:0.97414
[45] validation-logloss:0.25112 validation-auc:0.97029 validation-aucpr:0.97421
[46] validation-logloss:0.24827 validation-auc:0.97065 validation-aucpr:0.97442
[47] validation-logloss:0.24585 validation-auc:0.97070 validation-aucpr:0.97444
[48] validation-logloss:0.24341 validation-auc:0.97076 validation-aucpr:0.97446
[49] validation-logloss:0.24108 validation-auc:0.97083 validation-aucpr:0.97453
[50] validation-logloss:0.23895 validation-auc:0.97087 validation-aucpr:0.97452
[51] validation-logloss:0.23704 validation-auc:0.97095 validation-aucpr:0.97457
[52] validation-logloss:0.23507 validation-auc:0.97096 validation-aucpr:0.97458
[53] validation-logloss:0.23329 validation-auc:0.97105 validation-aucpr:0.97467
[54] validation-logloss:0.23179 validation-auc:0.97105 validation-aucpr:0.97464
[55] validation-logloss:0.23006 validation-auc:0.97101 validation-aucpr:0.97460
[56] validation-logloss:0.22855 validation-auc:0.97106 validation-aucpr:0.97463
[57] validation-logloss:0.22672 validation-auc:0.97126 validation-aucpr:0.97478
[58] validation-logloss:0.22527 validation-auc:0.97126 validation-aucpr:0.97479
[59] validation-logloss:0.22382 validation-auc:0.97128 validation-aucpr:0.97484
[60] validation-logloss:0.22257 validation-auc:0.97128 validation-aucpr:0.97481
[61] validation-logloss:0.22140 validation-auc:0.97133 validation-aucpr:0.97484
[62] validation-logloss:0.22029 validation-auc:0.97123 validation-aucpr:0.97477
[63] validation-logloss:0.21911 validation-auc:0.97118 validation-aucpr:0.97474
[64] validation-logloss:0.21802 validation-auc:0.97116 validation-aucpr:0.97472
[65] validation-logloss:0.21715 validation-auc:0.97105 validation-aucpr:0.97464
[66] validation-logloss:0.21604 validation-auc:0.97114 validation-aucpr:0.97471
[67] validation-logloss:0.21519 validation-auc:0.97109 validation-aucpr:0.97465
[68] validation-logloss:0.21439 validation-auc:0.97102 validation-aucpr:0.97461
[69] validation-logloss:0.21353 validation-auc:0.97097 validation-aucpr:0.97458
[70] validation-logloss:0.21268 validation-auc:0.97091 validation-aucpr:0.97453
[71] validation-logloss:0.21194 validation-auc:0.97086 validation-aucpr:0.97449
[72] validation-logloss:0.21111 validation-auc:0.97088 validation-aucpr:0.97452
[73] validation-logloss:0.21016 validation-auc:0.97097 validation-aucpr:0.97460
[74] validation-logloss:0.20952 validation-auc:0.97096 validation-aucpr:0.97457
[75] validation-logloss:0.20900 validation-auc:0.97092 validation-aucpr:0.97453
[76] validation-logloss:0.20829 validation-auc:0.97089 validation-aucpr:0.97451
[77] validation-logloss:0.20762 validation-auc:0.97092 validation-aucpr:0.97452
[78] validation-logloss:0.20690 validation-auc:0.97091 validation-aucpr:0.97449
[79] validation-logloss:0.20638 validation-auc:0.97091 validation-aucpr:0.97446
[80] validation-logloss:0.20580 validation-auc:0.97099 validation-aucpr:0.97450
[81] validation-logloss:0.20520 validation-auc:0.97107 validation-aucpr:0.97455
[82] validation-logloss:0.20476 validation-auc:0.97105 validation-aucpr:0.97456
[83] validation-logloss:0.20413 validation-auc:0.97117 validation-aucpr:0.97475
{'best_iteration': '61', 'best_score': '0.9748406069880208'}
Trial 49, Fold 5: Log loss = 0.20412901026625851, Average precision = 0.9746657449146889, ROC-AUC = 0.9711680136744514, Elapsed Time = 7.984125700000732 seconds
Optimization Progress: 50%|##### | 50/100 [2:49:00<1:55:06, 138.13s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 50, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68362 validation-auc:0.93883 validation-aucpr:0.94319
[1] validation-logloss:0.67417 validation-auc:0.94860 validation-aucpr:0.95309
[2] validation-logloss:0.66495 validation-auc:0.95193 validation-aucpr:0.95658
[3] validation-logloss:0.65565 validation-auc:0.95530 validation-aucpr:0.95966
[4] validation-logloss:0.64702 validation-auc:0.95608 validation-aucpr:0.96053
[5] validation-logloss:0.63888 validation-auc:0.95603 validation-aucpr:0.96063
[6] validation-logloss:0.62932 validation-auc:0.96132 validation-aucpr:0.96664
[7] validation-logloss:0.62109 validation-auc:0.96145 validation-aucpr:0.96677
[8] validation-logloss:0.61321 validation-auc:0.96188 validation-aucpr:0.96707
[9] validation-logloss:0.60432 validation-auc:0.96388 validation-aucpr:0.96926
[10] validation-logloss:0.59572 validation-auc:0.96492 validation-aucpr:0.97040
[11] validation-logloss:0.58729 validation-auc:0.96544 validation-aucpr:0.97095
[12] validation-logloss:0.58058 validation-auc:0.96508 validation-aucpr:0.97066
[13] validation-logloss:0.57338 validation-auc:0.96524 validation-aucpr:0.97076
[14] validation-logloss:0.56675 validation-auc:0.96502 validation-aucpr:0.97055
[15] validation-logloss:0.56027 validation-auc:0.96487 validation-aucpr:0.97036
[16] validation-logloss:0.55254 validation-auc:0.96550 validation-aucpr:0.97099
[17] validation-logloss:0.54638 validation-auc:0.96553 validation-aucpr:0.97097
[18] validation-logloss:0.54026 validation-auc:0.96552 validation-aucpr:0.97092
[19] validation-logloss:0.53432 validation-auc:0.96545 validation-aucpr:0.97080
[20] validation-logloss:0.52749 validation-auc:0.96584 validation-aucpr:0.97120
[21] validation-logloss:0.52085 validation-auc:0.96597 validation-aucpr:0.97139
[22] validation-logloss:0.51411 validation-auc:0.96626 validation-aucpr:0.97166
[23] validation-logloss:0.50896 validation-auc:0.96620 validation-aucpr:0.97165
[24] validation-logloss:0.50357 validation-auc:0.96622 validation-aucpr:0.97163
[25] validation-logloss:0.49860 validation-auc:0.96617 validation-aucpr:0.97159
[26] validation-logloss:0.49245 validation-auc:0.96651 validation-aucpr:0.97189
[27] validation-logloss:0.48636 validation-auc:0.96688 validation-aucpr:0.97221
[28] validation-logloss:0.48143 validation-auc:0.96701 validation-aucpr:0.97229
[29] validation-logloss:0.47686 validation-auc:0.96695 validation-aucpr:0.97219
[30] validation-logloss:0.47238 validation-auc:0.96684 validation-aucpr:0.97211
[31] validation-logloss:0.46785 validation-auc:0.96676 validation-aucpr:0.97202
[32] validation-logloss:0.46337 validation-auc:0.96672 validation-aucpr:0.97198
[33] validation-logloss:0.45831 validation-auc:0.96681 validation-aucpr:0.97213
[34] validation-logloss:0.45392 validation-auc:0.96685 validation-aucpr:0.97214
[35] validation-logloss:0.44982 validation-auc:0.96680 validation-aucpr:0.97208
[36] validation-logloss:0.44570 validation-auc:0.96680 validation-aucpr:0.97207
[37] validation-logloss:0.44085 validation-auc:0.96691 validation-aucpr:0.97218
[38] validation-logloss:0.43690 validation-auc:0.96695 validation-aucpr:0.97218
[39] validation-logloss:0.43313 validation-auc:0.96692 validation-aucpr:0.97214
[40] validation-logloss:0.42954 validation-auc:0.96692 validation-aucpr:0.97211
[41] validation-logloss:0.42593 validation-auc:0.96691 validation-aucpr:0.97208
[42] validation-logloss:0.42142 validation-auc:0.96706 validation-aucpr:0.97224
[43] validation-logloss:0.41807 validation-auc:0.96707 validation-aucpr:0.97226
[44] validation-logloss:0.41456 validation-auc:0.96712 validation-aucpr:0.97228
[45] validation-logloss:0.41026 validation-auc:0.96734 validation-aucpr:0.97248
[46] validation-logloss:0.40607 validation-auc:0.96749 validation-aucpr:0.97262
{'best_iteration': '46', 'best_score': '0.9726204836170593'}
Trial 50, Fold 1: Log loss = 0.40606661302723657, Average precision = 0.9726251530010052, ROC-AUC = 0.9674923705806724, Elapsed Time = 1.0990195000013046 seconds
Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 50, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68375 validation-auc:0.94128 validation-aucpr:0.94405
[1] validation-logloss:0.67298 validation-auc:0.96227 validation-aucpr:0.96690
[2] validation-logloss:0.66346 validation-auc:0.96311 validation-aucpr:0.96744
[3] validation-logloss:0.65439 validation-auc:0.96359 validation-aucpr:0.96757
[4] validation-logloss:0.64435 validation-auc:0.96496 validation-aucpr:0.96920
[5] validation-logloss:0.63600 validation-auc:0.96431 validation-aucpr:0.96830
[6] validation-logloss:0.62626 validation-auc:0.96564 validation-aucpr:0.96950
[7] validation-logloss:0.61849 validation-auc:0.96518 validation-aucpr:0.96898
[8] validation-logloss:0.60944 validation-auc:0.96597 validation-aucpr:0.96974
[9] validation-logloss:0.60186 validation-auc:0.96600 validation-aucpr:0.96967
[10] validation-logloss:0.59421 validation-auc:0.96610 validation-aucpr:0.96971
[11] validation-logloss:0.58574 validation-auc:0.96672 validation-aucpr:0.97059
[12] validation-logloss:0.57885 validation-auc:0.96666 validation-aucpr:0.97068
[13] validation-logloss:0.57186 validation-auc:0.96654 validation-aucpr:0.97047
[14] validation-logloss:0.56505 validation-auc:0.96636 validation-aucpr:0.97024
[15] validation-logloss:0.55775 validation-auc:0.96646 validation-aucpr:0.97038
[16] validation-logloss:0.55150 validation-auc:0.96624 validation-aucpr:0.97013
[17] validation-logloss:0.54409 validation-auc:0.96670 validation-aucpr:0.97060
[18] validation-logloss:0.53691 validation-auc:0.96691 validation-aucpr:0.97087
[19] validation-logloss:0.53141 validation-auc:0.96665 validation-aucpr:0.97057
[20] validation-logloss:0.52586 validation-auc:0.96657 validation-aucpr:0.97045
[21] validation-logloss:0.51949 validation-auc:0.96680 validation-aucpr:0.97061
[22] validation-logloss:0.51407 validation-auc:0.96682 validation-aucpr:0.97062
[23] validation-logloss:0.50860 validation-auc:0.96686 validation-aucpr:0.97066
[24] validation-logloss:0.50329 validation-auc:0.96672 validation-aucpr:0.97051
[25] validation-logloss:0.49796 validation-auc:0.96679 validation-aucpr:0.97053
[26] validation-logloss:0.49300 validation-auc:0.96673 validation-aucpr:0.97041
[27] validation-logloss:0.48795 validation-auc:0.96676 validation-aucpr:0.97041
[28] validation-logloss:0.48193 validation-auc:0.96711 validation-aucpr:0.97075
[29] validation-logloss:0.47721 validation-auc:0.96714 validation-aucpr:0.97078
[30] validation-logloss:0.47292 validation-auc:0.96706 validation-aucpr:0.97067
[31] validation-logloss:0.46739 validation-auc:0.96732 validation-aucpr:0.97091
[32] validation-logloss:0.46292 validation-auc:0.96740 validation-aucpr:0.97094
[33] validation-logloss:0.45841 validation-auc:0.96740 validation-aucpr:0.97094
[34] validation-logloss:0.45420 validation-auc:0.96744 validation-aucpr:0.97094
[35] validation-logloss:0.44991 validation-auc:0.96746 validation-aucpr:0.97095
[36] validation-logloss:0.44494 validation-auc:0.96769 validation-aucpr:0.97118
[37] validation-logloss:0.44081 validation-auc:0.96772 validation-aucpr:0.97117
[38] validation-logloss:0.43691 validation-auc:0.96770 validation-aucpr:0.97104
[39] validation-logloss:0.43224 validation-auc:0.96781 validation-aucpr:0.97119
[40] validation-logloss:0.42867 validation-auc:0.96763 validation-aucpr:0.97103
[41] validation-logloss:0.42505 validation-auc:0.96758 validation-aucpr:0.97095
[42] validation-logloss:0.42140 validation-auc:0.96760 validation-aucpr:0.97094
[43] validation-logloss:0.41730 validation-auc:0.96764 validation-aucpr:0.97094
[44] validation-logloss:0.41384 validation-auc:0.96764 validation-aucpr:0.97089
[45] validation-logloss:0.40966 validation-auc:0.96779 validation-aucpr:0.97104
[46] validation-logloss:0.40630 validation-auc:0.96780 validation-aucpr:0.97104
{'best_iteration': '39', 'best_score': '0.9711898911116464'}
Trial 50, Fold 2: Log loss = 0.406295763397056, Average precision = 0.9710449059893823, ROC-AUC = 0.9677950986072286, Elapsed Time = 1.3552650999990874 seconds
Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 50, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68338 validation-auc:0.94309 validation-aucpr:0.94693
[1] validation-logloss:0.67353 validation-auc:0.95311 validation-aucpr:0.95467
[2] validation-logloss:0.66305 validation-auc:0.96262 validation-aucpr:0.96503
[3] validation-logloss:0.65411 validation-auc:0.96252 validation-aucpr:0.96496
[4] validation-logloss:0.64533 validation-auc:0.96305 validation-aucpr:0.96633
[5] validation-logloss:0.63660 validation-auc:0.96315 validation-aucpr:0.96738
[6] validation-logloss:0.62847 validation-auc:0.96301 validation-aucpr:0.96636
[7] validation-logloss:0.62023 validation-auc:0.96287 validation-aucpr:0.96619
[8] validation-logloss:0.61093 validation-auc:0.96504 validation-aucpr:0.96851
[9] validation-logloss:0.60211 validation-auc:0.96602 validation-aucpr:0.96951
[10] validation-logloss:0.59335 validation-auc:0.96682 validation-aucpr:0.97029
[11] validation-logloss:0.58544 validation-auc:0.96738 validation-aucpr:0.97222
[12] validation-logloss:0.57889 validation-auc:0.96685 validation-aucpr:0.97185
[13] validation-logloss:0.57071 validation-auc:0.96745 validation-aucpr:0.97238
[14] validation-logloss:0.56278 validation-auc:0.96783 validation-aucpr:0.97274
[15] validation-logloss:0.55631 validation-auc:0.96772 validation-aucpr:0.97257
[16] validation-logloss:0.55018 validation-auc:0.96750 validation-aucpr:0.97238
[17] validation-logloss:0.54415 validation-auc:0.96723 validation-aucpr:0.97220
[18] validation-logloss:0.53795 validation-auc:0.96714 validation-aucpr:0.97210
[19] validation-logloss:0.53206 validation-auc:0.96702 validation-aucpr:0.97203
[20] validation-logloss:0.52632 validation-auc:0.96695 validation-aucpr:0.97197
[21] validation-logloss:0.52062 validation-auc:0.96692 validation-aucpr:0.97189
[22] validation-logloss:0.51494 validation-auc:0.96685 validation-aucpr:0.97179
[23] validation-logloss:0.50943 validation-auc:0.96691 validation-aucpr:0.97182
[24] validation-logloss:0.50296 validation-auc:0.96728 validation-aucpr:0.97217
[25] validation-logloss:0.49677 validation-auc:0.96751 validation-aucpr:0.97241
[26] validation-logloss:0.49086 validation-auc:0.96764 validation-aucpr:0.97253
[27] validation-logloss:0.48601 validation-auc:0.96761 validation-aucpr:0.97253
[28] validation-logloss:0.48126 validation-auc:0.96759 validation-aucpr:0.97250
[29] validation-logloss:0.47562 validation-auc:0.96779 validation-aucpr:0.97267
[30] validation-logloss:0.47005 validation-auc:0.96800 validation-aucpr:0.97281
[31] validation-logloss:0.46550 validation-auc:0.96798 validation-aucpr:0.97279
[32] validation-logloss:0.46007 validation-auc:0.96819 validation-aucpr:0.97298
[33] validation-logloss:0.45563 validation-auc:0.96824 validation-aucpr:0.97299
[34] validation-logloss:0.45150 validation-auc:0.96825 validation-aucpr:0.97298
[35] validation-logloss:0.44719 validation-auc:0.96832 validation-aucpr:0.97304
[36] validation-logloss:0.44297 validation-auc:0.96832 validation-aucpr:0.97304
[37] validation-logloss:0.43794 validation-auc:0.96859 validation-aucpr:0.97327
[38] validation-logloss:0.43416 validation-auc:0.96852 validation-aucpr:0.97319
[39] validation-logloss:0.43048 validation-auc:0.96852 validation-aucpr:0.97318
[40] validation-logloss:0.42678 validation-auc:0.96847 validation-aucpr:0.97317
[41] validation-logloss:0.42216 validation-auc:0.96866 validation-aucpr:0.97334
[42] validation-logloss:0.41780 validation-auc:0.96875 validation-aucpr:0.97343
[43] validation-logloss:0.41427 validation-auc:0.96871 validation-aucpr:0.97341
[44] validation-logloss:0.41021 validation-auc:0.96879 validation-aucpr:0.97348
[45] validation-logloss:0.40656 validation-auc:0.96889 validation-aucpr:0.97354
[46] validation-logloss:0.40306 validation-auc:0.96890 validation-aucpr:0.97355
{'best_iteration': '46', 'best_score': '0.97354798914545'}
Trial 50, Fold 3: Log loss = 0.4030647629357849, Average precision = 0.9735525545776742, ROC-AUC = 0.9688970237693946, Elapsed Time = 1.3142243000002054 seconds
Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 50, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68349 validation-auc:0.94204 validation-aucpr:0.94628
[1] validation-logloss:0.67442 validation-auc:0.94667 validation-aucpr:0.95214
[2] validation-logloss:0.66581 validation-auc:0.94897 validation-aucpr:0.95443
[3] validation-logloss:0.65630 validation-auc:0.95477 validation-aucpr:0.95994
[4] validation-logloss:0.64760 validation-auc:0.95499 validation-aucpr:0.96006
[5] validation-logloss:0.63920 validation-auc:0.95576 validation-aucpr:0.96085
[6] validation-logloss:0.63087 validation-auc:0.95639 validation-aucpr:0.96143
[7] validation-logloss:0.62133 validation-auc:0.96113 validation-aucpr:0.96650
[8] validation-logloss:0.61210 validation-auc:0.96282 validation-aucpr:0.96853
[9] validation-logloss:0.60443 validation-auc:0.96271 validation-aucpr:0.96834
[10] validation-logloss:0.59695 validation-auc:0.96290 validation-aucpr:0.96838
[11] validation-logloss:0.58957 validation-auc:0.96281 validation-aucpr:0.96822
[12] validation-logloss:0.58228 validation-auc:0.96297 validation-aucpr:0.96828
[13] validation-logloss:0.57531 validation-auc:0.96273 validation-aucpr:0.96805
[14] validation-logloss:0.56864 validation-auc:0.96263 validation-aucpr:0.96788
[15] validation-logloss:0.56211 validation-auc:0.96253 validation-aucpr:0.96772
[16] validation-logloss:0.55470 validation-auc:0.96339 validation-aucpr:0.96874
[17] validation-logloss:0.54744 validation-auc:0.96393 validation-aucpr:0.96936
[18] validation-logloss:0.54134 validation-auc:0.96368 validation-aucpr:0.96908
[19] validation-logloss:0.53522 validation-auc:0.96368 validation-aucpr:0.96904
[20] validation-logloss:0.52952 validation-auc:0.96350 validation-aucpr:0.96889
[21] validation-logloss:0.52278 validation-auc:0.96394 validation-aucpr:0.96944
[22] validation-logloss:0.51618 validation-auc:0.96442 validation-aucpr:0.96994
[23] validation-logloss:0.50987 validation-auc:0.96469 validation-aucpr:0.97023
[24] validation-logloss:0.50464 validation-auc:0.96461 validation-aucpr:0.97012
[25] validation-logloss:0.49952 validation-auc:0.96451 validation-aucpr:0.97003
[26] validation-logloss:0.49459 validation-auc:0.96447 validation-aucpr:0.97000
[27] validation-logloss:0.48864 validation-auc:0.96485 validation-aucpr:0.97037
[28] validation-logloss:0.48291 validation-auc:0.96507 validation-aucpr:0.97060
[29] validation-logloss:0.47819 validation-auc:0.96504 validation-aucpr:0.97057
[30] validation-logloss:0.47277 validation-auc:0.96517 validation-aucpr:0.97073
[31] validation-logloss:0.46834 validation-auc:0.96514 validation-aucpr:0.97071
[32] validation-logloss:0.46308 validation-auc:0.96535 validation-aucpr:0.97091
[33] validation-logloss:0.45862 validation-auc:0.96543 validation-aucpr:0.97097
[34] validation-logloss:0.45433 validation-auc:0.96547 validation-aucpr:0.97097
[35] validation-logloss:0.45025 validation-auc:0.96535 validation-aucpr:0.97087
[36] validation-logloss:0.44597 validation-auc:0.96545 validation-aucpr:0.97096
[37] validation-logloss:0.44207 validation-auc:0.96550 validation-aucpr:0.97099
[38] validation-logloss:0.43728 validation-auc:0.96570 validation-aucpr:0.97118
[39] validation-logloss:0.43352 validation-auc:0.96569 validation-aucpr:0.97115
[40] validation-logloss:0.42890 validation-auc:0.96584 validation-aucpr:0.97132
[41] validation-logloss:0.42518 validation-auc:0.96589 validation-aucpr:0.97135
[42] validation-logloss:0.42163 validation-auc:0.96593 validation-aucpr:0.97133
[43] validation-logloss:0.41817 validation-auc:0.96585 validation-aucpr:0.97127
[44] validation-logloss:0.41389 validation-auc:0.96603 validation-aucpr:0.97144
[45] validation-logloss:0.41068 validation-auc:0.96599 validation-aucpr:0.97141
[46] validation-logloss:0.40753 validation-auc:0.96593 validation-aucpr:0.97135
{'best_iteration': '44', 'best_score': '0.9714400273714907'}
Trial 50, Fold 4: Log loss = 0.4075332225921297, Average precision = 0.9713529212958817, ROC-AUC = 0.9659268588262505, Elapsed Time = 1.3637277999987418 seconds
Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 50, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68359 validation-auc:0.93834 validation-aucpr:0.93939
[1] validation-logloss:0.67398 validation-auc:0.95067 validation-aucpr:0.95333
[2] validation-logloss:0.66444 validation-auc:0.95413 validation-aucpr:0.95852
[3] validation-logloss:0.65589 validation-auc:0.95368 validation-aucpr:0.95783
[4] validation-logloss:0.64699 validation-auc:0.95520 validation-aucpr:0.95874
[5] validation-logloss:0.63728 validation-auc:0.96073 validation-aucpr:0.96504
[6] validation-logloss:0.62902 validation-auc:0.96101 validation-aucpr:0.96520
[7] validation-logloss:0.62113 validation-auc:0.96095 validation-aucpr:0.96444
[8] validation-logloss:0.61208 validation-auc:0.96247 validation-aucpr:0.96613
[9] validation-logloss:0.60432 validation-auc:0.96253 validation-aucpr:0.96615
[10] validation-logloss:0.59560 validation-auc:0.96339 validation-aucpr:0.96714
[11] validation-logloss:0.58859 validation-auc:0.96317 validation-aucpr:0.96684
[12] validation-logloss:0.58046 validation-auc:0.96357 validation-aucpr:0.96729
[13] validation-logloss:0.57249 validation-auc:0.96398 validation-aucpr:0.96733
[14] validation-logloss:0.56565 validation-auc:0.96402 validation-aucpr:0.96732
[15] validation-logloss:0.55915 validation-auc:0.96402 validation-aucpr:0.96690
[16] validation-logloss:0.55277 validation-auc:0.96398 validation-aucpr:0.96717
[17] validation-logloss:0.54631 validation-auc:0.96412 validation-aucpr:0.96793
[18] validation-logloss:0.54023 validation-auc:0.96403 validation-aucpr:0.96858
[19] validation-logloss:0.53332 validation-auc:0.96420 validation-aucpr:0.96882
[20] validation-logloss:0.52745 validation-auc:0.96416 validation-aucpr:0.96879
[21] validation-logloss:0.52059 validation-auc:0.96457 validation-aucpr:0.96896
[22] validation-logloss:0.51528 validation-auc:0.96441 validation-aucpr:0.96837
[23] validation-logloss:0.51020 validation-auc:0.96422 validation-aucpr:0.96811
[24] validation-logloss:0.50399 validation-auc:0.96439 validation-aucpr:0.96827
[25] validation-logloss:0.49908 validation-auc:0.96432 validation-aucpr:0.96823
[26] validation-logloss:0.49399 validation-auc:0.96433 validation-aucpr:0.96829
[27] validation-logloss:0.48912 validation-auc:0.96428 validation-aucpr:0.96819
[28] validation-logloss:0.48433 validation-auc:0.96433 validation-aucpr:0.96815
[29] validation-logloss:0.47871 validation-auc:0.96444 validation-aucpr:0.96830
[30] validation-logloss:0.47438 validation-auc:0.96426 validation-aucpr:0.96814
[31] validation-logloss:0.46987 validation-auc:0.96431 validation-aucpr:0.96809
[32] validation-logloss:0.46460 validation-auc:0.96447 validation-aucpr:0.96824
[33] validation-logloss:0.46030 validation-auc:0.96446 validation-aucpr:0.96817
[34] validation-logloss:0.45616 validation-auc:0.96437 validation-aucpr:0.96810
[35] validation-logloss:0.45114 validation-auc:0.96450 validation-aucpr:0.96821
[36] validation-logloss:0.44640 validation-auc:0.96463 validation-aucpr:0.96812
[37] validation-logloss:0.44244 validation-auc:0.96471 validation-aucpr:0.96938
[38] validation-logloss:0.43856 validation-auc:0.96466 validation-aucpr:0.96935
[39] validation-logloss:0.43402 validation-auc:0.96478 validation-aucpr:0.96942
[40] validation-logloss:0.42934 validation-auc:0.96501 validation-aucpr:0.96962
[41] validation-logloss:0.42498 validation-auc:0.96525 validation-aucpr:0.96996
[42] validation-logloss:0.42063 validation-auc:0.96545 validation-aucpr:0.97023
[43] validation-logloss:0.41623 validation-auc:0.96564 validation-aucpr:0.97038
[44] validation-logloss:0.41290 validation-auc:0.96559 validation-aucpr:0.97034
[45] validation-logloss:0.40871 validation-auc:0.96574 validation-aucpr:0.97047
[46] validation-logloss:0.40533 validation-auc:0.96573 validation-aucpr:0.97042
{'best_iteration': '45', 'best_score': '0.9704737172269167'}
Trial 50, Fold 5: Log loss = 0.405331832251526, Average precision = 0.9704301972342224, ROC-AUC = 0.9657291492484625, Elapsed Time = 1.3081268000023556 seconds
Optimization Progress: 51%|#####1 | 51/100 [2:49:14<1:22:31, 101.05s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 51, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68291 validation-auc:0.95205 validation-aucpr:0.95616
[1] validation-logloss:0.67265 validation-auc:0.95976 validation-aucpr:0.96540
[2] validation-logloss:0.66447 validation-auc:0.95993 validation-aucpr:0.96584
[3] validation-logloss:0.65478 validation-auc:0.96073 validation-aucpr:0.96659
[4] validation-logloss:0.64542 validation-auc:0.96077 validation-aucpr:0.96667
[5] validation-logloss:0.63731 validation-auc:0.96148 validation-aucpr:0.96697
[6] validation-logloss:0.62850 validation-auc:0.96148 validation-aucpr:0.96707
[7] validation-logloss:0.62023 validation-auc:0.96169 validation-aucpr:0.96733
[8] validation-logloss:0.61176 validation-auc:0.96215 validation-aucpr:0.96777
[9] validation-logloss:0.60441 validation-auc:0.96229 validation-aucpr:0.96770
[10] validation-logloss:0.59730 validation-auc:0.96171 validation-aucpr:0.96716
[11] validation-logloss:0.59040 validation-auc:0.96166 validation-aucpr:0.96700
[12] validation-logloss:0.58286 validation-auc:0.96205 validation-aucpr:0.96727
[13] validation-logloss:0.57540 validation-auc:0.96195 validation-aucpr:0.96468
[14] validation-logloss:0.56796 validation-auc:0.96199 validation-aucpr:0.96482
[15] validation-logloss:0.56073 validation-auc:0.96219 validation-aucpr:0.96497
[16] validation-logloss:0.55482 validation-auc:0.96246 validation-aucpr:0.96850
[17] validation-logloss:0.54798 validation-auc:0.96249 validation-aucpr:0.96856
[18] validation-logloss:0.54217 validation-auc:0.96246 validation-aucpr:0.96847
[19] validation-logloss:0.53597 validation-auc:0.96236 validation-aucpr:0.96843
[20] validation-logloss:0.52981 validation-auc:0.96217 validation-aucpr:0.96834
[21] validation-logloss:0.52451 validation-auc:0.96204 validation-aucpr:0.96820
[22] validation-logloss:0.51833 validation-auc:0.96214 validation-aucpr:0.96829
[23] validation-logloss:0.51302 validation-auc:0.96236 validation-aucpr:0.96843
[24] validation-logloss:0.50816 validation-auc:0.96214 validation-aucpr:0.96821
[25] validation-logloss:0.50237 validation-auc:0.96227 validation-aucpr:0.96830
[26] validation-logloss:0.49685 validation-auc:0.96239 validation-aucpr:0.96841
[27] validation-logloss:0.49238 validation-auc:0.96240 validation-aucpr:0.96847
[28] validation-logloss:0.48712 validation-auc:0.96238 validation-aucpr:0.96850
[29] validation-logloss:0.48192 validation-auc:0.96254 validation-aucpr:0.96865
[30] validation-logloss:0.47689 validation-auc:0.96261 validation-aucpr:0.96874
[31] validation-logloss:0.47189 validation-auc:0.96265 validation-aucpr:0.96880
[32] validation-logloss:0.46754 validation-auc:0.96266 validation-aucpr:0.96880
[33] validation-logloss:0.46335 validation-auc:0.96261 validation-aucpr:0.96874
[34] validation-logloss:0.45853 validation-auc:0.96268 validation-aucpr:0.96881
[35] validation-logloss:0.45444 validation-auc:0.96273 validation-aucpr:0.96882
[36] validation-logloss:0.44987 validation-auc:0.96281 validation-aucpr:0.96892
[37] validation-logloss:0.44598 validation-auc:0.96273 validation-aucpr:0.96882
[38] validation-logloss:0.44168 validation-auc:0.96280 validation-aucpr:0.96890
[39] validation-logloss:0.43754 validation-auc:0.96311 validation-aucpr:0.96955
[40] validation-logloss:0.43336 validation-auc:0.96317 validation-aucpr:0.96960
[41] validation-logloss:0.42915 validation-auc:0.96335 validation-aucpr:0.96973
[42] validation-logloss:0.42502 validation-auc:0.96348 validation-aucpr:0.96986
[43] validation-logloss:0.42167 validation-auc:0.96351 validation-aucpr:0.96986
[44] validation-logloss:0.41769 validation-auc:0.96366 validation-aucpr:0.96997
[45] validation-logloss:0.41441 validation-auc:0.96381 validation-aucpr:0.97007
[46] validation-logloss:0.41057 validation-auc:0.96390 validation-aucpr:0.97017
[47] validation-logloss:0.40694 validation-auc:0.96395 validation-aucpr:0.97025
[48] validation-logloss:0.40335 validation-auc:0.96406 validation-aucpr:0.97035
[49] validation-logloss:0.40034 validation-auc:0.96402 validation-aucpr:0.97030
[50] validation-logloss:0.39691 validation-auc:0.96415 validation-aucpr:0.97040
[51] validation-logloss:0.39351 validation-auc:0.96430 validation-aucpr:0.97055
[52] validation-logloss:0.39025 validation-auc:0.96427 validation-aucpr:0.97053
[53] validation-logloss:0.38691 validation-auc:0.96435 validation-aucpr:0.97061
[54] validation-logloss:0.38378 validation-auc:0.96436 validation-aucpr:0.97069
[55] validation-logloss:0.38122 validation-auc:0.96428 validation-aucpr:0.97060
{'best_iteration': '54', 'best_score': '0.97069156848798'}
Trial 51, Fold 1: Log loss = 0.3812171714360218, Average precision = 0.9703871434420608, ROC-AUC = 0.9642796651180798, Elapsed Time = 0.7973453000013251 seconds
Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 51, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68236 validation-auc:0.95602 validation-aucpr:0.95911
[1] validation-logloss:0.67211 validation-auc:0.96113 validation-aucpr:0.96672
[2] validation-logloss:0.66210 validation-auc:0.96291 validation-aucpr:0.96763
[3] validation-logloss:0.65293 validation-auc:0.96248 validation-aucpr:0.96502
[4] validation-logloss:0.64365 validation-auc:0.96223 validation-aucpr:0.96440
[5] validation-logloss:0.63554 validation-auc:0.96194 validation-aucpr:0.96443
[6] validation-logloss:0.62720 validation-auc:0.96301 validation-aucpr:0.96739
[7] validation-logloss:0.61892 validation-auc:0.96373 validation-aucpr:0.96800
[8] validation-logloss:0.61041 validation-auc:0.96439 validation-aucpr:0.96851
[9] validation-logloss:0.60304 validation-auc:0.96383 validation-aucpr:0.96794
[10] validation-logloss:0.59576 validation-auc:0.96307 validation-aucpr:0.96719
[11] validation-logloss:0.58901 validation-auc:0.96296 validation-aucpr:0.96705
[12] validation-logloss:0.58139 validation-auc:0.96323 validation-aucpr:0.96729
[13] validation-logloss:0.57480 validation-auc:0.96339 validation-aucpr:0.96749
[14] validation-logloss:0.56744 validation-auc:0.96348 validation-aucpr:0.96758
[15] validation-logloss:0.56120 validation-auc:0.96355 validation-aucpr:0.96760
[16] validation-logloss:0.55417 validation-auc:0.96355 validation-aucpr:0.96761
[17] validation-logloss:0.54723 validation-auc:0.96359 validation-aucpr:0.96768
[18] validation-logloss:0.54042 validation-auc:0.96374 validation-aucpr:0.96781
[19] validation-logloss:0.53391 validation-auc:0.96378 validation-aucpr:0.96790
[20] validation-logloss:0.52767 validation-auc:0.96392 validation-aucpr:0.96793
[21] validation-logloss:0.52233 validation-auc:0.96374 validation-aucpr:0.96768
[22] validation-logloss:0.51623 validation-auc:0.96373 validation-aucpr:0.96774
[23] validation-logloss:0.51016 validation-auc:0.96397 validation-aucpr:0.96794
[24] validation-logloss:0.50455 validation-auc:0.96423 validation-aucpr:0.96835
[25] validation-logloss:0.49884 validation-auc:0.96430 validation-aucpr:0.96842
[26] validation-logloss:0.49312 validation-auc:0.96447 validation-aucpr:0.96859
[27] validation-logloss:0.48843 validation-auc:0.96418 validation-aucpr:0.96829
[28] validation-logloss:0.48317 validation-auc:0.96429 validation-aucpr:0.96836
[29] validation-logloss:0.47862 validation-auc:0.96412 validation-aucpr:0.96814
[30] validation-logloss:0.47347 validation-auc:0.96409 validation-aucpr:0.96812
[31] validation-logloss:0.46914 validation-auc:0.96403 validation-aucpr:0.96803
[32] validation-logloss:0.46449 validation-auc:0.96407 validation-aucpr:0.96810
[33] validation-logloss:0.46030 validation-auc:0.96410 validation-aucpr:0.96811
[34] validation-logloss:0.45558 validation-auc:0.96409 validation-aucpr:0.96800
[35] validation-logloss:0.45151 validation-auc:0.96422 validation-aucpr:0.96810
[36] validation-logloss:0.44692 validation-auc:0.96426 validation-aucpr:0.96816
[37] validation-logloss:0.44305 validation-auc:0.96428 validation-aucpr:0.96813
[38] validation-logloss:0.43880 validation-auc:0.96441 validation-aucpr:0.96820
[39] validation-logloss:0.43448 validation-auc:0.96444 validation-aucpr:0.96824
[40] validation-logloss:0.43039 validation-auc:0.96455 validation-aucpr:0.96835
[41] validation-logloss:0.42628 validation-auc:0.96470 validation-aucpr:0.96847
[42] validation-logloss:0.42219 validation-auc:0.96486 validation-aucpr:0.96869
[43] validation-logloss:0.41821 validation-auc:0.96496 validation-aucpr:0.96879
[44] validation-logloss:0.41431 validation-auc:0.96508 validation-aucpr:0.96891
[45] validation-logloss:0.41078 validation-auc:0.96518 validation-aucpr:0.96900
[46] validation-logloss:0.40702 validation-auc:0.96526 validation-aucpr:0.96907
[47] validation-logloss:0.40409 validation-auc:0.96517 validation-aucpr:0.96898
[48] validation-logloss:0.40063 validation-auc:0.96524 validation-aucpr:0.96905
[49] validation-logloss:0.39711 validation-auc:0.96535 validation-aucpr:0.96914
[50] validation-logloss:0.39358 validation-auc:0.96558 validation-aucpr:0.96935
[51] validation-logloss:0.39066 validation-auc:0.96546 validation-aucpr:0.96922
[52] validation-logloss:0.38725 validation-auc:0.96566 validation-aucpr:0.96938
[53] validation-logloss:0.38446 validation-auc:0.96561 validation-aucpr:0.96935
[54] validation-logloss:0.38115 validation-auc:0.96573 validation-aucpr:0.96945
[55] validation-logloss:0.37860 validation-auc:0.96575 validation-aucpr:0.96945
{'best_iteration': '54', 'best_score': '0.9694506681393924'}
Trial 51, Fold 2: Log loss = 0.37859739271133297, Average precision = 0.9692820372580925, ROC-AUC = 0.9657487797201831, Elapsed Time = 1.055651200000284 seconds
Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 51, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68267 validation-auc:0.95690 validation-aucpr:0.96154
[1] validation-logloss:0.67228 validation-auc:0.96240 validation-aucpr:0.96740
[2] validation-logloss:0.66229 validation-auc:0.96191 validation-aucpr:0.96718
[3] validation-logloss:0.65256 validation-auc:0.96203 validation-aucpr:0.96732
[4] validation-logloss:0.64333 validation-auc:0.96423 validation-aucpr:0.96932
[5] validation-logloss:0.63419 validation-auc:0.96451 validation-aucpr:0.96946
[6] validation-logloss:0.62519 validation-auc:0.96492 validation-aucpr:0.96969
[7] validation-logloss:0.61746 validation-auc:0.96523 validation-aucpr:0.96998
[8] validation-logloss:0.60900 validation-auc:0.96533 validation-aucpr:0.97014
[9] validation-logloss:0.60075 validation-auc:0.96528 validation-aucpr:0.97072
[10] validation-logloss:0.59267 validation-auc:0.96608 validation-aucpr:0.97123
[11] validation-logloss:0.58475 validation-auc:0.96581 validation-aucpr:0.97107
[12] validation-logloss:0.57724 validation-auc:0.96559 validation-aucpr:0.97085
[13] validation-logloss:0.57071 validation-auc:0.96565 validation-aucpr:0.97093
[14] validation-logloss:0.56427 validation-auc:0.96531 validation-aucpr:0.97059
[15] validation-logloss:0.55749 validation-auc:0.96514 validation-aucpr:0.97048
[16] validation-logloss:0.55162 validation-auc:0.96466 validation-aucpr:0.96997
[17] validation-logloss:0.54552 validation-auc:0.96477 validation-aucpr:0.97001
[18] validation-logloss:0.53961 validation-auc:0.96468 validation-aucpr:0.96984
[19] validation-logloss:0.53316 validation-auc:0.96482 validation-aucpr:0.96995
[20] validation-logloss:0.52671 validation-auc:0.96489 validation-aucpr:0.97002
[21] validation-logloss:0.52069 validation-auc:0.96481 validation-aucpr:0.96998
[22] validation-logloss:0.51523 validation-auc:0.96477 validation-aucpr:0.96992
[23] validation-logloss:0.50917 validation-auc:0.96486 validation-aucpr:0.97001
[24] validation-logloss:0.50329 validation-auc:0.96469 validation-aucpr:0.96987
[25] validation-logloss:0.49831 validation-auc:0.96480 validation-aucpr:0.97004
[26] validation-logloss:0.49268 validation-auc:0.96485 validation-aucpr:0.97005
[27] validation-logloss:0.48801 validation-auc:0.96496 validation-aucpr:0.97016
[28] validation-logloss:0.48255 validation-auc:0.96501 validation-aucpr:0.97020
[29] validation-logloss:0.47805 validation-auc:0.96495 validation-aucpr:0.97014
[30] validation-logloss:0.47341 validation-auc:0.96515 validation-aucpr:0.97024
[31] validation-logloss:0.46852 validation-auc:0.96527 validation-aucpr:0.97038
[32] validation-logloss:0.46348 validation-auc:0.96542 validation-aucpr:0.97048
[33] validation-logloss:0.45852 validation-auc:0.96565 validation-aucpr:0.97062
[34] validation-logloss:0.45391 validation-auc:0.96567 validation-aucpr:0.97063
[35] validation-logloss:0.44926 validation-auc:0.96584 validation-aucpr:0.97080
[36] validation-logloss:0.44528 validation-auc:0.96576 validation-aucpr:0.97067
[37] validation-logloss:0.44103 validation-auc:0.96583 validation-aucpr:0.97075
[38] validation-logloss:0.43685 validation-auc:0.96585 validation-aucpr:0.97077
[39] validation-logloss:0.43245 validation-auc:0.96594 validation-aucpr:0.97085
[40] validation-logloss:0.42887 validation-auc:0.96590 validation-aucpr:0.97076
[41] validation-logloss:0.42482 validation-auc:0.96600 validation-aucpr:0.97084
[42] validation-logloss:0.42162 validation-auc:0.96591 validation-aucpr:0.97074
[43] validation-logloss:0.41758 validation-auc:0.96594 validation-aucpr:0.97076
[44] validation-logloss:0.41360 validation-auc:0.96599 validation-aucpr:0.97082
[45] validation-logloss:0.40968 validation-auc:0.96609 validation-aucpr:0.97090
[46] validation-logloss:0.40640 validation-auc:0.96603 validation-aucpr:0.97085
[47] validation-logloss:0.40267 validation-auc:0.96616 validation-aucpr:0.97095
[48] validation-logloss:0.39905 validation-auc:0.96617 validation-aucpr:0.97098
[49] validation-logloss:0.39541 validation-auc:0.96620 validation-aucpr:0.97102
[50] validation-logloss:0.39227 validation-auc:0.96629 validation-aucpr:0.97106
[51] validation-logloss:0.38923 validation-auc:0.96637 validation-aucpr:0.97110
[52] validation-logloss:0.38594 validation-auc:0.96636 validation-aucpr:0.97112
[53] validation-logloss:0.38261 validation-auc:0.96644 validation-aucpr:0.97119
[54] validation-logloss:0.37937 validation-auc:0.96650 validation-aucpr:0.97120
[55] validation-logloss:0.37612 validation-auc:0.96660 validation-aucpr:0.97130
{'best_iteration': '55', 'best_score': '0.9712978997806914'}
Trial 51, Fold 3: Log loss = 0.37611588215876013, Average precision = 0.9711011629204486, ROC-AUC = 0.9665986675463012, Elapsed Time = 1.0364141000027303 seconds
Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 51, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68242 validation-auc:0.95750 validation-aucpr:0.96134
[1] validation-logloss:0.67212 validation-auc:0.96272 validation-aucpr:0.96928
[2] validation-logloss:0.66378 validation-auc:0.96068 validation-aucpr:0.96725
[3] validation-logloss:0.65554 validation-auc:0.96049 validation-aucpr:0.96693
[4] validation-logloss:0.64616 validation-auc:0.95988 validation-aucpr:0.96668
[5] validation-logloss:0.63799 validation-auc:0.96061 validation-aucpr:0.96709
[6] validation-logloss:0.62904 validation-auc:0.96097 validation-aucpr:0.96739
[7] validation-logloss:0.62072 validation-auc:0.96165 validation-aucpr:0.96801
[8] validation-logloss:0.61223 validation-auc:0.96286 validation-aucpr:0.96881
[9] validation-logloss:0.60501 validation-auc:0.96256 validation-aucpr:0.96841
[10] validation-logloss:0.59690 validation-auc:0.96274 validation-aucpr:0.96859
[11] validation-logloss:0.59020 validation-auc:0.96272 validation-aucpr:0.96847
[12] validation-logloss:0.58233 validation-auc:0.96297 validation-aucpr:0.96880
[13] validation-logloss:0.57495 validation-auc:0.96308 validation-aucpr:0.96898
[14] validation-logloss:0.56853 validation-auc:0.96319 validation-aucpr:0.96894
[15] validation-logloss:0.56241 validation-auc:0.96297 validation-aucpr:0.96865
[16] validation-logloss:0.55551 validation-auc:0.96300 validation-aucpr:0.96867
[17] validation-logloss:0.54863 validation-auc:0.96290 validation-aucpr:0.96866
[18] validation-logloss:0.54290 validation-auc:0.96291 validation-aucpr:0.96867
[19] validation-logloss:0.53635 validation-auc:0.96288 validation-aucpr:0.96867
[20] validation-logloss:0.53085 validation-auc:0.96263 validation-aucpr:0.96844
[21] validation-logloss:0.52526 validation-auc:0.96281 validation-aucpr:0.96857
[22] validation-logloss:0.51906 validation-auc:0.96322 validation-aucpr:0.96891
[23] validation-logloss:0.51378 validation-auc:0.96307 validation-aucpr:0.96880
[24] validation-logloss:0.50888 validation-auc:0.96311 validation-aucpr:0.96885
[25] validation-logloss:0.50293 validation-auc:0.96340 validation-aucpr:0.96906
[26] validation-logloss:0.49720 validation-auc:0.96340 validation-aucpr:0.96909
[27] validation-logloss:0.49165 validation-auc:0.96361 validation-aucpr:0.96928
[28] validation-logloss:0.48646 validation-auc:0.96357 validation-aucpr:0.96932
[29] validation-logloss:0.48104 validation-auc:0.96369 validation-aucpr:0.96943
[30] validation-logloss:0.47652 validation-auc:0.96371 validation-aucpr:0.96941
[31] validation-logloss:0.47240 validation-auc:0.96366 validation-aucpr:0.96932
[32] validation-logloss:0.46735 validation-auc:0.96379 validation-aucpr:0.96948
[33] validation-logloss:0.46241 validation-auc:0.96388 validation-aucpr:0.96961
[34] validation-logloss:0.45836 validation-auc:0.96370 validation-aucpr:0.96947
[35] validation-logloss:0.45359 validation-auc:0.96383 validation-aucpr:0.96960
[36] validation-logloss:0.44967 validation-auc:0.96381 validation-aucpr:0.96954
[37] validation-logloss:0.44507 validation-auc:0.96404 validation-aucpr:0.96973
[38] validation-logloss:0.44146 validation-auc:0.96396 validation-aucpr:0.96968
[39] validation-logloss:0.43728 validation-auc:0.96402 validation-aucpr:0.96979
[40] validation-logloss:0.43312 validation-auc:0.96408 validation-aucpr:0.96984
[41] validation-logloss:0.42903 validation-auc:0.96415 validation-aucpr:0.96992
[42] validation-logloss:0.42565 validation-auc:0.96407 validation-aucpr:0.96984
[43] validation-logloss:0.42224 validation-auc:0.96398 validation-aucpr:0.96979
[44] validation-logloss:0.41820 validation-auc:0.96410 validation-aucpr:0.96987
[45] validation-logloss:0.41428 validation-auc:0.96415 validation-aucpr:0.96993
[46] validation-logloss:0.41119 validation-auc:0.96409 validation-aucpr:0.96986
[47] validation-logloss:0.40746 validation-auc:0.96413 validation-aucpr:0.96991
[48] validation-logloss:0.40442 validation-auc:0.96402 validation-aucpr:0.96984
[49] validation-logloss:0.40079 validation-auc:0.96421 validation-aucpr:0.96998
[50] validation-logloss:0.39723 validation-auc:0.96430 validation-aucpr:0.97005
[51] validation-logloss:0.39386 validation-auc:0.96430 validation-aucpr:0.97007
[52] validation-logloss:0.39118 validation-auc:0.96415 validation-aucpr:0.96996
[53] validation-logloss:0.38790 validation-auc:0.96424 validation-aucpr:0.97004
[54] validation-logloss:0.38449 validation-auc:0.96435 validation-aucpr:0.97015
[55] validation-logloss:0.38126 validation-auc:0.96433 validation-aucpr:0.97017
{'best_iteration': '55', 'best_score': '0.970168208440948'}
Trial 51, Fold 4: Log loss = 0.3812629807783591, Average precision = 0.9699755871299118, ROC-AUC = 0.9643334024812169, Elapsed Time = 1.0350413000014669 seconds
Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 51, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68251 validation-auc:0.95788 validation-aucpr:0.96177
[1] validation-logloss:0.67234 validation-auc:0.96178 validation-aucpr:0.96709
[2] validation-logloss:0.66242 validation-auc:0.96310 validation-aucpr:0.96796
[3] validation-logloss:0.65281 validation-auc:0.96369 validation-aucpr:0.96845
[4] validation-logloss:0.64371 validation-auc:0.96500 validation-aucpr:0.96907
[5] validation-logloss:0.63572 validation-auc:0.96209 validation-aucpr:0.96708
[6] validation-logloss:0.62687 validation-auc:0.96195 validation-aucpr:0.96705
[7] validation-logloss:0.61931 validation-auc:0.96172 validation-aucpr:0.96669
[8] validation-logloss:0.61088 validation-auc:0.96260 validation-aucpr:0.96728
[9] validation-logloss:0.60271 validation-auc:0.96276 validation-aucpr:0.96771
[10] validation-logloss:0.59472 validation-auc:0.96272 validation-aucpr:0.96756
[11] validation-logloss:0.58807 validation-auc:0.96221 validation-aucpr:0.96717
[12] validation-logloss:0.58043 validation-auc:0.96238 validation-aucpr:0.96735
[13] validation-logloss:0.57322 validation-auc:0.96219 validation-aucpr:0.96718
[14] validation-logloss:0.56688 validation-auc:0.96230 validation-aucpr:0.96720
[15] validation-logloss:0.56067 validation-auc:0.96199 validation-aucpr:0.96684
[16] validation-logloss:0.55497 validation-auc:0.96153 validation-aucpr:0.96673
[17] validation-logloss:0.54901 validation-auc:0.96144 validation-aucpr:0.96670
[18] validation-logloss:0.54327 validation-auc:0.96128 validation-aucpr:0.96658
[19] validation-logloss:0.53793 validation-auc:0.96111 validation-aucpr:0.96643
[20] validation-logloss:0.53258 validation-auc:0.96094 validation-aucpr:0.96630
[21] validation-logloss:0.52627 validation-auc:0.96101 validation-aucpr:0.96640
[22] validation-logloss:0.52022 validation-auc:0.96110 validation-aucpr:0.96646
[23] validation-logloss:0.51437 validation-auc:0.96113 validation-aucpr:0.96651
[24] validation-logloss:0.50879 validation-auc:0.96123 validation-aucpr:0.96657
[25] validation-logloss:0.50309 validation-auc:0.96124 validation-aucpr:0.96661
[26] validation-logloss:0.49759 validation-auc:0.96134 validation-aucpr:0.96668
[27] validation-logloss:0.49289 validation-auc:0.96146 validation-aucpr:0.96676
[28] validation-logloss:0.48741 validation-auc:0.96165 validation-aucpr:0.96695
[29] validation-logloss:0.48290 validation-auc:0.96159 validation-aucpr:0.96686
[30] validation-logloss:0.47775 validation-auc:0.96164 validation-aucpr:0.96690
[31] validation-logloss:0.47264 validation-auc:0.96177 validation-aucpr:0.96701
[32] validation-logloss:0.46776 validation-auc:0.96185 validation-aucpr:0.96710
[33] validation-logloss:0.46295 validation-auc:0.96176 validation-aucpr:0.96687
[34] validation-logloss:0.45841 validation-auc:0.96175 validation-aucpr:0.96687
[35] validation-logloss:0.45460 validation-auc:0.96168 validation-aucpr:0.96716
[36] validation-logloss:0.45027 validation-auc:0.96170 validation-aucpr:0.96723
[37] validation-logloss:0.44655 validation-auc:0.96169 validation-aucpr:0.96718
[38] validation-logloss:0.44286 validation-auc:0.96183 validation-aucpr:0.96764
[39] validation-logloss:0.43850 validation-auc:0.96206 validation-aucpr:0.96783
[40] validation-logloss:0.43495 validation-auc:0.96194 validation-aucpr:0.96773
[41] validation-logloss:0.43155 validation-auc:0.96189 validation-aucpr:0.96772
[42] validation-logloss:0.42830 validation-auc:0.96183 validation-aucpr:0.96766
[43] validation-logloss:0.42497 validation-auc:0.96186 validation-aucpr:0.96768
[44] validation-logloss:0.42099 validation-auc:0.96196 validation-aucpr:0.96778
[45] validation-logloss:0.41785 validation-auc:0.96195 validation-aucpr:0.96777
[46] validation-logloss:0.41407 validation-auc:0.96195 validation-aucpr:0.96777
[47] validation-logloss:0.41027 validation-auc:0.96208 validation-aucpr:0.96791
[48] validation-logloss:0.40660 validation-auc:0.96218 validation-aucpr:0.96799
[49] validation-logloss:0.40378 validation-auc:0.96215 validation-aucpr:0.96797
[50] validation-logloss:0.40025 validation-auc:0.96218 validation-aucpr:0.96802
[51] validation-logloss:0.39694 validation-auc:0.96223 validation-aucpr:0.96808
[52] validation-logloss:0.39348 validation-auc:0.96226 validation-aucpr:0.96811
[53] validation-logloss:0.39074 validation-auc:0.96231 validation-aucpr:0.96812
{'best_iteration': '4', 'best_score': '0.9690651836602294'}
Trial 51, Fold 5: Log loss = 0.38745012429890546, Average precision = 0.9680018802991691, ROC-AUC = 0.9624831763372536, Elapsed Time = 1.0867137000022922 seconds
Optimization Progress: 52%|#####2 | 52/100 [2:49:27<59:41, 74.61s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 52, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66455 validation-auc:0.95712 validation-aucpr:0.96324
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63859 validation-auc:0.96221 validation-aucpr:0.96793
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61703 validation-auc:0.96202 validation-aucpr:0.96628
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59423 validation-auc:0.96319 validation-aucpr:0.96731
[20:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57256 validation-auc:0.96511 validation-aucpr:0.97001
[20:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55286 validation-auc:0.96614 validation-aucpr:0.97147
[20:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53601 validation-auc:0.96659 validation-aucpr:0.97172
[20:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52017 validation-auc:0.96653 validation-aucpr:0.97175
[20:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50599 validation-auc:0.96628 validation-aucpr:0.97166
[20:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.49097 validation-auc:0.96631 validation-aucpr:0.97181
[20:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47623 validation-auc:0.96653 validation-aucpr:0.97199
[20:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.46420 validation-auc:0.96635 validation-aucpr:0.97175
[20:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.45319 validation-auc:0.96608 validation-aucpr:0.97150
[20:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.44194 validation-auc:0.96659 validation-aucpr:0.97185
[20:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.42956 validation-auc:0.96706 validation-aucpr:0.97223
[20:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.41983 validation-auc:0.96708 validation-aucpr:0.97230
[20:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.41093 validation-auc:0.96676 validation-aucpr:0.97202
[20:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.40043 validation-auc:0.96689 validation-aucpr:0.97215
[20:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.39095 validation-auc:0.96714 validation-aucpr:0.97234
[20:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.38330 validation-auc:0.96717 validation-aucpr:0.97242
[20:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.37393 validation-auc:0.96760 validation-aucpr:0.97272
[20:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.36515 validation-auc:0.96792 validation-aucpr:0.97303
[20:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.35697 validation-auc:0.96828 validation-aucpr:0.97333
[20:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.34930 validation-auc:0.96854 validation-aucpr:0.97354
[20:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.34198 validation-auc:0.96874 validation-aucpr:0.97372
[20:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.33513 validation-auc:0.96906 validation-aucpr:0.97398
[20:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.32848 validation-auc:0.96919 validation-aucpr:0.97405
[20:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.32323 validation-auc:0.96923 validation-aucpr:0.97406
[20:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.31842 validation-auc:0.96919 validation-aucpr:0.97404
[20:48:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.31239 validation-auc:0.96954 validation-aucpr:0.97431
[20:48:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.30660 validation-auc:0.96976 validation-aucpr:0.97452
[20:48:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.30184 validation-auc:0.96971 validation-aucpr:0.97451
[20:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.29659 validation-auc:0.96978 validation-aucpr:0.97460
[20:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.29264 validation-auc:0.96975 validation-aucpr:0.97457
[20:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.28819 validation-auc:0.96983 validation-aucpr:0.97464
[20:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.28483 validation-auc:0.96974 validation-aucpr:0.97451
[20:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.28054 validation-auc:0.96987 validation-aucpr:0.97462
[20:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.27721 validation-auc:0.96980 validation-aucpr:0.97460
[20:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.27423 validation-auc:0.96974 validation-aucpr:0.97455
[20:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.27034 validation-auc:0.96996 validation-aucpr:0.97473
[20:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.26749 validation-auc:0.96986 validation-aucpr:0.97467
[20:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.26407 validation-auc:0.96987 validation-aucpr:0.97469
[20:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.26086 validation-auc:0.96992 validation-aucpr:0.97475
[20:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.25847 validation-auc:0.96989 validation-aucpr:0.97467
[20:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25559 validation-auc:0.96991 validation-aucpr:0.97470
[20:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.25274 validation-auc:0.97005 validation-aucpr:0.97481
[20:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.24985 validation-auc:0.97018 validation-aucpr:0.97490
[20:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.24777 validation-auc:0.97025 validation-aucpr:0.97494
[20:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.24531 validation-auc:0.97038 validation-aucpr:0.97502
[20:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.24299 validation-auc:0.97038 validation-aucpr:0.97501
[20:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.24067 validation-auc:0.97042 validation-aucpr:0.97505
[20:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.23903 validation-auc:0.97029 validation-aucpr:0.97495
[20:48:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.23716 validation-auc:0.97038 validation-aucpr:0.97501
[20:48:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.23498 validation-auc:0.97043 validation-aucpr:0.97505
[20:48:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.23335 validation-auc:0.97046 validation-aucpr:0.97507
[20:48:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.23191 validation-auc:0.97042 validation-aucpr:0.97504
[20:48:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.23048 validation-auc:0.97050 validation-aucpr:0.97509
{'best_iteration': '56', 'best_score': '0.9750851646174706'}
Trial 52, Fold 1: Log loss = 0.23047562636104363, Average precision = 0.9750894141029817, ROC-AUC = 0.9705026156302128, Elapsed Time = 22.141144799999893 seconds
Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 52, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:48:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66495 validation-auc:0.95334 validation-aucpr:0.95421
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63872 validation-auc:0.96041 validation-aucpr:0.96190
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61403 validation-auc:0.96421 validation-aucpr:0.96762
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59267 validation-auc:0.96583 validation-aucpr:0.96927
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57130 validation-auc:0.96674 validation-aucpr:0.97051
[20:48:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55173 validation-auc:0.96794 validation-aucpr:0.97170
[20:48:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53308 validation-auc:0.96861 validation-aucpr:0.97249
[20:48:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51687 validation-auc:0.96902 validation-aucpr:0.97287
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50033 validation-auc:0.96907 validation-aucpr:0.97291
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.48507 validation-auc:0.96925 validation-aucpr:0.97301
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47067 validation-auc:0.96949 validation-aucpr:0.97315
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.45832 validation-auc:0.96960 validation-aucpr:0.97314
[20:48:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44493 validation-auc:0.96997 validation-aucpr:0.97344
[20:48:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.43264 validation-auc:0.97045 validation-aucpr:0.97369
[20:48:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.42088 validation-auc:0.97058 validation-aucpr:0.97382
[20:48:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.40986 validation-auc:0.97059 validation-aucpr:0.97385
[20:48:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.39929 validation-auc:0.97063 validation-aucpr:0.97388
[20:48:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.38942 validation-auc:0.97057 validation-aucpr:0.97389
[20:48:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.38084 validation-auc:0.97070 validation-aucpr:0.97391
[20:48:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.37184 validation-auc:0.97089 validation-aucpr:0.97408
[20:48:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.36319 validation-auc:0.97099 validation-aucpr:0.97404
[20:48:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.35589 validation-auc:0.97108 validation-aucpr:0.97404
[20:48:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.34785 validation-auc:0.97143 validation-aucpr:0.97417
[20:48:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.34076 validation-auc:0.97129 validation-aucpr:0.97404
[20:48:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.33389 validation-auc:0.97129 validation-aucpr:0.97396
[20:48:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.32823 validation-auc:0.97113 validation-aucpr:0.97357
[20:48:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.32180 validation-auc:0.97116 validation-aucpr:0.97349
[20:48:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.31625 validation-auc:0.97115 validation-aucpr:0.97345
[20:48:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.31111 validation-auc:0.97129 validation-aucpr:0.97357
[20:48:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.30552 validation-auc:0.97133 validation-aucpr:0.97362
[20:48:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.30122 validation-auc:0.97114 validation-aucpr:0.97338
[20:48:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.29613 validation-auc:0.97109 validation-aucpr:0.97334
[20:48:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.29189 validation-auc:0.97115 validation-aucpr:0.97340
[20:48:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.28696 validation-auc:0.97134 validation-aucpr:0.97351
[20:48:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.28344 validation-auc:0.97124 validation-aucpr:0.97350
[20:48:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.27970 validation-auc:0.97139 validation-aucpr:0.97410
[20:48:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.27533 validation-auc:0.97154 validation-aucpr:0.97424
[20:48:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.27211 validation-auc:0.97149 validation-aucpr:0.97421
[20:48:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.26846 validation-auc:0.97149 validation-aucpr:0.97421
[20:48:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.26524 validation-auc:0.97154 validation-aucpr:0.97424
[20:48:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.26163 validation-auc:0.97166 validation-aucpr:0.97432
[20:48:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.25830 validation-auc:0.97167 validation-aucpr:0.97431
[20:48:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.25541 validation-auc:0.97164 validation-aucpr:0.97426
[20:48:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.25280 validation-auc:0.97163 validation-aucpr:0.97424
[20:49:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.24991 validation-auc:0.97166 validation-aucpr:0.97422
[20:49:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.24747 validation-auc:0.97178 validation-aucpr:0.97427
[20:49:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.24542 validation-auc:0.97175 validation-aucpr:0.97427
[20:49:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.24270 validation-auc:0.97181 validation-aucpr:0.97431
[20:49:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.24040 validation-auc:0.97193 validation-aucpr:0.97439
[20:49:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.23850 validation-auc:0.97199 validation-aucpr:0.97449
[20:49:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.23660 validation-auc:0.97195 validation-aucpr:0.97445
[20:49:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.23424 validation-auc:0.97200 validation-aucpr:0.97452
[20:49:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.23257 validation-auc:0.97205 validation-aucpr:0.97477
[20:49:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.23095 validation-auc:0.97210 validation-aucpr:0.97478
[20:49:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.22957 validation-auc:0.97198 validation-aucpr:0.97467
[20:49:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.22759 validation-auc:0.97206 validation-aucpr:0.97473
[20:49:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.22601 validation-auc:0.97213 validation-aucpr:0.97478
{'best_iteration': '56', 'best_score': '0.9747847674977682'}
Trial 52, Fold 2: Log loss = 0.22601226216187362, Average precision = 0.9747905876487796, ROC-AUC = 0.9721342506588131, Elapsed Time = 21.478866199999175 seconds
Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 52, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:49:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66451 validation-auc:0.95725 validation-aucpr:0.96033
[20:49:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63800 validation-auc:0.96229 validation-aucpr:0.96447
[20:49:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61414 validation-auc:0.96422 validation-aucpr:0.96874
[20:49:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59107 validation-auc:0.96563 validation-aucpr:0.97007
[20:49:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57216 validation-auc:0.96613 validation-aucpr:0.97121
[20:49:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55180 validation-auc:0.96675 validation-aucpr:0.97166
[20:49:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53332 validation-auc:0.96747 validation-aucpr:0.97233
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51555 validation-auc:0.96822 validation-aucpr:0.97278
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50039 validation-auc:0.96901 validation-aucpr:0.97352
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.48610 validation-auc:0.96937 validation-aucpr:0.97385
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47158 validation-auc:0.96910 validation-aucpr:0.97361
[20:49:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.45941 validation-auc:0.96941 validation-aucpr:0.97403
[20:49:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44798 validation-auc:0.96949 validation-aucpr:0.97402
[20:49:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.43518 validation-auc:0.96980 validation-aucpr:0.97434
[20:49:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.42490 validation-auc:0.96974 validation-aucpr:0.97433
[20:49:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.41358 validation-auc:0.96965 validation-aucpr:0.97424
[20:49:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.40277 validation-auc:0.96977 validation-aucpr:0.97440
[20:49:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.39425 validation-auc:0.96982 validation-aucpr:0.97433
[20:49:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.38432 validation-auc:0.97021 validation-aucpr:0.97441
[20:49:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.37662 validation-auc:0.97012 validation-aucpr:0.97434
[20:49:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.36744 validation-auc:0.97047 validation-aucpr:0.97351
[20:49:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.35875 validation-auc:0.97064 validation-aucpr:0.97323
[20:49:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.35176 validation-auc:0.97069 validation-aucpr:0.97328
[20:49:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.34417 validation-auc:0.97075 validation-aucpr:0.97339
[20:49:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.33817 validation-auc:0.97067 validation-aucpr:0.97408
[20:49:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.33229 validation-auc:0.97065 validation-aucpr:0.97401
[20:49:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.32554 validation-auc:0.97083 validation-aucpr:0.97409
[20:49:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.31902 validation-auc:0.97109 validation-aucpr:0.97429
[20:49:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.31331 validation-auc:0.97104 validation-aucpr:0.97425
[20:49:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.30833 validation-auc:0.97098 validation-aucpr:0.97418
[20:49:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.30246 validation-auc:0.97118 validation-aucpr:0.97431
[20:49:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.29719 validation-auc:0.97119 validation-aucpr:0.97431
[20:49:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.29300 validation-auc:0.97108 validation-aucpr:0.97421
[20:49:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.28918 validation-auc:0.97104 validation-aucpr:0.97529
[20:49:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.28431 validation-auc:0.97132 validation-aucpr:0.97550
[20:49:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.27978 validation-auc:0.97152 validation-aucpr:0.97562
[20:49:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.27599 validation-auc:0.97155 validation-aucpr:0.97560
[20:49:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.27289 validation-auc:0.97150 validation-aucpr:0.97551
[20:49:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.26900 validation-auc:0.97157 validation-aucpr:0.97559
[20:49:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.26519 validation-auc:0.97166 validation-aucpr:0.97566
[20:49:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.26233 validation-auc:0.97164 validation-aucpr:0.97571
[20:49:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.25952 validation-auc:0.97163 validation-aucpr:0.97569
[20:49:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.25632 validation-auc:0.97160 validation-aucpr:0.97568
[20:49:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.25367 validation-auc:0.97156 validation-aucpr:0.97565
[20:49:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25138 validation-auc:0.97146 validation-aucpr:0.97557
[20:49:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.24817 validation-auc:0.97170 validation-aucpr:0.97576
[20:49:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.24577 validation-auc:0.97185 validation-aucpr:0.97585
[20:49:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.24313 validation-auc:0.97173 validation-aucpr:0.97576
[20:49:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.24058 validation-auc:0.97174 validation-aucpr:0.97577
[20:49:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.23829 validation-auc:0.97173 validation-aucpr:0.97575
[20:49:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.23573 validation-auc:0.97191 validation-aucpr:0.97586
[20:49:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.23354 validation-auc:0.97192 validation-aucpr:0.97586
[20:49:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.23201 validation-auc:0.97182 validation-aucpr:0.97576
[20:49:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.23051 validation-auc:0.97183 validation-aucpr:0.97581
[20:49:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.22883 validation-auc:0.97183 validation-aucpr:0.97583
[20:49:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.22679 validation-auc:0.97198 validation-aucpr:0.97596
[20:49:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.22491 validation-auc:0.97200 validation-aucpr:0.97595
{'best_iteration': '55', 'best_score': '0.975958283888624'}
Trial 52, Fold 3: Log loss = 0.22490842763663127, Average precision = 0.9759580309108026, ROC-AUC = 0.972004386936464, Elapsed Time = 22.18626099999892 seconds
Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 52, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:49:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66434 validation-auc:0.95254 validation-aucpr:0.95559
[20:49:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63778 validation-auc:0.96034 validation-aucpr:0.96585
[20:49:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61346 validation-auc:0.96308 validation-aucpr:0.96743
[20:49:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59333 validation-auc:0.96395 validation-aucpr:0.96976
[20:49:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57452 validation-auc:0.96464 validation-aucpr:0.97037
[20:49:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55681 validation-auc:0.96457 validation-aucpr:0.97030
[20:49:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.54007 validation-auc:0.96426 validation-aucpr:0.97006
[20:49:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52215 validation-auc:0.96498 validation-aucpr:0.97080
[20:49:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50782 validation-auc:0.96467 validation-aucpr:0.97045
[20:49:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.49152 validation-auc:0.96591 validation-aucpr:0.97136
[20:49:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47869 validation-auc:0.96568 validation-aucpr:0.97119
[20:49:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.46647 validation-auc:0.96533 validation-aucpr:0.97093
[20:49:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.45316 validation-auc:0.96580 validation-aucpr:0.97131
[20:49:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.44224 validation-auc:0.96565 validation-aucpr:0.97119
[20:49:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.43202 validation-auc:0.96578 validation-aucpr:0.97123
[20:49:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.42012 validation-auc:0.96630 validation-aucpr:0.97174
[20:49:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.41092 validation-auc:0.96645 validation-aucpr:0.97184
[20:49:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.40176 validation-auc:0.96687 validation-aucpr:0.97215
[20:49:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.39333 validation-auc:0.96701 validation-aucpr:0.97221
[20:49:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.38392 validation-auc:0.96733 validation-aucpr:0.97246
[20:49:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.37640 validation-auc:0.96711 validation-aucpr:0.97227
[20:49:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.36941 validation-auc:0.96707 validation-aucpr:0.97227
[20:49:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.36244 validation-auc:0.96725 validation-aucpr:0.97239
[20:49:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.35444 validation-auc:0.96740 validation-aucpr:0.97258
[20:49:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.34802 validation-auc:0.96748 validation-aucpr:0.97261
[20:49:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.34056 validation-auc:0.96777 validation-aucpr:0.97284
[20:49:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.33341 validation-auc:0.96801 validation-aucpr:0.97304
[20:49:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.32681 validation-auc:0.96820 validation-aucpr:0.97319
[20:49:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.32083 validation-auc:0.96824 validation-aucpr:0.97326
[20:49:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.31482 validation-auc:0.96831 validation-aucpr:0.97336
[20:49:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.31021 validation-auc:0.96825 validation-aucpr:0.97331
[20:49:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.30433 validation-auc:0.96852 validation-aucpr:0.97356
[20:49:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.29905 validation-auc:0.96876 validation-aucpr:0.97377
[20:49:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.29422 validation-auc:0.96890 validation-aucpr:0.97387
[20:49:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.29028 validation-auc:0.96893 validation-aucpr:0.97387
[20:49:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.28677 validation-auc:0.96889 validation-aucpr:0.97384
[20:49:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.28257 validation-auc:0.96871 validation-aucpr:0.97376
[20:49:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.27835 validation-auc:0.96890 validation-aucpr:0.97390
[20:49:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.27537 validation-auc:0.96880 validation-aucpr:0.97384
[20:49:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.27150 validation-auc:0.96897 validation-aucpr:0.97395
[20:49:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.26857 validation-auc:0.96906 validation-aucpr:0.97398
[20:49:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.26477 validation-auc:0.96918 validation-aucpr:0.97409
[20:49:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.26116 validation-auc:0.96949 validation-aucpr:0.97432
[20:49:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.25856 validation-auc:0.96953 validation-aucpr:0.97434
[20:49:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25539 validation-auc:0.96966 validation-aucpr:0.97445
[20:49:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.25305 validation-auc:0.96973 validation-aucpr:0.97451
[20:49:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.25004 validation-auc:0.96990 validation-aucpr:0.97462
[20:49:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.24715 validation-auc:0.97012 validation-aucpr:0.97476
[20:49:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.24458 validation-auc:0.97015 validation-aucpr:0.97480
[20:49:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.24250 validation-auc:0.97010 validation-aucpr:0.97478
[20:49:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.24074 validation-auc:0.97002 validation-aucpr:0.97469
[20:49:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.23841 validation-auc:0.97007 validation-aucpr:0.97472
[20:49:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.23591 validation-auc:0.97021 validation-aucpr:0.97483
[20:49:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.23431 validation-auc:0.97027 validation-aucpr:0.97488
[20:49:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.23217 validation-auc:0.97039 validation-aucpr:0.97498
[20:49:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.23003 validation-auc:0.97055 validation-aucpr:0.97509
[20:49:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.22803 validation-auc:0.97062 validation-aucpr:0.97514
{'best_iteration': '56', 'best_score': '0.9751414398190122'}
Trial 52, Fold 4: Log loss = 0.22802817105373885, Average precision = 0.9751453337884511, ROC-AUC = 0.9706172163923403, Elapsed Time = 26.99545769999895 seconds
Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 52, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:49:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66470 validation-auc:0.95401 validation-aucpr:0.95795
[20:49:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64216 validation-auc:0.95653 validation-aucpr:0.96240
[20:49:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61735 validation-auc:0.96173 validation-aucpr:0.96699
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59767 validation-auc:0.96169 validation-aucpr:0.96656
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57658 validation-auc:0.96331 validation-aucpr:0.96798
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55899 validation-auc:0.96339 validation-aucpr:0.96800
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53999 validation-auc:0.96417 validation-aucpr:0.96870
[20:49:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52215 validation-auc:0.96490 validation-aucpr:0.96959
[20:49:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50791 validation-auc:0.96415 validation-aucpr:0.96899
[20:49:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.49176 validation-auc:0.96486 validation-aucpr:0.96963
[20:50:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47686 validation-auc:0.96543 validation-aucpr:0.97003
[20:50:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.46490 validation-auc:0.96474 validation-aucpr:0.96944
[20:50:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.45146 validation-auc:0.96512 validation-aucpr:0.96976
[20:50:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.43874 validation-auc:0.96543 validation-aucpr:0.97001
[20:50:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.42667 validation-auc:0.96567 validation-aucpr:0.97030
[20:50:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.41711 validation-auc:0.96563 validation-aucpr:0.97005
[20:50:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.40640 validation-auc:0.96605 validation-aucpr:0.97044
[20:50:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.39647 validation-auc:0.96621 validation-aucpr:0.97053
[20:50:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.38828 validation-auc:0.96607 validation-aucpr:0.97040
[20:50:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.37899 validation-auc:0.96642 validation-aucpr:0.97066
[20:50:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.37165 validation-auc:0.96624 validation-aucpr:0.97054
[20:50:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.36370 validation-auc:0.96633 validation-aucpr:0.97063
[20:50:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.35726 validation-auc:0.96638 validation-aucpr:0.97059
[20:50:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.35071 validation-auc:0.96662 validation-aucpr:0.97068
[20:50:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.34446 validation-auc:0.96705 validation-aucpr:0.97098
[20:50:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.33908 validation-auc:0.96713 validation-aucpr:0.97165
[20:50:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.33343 validation-auc:0.96719 validation-aucpr:0.97167
[20:50:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.32804 validation-auc:0.96735 validation-aucpr:0.97180
[20:50:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.32201 validation-auc:0.96756 validation-aucpr:0.97206
[20:50:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.31724 validation-auc:0.96751 validation-aucpr:0.97206
[20:50:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.31139 validation-auc:0.96777 validation-aucpr:0.97230
[20:50:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.30604 validation-auc:0.96790 validation-aucpr:0.97239
[20:50:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.30090 validation-auc:0.96817 validation-aucpr:0.97256
[20:50:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.29658 validation-auc:0.96820 validation-aucpr:0.97259
[20:50:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.29278 validation-auc:0.96830 validation-aucpr:0.97267
[20:50:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.28913 validation-auc:0.96824 validation-aucpr:0.97263
[20:50:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.28489 validation-auc:0.96828 validation-aucpr:0.97269
[20:50:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.28071 validation-auc:0.96844 validation-aucpr:0.97281
[20:50:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.27669 validation-auc:0.96870 validation-aucpr:0.97299
[20:50:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.27340 validation-auc:0.96879 validation-aucpr:0.97303
[20:50:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.27059 validation-auc:0.96874 validation-aucpr:0.97298
[20:50:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.26703 validation-auc:0.96889 validation-aucpr:0.97308
[20:50:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.26359 validation-auc:0.96904 validation-aucpr:0.97318
[20:50:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.26025 validation-auc:0.96928 validation-aucpr:0.97338
[20:50:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25720 validation-auc:0.96940 validation-aucpr:0.97345
[20:50:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.25407 validation-auc:0.96970 validation-aucpr:0.97368
[20:50:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.25126 validation-auc:0.96990 validation-aucpr:0.97384
[20:50:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.24862 validation-auc:0.97006 validation-aucpr:0.97393
[20:50:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.24605 validation-auc:0.97017 validation-aucpr:0.97399
[20:50:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.24363 validation-auc:0.97026 validation-aucpr:0.97403
[20:50:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.24133 validation-auc:0.97037 validation-aucpr:0.97409
[20:50:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.23914 validation-auc:0.97045 validation-aucpr:0.97413
[20:50:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.23750 validation-auc:0.97051 validation-aucpr:0.97417
[20:50:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.23558 validation-auc:0.97052 validation-aucpr:0.97418
[20:50:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.23412 validation-auc:0.97055 validation-aucpr:0.97421
[20:50:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.23225 validation-auc:0.97068 validation-aucpr:0.97429
[20:50:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.23030 validation-auc:0.97080 validation-aucpr:0.97440
{'best_iteration': '56', 'best_score': '0.9744025313585415'}
Trial 52, Fold 5: Log loss = 0.23029926692140149, Average precision = 0.9744071415557151, ROC-AUC = 0.9707958307786633, Elapsed Time = 21.70401050000146 seconds
Optimization Progress: 53%|#####3 | 53/100 [2:51:30<1:09:43, 89.01s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 53, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67632 validation-auc:0.94589 validation-aucpr:0.94841
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66148 validation-auc:0.95293 validation-aucpr:0.95900
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64589 validation-auc:0.95853 validation-aucpr:0.96463
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63284 validation-auc:0.95921 validation-aucpr:0.96494
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62094 validation-auc:0.95847 validation-aucpr:0.96314
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60894 validation-auc:0.95930 validation-aucpr:0.96523
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59628 validation-auc:0.96093 validation-aucpr:0.96676
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58542 validation-auc:0.96114 validation-aucpr:0.96710
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57393 validation-auc:0.96208 validation-aucpr:0.96788
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56345 validation-auc:0.96229 validation-aucpr:0.96812
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55222 validation-auc:0.96262 validation-aucpr:0.96862
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54278 validation-auc:0.96239 validation-aucpr:0.96846
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53371 validation-auc:0.96228 validation-aucpr:0.96838
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52324 validation-auc:0.96283 validation-aucpr:0.96882
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.51355 validation-auc:0.96319 validation-aucpr:0.96906
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.50558 validation-auc:0.96325 validation-aucpr:0.96922
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.49681 validation-auc:0.96341 validation-aucpr:0.96940
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.48831 validation-auc:0.96350 validation-aucpr:0.96942
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48068 validation-auc:0.96373 validation-aucpr:0.96955
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47318 validation-auc:0.96378 validation-aucpr:0.96958
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.46668 validation-auc:0.96368 validation-aucpr:0.96947
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46003 validation-auc:0.96364 validation-aucpr:0.96941
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45362 validation-auc:0.96357 validation-aucpr:0.96944
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.44623 validation-auc:0.96385 validation-aucpr:0.96961
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.43976 validation-auc:0.96388 validation-aucpr:0.96971
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.43409 validation-auc:0.96389 validation-aucpr:0.96967
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.42716 validation-auc:0.96424 validation-aucpr:0.97000
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42124 validation-auc:0.96457 validation-aucpr:0.97005
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.41596 validation-auc:0.96464 validation-aucpr:0.96996
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41002 validation-auc:0.96479 validation-aucpr:0.97008
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.40511 validation-auc:0.96464 validation-aucpr:0.96997
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40070 validation-auc:0.96446 validation-aucpr:0.96982
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.39550 validation-auc:0.96444 validation-aucpr:0.96980
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39124 validation-auc:0.96441 validation-aucpr:0.96975
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.38569 validation-auc:0.96456 validation-aucpr:0.96992
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38114 validation-auc:0.96451 validation-aucpr:0.96829
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.37687 validation-auc:0.96459 validation-aucpr:0.96835
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37190 validation-auc:0.96471 validation-aucpr:0.96848
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.36813 validation-auc:0.96474 validation-aucpr:0.96848
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36308 validation-auc:0.96494 validation-aucpr:0.96866
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.35834 validation-auc:0.96514 validation-aucpr:0.96897
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.35461 validation-auc:0.96518 validation-aucpr:0.96892
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35113 validation-auc:0.96517 validation-aucpr:0.96909
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.34779 validation-auc:0.96522 validation-aucpr:0.96908
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.34344 validation-auc:0.96537 validation-aucpr:0.96930
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.34024 validation-auc:0.96540 validation-aucpr:0.96922
[20:50:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.33733 validation-auc:0.96554 validation-aucpr:0.96927
{'best_iteration': '29', 'best_score': '0.9700769090840297'}
Trial 53, Fold 1: Log loss = 0.33733213328401895, Average precision = 0.9693284708448289, ROC-AUC = 0.9655407298671752, Elapsed Time = 14.781939100001182 seconds
Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 53, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67573 validation-auc:0.94920 validation-aucpr:0.95315
[20:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.65968 validation-auc:0.95868 validation-aucpr:0.96208
[20:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64422 validation-auc:0.96191 validation-aucpr:0.96634
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63089 validation-auc:0.96299 validation-aucpr:0.96643
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.61864 validation-auc:0.96324 validation-aucpr:0.96574
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60691 validation-auc:0.96295 validation-aucpr:0.96554
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59383 validation-auc:0.96408 validation-aucpr:0.96649
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58077 validation-auc:0.96513 validation-aucpr:0.96760
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57019 validation-auc:0.96493 validation-aucpr:0.96914
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.55994 validation-auc:0.96541 validation-aucpr:0.96941
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.54842 validation-auc:0.96596 validation-aucpr:0.96991
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.53914 validation-auc:0.96559 validation-aucpr:0.96961
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53012 validation-auc:0.96579 validation-aucpr:0.96958
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.51986 validation-auc:0.96606 validation-aucpr:0.96976
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.50998 validation-auc:0.96660 validation-aucpr:0.97032
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.50138 validation-auc:0.96677 validation-aucpr:0.97036
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.49209 validation-auc:0.96752 validation-aucpr:0.97099
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.48462 validation-auc:0.96762 validation-aucpr:0.97092
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.47744 validation-auc:0.96737 validation-aucpr:0.97081
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47018 validation-auc:0.96744 validation-aucpr:0.97081
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.46361 validation-auc:0.96717 validation-aucpr:0.97047
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.45684 validation-auc:0.96715 validation-aucpr:0.97041
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45019 validation-auc:0.96725 validation-aucpr:0.97042
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.44259 validation-auc:0.96748 validation-aucpr:0.97081
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.43631 validation-auc:0.96754 validation-aucpr:0.97087
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.43034 validation-auc:0.96749 validation-aucpr:0.97082
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.42453 validation-auc:0.96764 validation-aucpr:0.97090
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.41815 validation-auc:0.96765 validation-aucpr:0.97099
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.41267 validation-auc:0.96777 validation-aucpr:0.97104
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.40704 validation-auc:0.96770 validation-aucpr:0.97096
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.40149 validation-auc:0.96772 validation-aucpr:0.97107
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.39700 validation-auc:0.96769 validation-aucpr:0.97112
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.39173 validation-auc:0.96780 validation-aucpr:0.97134
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.38704 validation-auc:0.96772 validation-aucpr:0.97127
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.38163 validation-auc:0.96789 validation-aucpr:0.97139
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.37734 validation-auc:0.96808 validation-aucpr:0.97150
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.37358 validation-auc:0.96788 validation-aucpr:0.97137
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.36829 validation-auc:0.96821 validation-aucpr:0.97170
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.36403 validation-auc:0.96832 validation-aucpr:0.97182
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.35979 validation-auc:0.96836 validation-aucpr:0.97184
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.35628 validation-auc:0.96826 validation-aucpr:0.97174
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.35248 validation-auc:0.96833 validation-aucpr:0.97175
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.34900 validation-auc:0.96837 validation-aucpr:0.97177
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.34474 validation-auc:0.96842 validation-aucpr:0.97186
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.34168 validation-auc:0.96832 validation-aucpr:0.97177
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.33750 validation-auc:0.96846 validation-aucpr:0.97187
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.33439 validation-auc:0.96850 validation-aucpr:0.97188
{'best_iteration': '46', 'best_score': '0.9718843925984001'}
Trial 53, Fold 2: Log loss = 0.3343866096678563, Average precision = 0.971889660200405, ROC-AUC = 0.9685029261525473, Elapsed Time = 14.036364999999932 seconds
Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 53, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67775 validation-auc:0.92623 validation-aucpr:0.93190
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66221 validation-auc:0.94945 validation-aucpr:0.95479
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64725 validation-auc:0.95836 validation-aucpr:0.96343
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63406 validation-auc:0.96058 validation-aucpr:0.96602
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62068 validation-auc:0.96274 validation-aucpr:0.96742
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60889 validation-auc:0.96230 validation-aucpr:0.96737
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59709 validation-auc:0.96258 validation-aucpr:0.96765
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58609 validation-auc:0.96274 validation-aucpr:0.96778
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57535 validation-auc:0.96206 validation-aucpr:0.96721
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56527 validation-auc:0.96183 validation-aucpr:0.96717
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55486 validation-auc:0.96244 validation-aucpr:0.96783
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54568 validation-auc:0.96230 validation-aucpr:0.96775
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53458 validation-auc:0.96357 validation-aucpr:0.96891
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52590 validation-auc:0.96373 validation-aucpr:0.96895
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.51759 validation-auc:0.96366 validation-aucpr:0.96882
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.50950 validation-auc:0.96373 validation-aucpr:0.96875
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50155 validation-auc:0.96383 validation-aucpr:0.96887
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49388 validation-auc:0.96388 validation-aucpr:0.96882
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48680 validation-auc:0.96368 validation-aucpr:0.96865
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47834 validation-auc:0.96425 validation-aucpr:0.96919
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47048 validation-auc:0.96460 validation-aucpr:0.96943
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.46376 validation-auc:0.96452 validation-aucpr:0.96929
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45724 validation-auc:0.96463 validation-aucpr:0.96936
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.44999 validation-auc:0.96484 validation-aucpr:0.96961
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44236 validation-auc:0.96514 validation-aucpr:0.96994
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.43507 validation-auc:0.96544 validation-aucpr:0.97022
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.42793 validation-auc:0.96577 validation-aucpr:0.97052
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42094 validation-auc:0.96605 validation-aucpr:0.97075
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.41535 validation-auc:0.96615 validation-aucpr:0.97086
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.40925 validation-auc:0.96639 validation-aucpr:0.97108
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.40426 validation-auc:0.96649 validation-aucpr:0.97109
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.39912 validation-auc:0.96651 validation-aucpr:0.97110
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.39422 validation-auc:0.96650 validation-aucpr:0.97107
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.38946 validation-auc:0.96661 validation-aucpr:0.97114
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.38462 validation-auc:0.96681 validation-aucpr:0.97127
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38028 validation-auc:0.96675 validation-aucpr:0.97125
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.37623 validation-auc:0.96664 validation-aucpr:0.97110
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37126 validation-auc:0.96679 validation-aucpr:0.97125
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.36700 validation-auc:0.96691 validation-aucpr:0.97134
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36305 validation-auc:0.96708 validation-aucpr:0.97148
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.35924 validation-auc:0.96716 validation-aucpr:0.97152
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.35566 validation-auc:0.96724 validation-aucpr:0.97160
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35080 validation-auc:0.96744 validation-aucpr:0.97175
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.34738 validation-auc:0.96753 validation-aucpr:0.97191
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.34311 validation-auc:0.96775 validation-aucpr:0.97211
[20:51:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.33961 validation-auc:0.96791 validation-aucpr:0.97223
[20:51:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.33617 validation-auc:0.96799 validation-aucpr:0.97231
{'best_iteration': '46', 'best_score': '0.9723141068097029'}
Trial 53, Fold 3: Log loss = 0.3361675740453154, Average precision = 0.9723190719744764, ROC-AUC = 0.9679867404724987, Elapsed Time = 14.098342500001309 seconds
Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 53, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67702 validation-auc:0.93491 validation-aucpr:0.93034
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66280 validation-auc:0.94355 validation-aucpr:0.95187
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64932 validation-auc:0.95139 validation-aucpr:0.95804
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63610 validation-auc:0.95323 validation-aucpr:0.95974
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62162 validation-auc:0.95866 validation-aucpr:0.96565
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60771 validation-auc:0.96075 validation-aucpr:0.96751
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59461 validation-auc:0.96233 validation-aucpr:0.96874
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58410 validation-auc:0.96215 validation-aucpr:0.96858
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57353 validation-auc:0.96217 validation-aucpr:0.96850
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56189 validation-auc:0.96257 validation-aucpr:0.96891
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55199 validation-auc:0.96250 validation-aucpr:0.96883
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54260 validation-auc:0.96269 validation-aucpr:0.96889
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53381 validation-auc:0.96283 validation-aucpr:0.96897
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52492 validation-auc:0.96280 validation-aucpr:0.96891
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.51647 validation-auc:0.96279 validation-aucpr:0.96880
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.50796 validation-auc:0.96282 validation-aucpr:0.96876
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.49884 validation-auc:0.96301 validation-aucpr:0.96916
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49076 validation-auc:0.96281 validation-aucpr:0.96905
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48326 validation-auc:0.96272 validation-aucpr:0.96896
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47504 validation-auc:0.96276 validation-aucpr:0.96906
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.46706 validation-auc:0.96269 validation-aucpr:0.96905
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.45952 validation-auc:0.96323 validation-aucpr:0.96948
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45246 validation-auc:0.96361 validation-aucpr:0.96978
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.44570 validation-auc:0.96380 validation-aucpr:0.96992
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.43919 validation-auc:0.96385 validation-aucpr:0.96995
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.43360 validation-auc:0.96398 validation-aucpr:0.97000
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.42658 validation-auc:0.96427 validation-aucpr:0.97027
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42000 validation-auc:0.96432 validation-aucpr:0.97036
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.41452 validation-auc:0.96465 validation-aucpr:0.97057
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.40892 validation-auc:0.96460 validation-aucpr:0.97052
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.40397 validation-auc:0.96454 validation-aucpr:0.97046
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.39767 validation-auc:0.96497 validation-aucpr:0.97086
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.39332 validation-auc:0.96462 validation-aucpr:0.97059
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.38872 validation-auc:0.96480 validation-aucpr:0.97067
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.38339 validation-auc:0.96496 validation-aucpr:0.97081
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.37866 validation-auc:0.96503 validation-aucpr:0.97092
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.37347 validation-auc:0.96516 validation-aucpr:0.97106
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.36946 validation-auc:0.96513 validation-aucpr:0.97101
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.36529 validation-auc:0.96510 validation-aucpr:0.97099
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36120 validation-auc:0.96525 validation-aucpr:0.97108
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.35756 validation-auc:0.96525 validation-aucpr:0.97106
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.35406 validation-auc:0.96516 validation-aucpr:0.97098
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35051 validation-auc:0.96523 validation-aucpr:0.97104
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.34581 validation-auc:0.96560 validation-aucpr:0.97133
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.34210 validation-auc:0.96570 validation-aucpr:0.97138
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.33803 validation-auc:0.96585 validation-aucpr:0.97154
[20:51:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.33491 validation-auc:0.96588 validation-aucpr:0.97157
{'best_iteration': '46', 'best_score': '0.9715733178238387'}
Trial 53, Fold 4: Log loss = 0.3349134832867913, Average precision = 0.9715754250844988, ROC-AUC = 0.9658764136569453, Elapsed Time = 14.17093129999921 seconds
Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 53, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67761 validation-auc:0.92559 validation-aucpr:0.93521
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66160 validation-auc:0.94911 validation-aucpr:0.95521
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64580 validation-auc:0.95579 validation-aucpr:0.96039
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63119 validation-auc:0.95911 validation-aucpr:0.96446
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.61837 validation-auc:0.96096 validation-aucpr:0.96666
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.60470 validation-auc:0.96196 validation-aucpr:0.96778
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59281 validation-auc:0.96181 validation-aucpr:0.96750
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58173 validation-auc:0.96163 validation-aucpr:0.96734
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57113 validation-auc:0.96146 validation-aucpr:0.96730
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56089 validation-auc:0.96106 validation-aucpr:0.96693
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55140 validation-auc:0.96111 validation-aucpr:0.96690
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54228 validation-auc:0.96119 validation-aucpr:0.96697
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53211 validation-auc:0.96151 validation-aucpr:0.96735
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52333 validation-auc:0.96161 validation-aucpr:0.96735
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.51356 validation-auc:0.96157 validation-aucpr:0.96731
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.50541 validation-auc:0.96162 validation-aucpr:0.96732
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.49766 validation-auc:0.96148 validation-aucpr:0.96727
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.48856 validation-auc:0.96187 validation-aucpr:0.96758
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.48063 validation-auc:0.96190 validation-aucpr:0.96770
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.47375 validation-auc:0.96196 validation-aucpr:0.96765
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.46592 validation-auc:0.96221 validation-aucpr:0.96809
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.45840 validation-auc:0.96218 validation-aucpr:0.96814
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.45195 validation-auc:0.96233 validation-aucpr:0.96821
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.44622 validation-auc:0.96239 validation-aucpr:0.96822
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.44048 validation-auc:0.96234 validation-aucpr:0.96815
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.43470 validation-auc:0.96242 validation-aucpr:0.96817
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.42863 validation-auc:0.96268 validation-aucpr:0.96836
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.42240 validation-auc:0.96286 validation-aucpr:0.96852
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.41708 validation-auc:0.96300 validation-aucpr:0.96869
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.41209 validation-auc:0.96303 validation-aucpr:0.96867
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.40701 validation-auc:0.96324 validation-aucpr:0.96880
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.40188 validation-auc:0.96314 validation-aucpr:0.96867
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.39720 validation-auc:0.96308 validation-aucpr:0.96862
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.39304 validation-auc:0.96298 validation-aucpr:0.96856
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.38861 validation-auc:0.96301 validation-aucpr:0.96856
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.38322 validation-auc:0.96321 validation-aucpr:0.96870
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.37904 validation-auc:0.96332 validation-aucpr:0.96877
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.37502 validation-auc:0.96350 validation-aucpr:0.96890
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37055 validation-auc:0.96355 validation-aucpr:0.96892
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.36638 validation-auc:0.96373 validation-aucpr:0.96907
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.36252 validation-auc:0.96369 validation-aucpr:0.96903
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.35847 validation-auc:0.96383 validation-aucpr:0.96910
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.35410 validation-auc:0.96389 validation-aucpr:0.96917
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.35066 validation-auc:0.96391 validation-aucpr:0.96918
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.34746 validation-auc:0.96392 validation-aucpr:0.96917
[20:51:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.34420 validation-auc:0.96404 validation-aucpr:0.96918
[20:51:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.34149 validation-auc:0.96385 validation-aucpr:0.96885
{'best_iteration': '43', 'best_score': '0.9691820538568151'}
Trial 53, Fold 5: Log loss = 0.34148940877067663, Average precision = 0.9688621797793956, ROC-AUC = 0.9638521821526114, Elapsed Time = 16.684623099998134 seconds
Optimization Progress: 54%|#####4 | 54/100 [2:52:52<1:06:36, 86.88s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 54, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67402 validation-auc:0.92686 validation-aucpr:0.90773
[1] validation-logloss:0.65665 validation-auc:0.95221 validation-aucpr:0.95385
[2] validation-logloss:0.63894 validation-auc:0.95776 validation-aucpr:0.95910
[3] validation-logloss:0.62399 validation-auc:0.95890 validation-aucpr:0.96099
[4] validation-logloss:0.60903 validation-auc:0.96046 validation-aucpr:0.96456
[5] validation-logloss:0.59579 validation-auc:0.96061 validation-aucpr:0.96533
[6] validation-logloss:0.58195 validation-auc:0.96187 validation-aucpr:0.96646
[7] validation-logloss:0.56959 validation-auc:0.96171 validation-aucpr:0.96615
[8] validation-logloss:0.55747 validation-auc:0.96237 validation-aucpr:0.96731
[9] validation-logloss:0.54655 validation-auc:0.96174 validation-aucpr:0.96680
[10] validation-logloss:0.53579 validation-auc:0.96194 validation-aucpr:0.96694
[11] validation-logloss:0.52545 validation-auc:0.96189 validation-aucpr:0.96689
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[14] validation-logloss:0.49390 validation-auc:0.96352 validation-aucpr:0.96877
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[18] validation-logloss:0.45530 validation-auc:0.96477 validation-aucpr:0.97030
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[84] validation-logloss:0.23751 validation-auc:0.96854 validation-aucpr:0.97350
[85] validation-logloss:0.23629 validation-auc:0.96856 validation-aucpr:0.97352
[86] validation-logloss:0.23534 validation-auc:0.96866 validation-aucpr:0.97359
[87] validation-logloss:0.23437 validation-auc:0.96866 validation-aucpr:0.97361
[88] validation-logloss:0.23289 validation-auc:0.96879 validation-aucpr:0.97375
[89] validation-logloss:0.23202 validation-auc:0.96887 validation-aucpr:0.97380
[90] validation-logloss:0.23120 validation-auc:0.96888 validation-aucpr:0.97379
[91] validation-logloss:0.23058 validation-auc:0.96886 validation-aucpr:0.97378
[92] validation-logloss:0.22989 validation-auc:0.96886 validation-aucpr:0.97376
[93] validation-logloss:0.22904 validation-auc:0.96883 validation-aucpr:0.97373
[94] validation-logloss:0.22831 validation-auc:0.96885 validation-aucpr:0.97375
[95] validation-logloss:0.22726 validation-auc:0.96888 validation-aucpr:0.97380
[96] validation-logloss:0.22649 validation-auc:0.96887 validation-aucpr:0.97379
[97] validation-logloss:0.22585 validation-auc:0.96889 validation-aucpr:0.97380
{'best_iteration': '89', 'best_score': '0.9738037843926355'}
Trial 54, Fold 1: Log loss = 0.22584576867637168, Average precision = 0.973802441655735, ROC-AUC = 0.9688949559799831, Elapsed Time = 16.8418285000007 seconds
Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 54, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67522 validation-auc:0.92656 validation-aucpr:0.89492
[1] validation-logloss:0.65811 validation-auc:0.94887 validation-aucpr:0.94476
[2] validation-logloss:0.64196 validation-auc:0.95633 validation-aucpr:0.95859
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[25] validation-logloss:0.40124 validation-auc:0.96843 validation-aucpr:0.97231
[26] validation-logloss:0.39539 validation-auc:0.96838 validation-aucpr:0.97225
[27] validation-logloss:0.38944 validation-auc:0.96842 validation-aucpr:0.97226
[28] validation-logloss:0.38287 validation-auc:0.96860 validation-aucpr:0.97215
[29] validation-logloss:0.37775 validation-auc:0.96844 validation-aucpr:0.97207
[30] validation-logloss:0.37171 validation-auc:0.96849 validation-aucpr:0.97210
[31] validation-logloss:0.36712 validation-auc:0.96843 validation-aucpr:0.97201
[32] validation-logloss:0.36136 validation-auc:0.96856 validation-aucpr:0.97214
[33] validation-logloss:0.35663 validation-auc:0.96851 validation-aucpr:0.97209
[34] validation-logloss:0.35220 validation-auc:0.96842 validation-aucpr:0.97202
[35] validation-logloss:0.34799 validation-auc:0.96845 validation-aucpr:0.97200
[36] validation-logloss:0.34329 validation-auc:0.96840 validation-aucpr:0.97186
[37] validation-logloss:0.33920 validation-auc:0.96850 validation-aucpr:0.97192
[38] validation-logloss:0.33552 validation-auc:0.96848 validation-aucpr:0.97188
[39] validation-logloss:0.33156 validation-auc:0.96849 validation-aucpr:0.97185
[40] validation-logloss:0.32808 validation-auc:0.96847 validation-aucpr:0.97180
[41] validation-logloss:0.32406 validation-auc:0.96856 validation-aucpr:0.97228
[42] validation-logloss:0.32084 validation-auc:0.96859 validation-aucpr:0.97228
[43] validation-logloss:0.31754 validation-auc:0.96860 validation-aucpr:0.97232
[44] validation-logloss:0.31378 validation-auc:0.96866 validation-aucpr:0.97232
[45] validation-logloss:0.31037 validation-auc:0.96885 validation-aucpr:0.97248
[46] validation-logloss:0.30661 validation-auc:0.96894 validation-aucpr:0.97256
[47] validation-logloss:0.30283 validation-auc:0.96909 validation-aucpr:0.97268
[48] validation-logloss:0.29991 validation-auc:0.96918 validation-aucpr:0.97277
[49] validation-logloss:0.29734 validation-auc:0.96921 validation-aucpr:0.97279
[50] validation-logloss:0.29465 validation-auc:0.96927 validation-aucpr:0.97283
[51] validation-logloss:0.29206 validation-auc:0.96932 validation-aucpr:0.97286
[52] validation-logloss:0.28873 validation-auc:0.96945 validation-aucpr:0.97297
[53] validation-logloss:0.28627 validation-auc:0.96947 validation-aucpr:0.97302
[54] validation-logloss:0.28399 validation-auc:0.96945 validation-aucpr:0.97302
[55] validation-logloss:0.28163 validation-auc:0.96953 validation-aucpr:0.97309
[56] validation-logloss:0.27917 validation-auc:0.96958 validation-aucpr:0.97314
[57] validation-logloss:0.27709 validation-auc:0.96955 validation-aucpr:0.97314
[58] validation-logloss:0.27493 validation-auc:0.96962 validation-aucpr:0.97319
[59] validation-logloss:0.27278 validation-auc:0.96964 validation-aucpr:0.97317
[60] validation-logloss:0.27013 validation-auc:0.96970 validation-aucpr:0.97324
[61] validation-logloss:0.26728 validation-auc:0.96988 validation-aucpr:0.97340
[62] validation-logloss:0.26483 validation-auc:0.96992 validation-aucpr:0.97343
[63] validation-logloss:0.26308 validation-auc:0.96991 validation-aucpr:0.97342
[64] validation-logloss:0.26133 validation-auc:0.97007 validation-aucpr:0.97356
[65] validation-logloss:0.25988 validation-auc:0.97002 validation-aucpr:0.97349
[66] validation-logloss:0.25756 validation-auc:0.97008 validation-aucpr:0.97356
[67] validation-logloss:0.25538 validation-auc:0.97010 validation-aucpr:0.97358
[68] validation-logloss:0.25331 validation-auc:0.97014 validation-aucpr:0.97360
[69] validation-logloss:0.25175 validation-auc:0.97017 validation-aucpr:0.97362
[70] validation-logloss:0.25011 validation-auc:0.97017 validation-aucpr:0.97368
[71] validation-logloss:0.24876 validation-auc:0.97017 validation-aucpr:0.97372
[72] validation-logloss:0.24721 validation-auc:0.97027 validation-aucpr:0.97380
[73] validation-logloss:0.24586 validation-auc:0.97027 validation-aucpr:0.97381
[74] validation-logloss:0.24462 validation-auc:0.97024 validation-aucpr:0.97379
[75] validation-logloss:0.24310 validation-auc:0.97024 validation-aucpr:0.97379
[76] validation-logloss:0.24121 validation-auc:0.97038 validation-aucpr:0.97387
[77] validation-logloss:0.24006 validation-auc:0.97034 validation-aucpr:0.97384
[78] validation-logloss:0.23882 validation-auc:0.97043 validation-aucpr:0.97388
[79] validation-logloss:0.23816 validation-auc:0.97034 validation-aucpr:0.97379
[80] validation-logloss:0.23679 validation-auc:0.97042 validation-aucpr:0.97384
[81] validation-logloss:0.23571 validation-auc:0.97042 validation-aucpr:0.97385
[82] validation-logloss:0.23462 validation-auc:0.97046 validation-aucpr:0.97388
[83] validation-logloss:0.23341 validation-auc:0.97048 validation-aucpr:0.97389
[84] validation-logloss:0.23235 validation-auc:0.97050 validation-aucpr:0.97391
[85] validation-logloss:0.23139 validation-auc:0.97053 validation-aucpr:0.97393
[86] validation-logloss:0.22999 validation-auc:0.97057 validation-aucpr:0.97394
[87] validation-logloss:0.22901 validation-auc:0.97061 validation-aucpr:0.97397
[88] validation-logloss:0.22774 validation-auc:0.97067 validation-aucpr:0.97401
[89] validation-logloss:0.22632 validation-auc:0.97074 validation-aucpr:0.97407
[90] validation-logloss:0.22496 validation-auc:0.97078 validation-aucpr:0.97411
[91] validation-logloss:0.22376 validation-auc:0.97082 validation-aucpr:0.97413
[92] validation-logloss:0.22266 validation-auc:0.97078 validation-aucpr:0.97411
[93] validation-logloss:0.22168 validation-auc:0.97082 validation-aucpr:0.97415
[94] validation-logloss:0.22109 validation-auc:0.97083 validation-aucpr:0.97416
[95] validation-logloss:0.22027 validation-auc:0.97093 validation-aucpr:0.97423
[96] validation-logloss:0.21954 validation-auc:0.97097 validation-aucpr:0.97425
[97] validation-logloss:0.21868 validation-auc:0.97099 validation-aucpr:0.97426
{'best_iteration': '97', 'best_score': '0.9742619592192957'}
Trial 54, Fold 2: Log loss = 0.21867937219408498, Average precision = 0.9742667819552169, ROC-AUC = 0.9709876443918116, Elapsed Time = 16.60435570000118 seconds
Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 54, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67524 validation-auc:0.92396 validation-aucpr:0.89424
[1] validation-logloss:0.65846 validation-auc:0.94801 validation-aucpr:0.95016
[2] validation-logloss:0.64193 validation-auc:0.95385 validation-aucpr:0.95537
[3] validation-logloss:0.62614 validation-auc:0.95819 validation-aucpr:0.96162
[4] validation-logloss:0.61126 validation-auc:0.96074 validation-aucpr:0.96425
[5] validation-logloss:0.59561 validation-auc:0.96404 validation-aucpr:0.96802
[6] validation-logloss:0.58261 validation-auc:0.96449 validation-aucpr:0.96775
[7] validation-logloss:0.56775 validation-auc:0.96627 validation-aucpr:0.96947
[8] validation-logloss:0.55604 validation-auc:0.96620 validation-aucpr:0.96949
[9] validation-logloss:0.54348 validation-auc:0.96647 validation-aucpr:0.96967
[10] validation-logloss:0.53034 validation-auc:0.96721 validation-aucpr:0.97044
[11] validation-logloss:0.51804 validation-auc:0.96784 validation-aucpr:0.97103
[12] validation-logloss:0.50665 validation-auc:0.96822 validation-aucpr:0.97146
[13] validation-logloss:0.49634 validation-auc:0.96878 validation-aucpr:0.97188
[14] validation-logloss:0.48723 validation-auc:0.96862 validation-aucpr:0.97179
[15] validation-logloss:0.47821 validation-auc:0.96857 validation-aucpr:0.97157
[16] validation-logloss:0.46797 validation-auc:0.96877 validation-aucpr:0.97190
[17] validation-logloss:0.45799 validation-auc:0.96896 validation-aucpr:0.97213
[18] validation-logloss:0.44867 validation-auc:0.96926 validation-aucpr:0.97285
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[31] validation-logloss:0.36485 validation-auc:0.96962 validation-aucpr:0.97339
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[34] validation-logloss:0.35135 validation-auc:0.96977 validation-aucpr:0.97338
[35] validation-logloss:0.34602 validation-auc:0.96982 validation-aucpr:0.97342
[36] validation-logloss:0.34194 validation-auc:0.96983 validation-aucpr:0.97350
[37] validation-logloss:0.33722 validation-auc:0.96978 validation-aucpr:0.97354
[38] validation-logloss:0.33216 validation-auc:0.96997 validation-aucpr:0.97371
[39] validation-logloss:0.32811 validation-auc:0.97007 validation-aucpr:0.97379
[40] validation-logloss:0.32419 validation-auc:0.97007 validation-aucpr:0.97380
[41] validation-logloss:0.32110 validation-auc:0.96992 validation-aucpr:0.97362
[42] validation-logloss:0.31704 validation-auc:0.97005 validation-aucpr:0.97382
[43] validation-logloss:0.31408 validation-auc:0.96999 validation-aucpr:0.97369
[44] validation-logloss:0.31104 validation-auc:0.96992 validation-aucpr:0.97365
[45] validation-logloss:0.30818 validation-auc:0.96974 validation-aucpr:0.97349
[46] validation-logloss:0.30430 validation-auc:0.96977 validation-aucpr:0.97357
[47] validation-logloss:0.30137 validation-auc:0.96981 validation-aucpr:0.97358
[48] validation-logloss:0.29852 validation-auc:0.96992 validation-aucpr:0.97391
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[50] validation-logloss:0.29319 validation-auc:0.96993 validation-aucpr:0.97394
[51] validation-logloss:0.29075 validation-auc:0.96994 validation-aucpr:0.97396
[52] validation-logloss:0.28804 validation-auc:0.96994 validation-aucpr:0.97397
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[55] validation-logloss:0.27964 validation-auc:0.97006 validation-aucpr:0.97415
[56] validation-logloss:0.27751 validation-auc:0.97008 validation-aucpr:0.97416
[57] validation-logloss:0.27550 validation-auc:0.97004 validation-aucpr:0.97412
[58] validation-logloss:0.27279 validation-auc:0.97012 validation-aucpr:0.97419
[59] validation-logloss:0.27051 validation-auc:0.97017 validation-aucpr:0.97422
[60] validation-logloss:0.26818 validation-auc:0.97017 validation-aucpr:0.97420
[61] validation-logloss:0.26628 validation-auc:0.97020 validation-aucpr:0.97422
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[63] validation-logloss:0.26320 validation-auc:0.97019 validation-aucpr:0.97423
[64] validation-logloss:0.26143 validation-auc:0.97022 validation-aucpr:0.97423
[65] validation-logloss:0.25902 validation-auc:0.97030 validation-aucpr:0.97431
[66] validation-logloss:0.25731 validation-auc:0.97036 validation-aucpr:0.97434
[67] validation-logloss:0.25585 validation-auc:0.97030 validation-aucpr:0.97426
[68] validation-logloss:0.25424 validation-auc:0.97026 validation-aucpr:0.97423
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[70] validation-logloss:0.25107 validation-auc:0.97032 validation-aucpr:0.97429
[71] validation-logloss:0.24924 validation-auc:0.97039 validation-aucpr:0.97437
[72] validation-logloss:0.24783 validation-auc:0.97040 validation-aucpr:0.97437
[73] validation-logloss:0.24584 validation-auc:0.97048 validation-aucpr:0.97444
[74] validation-logloss:0.24439 validation-auc:0.97049 validation-aucpr:0.97445
[75] validation-logloss:0.24255 validation-auc:0.97059 validation-aucpr:0.97452
[76] validation-logloss:0.24085 validation-auc:0.97065 validation-aucpr:0.97456
[77] validation-logloss:0.23904 validation-auc:0.97080 validation-aucpr:0.97478
[78] validation-logloss:0.23760 validation-auc:0.97091 validation-aucpr:0.97487
[79] validation-logloss:0.23592 validation-auc:0.97098 validation-aucpr:0.97497
[80] validation-logloss:0.23427 validation-auc:0.97103 validation-aucpr:0.97501
[81] validation-logloss:0.23324 validation-auc:0.97102 validation-aucpr:0.97499
[82] validation-logloss:0.23173 validation-auc:0.97103 validation-aucpr:0.97499
[83] validation-logloss:0.23059 validation-auc:0.97106 validation-aucpr:0.97501
[84] validation-logloss:0.22960 validation-auc:0.97111 validation-aucpr:0.97509
[85] validation-logloss:0.22811 validation-auc:0.97117 validation-aucpr:0.97516
[86] validation-logloss:0.22683 validation-auc:0.97122 validation-aucpr:0.97520
[87] validation-logloss:0.22602 validation-auc:0.97124 validation-aucpr:0.97518
[88] validation-logloss:0.22458 validation-auc:0.97134 validation-aucpr:0.97526
[89] validation-logloss:0.22331 validation-auc:0.97138 validation-aucpr:0.97530
[90] validation-logloss:0.22259 validation-auc:0.97134 validation-aucpr:0.97526
[91] validation-logloss:0.22159 validation-auc:0.97143 validation-aucpr:0.97533
[92] validation-logloss:0.22106 validation-auc:0.97136 validation-aucpr:0.97530
[93] validation-logloss:0.21988 validation-auc:0.97144 validation-aucpr:0.97535
[94] validation-logloss:0.21889 validation-auc:0.97146 validation-aucpr:0.97535
[95] validation-logloss:0.21819 validation-auc:0.97144 validation-aucpr:0.97532
[96] validation-logloss:0.21744 validation-auc:0.97146 validation-aucpr:0.97533
[97] validation-logloss:0.21686 validation-auc:0.97142 validation-aucpr:0.97529
{'best_iteration': '93', 'best_score': '0.9753493181523499'}
Trial 54, Fold 3: Log loss = 0.216855898710009, Average precision = 0.9752910810816002, ROC-AUC = 0.97141651758627, Elapsed Time = 16.7057109999987 seconds
Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 54, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67554 validation-auc:0.91778 validation-aucpr:0.88855
[1] validation-logloss:0.65872 validation-auc:0.94242 validation-aucpr:0.93678
[2] validation-logloss:0.64088 validation-auc:0.95546 validation-aucpr:0.95899
[3] validation-logloss:0.62504 validation-auc:0.95806 validation-aucpr:0.96390
[4] validation-logloss:0.60880 validation-auc:0.96062 validation-aucpr:0.96698
[5] validation-logloss:0.59336 validation-auc:0.96235 validation-aucpr:0.96854
[6] validation-logloss:0.57992 validation-auc:0.96340 validation-aucpr:0.96906
[7] validation-logloss:0.56731 validation-auc:0.96386 validation-aucpr:0.96927
[8] validation-logloss:0.55493 validation-auc:0.96421 validation-aucpr:0.96956
[9] validation-logloss:0.54190 validation-auc:0.96486 validation-aucpr:0.97034
[10] validation-logloss:0.53016 validation-auc:0.96501 validation-aucpr:0.97046
[11] validation-logloss:0.52017 validation-auc:0.96464 validation-aucpr:0.97019
[12] validation-logloss:0.51020 validation-auc:0.96484 validation-aucpr:0.97031
[13] validation-logloss:0.50048 validation-auc:0.96491 validation-aucpr:0.97030
[14] validation-logloss:0.49158 validation-auc:0.96464 validation-aucpr:0.97017
[15] validation-logloss:0.48089 validation-auc:0.96528 validation-aucpr:0.97076
[16] validation-logloss:0.47242 validation-auc:0.96512 validation-aucpr:0.97063
[17] validation-logloss:0.46419 validation-auc:0.96507 validation-aucpr:0.97056
[18] validation-logloss:0.45609 validation-auc:0.96506 validation-aucpr:0.97059
[19] validation-logloss:0.44869 validation-auc:0.96526 validation-aucpr:0.97067
[20] validation-logloss:0.44161 validation-auc:0.96540 validation-aucpr:0.97076
[21] validation-logloss:0.43445 validation-auc:0.96546 validation-aucpr:0.97082
[22] validation-logloss:0.42596 validation-auc:0.96595 validation-aucpr:0.97127
[23] validation-logloss:0.41926 validation-auc:0.96579 validation-aucpr:0.97114
[24] validation-logloss:0.41342 validation-auc:0.96569 validation-aucpr:0.97106
[25] validation-logloss:0.40593 validation-auc:0.96594 validation-aucpr:0.97135
[26] validation-logloss:0.40029 validation-auc:0.96594 validation-aucpr:0.97134
[27] validation-logloss:0.39387 validation-auc:0.96610 validation-aucpr:0.97152
[28] validation-logloss:0.38821 validation-auc:0.96621 validation-aucpr:0.97162
[29] validation-logloss:0.38323 validation-auc:0.96608 validation-aucpr:0.97149
[30] validation-logloss:0.37689 validation-auc:0.96615 validation-aucpr:0.97160
[31] validation-logloss:0.37241 validation-auc:0.96604 validation-aucpr:0.97153
[32] validation-logloss:0.36732 validation-auc:0.96623 validation-aucpr:0.97167
[33] validation-logloss:0.36288 validation-auc:0.96619 validation-aucpr:0.97164
[34] validation-logloss:0.35861 validation-auc:0.96615 validation-aucpr:0.97163
[35] validation-logloss:0.35427 validation-auc:0.96629 validation-aucpr:0.97171
[36] validation-logloss:0.34893 validation-auc:0.96643 validation-aucpr:0.97186
[37] validation-logloss:0.34525 validation-auc:0.96639 validation-aucpr:0.97184
[38] validation-logloss:0.34154 validation-auc:0.96631 validation-aucpr:0.97176
[39] validation-logloss:0.33762 validation-auc:0.96633 validation-aucpr:0.97176
[40] validation-logloss:0.33412 validation-auc:0.96649 validation-aucpr:0.97186
[41] validation-logloss:0.32961 validation-auc:0.96671 validation-aucpr:0.97202
[42] validation-logloss:0.32605 validation-auc:0.96677 validation-aucpr:0.97206
[43] validation-logloss:0.32257 validation-auc:0.96671 validation-aucpr:0.97202
[44] validation-logloss:0.31934 validation-auc:0.96681 validation-aucpr:0.97208
[45] validation-logloss:0.31627 validation-auc:0.96676 validation-aucpr:0.97206
[46] validation-logloss:0.31350 validation-auc:0.96667 validation-aucpr:0.97200
[47] validation-logloss:0.30990 validation-auc:0.96671 validation-aucpr:0.97207
[48] validation-logloss:0.30612 validation-auc:0.96690 validation-aucpr:0.97224
[49] validation-logloss:0.30380 validation-auc:0.96682 validation-aucpr:0.97216
[50] validation-logloss:0.30024 validation-auc:0.96689 validation-aucpr:0.97225
[51] validation-logloss:0.29675 validation-auc:0.96694 validation-aucpr:0.97233
[52] validation-logloss:0.29401 validation-auc:0.96695 validation-aucpr:0.97236
[53] validation-logloss:0.29218 validation-auc:0.96680 validation-aucpr:0.97224
[54] validation-logloss:0.28912 validation-auc:0.96694 validation-aucpr:0.97234
[55] validation-logloss:0.28648 validation-auc:0.96711 validation-aucpr:0.97246
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[60] validation-logloss:0.27568 validation-auc:0.96730 validation-aucpr:0.97261
[61] validation-logloss:0.27386 validation-auc:0.96736 validation-aucpr:0.97264
[62] validation-logloss:0.27181 validation-auc:0.96737 validation-aucpr:0.97264
[63] validation-logloss:0.26910 validation-auc:0.96750 validation-aucpr:0.97275
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[65] validation-logloss:0.26446 validation-auc:0.96759 validation-aucpr:0.97289
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[70] validation-logloss:0.25649 validation-auc:0.96773 validation-aucpr:0.97297
[71] validation-logloss:0.25527 validation-auc:0.96774 validation-aucpr:0.97299
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[76] validation-logloss:0.24712 validation-auc:0.96812 validation-aucpr:0.97330
[77] validation-logloss:0.24584 validation-auc:0.96816 validation-aucpr:0.97333
[78] validation-logloss:0.24388 validation-auc:0.96827 validation-aucpr:0.97345
[79] validation-logloss:0.24202 validation-auc:0.96842 validation-aucpr:0.97357
[80] validation-logloss:0.24088 validation-auc:0.96842 validation-aucpr:0.97356
[81] validation-logloss:0.23949 validation-auc:0.96850 validation-aucpr:0.97362
[82] validation-logloss:0.23853 validation-auc:0.96847 validation-aucpr:0.97361
[83] validation-logloss:0.23703 validation-auc:0.96853 validation-aucpr:0.97368
[84] validation-logloss:0.23580 validation-auc:0.96862 validation-aucpr:0.97373
[85] validation-logloss:0.23491 validation-auc:0.96861 validation-aucpr:0.97371
[86] validation-logloss:0.23346 validation-auc:0.96872 validation-aucpr:0.97380
[87] validation-logloss:0.23187 validation-auc:0.96883 validation-aucpr:0.97389
[88] validation-logloss:0.23043 validation-auc:0.96891 validation-aucpr:0.97397
[89] validation-logloss:0.22913 validation-auc:0.96892 validation-aucpr:0.97398
[90] validation-logloss:0.22798 validation-auc:0.96898 validation-aucpr:0.97404
[91] validation-logloss:0.22705 validation-auc:0.96903 validation-aucpr:0.97408
[92] validation-logloss:0.22650 validation-auc:0.96900 validation-aucpr:0.97405
[93] validation-logloss:0.22575 validation-auc:0.96899 validation-aucpr:0.97405
[94] validation-logloss:0.22502 validation-auc:0.96897 validation-aucpr:0.97404
[95] validation-logloss:0.22394 validation-auc:0.96902 validation-aucpr:0.97408
[96] validation-logloss:0.22324 validation-auc:0.96904 validation-aucpr:0.97409
[97] validation-logloss:0.22246 validation-auc:0.96912 validation-aucpr:0.97414
{'best_iteration': '97', 'best_score': '0.9741356325292141'}
Trial 54, Fold 4: Log loss = 0.22246427317922213, Average precision = 0.9741398406367417, ROC-AUC = 0.9691153362852326, Elapsed Time = 16.831737099997554 seconds
Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 54, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67576 validation-auc:0.92141 validation-aucpr:0.90680
[1] validation-logloss:0.65868 validation-auc:0.94379 validation-aucpr:0.94201
[2] validation-logloss:0.64288 validation-auc:0.94958 validation-aucpr:0.95391
[3] validation-logloss:0.62788 validation-auc:0.95220 validation-aucpr:0.95693
[4] validation-logloss:0.61293 validation-auc:0.95519 validation-aucpr:0.95990
[5] validation-logloss:0.59880 validation-auc:0.95692 validation-aucpr:0.96154
[6] validation-logloss:0.58422 validation-auc:0.96008 validation-aucpr:0.96514
[7] validation-logloss:0.57016 validation-auc:0.96099 validation-aucpr:0.96611
[8] validation-logloss:0.55795 validation-auc:0.96134 validation-aucpr:0.96635
[9] validation-logloss:0.54675 validation-auc:0.96150 validation-aucpr:0.96630
[10] validation-logloss:0.53505 validation-auc:0.96201 validation-aucpr:0.96663
[11] validation-logloss:0.52494 validation-auc:0.96208 validation-aucpr:0.96661
[12] validation-logloss:0.51543 validation-auc:0.96197 validation-aucpr:0.96645
[13] validation-logloss:0.50561 validation-auc:0.96211 validation-aucpr:0.96653
[14] validation-logloss:0.49574 validation-auc:0.96242 validation-aucpr:0.96734
[15] validation-logloss:0.48703 validation-auc:0.96240 validation-aucpr:0.96740
[16] validation-logloss:0.47656 validation-auc:0.96300 validation-aucpr:0.96812
[17] validation-logloss:0.46794 validation-auc:0.96304 validation-aucpr:0.96814
[18] validation-logloss:0.46004 validation-auc:0.96311 validation-aucpr:0.96813
[19] validation-logloss:0.45223 validation-auc:0.96300 validation-aucpr:0.96807
[20] validation-logloss:0.44368 validation-auc:0.96322 validation-aucpr:0.96837
[21] validation-logloss:0.43655 validation-auc:0.96348 validation-aucpr:0.96854
[22] validation-logloss:0.42984 validation-auc:0.96356 validation-aucpr:0.96860
[23] validation-logloss:0.42203 validation-auc:0.96386 validation-aucpr:0.96891
[24] validation-logloss:0.41437 validation-auc:0.96411 validation-aucpr:0.96910
[25] validation-logloss:0.40845 validation-auc:0.96412 validation-aucpr:0.96909
[26] validation-logloss:0.40288 validation-auc:0.96408 validation-aucpr:0.96894
[27] validation-logloss:0.39599 validation-auc:0.96430 validation-aucpr:0.96911
[28] validation-logloss:0.39079 validation-auc:0.96439 validation-aucpr:0.96912
[29] validation-logloss:0.38562 validation-auc:0.96447 validation-aucpr:0.96915
[30] validation-logloss:0.37925 validation-auc:0.96467 validation-aucpr:0.96931
[31] validation-logloss:0.37360 validation-auc:0.96490 validation-aucpr:0.96953
[32] validation-logloss:0.36786 validation-auc:0.96506 validation-aucpr:0.96962
[33] validation-logloss:0.36265 validation-auc:0.96507 validation-aucpr:0.96964
[34] validation-logloss:0.35727 validation-auc:0.96533 validation-aucpr:0.96980
[35] validation-logloss:0.35324 validation-auc:0.96545 validation-aucpr:0.96987
[36] validation-logloss:0.34822 validation-auc:0.96566 validation-aucpr:0.97004
[37] validation-logloss:0.34426 validation-auc:0.96561 validation-aucpr:0.96999
[38] validation-logloss:0.34009 validation-auc:0.96570 validation-aucpr:0.97024
[39] validation-logloss:0.33541 validation-auc:0.96602 validation-aucpr:0.97055
[40] validation-logloss:0.33147 validation-auc:0.96601 validation-aucpr:0.97054
[41] validation-logloss:0.32777 validation-auc:0.96603 validation-aucpr:0.97056
[42] validation-logloss:0.32461 validation-auc:0.96589 validation-aucpr:0.97045
[43] validation-logloss:0.32120 validation-auc:0.96595 validation-aucpr:0.97055
[44] validation-logloss:0.31752 validation-auc:0.96607 validation-aucpr:0.97066
[45] validation-logloss:0.31466 validation-auc:0.96602 validation-aucpr:0.97060
[46] validation-logloss:0.31179 validation-auc:0.96599 validation-aucpr:0.97057
[47] validation-logloss:0.30814 validation-auc:0.96609 validation-aucpr:0.97067
[48] validation-logloss:0.30550 validation-auc:0.96613 validation-aucpr:0.97063
[49] validation-logloss:0.30295 validation-auc:0.96608 validation-aucpr:0.97047
[50] validation-logloss:0.29959 validation-auc:0.96622 validation-aucpr:0.97066
[51] validation-logloss:0.29658 validation-auc:0.96622 validation-aucpr:0.97067
[52] validation-logloss:0.29352 validation-auc:0.96632 validation-aucpr:0.97074
[53] validation-logloss:0.29060 validation-auc:0.96638 validation-aucpr:0.97080
[54] validation-logloss:0.28856 validation-auc:0.96642 validation-aucpr:0.97080
[55] validation-logloss:0.28552 validation-auc:0.96665 validation-aucpr:0.97098
[56] validation-logloss:0.28368 validation-auc:0.96650 validation-aucpr:0.97084
[57] validation-logloss:0.28105 validation-auc:0.96655 validation-aucpr:0.97077
[58] validation-logloss:0.27861 validation-auc:0.96652 validation-aucpr:0.97076
[59] validation-logloss:0.27676 validation-auc:0.96650 validation-aucpr:0.97074
[60] validation-logloss:0.27485 validation-auc:0.96650 validation-aucpr:0.97077
[61] validation-logloss:0.27231 validation-auc:0.96663 validation-aucpr:0.97088
[62] validation-logloss:0.27052 validation-auc:0.96664 validation-aucpr:0.97089
[63] validation-logloss:0.26857 validation-auc:0.96661 validation-aucpr:0.97086
[64] validation-logloss:0.26676 validation-auc:0.96671 validation-aucpr:0.97094
[65] validation-logloss:0.26459 validation-auc:0.96678 validation-aucpr:0.97100
[66] validation-logloss:0.26285 validation-auc:0.96688 validation-aucpr:0.97109
[67] validation-logloss:0.26118 validation-auc:0.96698 validation-aucpr:0.97121
[68] validation-logloss:0.25978 validation-auc:0.96690 validation-aucpr:0.97114
[69] validation-logloss:0.25787 validation-auc:0.96701 validation-aucpr:0.97121
[70] validation-logloss:0.25595 validation-auc:0.96711 validation-aucpr:0.97140
[71] validation-logloss:0.25456 validation-auc:0.96718 validation-aucpr:0.97140
[72] validation-logloss:0.25311 validation-auc:0.96719 validation-aucpr:0.97140
[73] validation-logloss:0.25180 validation-auc:0.96720 validation-aucpr:0.97140
[74] validation-logloss:0.25044 validation-auc:0.96729 validation-aucpr:0.97154
[75] validation-logloss:0.24858 validation-auc:0.96740 validation-aucpr:0.97165
[76] validation-logloss:0.24744 validation-auc:0.96740 validation-aucpr:0.97164
[77] validation-logloss:0.24656 validation-auc:0.96735 validation-aucpr:0.97159
[78] validation-logloss:0.24498 validation-auc:0.96742 validation-aucpr:0.97158
[79] validation-logloss:0.24354 validation-auc:0.96741 validation-aucpr:0.97157
[80] validation-logloss:0.24224 validation-auc:0.96744 validation-aucpr:0.97160
[81] validation-logloss:0.24070 validation-auc:0.96755 validation-aucpr:0.97169
[82] validation-logloss:0.23930 validation-auc:0.96762 validation-aucpr:0.97178
[83] validation-logloss:0.23819 validation-auc:0.96770 validation-aucpr:0.97183
[84] validation-logloss:0.23741 validation-auc:0.96765 validation-aucpr:0.97178
[85] validation-logloss:0.23637 validation-auc:0.96774 validation-aucpr:0.97184
[86] validation-logloss:0.23550 validation-auc:0.96773 validation-aucpr:0.97183
[87] validation-logloss:0.23469 validation-auc:0.96774 validation-aucpr:0.97183
[88] validation-logloss:0.23383 validation-auc:0.96777 validation-aucpr:0.97189
[89] validation-logloss:0.23269 validation-auc:0.96784 validation-aucpr:0.97194
[90] validation-logloss:0.23188 validation-auc:0.96786 validation-aucpr:0.97180
[91] validation-logloss:0.23085 validation-auc:0.96788 validation-aucpr:0.97184
[92] validation-logloss:0.23022 validation-auc:0.96786 validation-aucpr:0.97181
[93] validation-logloss:0.22955 validation-auc:0.96788 validation-aucpr:0.97162
[94] validation-logloss:0.22889 validation-auc:0.96786 validation-aucpr:0.97177
[95] validation-logloss:0.22824 validation-auc:0.96787 validation-aucpr:0.97188
[96] validation-logloss:0.22720 validation-auc:0.96794 validation-aucpr:0.97195
[97] validation-logloss:0.22612 validation-auc:0.96799 validation-aucpr:0.97200
{'best_iteration': '97', 'best_score': '0.9719967325717643'}
Trial 54, Fold 5: Log loss = 0.2261180343303365, Average precision = 0.9720025766905903, ROC-AUC = 0.9679936086803038, Elapsed Time = 16.96568880000268 seconds
Optimization Progress: 55%|#####5 | 55/100 [2:54:24<1:06:21, 88.47s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 55, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68388 validation-auc:0.94851 validation-aucpr:0.94833
[1] validation-logloss:0.67489 validation-auc:0.96102 validation-aucpr:0.96180
[2] validation-logloss:0.66598 validation-auc:0.96393 validation-aucpr:0.96686
[3] validation-logloss:0.65723 validation-auc:0.96541 validation-aucpr:0.96763
[4] validation-logloss:0.64867 validation-auc:0.96675 validation-aucpr:0.97182
[5] validation-logloss:0.64021 validation-auc:0.96787 validation-aucpr:0.97176
[6] validation-logloss:0.63302 validation-auc:0.96805 validation-aucpr:0.97183
[7] validation-logloss:0.62502 validation-auc:0.96887 validation-aucpr:0.97372
[8] validation-logloss:0.61730 validation-auc:0.96873 validation-aucpr:0.97362
[9] validation-logloss:0.60971 validation-auc:0.96859 validation-aucpr:0.97364
[10] validation-logloss:0.60221 validation-auc:0.96868 validation-aucpr:0.97368
[11] validation-logloss:0.59507 validation-auc:0.96903 validation-aucpr:0.97391
[12] validation-logloss:0.58874 validation-auc:0.96889 validation-aucpr:0.97378
[13] validation-logloss:0.58168 validation-auc:0.96872 validation-aucpr:0.97366
[14] validation-logloss:0.57491 validation-auc:0.96870 validation-aucpr:0.97367
[15] validation-logloss:0.56824 validation-auc:0.96880 validation-aucpr:0.97376
[16] validation-logloss:0.56202 validation-auc:0.96901 validation-aucpr:0.97400
[17] validation-logloss:0.55561 validation-auc:0.96918 validation-aucpr:0.97410
[18] validation-logloss:0.54946 validation-auc:0.96916 validation-aucpr:0.97414
[19] validation-logloss:0.54334 validation-auc:0.96894 validation-aucpr:0.97399
[20] validation-logloss:0.53735 validation-auc:0.96919 validation-aucpr:0.97414
[21] validation-logloss:0.53161 validation-auc:0.96928 validation-aucpr:0.97421
[22] validation-logloss:0.52582 validation-auc:0.96944 validation-aucpr:0.97435
[23] validation-logloss:0.52031 validation-auc:0.96951 validation-aucpr:0.97439
[24] validation-logloss:0.51475 validation-auc:0.96970 validation-aucpr:0.97448
[25] validation-logloss:0.50942 validation-auc:0.96970 validation-aucpr:0.97450
[26] validation-logloss:0.50412 validation-auc:0.96975 validation-aucpr:0.97455
[27] validation-logloss:0.49963 validation-auc:0.96961 validation-aucpr:0.97445
[28] validation-logloss:0.49458 validation-auc:0.96970 validation-aucpr:0.97451
[29] validation-logloss:0.48953 validation-auc:0.96972 validation-aucpr:0.97452
[30] validation-logloss:0.48514 validation-auc:0.96973 validation-aucpr:0.97454
[31] validation-logloss:0.48030 validation-auc:0.96974 validation-aucpr:0.97452
[32] validation-logloss:0.47550 validation-auc:0.96986 validation-aucpr:0.97458
[33] validation-logloss:0.47086 validation-auc:0.96988 validation-aucpr:0.97460
[34] validation-logloss:0.46641 validation-auc:0.96987 validation-aucpr:0.97458
[35] validation-logloss:0.46235 validation-auc:0.97004 validation-aucpr:0.97470
[36] validation-logloss:0.45799 validation-auc:0.97003 validation-aucpr:0.97470
[37] validation-logloss:0.45383 validation-auc:0.96987 validation-aucpr:0.97459
[38] validation-logloss:0.45003 validation-auc:0.96990 validation-aucpr:0.97461
[39] validation-logloss:0.44587 validation-auc:0.96996 validation-aucpr:0.97468
[40] validation-logloss:0.44176 validation-auc:0.97000 validation-aucpr:0.97470
[41] validation-logloss:0.43807 validation-auc:0.97010 validation-aucpr:0.97478
[42] validation-logloss:0.43472 validation-auc:0.97001 validation-aucpr:0.97469
[43] validation-logloss:0.43077 validation-auc:0.96996 validation-aucpr:0.97466
[44] validation-logloss:0.42731 validation-auc:0.97005 validation-aucpr:0.97473
[45] validation-logloss:0.42390 validation-auc:0.97007 validation-aucpr:0.97474
[46] validation-logloss:0.42023 validation-auc:0.97010 validation-aucpr:0.97477
[47] validation-logloss:0.41712 validation-auc:0.97005 validation-aucpr:0.97470
[48] validation-logloss:0.41392 validation-auc:0.97005 validation-aucpr:0.97470
[49] validation-logloss:0.41081 validation-auc:0.97003 validation-aucpr:0.97466
[50] validation-logloss:0.40787 validation-auc:0.96997 validation-aucpr:0.97468
[51] validation-logloss:0.40445 validation-auc:0.97001 validation-aucpr:0.97470
[52] validation-logloss:0.40113 validation-auc:0.96999 validation-aucpr:0.97468
[53] validation-logloss:0.39831 validation-auc:0.96992 validation-aucpr:0.97461
[54] validation-logloss:0.39510 validation-auc:0.96995 validation-aucpr:0.97464
[55] validation-logloss:0.39237 validation-auc:0.96992 validation-aucpr:0.97467
[56] validation-logloss:0.38942 validation-auc:0.96995 validation-aucpr:0.97470
[57] validation-logloss:0.38665 validation-auc:0.96997 validation-aucpr:0.97470
[58] validation-logloss:0.38358 validation-auc:0.97001 validation-aucpr:0.97473
[59] validation-logloss:0.38068 validation-auc:0.97002 validation-aucpr:0.97474
[60] validation-logloss:0.37812 validation-auc:0.97000 validation-aucpr:0.97470
[61] validation-logloss:0.37521 validation-auc:0.97010 validation-aucpr:0.97478
[62] validation-logloss:0.37232 validation-auc:0.97015 validation-aucpr:0.97482
[63] validation-logloss:0.36953 validation-auc:0.97014 validation-aucpr:0.97481
[64] validation-logloss:0.36677 validation-auc:0.97019 validation-aucpr:0.97486
[65] validation-logloss:0.36418 validation-auc:0.97020 validation-aucpr:0.97490
{'best_iteration': '65', 'best_score': '0.9748960585897559'}
Trial 55, Fold 1: Log loss = 0.3641809402106429, Average precision = 0.9748999805952879, ROC-AUC = 0.9701964231943906, Elapsed Time = 2.353945900002145 seconds
Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 55, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68338 validation-auc:0.95430 validation-aucpr:0.95662
[1] validation-logloss:0.67423 validation-auc:0.96548 validation-aucpr:0.96508
[2] validation-logloss:0.66602 validation-auc:0.96689 validation-aucpr:0.97055
[3] validation-logloss:0.65723 validation-auc:0.96778 validation-aucpr:0.97152
[4] validation-logloss:0.64881 validation-auc:0.96885 validation-aucpr:0.97255
[5] validation-logloss:0.64055 validation-auc:0.96889 validation-aucpr:0.97252
[6] validation-logloss:0.63234 validation-auc:0.96931 validation-aucpr:0.97313
[7] validation-logloss:0.62435 validation-auc:0.96930 validation-aucpr:0.97319
[8] validation-logloss:0.61767 validation-auc:0.96872 validation-aucpr:0.97269
[9] validation-logloss:0.61097 validation-auc:0.96871 validation-aucpr:0.97268
[10] validation-logloss:0.60354 validation-auc:0.96905 validation-aucpr:0.97292
[11] validation-logloss:0.59705 validation-auc:0.96901 validation-aucpr:0.97291
[12] validation-logloss:0.58995 validation-auc:0.96915 validation-aucpr:0.97301
[13] validation-logloss:0.58301 validation-auc:0.96936 validation-aucpr:0.97314
[14] validation-logloss:0.57659 validation-auc:0.96979 validation-aucpr:0.97337
[15] validation-logloss:0.57050 validation-auc:0.96952 validation-aucpr:0.97313
[16] validation-logloss:0.56466 validation-auc:0.96953 validation-aucpr:0.97333
[17] validation-logloss:0.55819 validation-auc:0.96956 validation-aucpr:0.97336
[18] validation-logloss:0.55274 validation-auc:0.96947 validation-aucpr:0.97323
[19] validation-logloss:0.54686 validation-auc:0.96967 validation-aucpr:0.97338
[20] validation-logloss:0.54081 validation-auc:0.96986 validation-aucpr:0.97355
[21] validation-logloss:0.53481 validation-auc:0.97006 validation-aucpr:0.97371
[22] validation-logloss:0.52908 validation-auc:0.97009 validation-aucpr:0.97377
[23] validation-logloss:0.52354 validation-auc:0.97025 validation-aucpr:0.97391
[24] validation-logloss:0.51789 validation-auc:0.97048 validation-aucpr:0.97413
[25] validation-logloss:0.51236 validation-auc:0.97061 validation-aucpr:0.97424
[26] validation-logloss:0.50693 validation-auc:0.97068 validation-aucpr:0.97425
[27] validation-logloss:0.50239 validation-auc:0.97038 validation-aucpr:0.97393
[28] validation-logloss:0.49748 validation-auc:0.97039 validation-aucpr:0.97394
[29] validation-logloss:0.49294 validation-auc:0.97034 validation-aucpr:0.97389
[30] validation-logloss:0.48791 validation-auc:0.97045 validation-aucpr:0.97399
[31] validation-logloss:0.48300 validation-auc:0.97069 validation-aucpr:0.97418
[32] validation-logloss:0.47877 validation-auc:0.97068 validation-aucpr:0.97415
[33] validation-logloss:0.47451 validation-auc:0.97063 validation-aucpr:0.97411
[34] validation-logloss:0.46992 validation-auc:0.97064 validation-aucpr:0.97401
[35] validation-logloss:0.46532 validation-auc:0.97085 validation-aucpr:0.97416
[36] validation-logloss:0.46085 validation-auc:0.97103 validation-aucpr:0.97430
[37] validation-logloss:0.45646 validation-auc:0.97104 validation-aucpr:0.97432
[38] validation-logloss:0.45236 validation-auc:0.97101 validation-aucpr:0.97429
[39] validation-logloss:0.44809 validation-auc:0.97109 validation-aucpr:0.97437
[40] validation-logloss:0.44394 validation-auc:0.97112 validation-aucpr:0.97438
[41] validation-logloss:0.44012 validation-auc:0.97109 validation-aucpr:0.97434
[42] validation-logloss:0.43656 validation-auc:0.97107 validation-aucpr:0.97424
[43] validation-logloss:0.43254 validation-auc:0.97124 validation-aucpr:0.97438
[44] validation-logloss:0.42915 validation-auc:0.97115 validation-aucpr:0.97431
[45] validation-logloss:0.42534 validation-auc:0.97126 validation-aucpr:0.97440
[46] validation-logloss:0.42148 validation-auc:0.97144 validation-aucpr:0.97453
[47] validation-logloss:0.41774 validation-auc:0.97153 validation-aucpr:0.97461
[48] validation-logloss:0.41411 validation-auc:0.97168 validation-aucpr:0.97471
[49] validation-logloss:0.41058 validation-auc:0.97163 validation-aucpr:0.97465
[50] validation-logloss:0.40752 validation-auc:0.97148 validation-aucpr:0.97454
[51] validation-logloss:0.40430 validation-auc:0.97150 validation-aucpr:0.97454
[52] validation-logloss:0.40124 validation-auc:0.97153 validation-aucpr:0.97456
[53] validation-logloss:0.39829 validation-auc:0.97147 validation-aucpr:0.97450
[54] validation-logloss:0.39508 validation-auc:0.97146 validation-aucpr:0.97450
[55] validation-logloss:0.39189 validation-auc:0.97157 validation-aucpr:0.97459
[56] validation-logloss:0.38870 validation-auc:0.97158 validation-aucpr:0.97459
[57] validation-logloss:0.38557 validation-auc:0.97160 validation-aucpr:0.97462
[58] validation-logloss:0.38257 validation-auc:0.97156 validation-aucpr:0.97459
[59] validation-logloss:0.37948 validation-auc:0.97155 validation-aucpr:0.97459
[60] validation-logloss:0.37650 validation-auc:0.97161 validation-aucpr:0.97461
[61] validation-logloss:0.37362 validation-auc:0.97164 validation-aucpr:0.97464
[62] validation-logloss:0.37076 validation-auc:0.97167 validation-aucpr:0.97466
[63] validation-logloss:0.36786 validation-auc:0.97176 validation-aucpr:0.97474
[64] validation-logloss:0.36537 validation-auc:0.97176 validation-aucpr:0.97442
[65] validation-logloss:0.36256 validation-auc:0.97181 validation-aucpr:0.97447
{'best_iteration': '63', 'best_score': '0.9747379643077329'}
Trial 55, Fold 2: Log loss = 0.36256205802843033, Average precision = 0.9745009545862788, ROC-AUC = 0.9718087516589826, Elapsed Time = 2.5463577000009536 seconds
Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 55, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68367 validation-auc:0.94844 validation-aucpr:0.95205
[1] validation-logloss:0.67453 validation-auc:0.96217 validation-aucpr:0.96193
[2] validation-logloss:0.66633 validation-auc:0.96459 validation-aucpr:0.96725
[3] validation-logloss:0.65755 validation-auc:0.96635 validation-aucpr:0.96968
[4] validation-logloss:0.64890 validation-auc:0.96834 validation-aucpr:0.97264
[5] validation-logloss:0.64060 validation-auc:0.96913 validation-aucpr:0.97333
[6] validation-logloss:0.63325 validation-auc:0.96940 validation-aucpr:0.97358
[7] validation-logloss:0.62529 validation-auc:0.96949 validation-aucpr:0.97362
[8] validation-logloss:0.61752 validation-auc:0.96956 validation-aucpr:0.97367
[9] validation-logloss:0.61002 validation-auc:0.96977 validation-aucpr:0.97384
[10] validation-logloss:0.60243 validation-auc:0.96986 validation-aucpr:0.97397
[11] validation-logloss:0.59581 validation-auc:0.96985 validation-aucpr:0.97399
[12] validation-logloss:0.58874 validation-auc:0.97002 validation-aucpr:0.97412
[13] validation-logloss:0.58175 validation-auc:0.97018 validation-aucpr:0.97423
[14] validation-logloss:0.57510 validation-auc:0.97011 validation-aucpr:0.97416
[15] validation-logloss:0.56841 validation-auc:0.97001 validation-aucpr:0.97413
[16] validation-logloss:0.56233 validation-auc:0.97001 validation-aucpr:0.97412
[17] validation-logloss:0.55655 validation-auc:0.96984 validation-aucpr:0.97389
[18] validation-logloss:0.55107 validation-auc:0.96974 validation-aucpr:0.97377
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[20] validation-logloss:0.53940 validation-auc:0.96983 validation-aucpr:0.97387
[21] validation-logloss:0.53340 validation-auc:0.96998 validation-aucpr:0.97407
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[23] validation-logloss:0.52231 validation-auc:0.97007 validation-aucpr:0.97428
[24] validation-logloss:0.51673 validation-auc:0.97007 validation-aucpr:0.97427
[25] validation-logloss:0.51128 validation-auc:0.97013 validation-aucpr:0.97431
[26] validation-logloss:0.50594 validation-auc:0.97026 validation-aucpr:0.97443
[27] validation-logloss:0.50056 validation-auc:0.97037 validation-aucpr:0.97454
[28] validation-logloss:0.49532 validation-auc:0.97051 validation-aucpr:0.97467
[29] validation-logloss:0.49032 validation-auc:0.97052 validation-aucpr:0.97467
[30] validation-logloss:0.48527 validation-auc:0.97066 validation-aucpr:0.97478
[31] validation-logloss:0.48084 validation-auc:0.97066 validation-aucpr:0.97478
[32] validation-logloss:0.47600 validation-auc:0.97076 validation-aucpr:0.97486
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[34] validation-logloss:0.46750 validation-auc:0.97098 validation-aucpr:0.97501
[35] validation-logloss:0.46303 validation-auc:0.97106 validation-aucpr:0.97507
[36] validation-logloss:0.45866 validation-auc:0.97112 validation-aucpr:0.97509
[37] validation-logloss:0.45476 validation-auc:0.97111 validation-aucpr:0.97507
[38] validation-logloss:0.45034 validation-auc:0.97122 validation-aucpr:0.97515
[39] validation-logloss:0.44598 validation-auc:0.97133 validation-aucpr:0.97523
[40] validation-logloss:0.44233 validation-auc:0.97138 validation-aucpr:0.97528
[41] validation-logloss:0.43817 validation-auc:0.97159 validation-aucpr:0.97542
[42] validation-logloss:0.43406 validation-auc:0.97173 validation-aucpr:0.97553
[43] validation-logloss:0.43031 validation-auc:0.97162 validation-aucpr:0.97547
[44] validation-logloss:0.42638 validation-auc:0.97173 validation-aucpr:0.97556
[45] validation-logloss:0.42262 validation-auc:0.97171 validation-aucpr:0.97554
[46] validation-logloss:0.41886 validation-auc:0.97180 validation-aucpr:0.97562
[47] validation-logloss:0.41567 validation-auc:0.97179 validation-aucpr:0.97560
[48] validation-logloss:0.41202 validation-auc:0.97182 validation-aucpr:0.97562
[49] validation-logloss:0.40902 validation-auc:0.97172 validation-aucpr:0.97553
[50] validation-logloss:0.40569 validation-auc:0.97179 validation-aucpr:0.97559
[51] validation-logloss:0.40276 validation-auc:0.97168 validation-aucpr:0.97548
[52] validation-logloss:0.39936 validation-auc:0.97166 validation-aucpr:0.97548
[53] validation-logloss:0.39603 validation-auc:0.97169 validation-aucpr:0.97546
[54] validation-logloss:0.39292 validation-auc:0.97166 validation-aucpr:0.97543
[55] validation-logloss:0.38959 validation-auc:0.97179 validation-aucpr:0.97554
[56] validation-logloss:0.38648 validation-auc:0.97186 validation-aucpr:0.97562
[57] validation-logloss:0.38334 validation-auc:0.97190 validation-aucpr:0.97564
[58] validation-logloss:0.38030 validation-auc:0.97194 validation-aucpr:0.97566
[59] validation-logloss:0.37720 validation-auc:0.97202 validation-aucpr:0.97572
[60] validation-logloss:0.37423 validation-auc:0.97207 validation-aucpr:0.97576
[61] validation-logloss:0.37127 validation-auc:0.97211 validation-aucpr:0.97577
[62] validation-logloss:0.36839 validation-auc:0.97211 validation-aucpr:0.97578
[63] validation-logloss:0.36550 validation-auc:0.97218 validation-aucpr:0.97583
[64] validation-logloss:0.36271 validation-auc:0.97222 validation-aucpr:0.97586
[65] validation-logloss:0.36009 validation-auc:0.97215 validation-aucpr:0.97580
{'best_iteration': '64', 'best_score': '0.9758630569815583'}
Trial 55, Fold 3: Log loss = 0.3600912741941957, Average precision = 0.9758044092545942, ROC-AUC = 0.9721529155550848, Elapsed Time = 2.635833000000275 seconds
Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 55, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68357 validation-auc:0.94645 validation-aucpr:0.93736
[1] validation-logloss:0.67441 validation-auc:0.95971 validation-aucpr:0.95893
[2] validation-logloss:0.66542 validation-auc:0.96524 validation-aucpr:0.96967
[3] validation-logloss:0.65689 validation-auc:0.96651 validation-aucpr:0.97207
[4] validation-logloss:0.64943 validation-auc:0.96551 validation-aucpr:0.97122
[5] validation-logloss:0.64171 validation-auc:0.96616 validation-aucpr:0.97169
[6] validation-logloss:0.63356 validation-auc:0.96677 validation-aucpr:0.97227
[7] validation-logloss:0.62636 validation-auc:0.96672 validation-aucpr:0.97220
[8] validation-logloss:0.61846 validation-auc:0.96715 validation-aucpr:0.97258
[9] validation-logloss:0.61082 validation-auc:0.96777 validation-aucpr:0.97307
[10] validation-logloss:0.60342 validation-auc:0.96808 validation-aucpr:0.97342
[11] validation-logloss:0.59610 validation-auc:0.96819 validation-aucpr:0.97354
[12] validation-logloss:0.58938 validation-auc:0.96830 validation-aucpr:0.97369
[13] validation-logloss:0.58222 validation-auc:0.96892 validation-aucpr:0.97413
[14] validation-logloss:0.57540 validation-auc:0.96901 validation-aucpr:0.97423
[15] validation-logloss:0.56874 validation-auc:0.96927 validation-aucpr:0.97442
[16] validation-logloss:0.56217 validation-auc:0.96940 validation-aucpr:0.97451
[17] validation-logloss:0.55570 validation-auc:0.96963 validation-aucpr:0.97471
[18] validation-logloss:0.55019 validation-auc:0.96935 validation-aucpr:0.97448
[19] validation-logloss:0.54421 validation-auc:0.96941 validation-aucpr:0.97450
[20] validation-logloss:0.53826 validation-auc:0.96943 validation-aucpr:0.97450
[21] validation-logloss:0.53233 validation-auc:0.96963 validation-aucpr:0.97465
[22] validation-logloss:0.52676 validation-auc:0.96955 validation-aucpr:0.97460
[23] validation-logloss:0.52171 validation-auc:0.96943 validation-aucpr:0.97450
[24] validation-logloss:0.51609 validation-auc:0.96958 validation-aucpr:0.97460
[25] validation-logloss:0.51064 validation-auc:0.96965 validation-aucpr:0.97468
[26] validation-logloss:0.50524 validation-auc:0.96973 validation-aucpr:0.97476
[27] validation-logloss:0.50066 validation-auc:0.96964 validation-aucpr:0.97468
[28] validation-logloss:0.49553 validation-auc:0.96980 validation-aucpr:0.97485
[29] validation-logloss:0.49046 validation-auc:0.96993 validation-aucpr:0.97492
[30] validation-logloss:0.48614 validation-auc:0.96981 validation-aucpr:0.97482
[31] validation-logloss:0.48137 validation-auc:0.96985 validation-aucpr:0.97485
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[33] validation-logloss:0.47188 validation-auc:0.97001 validation-aucpr:0.97496
[34] validation-logloss:0.46783 validation-auc:0.96995 validation-aucpr:0.97490
[35] validation-logloss:0.46399 validation-auc:0.96977 validation-aucpr:0.97477
[36] validation-logloss:0.45971 validation-auc:0.96975 validation-aucpr:0.97475
[37] validation-logloss:0.45530 validation-auc:0.96986 validation-aucpr:0.97483
[38] validation-logloss:0.45109 validation-auc:0.96987 validation-aucpr:0.97485
[39] validation-logloss:0.44715 validation-auc:0.96989 validation-aucpr:0.97487
[40] validation-logloss:0.44363 validation-auc:0.96981 validation-aucpr:0.97479
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[42] validation-logloss:0.43572 validation-auc:0.96991 validation-aucpr:0.97487
[43] validation-logloss:0.43178 validation-auc:0.96985 validation-aucpr:0.97482
[44] validation-logloss:0.42800 validation-auc:0.96988 validation-aucpr:0.97484
[45] validation-logloss:0.42423 validation-auc:0.96984 validation-aucpr:0.97480
[46] validation-logloss:0.42099 validation-auc:0.96976 validation-aucpr:0.97475
[47] validation-logloss:0.41723 validation-auc:0.96997 validation-aucpr:0.97490
[48] validation-logloss:0.41378 validation-auc:0.96999 validation-aucpr:0.97493
[49] validation-logloss:0.41063 validation-auc:0.96984 validation-aucpr:0.97480
[50] validation-logloss:0.40721 validation-auc:0.96990 validation-aucpr:0.97485
[51] validation-logloss:0.40412 validation-auc:0.96993 validation-aucpr:0.97487
[52] validation-logloss:0.40121 validation-auc:0.96989 validation-aucpr:0.97483
[53] validation-logloss:0.39783 validation-auc:0.97004 validation-aucpr:0.97496
[54] validation-logloss:0.39462 validation-auc:0.97001 validation-aucpr:0.97496
[55] validation-logloss:0.39139 validation-auc:0.97003 validation-aucpr:0.97499
[56] validation-logloss:0.38867 validation-auc:0.97003 validation-aucpr:0.97497
[57] validation-logloss:0.38549 validation-auc:0.97019 validation-aucpr:0.97510
[58] validation-logloss:0.38243 validation-auc:0.97025 validation-aucpr:0.97513
[59] validation-logloss:0.37947 validation-auc:0.97025 validation-aucpr:0.97514
[60] validation-logloss:0.37661 validation-auc:0.97039 validation-aucpr:0.97525
[61] validation-logloss:0.37386 validation-auc:0.97041 validation-aucpr:0.97527
[62] validation-logloss:0.37095 validation-auc:0.97051 validation-aucpr:0.97535
[63] validation-logloss:0.36810 validation-auc:0.97054 validation-aucpr:0.97537
[64] validation-logloss:0.36534 validation-auc:0.97065 validation-aucpr:0.97545
[65] validation-logloss:0.36298 validation-auc:0.97060 validation-aucpr:0.97541
{'best_iteration': '64', 'best_score': '0.9754461952539737'}
Trial 55, Fold 4: Log loss = 0.3629805179052722, Average precision = 0.9754157021253169, ROC-AUC = 0.9705993333190268, Elapsed Time = 2.696198199999344 seconds
Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 55, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68385 validation-auc:0.93955 validation-aucpr:0.93653
[1] validation-logloss:0.67499 validation-auc:0.95723 validation-aucpr:0.95397
[2] validation-logloss:0.66600 validation-auc:0.96296 validation-aucpr:0.96347
[3] validation-logloss:0.65731 validation-auc:0.96436 validation-aucpr:0.96781
[4] validation-logloss:0.64894 validation-auc:0.96514 validation-aucpr:0.97009
[5] validation-logloss:0.64068 validation-auc:0.96584 validation-aucpr:0.97050
[6] validation-logloss:0.63275 validation-auc:0.96600 validation-aucpr:0.97010
[7] validation-logloss:0.62498 validation-auc:0.96649 validation-aucpr:0.97041
[8] validation-logloss:0.61727 validation-auc:0.96682 validation-aucpr:0.97070
[9] validation-logloss:0.60973 validation-auc:0.96702 validation-aucpr:0.97098
[10] validation-logloss:0.60228 validation-auc:0.96751 validation-aucpr:0.97138
[11] validation-logloss:0.59581 validation-auc:0.96772 validation-aucpr:0.97167
[12] validation-logloss:0.58935 validation-auc:0.96767 validation-aucpr:0.97154
[13] validation-logloss:0.58243 validation-auc:0.96786 validation-aucpr:0.97167
[14] validation-logloss:0.57567 validation-auc:0.96763 validation-aucpr:0.97151
[15] validation-logloss:0.56914 validation-auc:0.96764 validation-aucpr:0.97150
[16] validation-logloss:0.56281 validation-auc:0.96753 validation-aucpr:0.97150
[17] validation-logloss:0.55710 validation-auc:0.96746 validation-aucpr:0.97147
[18] validation-logloss:0.55134 validation-auc:0.96749 validation-aucpr:0.97147
[19] validation-logloss:0.54528 validation-auc:0.96743 validation-aucpr:0.97145
[20] validation-logloss:0.53932 validation-auc:0.96776 validation-aucpr:0.97162
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[22] validation-logloss:0.52840 validation-auc:0.96769 validation-aucpr:0.97103
[23] validation-logloss:0.52279 validation-auc:0.96795 validation-aucpr:0.97121
[24] validation-logloss:0.51784 validation-auc:0.96799 validation-aucpr:0.97123
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[30] validation-logloss:0.48749 validation-auc:0.96857 validation-aucpr:0.97308
[31] validation-logloss:0.48261 validation-auc:0.96876 validation-aucpr:0.97323
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[42] validation-logloss:0.43604 validation-auc:0.96953 validation-aucpr:0.97381
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[44] validation-logloss:0.42831 validation-auc:0.96962 validation-aucpr:0.97389
[45] validation-logloss:0.42495 validation-auc:0.96968 validation-aucpr:0.97390
[46] validation-logloss:0.42125 validation-auc:0.96969 validation-aucpr:0.97391
[47] validation-logloss:0.41763 validation-auc:0.96984 validation-aucpr:0.97402
[48] validation-logloss:0.41402 validation-auc:0.96994 validation-aucpr:0.97411
[49] validation-logloss:0.41055 validation-auc:0.97003 validation-aucpr:0.97417
[50] validation-logloss:0.40756 validation-auc:0.97000 validation-aucpr:0.97411
[51] validation-logloss:0.40432 validation-auc:0.96996 validation-aucpr:0.97410
[52] validation-logloss:0.40095 validation-auc:0.97006 validation-aucpr:0.97417
[53] validation-logloss:0.39771 validation-auc:0.97018 validation-aucpr:0.97425
[54] validation-logloss:0.39447 validation-auc:0.97026 validation-aucpr:0.97430
[55] validation-logloss:0.39135 validation-auc:0.97032 validation-aucpr:0.97434
[56] validation-logloss:0.38826 validation-auc:0.97033 validation-aucpr:0.97436
[57] validation-logloss:0.38565 validation-auc:0.97029 validation-aucpr:0.97432
[58] validation-logloss:0.38303 validation-auc:0.97026 validation-aucpr:0.97429
[59] validation-logloss:0.38014 validation-auc:0.97032 validation-aucpr:0.97431
[60] validation-logloss:0.37728 validation-auc:0.97039 validation-aucpr:0.97440
[61] validation-logloss:0.37442 validation-auc:0.97043 validation-aucpr:0.97444
[62] validation-logloss:0.37163 validation-auc:0.97043 validation-aucpr:0.97444
[63] validation-logloss:0.36893 validation-auc:0.97043 validation-aucpr:0.97443
[64] validation-logloss:0.36624 validation-auc:0.97043 validation-aucpr:0.97444
[65] validation-logloss:0.36350 validation-auc:0.97052 validation-aucpr:0.97451
{'best_iteration': '65', 'best_score': '0.9745086390557987'}
Trial 55, Fold 5: Log loss = 0.3635039653222269, Average precision = 0.974512820454051, ROC-AUC = 0.9705224903851514, Elapsed Time = 2.5660344999996596 seconds
Optimization Progress: 56%|#####6 | 56/100 [2:54:45<49:55, 68.08s/it]
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 56, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66058 validation-auc:0.94692 validation-aucpr:0.93916
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63109 validation-auc:0.96084 validation-aucpr:0.96099
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60382 validation-auc:0.96396 validation-aucpr:0.96927
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57872 validation-auc:0.96459 validation-aucpr:0.97005
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55551 validation-auc:0.96481 validation-aucpr:0.96872
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53392 validation-auc:0.96602 validation-aucpr:0.96837
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51583 validation-auc:0.96631 validation-aucpr:0.96879
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49709 validation-auc:0.96688 validation-aucpr:0.96920
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47959 validation-auc:0.96666 validation-aucpr:0.96512
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46324 validation-auc:0.96772 validation-aucpr:0.96759
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44945 validation-auc:0.96784 validation-aucpr:0.96776
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43532 validation-auc:0.96785 validation-aucpr:0.96779
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42201 validation-auc:0.96803 validation-aucpr:0.96801
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.41083 validation-auc:0.96764 validation-aucpr:0.96620
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39912 validation-auc:0.96780 validation-aucpr:0.96632
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38980 validation-auc:0.96804 validation-aucpr:0.96812
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37941 validation-auc:0.96844 validation-aucpr:0.97063
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36946 validation-auc:0.96862 validation-aucpr:0.97080
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.36019 validation-auc:0.96869 validation-aucpr:0.97084
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.35132 validation-auc:0.96868 validation-aucpr:0.97084
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34285 validation-auc:0.96876 validation-aucpr:0.97089
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33569 validation-auc:0.96882 validation-aucpr:0.97087
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32816 validation-auc:0.96931 validation-aucpr:0.97392
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.32117 validation-auc:0.96949 validation-aucpr:0.97403
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31469 validation-auc:0.96947 validation-aucpr:0.97402
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30918 validation-auc:0.96954 validation-aucpr:0.97402
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30378 validation-auc:0.96968 validation-aucpr:0.97405
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29798 validation-auc:0.96978 validation-aucpr:0.97410
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29254 validation-auc:0.96998 validation-aucpr:0.97427
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28744 validation-auc:0.96995 validation-aucpr:0.97427
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28246 validation-auc:0.97011 validation-aucpr:0.97435
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27798 validation-auc:0.97016 validation-aucpr:0.97441
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27340 validation-auc:0.97035 validation-aucpr:0.97454
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26919 validation-auc:0.97052 validation-aucpr:0.97466
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26531 validation-auc:0.97049 validation-aucpr:0.97463
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26168 validation-auc:0.97049 validation-aucpr:0.97360
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25811 validation-auc:0.97055 validation-aucpr:0.97366
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25475 validation-auc:0.97063 validation-aucpr:0.97368
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25150 validation-auc:0.97071 validation-aucpr:0.97374
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24857 validation-auc:0.97082 validation-aucpr:0.97388
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24594 validation-auc:0.97087 validation-aucpr:0.97532
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24311 validation-auc:0.97096 validation-aucpr:0.97539
[20:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.24062 validation-auc:0.97093 validation-aucpr:0.97537
{'best_iteration': '41', 'best_score': '0.975389821361592'}
Trial 56, Fold 1: Log loss = 0.24061675051511633, Average precision = 0.9753669075707132, ROC-AUC = 0.9709288952457424, Elapsed Time = 6.691964800000278 seconds
Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 56, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66045 validation-auc:0.95472 validation-aucpr:0.94123
[20:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63073 validation-auc:0.96408 validation-aucpr:0.95982
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60338 validation-auc:0.96654 validation-aucpr:0.96890
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57840 validation-auc:0.96627 validation-aucpr:0.96846
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55703 validation-auc:0.96781 validation-aucpr:0.97159
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53731 validation-auc:0.96831 validation-aucpr:0.97175
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51663 validation-auc:0.96931 validation-aucpr:0.97248
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49760 validation-auc:0.96978 validation-aucpr:0.97282
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.48159 validation-auc:0.97049 validation-aucpr:0.97375
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46460 validation-auc:0.97134 validation-aucpr:0.97434
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.45080 validation-auc:0.97079 validation-aucpr:0.97396
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43769 validation-auc:0.97068 validation-aucpr:0.97377
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42383 validation-auc:0.97101 validation-aucpr:0.97409
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.41090 validation-auc:0.97131 validation-aucpr:0.97438
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39882 validation-auc:0.97155 validation-aucpr:0.97458
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38737 validation-auc:0.97165 validation-aucpr:0.97465
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37791 validation-auc:0.97158 validation-aucpr:0.97457
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36773 validation-auc:0.97185 validation-aucpr:0.97478
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35819 validation-auc:0.97171 validation-aucpr:0.97476
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34923 validation-auc:0.97189 validation-aucpr:0.97497
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34054 validation-auc:0.97201 validation-aucpr:0.97507
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33265 validation-auc:0.97192 validation-aucpr:0.97500
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32505 validation-auc:0.97201 validation-aucpr:0.97505
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31776 validation-auc:0.97204 validation-aucpr:0.97508
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31084 validation-auc:0.97223 validation-aucpr:0.97528
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30420 validation-auc:0.97230 validation-aucpr:0.97530
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29811 validation-auc:0.97243 validation-aucpr:0.97550
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29235 validation-auc:0.97246 validation-aucpr:0.97542
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28689 validation-auc:0.97242 validation-aucpr:0.97538
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28180 validation-auc:0.97235 validation-aucpr:0.97531
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27681 validation-auc:0.97237 validation-aucpr:0.97545
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27230 validation-auc:0.97229 validation-aucpr:0.97537
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.26786 validation-auc:0.97232 validation-aucpr:0.97539
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26351 validation-auc:0.97247 validation-aucpr:0.97551
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.25952 validation-auc:0.97252 validation-aucpr:0.97553
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25552 validation-auc:0.97273 validation-aucpr:0.97568
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25187 validation-auc:0.97280 validation-aucpr:0.97575
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.24838 validation-auc:0.97275 validation-aucpr:0.97574
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24493 validation-auc:0.97283 validation-aucpr:0.97580
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24196 validation-auc:0.97270 validation-aucpr:0.97571
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.23894 validation-auc:0.97281 validation-aucpr:0.97577
[20:53:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23616 validation-auc:0.97280 validation-aucpr:0.97573
[20:53:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23346 validation-auc:0.97279 validation-aucpr:0.97571
{'best_iteration': '38', 'best_score': '0.9757950282631109'}
Trial 56, Fold 2: Log loss = 0.23345912524895623, Average precision = 0.9755510875905027, ROC-AUC = 0.9727877524969339, Elapsed Time = 5.648318900002778 seconds
Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 56, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66060 validation-auc:0.95031 validation-aucpr:0.94385
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63067 validation-auc:0.95917 validation-aucpr:0.94856
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60336 validation-auc:0.96377 validation-aucpr:0.95967
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57806 validation-auc:0.96458 validation-aucpr:0.96060
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55470 validation-auc:0.96615 validation-aucpr:0.96471
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53304 validation-auc:0.96699 validation-aucpr:0.96866
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51313 validation-auc:0.96758 validation-aucpr:0.97208
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49459 validation-auc:0.96756 validation-aucpr:0.96980
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47726 validation-auc:0.96761 validation-aucpr:0.96986
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46110 validation-auc:0.96798 validation-aucpr:0.97001
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44581 validation-auc:0.96831 validation-aucpr:0.97039
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43170 validation-auc:0.96861 validation-aucpr:0.97061
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41833 validation-auc:0.96877 validation-aucpr:0.97076
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40571 validation-auc:0.96916 validation-aucpr:0.96863
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39386 validation-auc:0.96949 validation-aucpr:0.97143
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38283 validation-auc:0.96967 validation-aucpr:0.97122
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37247 validation-auc:0.96963 validation-aucpr:0.97122
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36278 validation-auc:0.96957 validation-aucpr:0.97117
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35351 validation-auc:0.97014 validation-aucpr:0.97394
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34494 validation-auc:0.97015 validation-aucpr:0.97395
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33653 validation-auc:0.97045 validation-aucpr:0.97409
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.32933 validation-auc:0.97083 validation-aucpr:0.97432
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32196 validation-auc:0.97096 validation-aucpr:0.97441
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31517 validation-auc:0.97086 validation-aucpr:0.97435
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.30871 validation-auc:0.97096 validation-aucpr:0.97442
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30265 validation-auc:0.97099 validation-aucpr:0.97447
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29672 validation-auc:0.97122 validation-aucpr:0.97462
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29108 validation-auc:0.97139 validation-aucpr:0.97475
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28572 validation-auc:0.97152 validation-aucpr:0.97488
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28100 validation-auc:0.97170 validation-aucpr:0.97498
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27631 validation-auc:0.97161 validation-aucpr:0.97495
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27196 validation-auc:0.97159 validation-aucpr:0.97493
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.26802 validation-auc:0.97176 validation-aucpr:0.97503
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26408 validation-auc:0.97169 validation-aucpr:0.97496
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26034 validation-auc:0.97168 validation-aucpr:0.97487
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25657 validation-auc:0.97176 validation-aucpr:0.97500
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25313 validation-auc:0.97175 validation-aucpr:0.97495
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.24996 validation-auc:0.97173 validation-aucpr:0.97492
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24674 validation-auc:0.97183 validation-aucpr:0.97499
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24363 validation-auc:0.97189 validation-aucpr:0.97503
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24092 validation-auc:0.97195 validation-aucpr:0.97507
[20:53:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23835 validation-auc:0.97200 validation-aucpr:0.97509
[20:53:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23589 validation-auc:0.97197 validation-aucpr:0.97508
{'best_iteration': '41', 'best_score': '0.9750947247890471'}
Trial 56, Fold 3: Log loss = 0.2358894526760947, Average precision = 0.9749897931718626, ROC-AUC = 0.9719713554959502, Elapsed Time = 5.436695800002781 seconds
Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 56, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66077 validation-auc:0.94342 validation-aucpr:0.94184
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63116 validation-auc:0.95862 validation-aucpr:0.95123
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60602 validation-auc:0.96504 validation-aucpr:0.96958
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58052 validation-auc:0.96631 validation-aucpr:0.97024
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55740 validation-auc:0.96677 validation-aucpr:0.97222
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53578 validation-auc:0.96742 validation-aucpr:0.97254
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51761 validation-auc:0.96740 validation-aucpr:0.97239
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49867 validation-auc:0.96791 validation-aucpr:0.97161
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.48102 validation-auc:0.96826 validation-aucpr:0.97158
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46433 validation-auc:0.96889 validation-aucpr:0.97365
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44888 validation-auc:0.96888 validation-aucpr:0.97377
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43418 validation-auc:0.96935 validation-aucpr:0.97324
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42066 validation-auc:0.96965 validation-aucpr:0.97324
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40923 validation-auc:0.96938 validation-aucpr:0.97291
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39731 validation-auc:0.96956 validation-aucpr:0.97429
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38595 validation-auc:0.96974 validation-aucpr:0.97443
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37533 validation-auc:0.97000 validation-aucpr:0.97460
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36524 validation-auc:0.97021 validation-aucpr:0.97394
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35583 validation-auc:0.97022 validation-aucpr:0.97395
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34687 validation-auc:0.97044 validation-aucpr:0.97295
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33857 validation-auc:0.97033 validation-aucpr:0.97275
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33072 validation-auc:0.97044 validation-aucpr:0.97263
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32319 validation-auc:0.97050 validation-aucpr:0.97290
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31605 validation-auc:0.97069 validation-aucpr:0.97304
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.30930 validation-auc:0.97106 validation-aucpr:0.97471
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30307 validation-auc:0.97121 validation-aucpr:0.97471
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29700 validation-auc:0.97123 validation-aucpr:0.97472
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29136 validation-auc:0.97113 validation-aucpr:0.97464
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28610 validation-auc:0.97117 validation-aucpr:0.97456
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28164 validation-auc:0.97111 validation-aucpr:0.97414
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27684 validation-auc:0.97106 validation-aucpr:0.97412
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27215 validation-auc:0.97132 validation-aucpr:0.97480
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.26796 validation-auc:0.97117 validation-aucpr:0.97463
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26380 validation-auc:0.97129 validation-aucpr:0.97440
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26036 validation-auc:0.97126 validation-aucpr:0.97406
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25640 validation-auc:0.97169 validation-aucpr:0.97601
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25290 validation-auc:0.97174 validation-aucpr:0.97606
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25002 validation-auc:0.97170 validation-aucpr:0.97604
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24672 validation-auc:0.97181 validation-aucpr:0.97614
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24370 validation-auc:0.97194 validation-aucpr:0.97623
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24093 validation-auc:0.97204 validation-aucpr:0.97631
[20:54:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23842 validation-auc:0.97192 validation-aucpr:0.97622
[20:54:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23582 validation-auc:0.97196 validation-aucpr:0.97626
{'best_iteration': '40', 'best_score': '0.976311607155693'}
Trial 56, Fold 4: Log loss = 0.23582202103210947, Average precision = 0.9762461607065359, ROC-AUC = 0.9719560624810738, Elapsed Time = 5.414685199997621 seconds
Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 56, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66050 validation-auc:0.94956 validation-aucpr:0.95245
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63061 validation-auc:0.96168 validation-aucpr:0.95996
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60337 validation-auc:0.96329 validation-aucpr:0.96246
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57825 validation-auc:0.96536 validation-aucpr:0.96247
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55729 validation-auc:0.96621 validation-aucpr:0.97090
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53588 validation-auc:0.96742 validation-aucpr:0.97180
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51580 validation-auc:0.96789 validation-aucpr:0.97089
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49737 validation-auc:0.96792 validation-aucpr:0.97075
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47988 validation-auc:0.96871 validation-aucpr:0.97288
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46348 validation-auc:0.96919 validation-aucpr:0.97326
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44839 validation-auc:0.96926 validation-aucpr:0.97332
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43432 validation-auc:0.96961 validation-aucpr:0.97355
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42096 validation-auc:0.96998 validation-aucpr:0.97396
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40859 validation-auc:0.96989 validation-aucpr:0.97392
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39686 validation-auc:0.96989 validation-aucpr:0.97394
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38587 validation-auc:0.97000 validation-aucpr:0.97402
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37564 validation-auc:0.97010 validation-aucpr:0.97415
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36579 validation-auc:0.97035 validation-aucpr:0.97421
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35666 validation-auc:0.97053 validation-aucpr:0.97435
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34802 validation-auc:0.97072 validation-aucpr:0.97437
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33994 validation-auc:0.97070 validation-aucpr:0.97434
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33198 validation-auc:0.97092 validation-aucpr:0.97448
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32470 validation-auc:0.97096 validation-aucpr:0.97461
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31881 validation-auc:0.97090 validation-aucpr:0.97437
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31242 validation-auc:0.97088 validation-aucpr:0.97436
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30611 validation-auc:0.97102 validation-aucpr:0.97448
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30035 validation-auc:0.97099 validation-aucpr:0.97446
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29486 validation-auc:0.97104 validation-aucpr:0.97450
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28960 validation-auc:0.97102 validation-aucpr:0.97447
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28492 validation-auc:0.97100 validation-aucpr:0.97447
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28035 validation-auc:0.97099 validation-aucpr:0.97447
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27613 validation-auc:0.97095 validation-aucpr:0.97438
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27192 validation-auc:0.97097 validation-aucpr:0.97441
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26798 validation-auc:0.97100 validation-aucpr:0.97444
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26446 validation-auc:0.97106 validation-aucpr:0.97446
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26090 validation-auc:0.97103 validation-aucpr:0.97443
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25730 validation-auc:0.97112 validation-aucpr:0.97453
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25402 validation-auc:0.97119 validation-aucpr:0.97457
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25090 validation-auc:0.97118 validation-aucpr:0.97456
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24801 validation-auc:0.97112 validation-aucpr:0.97451
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24531 validation-auc:0.97115 validation-aucpr:0.97454
[20:54:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24258 validation-auc:0.97119 validation-aucpr:0.97457
[20:54:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.24017 validation-auc:0.97121 validation-aucpr:0.97457
{'best_iteration': '22', 'best_score': '0.9746072960651041'}
Trial 56, Fold 5: Log loss = 0.24016825303238032, Average precision = 0.9744614167127172, ROC-AUC = 0.9712105233821972, Elapsed Time = 5.48138789999939 seconds
Optimization Progress: 57%|#####6 | 57/100 [2:55:22<42:08, 58.81s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 57, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67453 validation-auc:0.92966 validation-aucpr:0.92412
[1] validation-logloss:0.65734 validation-auc:0.94220 validation-aucpr:0.94079
[2] validation-logloss:0.64190 validation-auc:0.94381 validation-aucpr:0.94793
[3] validation-logloss:0.62513 validation-auc:0.95034 validation-aucpr:0.95588
[4] validation-logloss:0.61047 validation-auc:0.95093 validation-aucpr:0.95709
[5] validation-logloss:0.59542 validation-auc:0.95285 validation-aucpr:0.95884
[6] validation-logloss:0.58235 validation-auc:0.95329 validation-aucpr:0.95917
[7] validation-logloss:0.56761 validation-auc:0.95724 validation-aucpr:0.96378
[8] validation-logloss:0.55330 validation-auc:0.95864 validation-aucpr:0.96548
[9] validation-logloss:0.54248 validation-auc:0.95811 validation-aucpr:0.96489
[10] validation-logloss:0.53158 validation-auc:0.95876 validation-aucpr:0.96530
[11] validation-logloss:0.51977 validation-auc:0.95895 validation-aucpr:0.96559
[12] validation-logloss:0.50953 validation-auc:0.95900 validation-aucpr:0.96570
[13] validation-logloss:0.50093 validation-auc:0.95857 validation-aucpr:0.96533
[14] validation-logloss:0.49037 validation-auc:0.95885 validation-aucpr:0.96570
[15] validation-logloss:0.48179 validation-auc:0.95892 validation-aucpr:0.96568
[16] validation-logloss:0.47358 validation-auc:0.95888 validation-aucpr:0.96558
[17] validation-logloss:0.46601 validation-auc:0.95864 validation-aucpr:0.96541
[18] validation-logloss:0.45761 validation-auc:0.95848 validation-aucpr:0.96536
[19] validation-logloss:0.44815 validation-auc:0.95907 validation-aucpr:0.96591
[20] validation-logloss:0.43952 validation-auc:0.95952 validation-aucpr:0.96641
[21] validation-logloss:0.43300 validation-auc:0.95957 validation-aucpr:0.96643
[22] validation-logloss:0.42651 validation-auc:0.95975 validation-aucpr:0.96658
[23] validation-logloss:0.41861 validation-auc:0.95982 validation-aucpr:0.96674
[24] validation-logloss:0.41035 validation-auc:0.96038 validation-aucpr:0.96730
{'best_iteration': '24', 'best_score': '0.967297137732974'}
Trial 57, Fold 1: Log loss = 0.41034771616691573, Average precision = 0.9673015836050309, ROC-AUC = 0.9603838517992763, Elapsed Time = 1.044937900001969 seconds
Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 57, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67493 validation-auc:0.92659 validation-aucpr:0.91603
[1] validation-logloss:0.65440 validation-auc:0.95558 validation-aucpr:0.96034
[2] validation-logloss:0.63804 validation-auc:0.95563 validation-aucpr:0.96028
[3] validation-logloss:0.62193 validation-auc:0.95796 validation-aucpr:0.96183
[4] validation-logloss:0.60719 validation-auc:0.95712 validation-aucpr:0.96109
[5] validation-logloss:0.59324 validation-auc:0.95693 validation-aucpr:0.96071
[6] validation-logloss:0.57882 validation-auc:0.95797 validation-aucpr:0.96157
[7] validation-logloss:0.56650 validation-auc:0.95736 validation-aucpr:0.96121
[8] validation-logloss:0.55490 validation-auc:0.95750 validation-aucpr:0.96111
[9] validation-logloss:0.54254 validation-auc:0.95863 validation-aucpr:0.96241
[10] validation-logloss:0.53208 validation-auc:0.95793 validation-aucpr:0.96168
[11] validation-logloss:0.51964 validation-auc:0.95966 validation-aucpr:0.96375
[12] validation-logloss:0.50705 validation-auc:0.96118 validation-aucpr:0.96578
[13] validation-logloss:0.49555 validation-auc:0.96162 validation-aucpr:0.96616
[14] validation-logloss:0.48666 validation-auc:0.96141 validation-aucpr:0.96585
[15] validation-logloss:0.47513 validation-auc:0.96223 validation-aucpr:0.96669
[16] validation-logloss:0.46474 validation-auc:0.96290 validation-aucpr:0.96736
[17] validation-logloss:0.45657 validation-auc:0.96301 validation-aucpr:0.96737
[18] validation-logloss:0.44932 validation-auc:0.96257 validation-aucpr:0.96691
[19] validation-logloss:0.44048 validation-auc:0.96280 validation-aucpr:0.96714
[20] validation-logloss:0.43353 validation-auc:0.96272 validation-aucpr:0.96704
[21] validation-logloss:0.42738 validation-auc:0.96251 validation-aucpr:0.96683
[22] validation-logloss:0.42151 validation-auc:0.96242 validation-aucpr:0.96672
[23] validation-logloss:0.41597 validation-auc:0.96235 validation-aucpr:0.96664
[24] validation-logloss:0.41013 validation-auc:0.96245 validation-aucpr:0.96669
{'best_iteration': '17', 'best_score': '0.9673667641165824'}
Trial 57, Fold 2: Log loss = 0.4101304550323175, Average precision = 0.9666330371412952, ROC-AUC = 0.9624493276207374, Elapsed Time = 1.3475201000001107 seconds
Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 57, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67449 validation-auc:0.93199 validation-aucpr:0.92687
[1] validation-logloss:0.65477 validation-auc:0.95745 validation-aucpr:0.96278
[2] validation-logloss:0.63839 validation-auc:0.95725 validation-aucpr:0.96317
[3] validation-logloss:0.62300 validation-auc:0.95666 validation-aucpr:0.96261
[4] validation-logloss:0.60752 validation-auc:0.95706 validation-aucpr:0.96320
[5] validation-logloss:0.59387 validation-auc:0.95680 validation-aucpr:0.96336
[6] validation-logloss:0.58044 validation-auc:0.95810 validation-aucpr:0.96379
[7] validation-logloss:0.56757 validation-auc:0.95870 validation-aucpr:0.96417
[8] validation-logloss:0.55673 validation-auc:0.95799 validation-aucpr:0.96354
[9] validation-logloss:0.54440 validation-auc:0.95855 validation-aucpr:0.96395
[10] validation-logloss:0.53330 validation-auc:0.95814 validation-aucpr:0.96352
[11] validation-logloss:0.52290 validation-auc:0.95761 validation-aucpr:0.96295
[12] validation-logloss:0.51239 validation-auc:0.95829 validation-aucpr:0.96379
[13] validation-logloss:0.50305 validation-auc:0.95823 validation-aucpr:0.96375
[14] validation-logloss:0.49428 validation-auc:0.95778 validation-aucpr:0.96334
[15] validation-logloss:0.48541 validation-auc:0.95800 validation-aucpr:0.96341
[16] validation-logloss:0.47637 validation-auc:0.95854 validation-aucpr:0.96369
[17] validation-logloss:0.46880 validation-auc:0.95807 validation-aucpr:0.96322
[18] validation-logloss:0.46171 validation-auc:0.95788 validation-aucpr:0.96306
[19] validation-logloss:0.45486 validation-auc:0.95767 validation-aucpr:0.96290
[20] validation-logloss:0.44828 validation-auc:0.95755 validation-aucpr:0.96278
[21] validation-logloss:0.43986 validation-auc:0.95857 validation-aucpr:0.96410
[22] validation-logloss:0.43406 validation-auc:0.95835 validation-aucpr:0.96395
[23] validation-logloss:0.42816 validation-auc:0.95807 validation-aucpr:0.96371
[24] validation-logloss:0.42208 validation-auc:0.95836 validation-aucpr:0.96387
{'best_iteration': '7', 'best_score': '0.9641735778234024'}
Trial 57, Fold 3: Log loss = 0.42207755835198346, Average precision = 0.9638774698876118, ROC-AUC = 0.9583618753069894, Elapsed Time = 1.2980142000014894 seconds
Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 57, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67487 validation-auc:0.92848 validation-aucpr:0.92519
[1] validation-logloss:0.65758 validation-auc:0.93797 validation-aucpr:0.93847
[2] validation-logloss:0.64007 validation-auc:0.95223 validation-aucpr:0.95651
[3] validation-logloss:0.62384 validation-auc:0.95442 validation-aucpr:0.96065
[4] validation-logloss:0.60735 validation-auc:0.95637 validation-aucpr:0.96316
[5] validation-logloss:0.59402 validation-auc:0.95596 validation-aucpr:0.96283
[6] validation-logloss:0.58079 validation-auc:0.95533 validation-aucpr:0.96204
[7] validation-logloss:0.56572 validation-auc:0.95708 validation-aucpr:0.96393
[8] validation-logloss:0.55312 validation-auc:0.95708 validation-aucpr:0.96397
[9] validation-logloss:0.53975 validation-auc:0.95803 validation-aucpr:0.96481
[10] validation-logloss:0.52881 validation-auc:0.95869 validation-aucpr:0.96522
[11] validation-logloss:0.51717 validation-auc:0.95914 validation-aucpr:0.96566
[12] validation-logloss:0.50687 validation-auc:0.95932 validation-aucpr:0.96581
[13] validation-logloss:0.49786 validation-auc:0.95874 validation-aucpr:0.96533
[14] validation-logloss:0.48824 validation-auc:0.95920 validation-aucpr:0.96564
[15] validation-logloss:0.47747 validation-auc:0.96011 validation-aucpr:0.96646
[16] validation-logloss:0.46911 validation-auc:0.96021 validation-aucpr:0.96650
[17] validation-logloss:0.46103 validation-auc:0.96070 validation-aucpr:0.96690
[18] validation-logloss:0.45382 validation-auc:0.96053 validation-aucpr:0.96667
[19] validation-logloss:0.44491 validation-auc:0.96111 validation-aucpr:0.96724
[20] validation-logloss:0.43808 validation-auc:0.96107 validation-aucpr:0.96717
[21] validation-logloss:0.42941 validation-auc:0.96140 validation-aucpr:0.96752
[22] validation-logloss:0.42274 validation-auc:0.96152 validation-aucpr:0.96760
[23] validation-logloss:0.41489 validation-auc:0.96189 validation-aucpr:0.96801
[24] validation-logloss:0.40935 validation-auc:0.96188 validation-aucpr:0.96799
{'best_iteration': '23', 'best_score': '0.9680064618656231'}
Trial 57, Fold 4: Log loss = 0.409350621791954, Average precision = 0.9679871158277571, ROC-AUC = 0.9618803664122502, Elapsed Time = 1.265817600000446 seconds
Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 57, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67495 validation-auc:0.91969 validation-aucpr:0.90610
[1] validation-logloss:0.65813 validation-auc:0.93588 validation-aucpr:0.93828
[2] validation-logloss:0.64248 validation-auc:0.93797 validation-aucpr:0.94127
[3] validation-logloss:0.62754 validation-auc:0.93957 validation-aucpr:0.94455
[4] validation-logloss:0.61008 validation-auc:0.94994 validation-aucpr:0.95687
[5] validation-logloss:0.59344 validation-auc:0.95363 validation-aucpr:0.96018
[6] validation-logloss:0.58120 validation-auc:0.95313 validation-aucpr:0.96021
[7] validation-logloss:0.56863 validation-auc:0.95286 validation-aucpr:0.96003
[8] validation-logloss:0.55453 validation-auc:0.95443 validation-aucpr:0.96131
[9] validation-logloss:0.54319 validation-auc:0.95455 validation-aucpr:0.96132
[10] validation-logloss:0.53230 validation-auc:0.95490 validation-aucpr:0.96160
[11] validation-logloss:0.52226 validation-auc:0.95486 validation-aucpr:0.96151
[12] validation-logloss:0.51321 validation-auc:0.95474 validation-aucpr:0.96134
[13] validation-logloss:0.50445 validation-auc:0.95453 validation-aucpr:0.96117
[14] validation-logloss:0.49649 validation-auc:0.95444 validation-aucpr:0.96145
[15] validation-logloss:0.48890 validation-auc:0.95432 validation-aucpr:0.96129
[16] validation-logloss:0.48053 validation-auc:0.95437 validation-aucpr:0.96124
[17] validation-logloss:0.47301 validation-auc:0.95430 validation-aucpr:0.96114
[18] validation-logloss:0.46379 validation-auc:0.95513 validation-aucpr:0.96189
[19] validation-logloss:0.45706 validation-auc:0.95482 validation-aucpr:0.96159
[20] validation-logloss:0.44790 validation-auc:0.95561 validation-aucpr:0.96244
[21] validation-logloss:0.44038 validation-auc:0.95607 validation-aucpr:0.96272
[22] validation-logloss:0.43436 validation-auc:0.95596 validation-aucpr:0.96254
[23] validation-logloss:0.42855 validation-auc:0.95596 validation-aucpr:0.96258
[24] validation-logloss:0.42321 validation-auc:0.95581 validation-aucpr:0.96247
{'best_iteration': '21', 'best_score': '0.9627214154090976'}
Trial 57, Fold 5: Log loss = 0.42320609848369695, Average precision = 0.9624687066772707, ROC-AUC = 0.9558063281496758, Elapsed Time = 1.2785884000004444 seconds
Optimization Progress: 58%|#####8 | 58/100 [2:55:36<31:44, 45.34s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 58, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68570 validation-auc:0.91254 validation-aucpr:0.88376
[1] validation-logloss:0.67832 validation-auc:0.94100 validation-aucpr:0.94213
[2] validation-logloss:0.67010 validation-auc:0.95528 validation-aucpr:0.95899
[3] validation-logloss:0.66274 validation-auc:0.95750 validation-aucpr:0.96288
[4] validation-logloss:0.65580 validation-auc:0.95827 validation-aucpr:0.96318
[5] validation-logloss:0.64802 validation-auc:0.96097 validation-aucpr:0.96472
[6] validation-logloss:0.64046 validation-auc:0.96242 validation-aucpr:0.96642
[7] validation-logloss:0.63304 validation-auc:0.96390 validation-aucpr:0.96787
[8] validation-logloss:0.62653 validation-auc:0.96393 validation-aucpr:0.96765
[9] validation-logloss:0.61939 validation-auc:0.96481 validation-aucpr:0.96922
[10] validation-logloss:0.61221 validation-auc:0.96560 validation-aucpr:0.96988
[11] validation-logloss:0.60527 validation-auc:0.96607 validation-aucpr:0.97025
[12] validation-logloss:0.59849 validation-auc:0.96652 validation-aucpr:0.97046
[13] validation-logloss:0.59273 validation-auc:0.96653 validation-aucpr:0.97034
[14] validation-logloss:0.58714 validation-auc:0.96627 validation-aucpr:0.97131
[15] validation-logloss:0.58070 validation-auc:0.96658 validation-aucpr:0.97166
[16] validation-logloss:0.57518 validation-auc:0.96624 validation-aucpr:0.97137
[17] validation-logloss:0.56915 validation-auc:0.96639 validation-aucpr:0.97148
[18] validation-logloss:0.56409 validation-auc:0.96615 validation-aucpr:0.97130
[19] validation-logloss:0.55798 validation-auc:0.96654 validation-aucpr:0.97166
[20] validation-logloss:0.55210 validation-auc:0.96683 validation-aucpr:0.97193
[21] validation-logloss:0.54720 validation-auc:0.96682 validation-aucpr:0.97183
[22] validation-logloss:0.54187 validation-auc:0.96674 validation-aucpr:0.97200
[23] validation-logloss:0.53698 validation-auc:0.96655 validation-aucpr:0.97188
[24] validation-logloss:0.53140 validation-auc:0.96689 validation-aucpr:0.97218
[25] validation-logloss:0.52682 validation-auc:0.96677 validation-aucpr:0.97205
[26] validation-logloss:0.52235 validation-auc:0.96681 validation-aucpr:0.97206
[27] validation-logloss:0.51763 validation-auc:0.96699 validation-aucpr:0.97226
[28] validation-logloss:0.51317 validation-auc:0.96691 validation-aucpr:0.97222
[29] validation-logloss:0.50894 validation-auc:0.96687 validation-aucpr:0.97215
[30] validation-logloss:0.50456 validation-auc:0.96687 validation-aucpr:0.97213
[31] validation-logloss:0.50034 validation-auc:0.96709 validation-aucpr:0.97229
[32] validation-logloss:0.49615 validation-auc:0.96708 validation-aucpr:0.97226
[33] validation-logloss:0.49220 validation-auc:0.96704 validation-aucpr:0.97221
[34] validation-logloss:0.48831 validation-auc:0.96701 validation-aucpr:0.97217
[35] validation-logloss:0.48379 validation-auc:0.96724 validation-aucpr:0.97236
[36] validation-logloss:0.47931 validation-auc:0.96735 validation-aucpr:0.97245
[37] validation-logloss:0.47550 validation-auc:0.96747 validation-aucpr:0.97251
[38] validation-logloss:0.47186 validation-auc:0.96742 validation-aucpr:0.97245
[39] validation-logloss:0.46818 validation-auc:0.96740 validation-aucpr:0.97243
[40] validation-logloss:0.46391 validation-auc:0.96767 validation-aucpr:0.97264
[41] validation-logloss:0.45976 validation-auc:0.96776 validation-aucpr:0.97275
[42] validation-logloss:0.45624 validation-auc:0.96792 validation-aucpr:0.97283
[43] validation-logloss:0.45283 validation-auc:0.96791 validation-aucpr:0.97281
[44] validation-logloss:0.44912 validation-auc:0.96793 validation-aucpr:0.97287
[45] validation-logloss:0.44534 validation-auc:0.96811 validation-aucpr:0.97302
[46] validation-logloss:0.44157 validation-auc:0.96822 validation-aucpr:0.97313
[47] validation-logloss:0.43845 validation-auc:0.96814 validation-aucpr:0.97304
[48] validation-logloss:0.43543 validation-auc:0.96803 validation-aucpr:0.97294
[49] validation-logloss:0.43252 validation-auc:0.96797 validation-aucpr:0.97286
[50] validation-logloss:0.42945 validation-auc:0.96801 validation-aucpr:0.97288
[51] validation-logloss:0.42646 validation-auc:0.96806 validation-aucpr:0.97293
{'best_iteration': '46', 'best_score': '0.9731310062419574'}
Trial 58, Fold 1: Log loss = 0.4264604090034749, Average precision = 0.9729299750562228, ROC-AUC = 0.9680568848788135, Elapsed Time = 9.525947300000553 seconds
Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 58, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68512 validation-auc:0.92130 validation-aucpr:0.88875
[1] validation-logloss:0.67730 validation-auc:0.95032 validation-aucpr:0.94661
[2] validation-logloss:0.66981 validation-auc:0.95501 validation-aucpr:0.95575
[3] validation-logloss:0.66258 validation-auc:0.95662 validation-aucpr:0.95999
[4] validation-logloss:0.65461 validation-auc:0.96271 validation-aucpr:0.96636
[5] validation-logloss:0.64768 validation-auc:0.96284 validation-aucpr:0.96632
[6] validation-logloss:0.64000 validation-auc:0.96516 validation-aucpr:0.96887
[7] validation-logloss:0.63255 validation-auc:0.96619 validation-aucpr:0.96992
[8] validation-logloss:0.62583 validation-auc:0.96630 validation-aucpr:0.96990
[9] validation-logloss:0.61871 validation-auc:0.96692 validation-aucpr:0.97051
[10] validation-logloss:0.61167 validation-auc:0.96710 validation-aucpr:0.97068
[11] validation-logloss:0.60537 validation-auc:0.96752 validation-aucpr:0.97093
[12] validation-logloss:0.59870 validation-auc:0.96786 validation-aucpr:0.97133
[13] validation-logloss:0.59291 validation-auc:0.96797 validation-aucpr:0.97130
[14] validation-logloss:0.58710 validation-auc:0.96825 validation-aucpr:0.97152
[15] validation-logloss:0.58073 validation-auc:0.96827 validation-aucpr:0.97163
[16] validation-logloss:0.57506 validation-auc:0.96863 validation-aucpr:0.97190
[17] validation-logloss:0.56945 validation-auc:0.96874 validation-aucpr:0.97196
[18] validation-logloss:0.56390 validation-auc:0.96882 validation-aucpr:0.97196
[19] validation-logloss:0.55861 validation-auc:0.96877 validation-aucpr:0.97190
[20] validation-logloss:0.55339 validation-auc:0.96879 validation-aucpr:0.97189
[21] validation-logloss:0.54818 validation-auc:0.96895 validation-aucpr:0.97195
[22] validation-logloss:0.54327 validation-auc:0.96897 validation-aucpr:0.97191
[23] validation-logloss:0.53834 validation-auc:0.96912 validation-aucpr:0.97197
[24] validation-logloss:0.53287 validation-auc:0.96938 validation-aucpr:0.97223
[25] validation-logloss:0.52752 validation-auc:0.96982 validation-aucpr:0.97265
[26] validation-logloss:0.52288 validation-auc:0.96971 validation-aucpr:0.97254
[27] validation-logloss:0.51762 validation-auc:0.96990 validation-aucpr:0.97270
[28] validation-logloss:0.51240 validation-auc:0.97010 validation-aucpr:0.97293
[29] validation-logloss:0.50791 validation-auc:0.97006 validation-aucpr:0.97290
[30] validation-logloss:0.50298 validation-auc:0.97012 validation-aucpr:0.97299
[31] validation-logloss:0.49875 validation-auc:0.97004 validation-aucpr:0.97289
[32] validation-logloss:0.49444 validation-auc:0.97015 validation-aucpr:0.97295
[33] validation-logloss:0.49041 validation-auc:0.97011 validation-aucpr:0.97292
[34] validation-logloss:0.48641 validation-auc:0.97007 validation-aucpr:0.97287
[35] validation-logloss:0.48178 validation-auc:0.97025 validation-aucpr:0.97306
[36] validation-logloss:0.47736 validation-auc:0.97034 validation-aucpr:0.97317
[37] validation-logloss:0.47303 validation-auc:0.97040 validation-aucpr:0.97325
[38] validation-logloss:0.46936 validation-auc:0.97053 validation-aucpr:0.97332
[39] validation-logloss:0.46493 validation-auc:0.97069 validation-aucpr:0.97348
[40] validation-logloss:0.46083 validation-auc:0.97075 validation-aucpr:0.97341
[41] validation-logloss:0.45732 validation-auc:0.97073 validation-aucpr:0.97339
[42] validation-logloss:0.45385 validation-auc:0.97080 validation-aucpr:0.97343
[43] validation-logloss:0.44977 validation-auc:0.97095 validation-aucpr:0.97355
[44] validation-logloss:0.44637 validation-auc:0.97099 validation-aucpr:0.97360
[45] validation-logloss:0.44316 validation-auc:0.97096 validation-aucpr:0.97357
[46] validation-logloss:0.43938 validation-auc:0.97102 validation-aucpr:0.97363
[47] validation-logloss:0.43614 validation-auc:0.97108 validation-aucpr:0.97366
[48] validation-logloss:0.43322 validation-auc:0.97106 validation-aucpr:0.97364
[49] validation-logloss:0.43010 validation-auc:0.97108 validation-aucpr:0.97367
[50] validation-logloss:0.42656 validation-auc:0.97108 validation-aucpr:0.97377
[51] validation-logloss:0.42303 validation-auc:0.97118 validation-aucpr:0.97386
{'best_iteration': '51', 'best_score': '0.9738633077380496'}
Trial 58, Fold 2: Log loss = 0.4230341057484318, Average precision = 0.9737971510136811, ROC-AUC = 0.9711782396103601, Elapsed Time = 9.858536900002946 seconds
Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 58, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68538 validation-auc:0.91583 validation-aucpr:0.87047
[1] validation-logloss:0.67757 validation-auc:0.94953 validation-aucpr:0.94006
[2] validation-logloss:0.67011 validation-auc:0.95516 validation-aucpr:0.95680
[3] validation-logloss:0.66266 validation-auc:0.95872 validation-aucpr:0.96160
[4] validation-logloss:0.65549 validation-auc:0.96005 validation-aucpr:0.96284
[5] validation-logloss:0.64748 validation-auc:0.96583 validation-aucpr:0.96858
[6] validation-logloss:0.63974 validation-auc:0.96826 validation-aucpr:0.97116
[7] validation-logloss:0.63209 validation-auc:0.96895 validation-aucpr:0.97183
[8] validation-logloss:0.62452 validation-auc:0.96928 validation-aucpr:0.97219
[9] validation-logloss:0.61846 validation-auc:0.96915 validation-aucpr:0.97346
[10] validation-logloss:0.61129 validation-auc:0.96941 validation-aucpr:0.97366
[11] validation-logloss:0.60511 validation-auc:0.96930 validation-aucpr:0.97353
[12] validation-logloss:0.59910 validation-auc:0.96904 validation-aucpr:0.97321
[13] validation-logloss:0.59231 validation-auc:0.96930 validation-aucpr:0.97346
[14] validation-logloss:0.58639 validation-auc:0.96944 validation-aucpr:0.97356
[15] validation-logloss:0.57991 validation-auc:0.96976 validation-aucpr:0.97383
[16] validation-logloss:0.57415 validation-auc:0.96973 validation-aucpr:0.97378
[17] validation-logloss:0.56786 validation-auc:0.96999 validation-aucpr:0.97409
[18] validation-logloss:0.56194 validation-auc:0.97000 validation-aucpr:0.97415
[19] validation-logloss:0.55605 validation-auc:0.97000 validation-aucpr:0.97419
[20] validation-logloss:0.55078 validation-auc:0.97018 validation-aucpr:0.97428
[21] validation-logloss:0.54561 validation-auc:0.97017 validation-aucpr:0.97424
[22] validation-logloss:0.54077 validation-auc:0.97004 validation-aucpr:0.97412
[23] validation-logloss:0.53579 validation-auc:0.97005 validation-aucpr:0.97409
[24] validation-logloss:0.53103 validation-auc:0.97013 validation-aucpr:0.97415
[25] validation-logloss:0.52574 validation-auc:0.97013 validation-aucpr:0.97417
[26] validation-logloss:0.52037 validation-auc:0.97022 validation-aucpr:0.97427
[27] validation-logloss:0.51515 validation-auc:0.97041 validation-aucpr:0.97444
[28] validation-logloss:0.51076 validation-auc:0.97038 validation-aucpr:0.97440
[29] validation-logloss:0.50583 validation-auc:0.97034 validation-aucpr:0.97438
[30] validation-logloss:0.50151 validation-auc:0.97045 validation-aucpr:0.97444
[31] validation-logloss:0.49740 validation-auc:0.97049 validation-aucpr:0.97445
[32] validation-logloss:0.49316 validation-auc:0.97048 validation-aucpr:0.97443
[33] validation-logloss:0.48905 validation-auc:0.97042 validation-aucpr:0.97438
[34] validation-logloss:0.48519 validation-auc:0.97035 validation-aucpr:0.97432
[35] validation-logloss:0.48063 validation-auc:0.97034 validation-aucpr:0.97431
[36] validation-logloss:0.47618 validation-auc:0.97038 validation-aucpr:0.97439
[37] validation-logloss:0.47175 validation-auc:0.97047 validation-aucpr:0.97446
[38] validation-logloss:0.46818 validation-auc:0.97036 validation-aucpr:0.97435
[39] validation-logloss:0.46417 validation-auc:0.97042 validation-aucpr:0.97441
[40] validation-logloss:0.45997 validation-auc:0.97047 validation-aucpr:0.97446
[41] validation-logloss:0.45604 validation-auc:0.97044 validation-aucpr:0.97448
[42] validation-logloss:0.45257 validation-auc:0.97037 validation-aucpr:0.97442
[43] validation-logloss:0.44853 validation-auc:0.97052 validation-aucpr:0.97455
[44] validation-logloss:0.44523 validation-auc:0.97049 validation-aucpr:0.97452
[45] validation-logloss:0.44188 validation-auc:0.97054 validation-aucpr:0.97461
[46] validation-logloss:0.43879 validation-auc:0.97045 validation-aucpr:0.97452
[47] validation-logloss:0.43504 validation-auc:0.97047 validation-aucpr:0.97455
[48] validation-logloss:0.43136 validation-auc:0.97057 validation-aucpr:0.97464
[49] validation-logloss:0.42786 validation-auc:0.97061 validation-aucpr:0.97467
[50] validation-logloss:0.42488 validation-auc:0.97049 validation-aucpr:0.97463
[51] validation-logloss:0.42190 validation-auc:0.97047 validation-aucpr:0.97458
{'best_iteration': '49', 'best_score': '0.9746702082923872'}
Trial 58, Fold 3: Log loss = 0.4218982546432991, Average precision = 0.9745859170622088, ROC-AUC = 0.9704738047885506, Elapsed Time = 9.829640899999504 seconds
Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 58, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68537 validation-auc:0.90713 validation-aucpr:0.86614
[1] validation-logloss:0.67684 validation-auc:0.94933 validation-aucpr:0.94181
[2] validation-logloss:0.66957 validation-auc:0.95704 validation-aucpr:0.95876
[3] validation-logloss:0.66130 validation-auc:0.96109 validation-aucpr:0.96457
[4] validation-logloss:0.65334 validation-auc:0.96373 validation-aucpr:0.96961
[5] validation-logloss:0.64557 validation-auc:0.96458 validation-aucpr:0.97034
[6] validation-logloss:0.63877 validation-auc:0.96470 validation-aucpr:0.97057
[7] validation-logloss:0.63130 validation-auc:0.96560 validation-aucpr:0.97136
[8] validation-logloss:0.62498 validation-auc:0.96554 validation-aucpr:0.97123
[9] validation-logloss:0.61895 validation-auc:0.96544 validation-aucpr:0.97113
[10] validation-logloss:0.61251 validation-auc:0.96604 validation-aucpr:0.97154
[11] validation-logloss:0.60555 validation-auc:0.96629 validation-aucpr:0.97177
[12] validation-logloss:0.59958 validation-auc:0.96604 validation-aucpr:0.97152
[13] validation-logloss:0.59275 validation-auc:0.96677 validation-aucpr:0.97212
[14] validation-logloss:0.58626 validation-auc:0.96713 validation-aucpr:0.97245
[15] validation-logloss:0.57995 validation-auc:0.96738 validation-aucpr:0.97267
[16] validation-logloss:0.57356 validation-auc:0.96791 validation-aucpr:0.97308
[17] validation-logloss:0.56808 validation-auc:0.96792 validation-aucpr:0.97308
[18] validation-logloss:0.56204 validation-auc:0.96814 validation-aucpr:0.97327
[19] validation-logloss:0.55603 validation-auc:0.96833 validation-aucpr:0.97345
[20] validation-logloss:0.55038 validation-auc:0.96842 validation-aucpr:0.97350
[21] validation-logloss:0.54470 validation-auc:0.96841 validation-aucpr:0.97353
[22] validation-logloss:0.53960 validation-auc:0.96851 validation-aucpr:0.97363
[23] validation-logloss:0.53410 validation-auc:0.96854 validation-aucpr:0.97370
[24] validation-logloss:0.52937 validation-auc:0.96854 validation-aucpr:0.97366
[25] validation-logloss:0.52409 validation-auc:0.96849 validation-aucpr:0.97365
[26] validation-logloss:0.51938 validation-auc:0.96853 validation-aucpr:0.97369
[27] validation-logloss:0.51480 validation-auc:0.96865 validation-aucpr:0.97377
[28] validation-logloss:0.51045 validation-auc:0.96860 validation-aucpr:0.97370
[29] validation-logloss:0.50556 validation-auc:0.96872 validation-aucpr:0.97380
[30] validation-logloss:0.50079 validation-auc:0.96884 validation-aucpr:0.97391
[31] validation-logloss:0.49589 validation-auc:0.96911 validation-aucpr:0.97413
[32] validation-logloss:0.49123 validation-auc:0.96919 validation-aucpr:0.97420
[33] validation-logloss:0.48665 validation-auc:0.96928 validation-aucpr:0.97429
[34] validation-logloss:0.48258 validation-auc:0.96937 validation-aucpr:0.97435
[35] validation-logloss:0.47814 validation-auc:0.96952 validation-aucpr:0.97444
[36] validation-logloss:0.47449 validation-auc:0.96946 validation-aucpr:0.97440
[37] validation-logloss:0.47055 validation-auc:0.96950 validation-aucpr:0.97441
[38] validation-logloss:0.46635 validation-auc:0.96958 validation-aucpr:0.97447
[39] validation-logloss:0.46288 validation-auc:0.96941 validation-aucpr:0.97435
[40] validation-logloss:0.45874 validation-auc:0.96945 validation-aucpr:0.97439
[41] validation-logloss:0.45467 validation-auc:0.96958 validation-aucpr:0.97452
[42] validation-logloss:0.45072 validation-auc:0.96965 validation-aucpr:0.97457
[43] validation-logloss:0.44691 validation-auc:0.96975 validation-aucpr:0.97463
[44] validation-logloss:0.44304 validation-auc:0.96987 validation-aucpr:0.97471
[45] validation-logloss:0.43926 validation-auc:0.96996 validation-aucpr:0.97477
[46] validation-logloss:0.43587 validation-auc:0.97009 validation-aucpr:0.97485
[47] validation-logloss:0.43221 validation-auc:0.97011 validation-aucpr:0.97486
[48] validation-logloss:0.42905 validation-auc:0.97013 validation-aucpr:0.97487
[49] validation-logloss:0.42549 validation-auc:0.97017 validation-aucpr:0.97490
[50] validation-logloss:0.42197 validation-auc:0.97032 validation-aucpr:0.97502
[51] validation-logloss:0.41858 validation-auc:0.97035 validation-aucpr:0.97505
{'best_iteration': '51', 'best_score': '0.9750538421258711'}
Trial 58, Fold 4: Log loss = 0.41858497123120914, Average precision = 0.9750528920264302, ROC-AUC = 0.9703511311639974, Elapsed Time = 10.098115500000858 seconds
Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 58, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68543 validation-auc:0.91494 validation-aucpr:0.88175
[1] validation-logloss:0.67780 validation-auc:0.94480 validation-aucpr:0.93673
[2] validation-logloss:0.66948 validation-auc:0.95846 validation-aucpr:0.95619
[3] validation-logloss:0.66231 validation-auc:0.95895 validation-aucpr:0.96348
[4] validation-logloss:0.65428 validation-auc:0.96190 validation-aucpr:0.96671
[5] validation-logloss:0.64748 validation-auc:0.96177 validation-aucpr:0.96650
[6] validation-logloss:0.63987 validation-auc:0.96323 validation-aucpr:0.96802
[7] validation-logloss:0.63319 validation-auc:0.96324 validation-aucpr:0.96798
[8] validation-logloss:0.62674 validation-auc:0.96331 validation-aucpr:0.96791
[9] validation-logloss:0.61949 validation-auc:0.96423 validation-aucpr:0.96881
[10] validation-logloss:0.61354 validation-auc:0.96399 validation-aucpr:0.96836
[11] validation-logloss:0.60681 validation-auc:0.96454 validation-aucpr:0.96887
[12] validation-logloss:0.60094 validation-auc:0.96416 validation-aucpr:0.96850
[13] validation-logloss:0.59417 validation-auc:0.96471 validation-aucpr:0.96909
[14] validation-logloss:0.58840 validation-auc:0.96490 validation-aucpr:0.96916
[15] validation-logloss:0.58220 validation-auc:0.96537 validation-aucpr:0.96948
[16] validation-logloss:0.57659 validation-auc:0.96532 validation-aucpr:0.96945
[17] validation-logloss:0.57114 validation-auc:0.96523 validation-aucpr:0.96981
[18] validation-logloss:0.56575 validation-auc:0.96513 validation-aucpr:0.96972
[19] validation-logloss:0.55976 validation-auc:0.96524 validation-aucpr:0.96988
[20] validation-logloss:0.55410 validation-auc:0.96527 validation-aucpr:0.96999
[21] validation-logloss:0.54920 validation-auc:0.96526 validation-aucpr:0.96989
[22] validation-logloss:0.54348 validation-auc:0.96570 validation-aucpr:0.97025
[23] validation-logloss:0.53790 validation-auc:0.96594 validation-aucpr:0.97046
[24] validation-logloss:0.53318 validation-auc:0.96590 validation-aucpr:0.97041
[25] validation-logloss:0.52790 validation-auc:0.96602 validation-aucpr:0.97055
[26] validation-logloss:0.52287 validation-auc:0.96604 validation-aucpr:0.97061
[27] validation-logloss:0.51843 validation-auc:0.96604 validation-aucpr:0.97059
[28] validation-logloss:0.51331 validation-auc:0.96634 validation-aucpr:0.97083
[29] validation-logloss:0.50931 validation-auc:0.96614 validation-aucpr:0.97066
[30] validation-logloss:0.50516 validation-auc:0.96605 validation-aucpr:0.97058
[31] validation-logloss:0.50083 validation-auc:0.96625 validation-aucpr:0.97074
[32] validation-logloss:0.49602 validation-auc:0.96653 validation-aucpr:0.97101
[33] validation-logloss:0.49224 validation-auc:0.96645 validation-aucpr:0.97093
[34] validation-logloss:0.48788 validation-auc:0.96638 validation-aucpr:0.97090
[35] validation-logloss:0.48425 validation-auc:0.96624 validation-aucpr:0.97077
[36] validation-logloss:0.47971 validation-auc:0.96647 validation-aucpr:0.97096
[37] validation-logloss:0.47543 validation-auc:0.96667 validation-aucpr:0.97138
[38] validation-logloss:0.47179 validation-auc:0.96665 validation-aucpr:0.97135
[39] validation-logloss:0.46751 validation-auc:0.96678 validation-aucpr:0.97147
[40] validation-logloss:0.46353 validation-auc:0.96667 validation-aucpr:0.97139
[41] validation-logloss:0.46004 validation-auc:0.96668 validation-aucpr:0.97141
[42] validation-logloss:0.45657 validation-auc:0.96677 validation-aucpr:0.97146
[43] validation-logloss:0.45256 validation-auc:0.96690 validation-aucpr:0.97159
[44] validation-logloss:0.44918 validation-auc:0.96696 validation-aucpr:0.97162
[45] validation-logloss:0.44598 validation-auc:0.96686 validation-aucpr:0.97155
[46] validation-logloss:0.44267 validation-auc:0.96679 validation-aucpr:0.97151
[47] validation-logloss:0.43958 validation-auc:0.96675 validation-aucpr:0.97147
[48] validation-logloss:0.43632 validation-auc:0.96680 validation-aucpr:0.97150
[49] validation-logloss:0.43273 validation-auc:0.96683 validation-aucpr:0.97155
[50] validation-logloss:0.42965 validation-auc:0.96686 validation-aucpr:0.97154
[51] validation-logloss:0.42667 validation-auc:0.96689 validation-aucpr:0.97162
{'best_iteration': '51', 'best_score': '0.9716200229220133'}
Trial 58, Fold 5: Log loss = 0.42666805150415726, Average precision = 0.9716291604384265, ROC-AUC = 0.9668851606190663, Elapsed Time = 9.674562900003366 seconds
Optimization Progress: 59%|#####8 | 59/100 [2:56:32<33:16, 48.70s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 59, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67899 validation-auc:0.92790 validation-aucpr:0.92841
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66477 validation-auc:0.94561 validation-aucpr:0.94981
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64949 validation-auc:0.95777 validation-aucpr:0.96367
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63710 validation-auc:0.95702 validation-aucpr:0.96296
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62566 validation-auc:0.95598 validation-aucpr:0.96186
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61183 validation-auc:0.95884 validation-aucpr:0.96522
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60139 validation-auc:0.95814 validation-aucpr:0.96473
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59172 validation-auc:0.95718 validation-aucpr:0.96394
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58145 validation-auc:0.95756 validation-aucpr:0.96419
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57007 validation-auc:0.95857 validation-aucpr:0.96552
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56014 validation-auc:0.95915 validation-aucpr:0.96575
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55085 validation-auc:0.95933 validation-aucpr:0.96571
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54272 validation-auc:0.95896 validation-aucpr:0.96524
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53227 validation-auc:0.95929 validation-aucpr:0.96572
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52406 validation-auc:0.95928 validation-aucpr:0.96576
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51473 validation-auc:0.95969 validation-aucpr:0.96618
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50589 validation-auc:0.95988 validation-aucpr:0.96641
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49852 validation-auc:0.95985 validation-aucpr:0.96647
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49187 validation-auc:0.95967 validation-aucpr:0.96628
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48482 validation-auc:0.95964 validation-aucpr:0.96614
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47792 validation-auc:0.96003 validation-aucpr:0.96645
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47136 validation-auc:0.96054 validation-aucpr:0.96680
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.46562 validation-auc:0.96050 validation-aucpr:0.96668
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.46021 validation-auc:0.96018 validation-aucpr:0.96640
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.45432 validation-auc:0.96020 validation-aucpr:0.96644
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44804 validation-auc:0.96079 validation-aucpr:0.96692
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.44086 validation-auc:0.96144 validation-aucpr:0.96750
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.43622 validation-auc:0.96134 validation-aucpr:0.96740
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43010 validation-auc:0.96148 validation-aucpr:0.96760
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.42475 validation-auc:0.96186 validation-aucpr:0.96790
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41999 validation-auc:0.96184 validation-aucpr:0.96786
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.41374 validation-auc:0.96235 validation-aucpr:0.96831
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40748 validation-auc:0.96266 validation-aucpr:0.96861
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.40208 validation-auc:0.96262 validation-aucpr:0.96865
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.39824 validation-auc:0.96249 validation-aucpr:0.96855
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.39417 validation-auc:0.96236 validation-aucpr:0.96841
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.38962 validation-auc:0.96262 validation-aucpr:0.96860
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.38586 validation-auc:0.96253 validation-aucpr:0.96854
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.38239 validation-auc:0.96243 validation-aucpr:0.96848
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.37886 validation-auc:0.96239 validation-aucpr:0.96840
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.37507 validation-auc:0.96239 validation-aucpr:0.96841
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.37205 validation-auc:0.96245 validation-aucpr:0.96844
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.36889 validation-auc:0.96238 validation-aucpr:0.96837
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.36558 validation-auc:0.96240 validation-aucpr:0.96838
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.36150 validation-auc:0.96254 validation-aucpr:0.96853
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.35818 validation-auc:0.96263 validation-aucpr:0.96862
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.35467 validation-auc:0.96289 validation-aucpr:0.96882
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.35203 validation-auc:0.96292 validation-aucpr:0.96883
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.34941 validation-auc:0.96294 validation-aucpr:0.96885
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.34667 validation-auc:0.96296 validation-aucpr:0.96888
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.34343 validation-auc:0.96307 validation-aucpr:0.96893
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.34070 validation-auc:0.96304 validation-aucpr:0.96890
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.33798 validation-auc:0.96309 validation-aucpr:0.96895
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.33579 validation-auc:0.96307 validation-aucpr:0.96891
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.33343 validation-auc:0.96305 validation-aucpr:0.96888
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.32938 validation-auc:0.96347 validation-aucpr:0.96926
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.32698 validation-auc:0.96355 validation-aucpr:0.96932
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.32443 validation-auc:0.96369 validation-aucpr:0.96942
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.32194 validation-auc:0.96379 validation-aucpr:0.96952
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.31897 validation-auc:0.96389 validation-aucpr:0.96965
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.31694 validation-auc:0.96396 validation-aucpr:0.96972
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.31474 validation-auc:0.96397 validation-aucpr:0.96972
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.31206 validation-auc:0.96409 validation-aucpr:0.96984
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.31013 validation-auc:0.96416 validation-aucpr:0.96991
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.30882 validation-auc:0.96401 validation-aucpr:0.96980
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.30704 validation-auc:0.96401 validation-aucpr:0.96980
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.30527 validation-auc:0.96401 validation-aucpr:0.96977
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.30285 validation-auc:0.96419 validation-aucpr:0.96991
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.30001 validation-auc:0.96439 validation-aucpr:0.97011
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.29832 validation-auc:0.96448 validation-aucpr:0.97019
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.29685 validation-auc:0.96443 validation-aucpr:0.97015
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.29457 validation-auc:0.96454 validation-aucpr:0.97028
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.29278 validation-auc:0.96456 validation-aucpr:0.97032
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.29124 validation-auc:0.96459 validation-aucpr:0.97034
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.28958 validation-auc:0.96475 validation-aucpr:0.97046
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.28846 validation-auc:0.96472 validation-aucpr:0.97043
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.28718 validation-auc:0.96471 validation-aucpr:0.97041
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.28585 validation-auc:0.96479 validation-aucpr:0.97046
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.28486 validation-auc:0.96475 validation-aucpr:0.97041
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.28372 validation-auc:0.96478 validation-aucpr:0.97041
[20:55:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.28263 validation-auc:0.96470 validation-aucpr:0.97034
[20:55:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.28055 validation-auc:0.96482 validation-aucpr:0.97044
[20:55:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.27859 validation-auc:0.96495 validation-aucpr:0.97057
[20:55:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.27728 validation-auc:0.96506 validation-aucpr:0.97063
[20:55:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.27609 validation-auc:0.96512 validation-aucpr:0.97068
[20:55:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.27490 validation-auc:0.96514 validation-aucpr:0.97068
[20:55:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.27299 validation-auc:0.96524 validation-aucpr:0.97079
[20:55:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.27184 validation-auc:0.96529 validation-aucpr:0.97082
[20:55:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.27078 validation-auc:0.96536 validation-aucpr:0.97087
[20:55:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.26895 validation-auc:0.96549 validation-aucpr:0.97098
[20:55:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.26708 validation-auc:0.96563 validation-aucpr:0.97111
[20:55:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.26582 validation-auc:0.96572 validation-aucpr:0.97116
[20:55:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.26486 validation-auc:0.96570 validation-aucpr:0.97114
[20:55:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93] validation-logloss:0.26314 validation-auc:0.96587 validation-aucpr:0.97129
[20:55:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94] validation-logloss:0.26224 validation-auc:0.96586 validation-aucpr:0.97129
{'best_iteration': '93', 'best_score': '0.9712917743104803'}
Trial 59, Fold 1: Log loss = 0.2622414109940505, Average precision = 0.9712959878669294, ROC-AUC = 0.9658647710815733, Elapsed Time = 19.233018799997808 seconds
Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 59, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67932 validation-auc:0.92449 validation-aucpr:0.92114
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66522 validation-auc:0.94170 validation-aucpr:0.94047
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65008 validation-auc:0.95676 validation-aucpr:0.95986
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63706 validation-auc:0.95940 validation-aucpr:0.96276
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62527 validation-auc:0.95850 validation-aucpr:0.96176
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61394 validation-auc:0.95821 validation-aucpr:0.96169
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60367 validation-auc:0.95682 validation-aucpr:0.96054
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59286 validation-auc:0.95709 validation-aucpr:0.96050
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58275 validation-auc:0.95661 validation-aucpr:0.96011
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57086 validation-auc:0.95914 validation-aucpr:0.96318
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56181 validation-auc:0.95871 validation-aucpr:0.96264
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55230 validation-auc:0.95952 validation-aucpr:0.96338
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54340 validation-auc:0.95989 validation-aucpr:0.96389
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53516 validation-auc:0.96001 validation-aucpr:0.96412
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52523 validation-auc:0.96102 validation-aucpr:0.96543
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51763 validation-auc:0.96096 validation-aucpr:0.96530
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50847 validation-auc:0.96172 validation-aucpr:0.96629
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.50124 validation-auc:0.96147 validation-aucpr:0.96608
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49359 validation-auc:0.96160 validation-aucpr:0.96622
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48659 validation-auc:0.96165 validation-aucpr:0.96622
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47800 validation-auc:0.96218 validation-aucpr:0.96680
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47133 validation-auc:0.96232 validation-aucpr:0.96684
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.46581 validation-auc:0.96208 validation-aucpr:0.96657
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45967 validation-auc:0.96220 validation-aucpr:0.96666
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.45270 validation-auc:0.96242 validation-aucpr:0.96684
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44727 validation-auc:0.96243 validation-aucpr:0.96687
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.44135 validation-auc:0.96264 validation-aucpr:0.96700
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.43663 validation-auc:0.96243 validation-aucpr:0.96683
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43170 validation-auc:0.96261 validation-aucpr:0.96694
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.42722 validation-auc:0.96256 validation-aucpr:0.96684
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.42141 validation-auc:0.96265 validation-aucpr:0.96697
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.41661 validation-auc:0.96273 validation-aucpr:0.96701
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.41232 validation-auc:0.96262 validation-aucpr:0.96688
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.40617 validation-auc:0.96291 validation-aucpr:0.96717
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.40151 validation-auc:0.96312 validation-aucpr:0.96730
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.39738 validation-auc:0.96318 validation-aucpr:0.96737
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.39314 validation-auc:0.96315 validation-aucpr:0.96730
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.38893 validation-auc:0.96322 validation-aucpr:0.96736
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.38568 validation-auc:0.96342 validation-aucpr:0.96753
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.38199 validation-auc:0.96338 validation-aucpr:0.96748
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.37691 validation-auc:0.96354 validation-aucpr:0.96767
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.37309 validation-auc:0.96370 validation-aucpr:0.96777
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.36978 validation-auc:0.96367 validation-aucpr:0.96772
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.36582 validation-auc:0.96394 validation-aucpr:0.96795
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.36234 validation-auc:0.96402 validation-aucpr:0.96803
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.35938 validation-auc:0.96406 validation-aucpr:0.96794
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.35598 validation-auc:0.96424 validation-aucpr:0.96808
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.35155 validation-auc:0.96450 validation-aucpr:0.96835
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.34850 validation-auc:0.96460 validation-aucpr:0.96848
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.34544 validation-auc:0.96470 validation-aucpr:0.96855
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.34288 validation-auc:0.96459 validation-aucpr:0.96846
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.34033 validation-auc:0.96458 validation-aucpr:0.96844
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.33802 validation-auc:0.96446 validation-aucpr:0.96832
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.33430 validation-auc:0.96454 validation-aucpr:0.96842
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.33208 validation-auc:0.96444 validation-aucpr:0.96837
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.32915 validation-auc:0.96460 validation-aucpr:0.96848
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.32547 validation-auc:0.96487 validation-aucpr:0.96871
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.32312 validation-auc:0.96497 validation-aucpr:0.96877
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.32086 validation-auc:0.96502 validation-aucpr:0.96879
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.31836 validation-auc:0.96520 validation-aucpr:0.96891
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.31642 validation-auc:0.96522 validation-aucpr:0.96888
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.31444 validation-auc:0.96523 validation-aucpr:0.96886
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.31259 validation-auc:0.96524 validation-aucpr:0.96890
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.31054 validation-auc:0.96534 validation-aucpr:0.96898
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.30887 validation-auc:0.96530 validation-aucpr:0.96893
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.30671 validation-auc:0.96528 validation-aucpr:0.96892
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.30490 validation-auc:0.96529 validation-aucpr:0.96897
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.30290 validation-auc:0.96540 validation-aucpr:0.96904
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.30109 validation-auc:0.96537 validation-aucpr:0.96900
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.29946 validation-auc:0.96534 validation-aucpr:0.96895
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.29767 validation-auc:0.96541 validation-aucpr:0.96899
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.29620 validation-auc:0.96544 validation-aucpr:0.96899
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.29476 validation-auc:0.96547 validation-aucpr:0.96899
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.29331 validation-auc:0.96550 validation-aucpr:0.96904
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.29188 validation-auc:0.96548 validation-aucpr:0.96902
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.29057 validation-auc:0.96547 validation-aucpr:0.96904
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.28927 validation-auc:0.96538 validation-aucpr:0.96895
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.28675 validation-auc:0.96557 validation-aucpr:0.96916
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.28547 validation-auc:0.96559 validation-aucpr:0.96933
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.28414 validation-auc:0.96561 validation-aucpr:0.96934
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.28170 validation-auc:0.96580 validation-aucpr:0.96952
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.27919 validation-auc:0.96604 validation-aucpr:0.96975
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.27801 validation-auc:0.96608 validation-aucpr:0.96978
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.27719 validation-auc:0.96607 validation-aucpr:0.96979
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.27578 validation-auc:0.96623 validation-aucpr:0.96990
[20:56:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.27470 validation-auc:0.96630 validation-aucpr:0.96994
[20:56:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.27376 validation-auc:0.96632 validation-aucpr:0.96995
[20:56:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.27258 validation-auc:0.96638 validation-aucpr:0.96999
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.27137 validation-auc:0.96647 validation-aucpr:0.97006
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.27048 validation-auc:0.96643 validation-aucpr:0.97002
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.26950 validation-auc:0.96649 validation-aucpr:0.97016
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.26862 validation-auc:0.96646 validation-aucpr:0.97008
[20:56:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.26752 validation-auc:0.96652 validation-aucpr:0.97012
[20:56:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93] validation-logloss:0.26556 validation-auc:0.96672 validation-aucpr:0.97032
[20:56:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94] validation-logloss:0.26463 validation-auc:0.96670 validation-aucpr:0.97027
{'best_iteration': '93', 'best_score': '0.9703206595234433'}
Trial 59, Fold 2: Log loss = 0.2646334476355985, Average precision = 0.9702728597285563, ROC-AUC = 0.9666965205143279, Elapsed Time = 17.663478499998746 seconds
Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 59, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67862 validation-auc:0.93487 validation-aucpr:0.93542
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66407 validation-auc:0.95108 validation-aucpr:0.94911
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.64926 validation-auc:0.95705 validation-aucpr:0.96172
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63720 validation-auc:0.95805 validation-aucpr:0.96305
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62432 validation-auc:0.96126 validation-aucpr:0.96610
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61216 validation-auc:0.96220 validation-aucpr:0.96693
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60128 validation-auc:0.96218 validation-aucpr:0.96715
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59030 validation-auc:0.96237 validation-aucpr:0.96722
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57927 validation-auc:0.96268 validation-aucpr:0.96760
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57012 validation-auc:0.96212 validation-aucpr:0.96706
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56130 validation-auc:0.96168 validation-aucpr:0.96674
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55239 validation-auc:0.96124 validation-aucpr:0.96628
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54219 validation-auc:0.96202 validation-aucpr:0.96717
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53377 validation-auc:0.96204 validation-aucpr:0.96719
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52553 validation-auc:0.96209 validation-aucpr:0.96720
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51737 validation-auc:0.96200 validation-aucpr:0.96710
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50973 validation-auc:0.96191 validation-aucpr:0.96699
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.50272 validation-auc:0.96164 validation-aucpr:0.96673
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49402 validation-auc:0.96223 validation-aucpr:0.96737
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48741 validation-auc:0.96212 validation-aucpr:0.96720
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47941 validation-auc:0.96253 validation-aucpr:0.96766
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47182 validation-auc:0.96264 validation-aucpr:0.96778
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.46651 validation-auc:0.96245 validation-aucpr:0.96758
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.45853 validation-auc:0.96293 validation-aucpr:0.96807
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.45272 validation-auc:0.96297 validation-aucpr:0.96807
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44688 validation-auc:0.96296 validation-aucpr:0.96804
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.44094 validation-auc:0.96309 validation-aucpr:0.96810
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.43581 validation-auc:0.96296 validation-aucpr:0.96796
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43060 validation-auc:0.96314 validation-aucpr:0.96807
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.42366 validation-auc:0.96353 validation-aucpr:0.96846
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41726 validation-auc:0.96368 validation-aucpr:0.96857
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.41077 validation-auc:0.96383 validation-aucpr:0.96880
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40630 validation-auc:0.96389 validation-aucpr:0.96879
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.40211 validation-auc:0.96382 validation-aucpr:0.96869
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.39791 validation-auc:0.96377 validation-aucpr:0.96866
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.39225 validation-auc:0.96394 validation-aucpr:0.96888
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.38717 validation-auc:0.96405 validation-aucpr:0.96900
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.38332 validation-auc:0.96400 validation-aucpr:0.96896
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.37964 validation-auc:0.96412 validation-aucpr:0.96905
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.37552 validation-auc:0.96430 validation-aucpr:0.96921
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.37056 validation-auc:0.96439 validation-aucpr:0.96937
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.36677 validation-auc:0.96442 validation-aucpr:0.96937
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.36212 validation-auc:0.96467 validation-aucpr:0.96960
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.35920 validation-auc:0.96462 validation-aucpr:0.96954
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.35440 validation-auc:0.96495 validation-aucpr:0.96987
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.35154 validation-auc:0.96490 validation-aucpr:0.96984
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.34820 validation-auc:0.96506 validation-aucpr:0.96998
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.34410 validation-auc:0.96514 validation-aucpr:0.97007
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.34063 validation-auc:0.96516 validation-aucpr:0.97019
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.33745 validation-auc:0.96534 validation-aucpr:0.97033
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.33475 validation-auc:0.96537 validation-aucpr:0.97035
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.33219 validation-auc:0.96537 validation-aucpr:0.97035
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.32958 validation-auc:0.96540 validation-aucpr:0.97035
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.32602 validation-auc:0.96549 validation-aucpr:0.97046
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.32261 validation-auc:0.96569 validation-aucpr:0.97065
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.31992 validation-auc:0.96583 validation-aucpr:0.97075
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.31732 validation-auc:0.96597 validation-aucpr:0.97093
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.31406 validation-auc:0.96607 validation-aucpr:0.97104
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.31213 validation-auc:0.96608 validation-aucpr:0.97102
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.31006 validation-auc:0.96608 validation-aucpr:0.97100
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.30788 validation-auc:0.96611 validation-aucpr:0.97101
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.30603 validation-auc:0.96623 validation-aucpr:0.97111
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.30441 validation-auc:0.96616 validation-aucpr:0.97107
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.30259 validation-auc:0.96618 validation-aucpr:0.97107
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.30075 validation-auc:0.96622 validation-aucpr:0.97108
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.29859 validation-auc:0.96626 validation-aucpr:0.97112
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.29647 validation-auc:0.96632 validation-aucpr:0.97116
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.29390 validation-auc:0.96634 validation-aucpr:0.97121
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.29227 validation-auc:0.96633 validation-aucpr:0.97119
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.28978 validation-auc:0.96641 validation-aucpr:0.97129
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.28740 validation-auc:0.96646 validation-aucpr:0.97135
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.28576 validation-auc:0.96656 validation-aucpr:0.97144
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.28397 validation-auc:0.96667 validation-aucpr:0.97151
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.28254 validation-auc:0.96662 validation-aucpr:0.97144
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.28100 validation-auc:0.96669 validation-aucpr:0.97149
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.27898 validation-auc:0.96685 validation-aucpr:0.97163
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.27757 validation-auc:0.96687 validation-aucpr:0.97163
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.27589 validation-auc:0.96696 validation-aucpr:0.97172
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.27461 validation-auc:0.96698 validation-aucpr:0.97175
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.27334 validation-auc:0.96696 validation-aucpr:0.97171
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.27219 validation-auc:0.96697 validation-aucpr:0.97171
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.27092 validation-auc:0.96702 validation-aucpr:0.97174
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.26970 validation-auc:0.96707 validation-aucpr:0.97181
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.26818 validation-auc:0.96718 validation-aucpr:0.97191
[20:56:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.26696 validation-auc:0.96723 validation-aucpr:0.97194
[20:56:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.26611 validation-auc:0.96720 validation-aucpr:0.97193
[20:56:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.26510 validation-auc:0.96720 validation-aucpr:0.97193
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.26425 validation-auc:0.96718 validation-aucpr:0.97189
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.26333 validation-auc:0.96711 validation-aucpr:0.97183
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.26228 validation-auc:0.96708 validation-aucpr:0.97182
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.26099 validation-auc:0.96719 validation-aucpr:0.97190
[20:56:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.25908 validation-auc:0.96733 validation-aucpr:0.97203
[20:56:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.25716 validation-auc:0.96750 validation-aucpr:0.97219
[20:56:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93] validation-logloss:0.25599 validation-auc:0.96754 validation-aucpr:0.97222
[20:56:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94] validation-logloss:0.25507 validation-auc:0.96758 validation-aucpr:0.97225
{'best_iteration': '94', 'best_score': '0.9722450704143667'}
Trial 59, Fold 3: Log loss = 0.25506957399390734, Average precision = 0.9722498977626997, ROC-AUC = 0.9675826884552783, Elapsed Time = 16.725995500000863 seconds
Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 59, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67895 validation-auc:0.92444 validation-aucpr:0.92797
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66482 validation-auc:0.94131 validation-aucpr:0.94623
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65203 validation-auc:0.94545 validation-aucpr:0.94943
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63918 validation-auc:0.94843 validation-aucpr:0.95268
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62454 validation-auc:0.95810 validation-aucpr:0.96384
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61036 validation-auc:0.96045 validation-aucpr:0.96660
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.59884 validation-auc:0.96002 validation-aucpr:0.96626
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.58798 validation-auc:0.96030 validation-aucpr:0.96647
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.57610 validation-auc:0.96120 validation-aucpr:0.96750
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.56643 validation-auc:0.96088 validation-aucpr:0.96719
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.55546 validation-auc:0.96079 validation-aucpr:0.96725
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.54647 validation-auc:0.96054 validation-aucpr:0.96710
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.53760 validation-auc:0.96064 validation-aucpr:0.96725
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.52897 validation-auc:0.96067 validation-aucpr:0.96717
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52174 validation-auc:0.96034 validation-aucpr:0.96686
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.51412 validation-auc:0.96005 validation-aucpr:0.96662
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.50671 validation-auc:0.96033 validation-aucpr:0.96674
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.49844 validation-auc:0.96057 validation-aucpr:0.96705
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49138 validation-auc:0.96070 validation-aucpr:0.96704
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.48431 validation-auc:0.96106 validation-aucpr:0.96725
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.47811 validation-auc:0.96069 validation-aucpr:0.96700
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47224 validation-auc:0.96033 validation-aucpr:0.96675
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.46632 validation-auc:0.96024 validation-aucpr:0.96668
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.46063 validation-auc:0.96032 validation-aucpr:0.96668
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.45328 validation-auc:0.96084 validation-aucpr:0.96718
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.44810 validation-auc:0.96062 validation-aucpr:0.96702
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.44087 validation-auc:0.96097 validation-aucpr:0.96742
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.43547 validation-auc:0.96103 validation-aucpr:0.96741
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43078 validation-auc:0.96087 validation-aucpr:0.96728
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.42404 validation-auc:0.96133 validation-aucpr:0.96767
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.41927 validation-auc:0.96150 validation-aucpr:0.96782
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.41470 validation-auc:0.96153 validation-aucpr:0.96781
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.40989 validation-auc:0.96171 validation-aucpr:0.96791
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.40544 validation-auc:0.96169 validation-aucpr:0.96787
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.40141 validation-auc:0.96158 validation-aucpr:0.96777
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.39764 validation-auc:0.96148 validation-aucpr:0.96769
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.39189 validation-auc:0.96176 validation-aucpr:0.96795
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.38812 validation-auc:0.96181 validation-aucpr:0.96797
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.38455 validation-auc:0.96174 validation-aucpr:0.96790
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.38086 validation-auc:0.96170 validation-aucpr:0.96786
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.37743 validation-auc:0.96166 validation-aucpr:0.96782
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.37383 validation-auc:0.96174 validation-aucpr:0.96787
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.37055 validation-auc:0.96174 validation-aucpr:0.96786
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.36743 validation-auc:0.96167 validation-aucpr:0.96782
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.36407 validation-auc:0.96175 validation-aucpr:0.96785
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.36093 validation-auc:0.96186 validation-aucpr:0.96790
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.35842 validation-auc:0.96173 validation-aucpr:0.96776
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.35574 validation-auc:0.96163 validation-aucpr:0.96766
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.35288 validation-auc:0.96163 validation-aucpr:0.96765
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.35021 validation-auc:0.96170 validation-aucpr:0.96771
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.34608 validation-auc:0.96183 validation-aucpr:0.96790
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.34291 validation-auc:0.96188 validation-aucpr:0.96797
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.34016 validation-auc:0.96198 validation-aucpr:0.96804
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.33791 validation-auc:0.96206 validation-aucpr:0.96808
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.33543 validation-auc:0.96217 validation-aucpr:0.96818
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.33130 validation-auc:0.96251 validation-aucpr:0.96851
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.32929 validation-auc:0.96256 validation-aucpr:0.96852
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.32704 validation-auc:0.96262 validation-aucpr:0.96857
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.32461 validation-auc:0.96263 validation-aucpr:0.96858
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.32226 validation-auc:0.96269 validation-aucpr:0.96862
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.32038 validation-auc:0.96265 validation-aucpr:0.96857
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.31861 validation-auc:0.96261 validation-aucpr:0.96853
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.31536 validation-auc:0.96292 validation-aucpr:0.96879
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.31322 validation-auc:0.96298 validation-aucpr:0.96882
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.30992 validation-auc:0.96318 validation-aucpr:0.96904
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.30813 validation-auc:0.96318 validation-aucpr:0.96903
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.30602 validation-auc:0.96327 validation-aucpr:0.96910
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.30442 validation-auc:0.96332 validation-aucpr:0.96913
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.30307 validation-auc:0.96327 validation-aucpr:0.96907
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.30008 validation-auc:0.96346 validation-aucpr:0.96927
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.29847 validation-auc:0.96356 validation-aucpr:0.96933
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.29691 validation-auc:0.96354 validation-aucpr:0.96932
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.29521 validation-auc:0.96357 validation-aucpr:0.96935
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.29410 validation-auc:0.96353 validation-aucpr:0.96933
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.29161 validation-auc:0.96361 validation-aucpr:0.96944
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.28932 validation-auc:0.96361 validation-aucpr:0.96949
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.28756 validation-auc:0.96375 validation-aucpr:0.96962
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.28611 validation-auc:0.96387 validation-aucpr:0.96970
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.28499 validation-auc:0.96376 validation-aucpr:0.96962
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.28358 validation-auc:0.96381 validation-aucpr:0.96964
[20:56:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.28229 validation-auc:0.96380 validation-aucpr:0.96965
[20:56:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.28117 validation-auc:0.96382 validation-aucpr:0.96966
[20:56:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.27893 validation-auc:0.96396 validation-aucpr:0.96982
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.27706 validation-auc:0.96406 validation-aucpr:0.96992
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.27480 validation-auc:0.96423 validation-aucpr:0.97007
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.27302 validation-auc:0.96435 validation-aucpr:0.97018
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.27175 validation-auc:0.96445 validation-aucpr:0.97028
[20:56:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.27043 validation-auc:0.96463 validation-aucpr:0.97040
[20:56:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.26939 validation-auc:0.96461 validation-aucpr:0.97038
[20:56:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.26818 validation-auc:0.96467 validation-aucpr:0.97044
[20:56:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.26674 validation-auc:0.96474 validation-aucpr:0.97052
[20:56:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.26581 validation-auc:0.96481 validation-aucpr:0.97057
[20:56:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.26400 validation-auc:0.96486 validation-aucpr:0.97063
[20:56:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93] validation-logloss:0.26308 validation-auc:0.96486 validation-aucpr:0.97064
[20:56:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94] validation-logloss:0.26146 validation-auc:0.96492 validation-aucpr:0.97070
{'best_iteration': '94', 'best_score': '0.9707029113327627'}
Trial 59, Fold 4: Log loss = 0.2614591833102355, Average precision = 0.9707076760835737, ROC-AUC = 0.9649216065676183, Elapsed Time = 16.850258499998745 seconds
Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 59, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.67920 validation-auc:0.92272 validation-aucpr:0.92042
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.66623 validation-auc:0.93540 validation-aucpr:0.93593
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65081 validation-auc:0.95197 validation-aucpr:0.95806
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.63840 validation-auc:0.95330 validation-aucpr:0.95919
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.62729 validation-auc:0.95354 validation-aucpr:0.95930
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.61550 validation-auc:0.95555 validation-aucpr:0.96082
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.60527 validation-auc:0.95521 validation-aucpr:0.96018
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.59564 validation-auc:0.95459 validation-aucpr:0.95941
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.58274 validation-auc:0.95754 validation-aucpr:0.96242
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.57273 validation-auc:0.95779 validation-aucpr:0.96249
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.56377 validation-auc:0.95793 validation-aucpr:0.96262
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.55540 validation-auc:0.95802 validation-aucpr:0.96279
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.54626 validation-auc:0.95827 validation-aucpr:0.96295
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.53700 validation-auc:0.95871 validation-aucpr:0.96340
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.52872 validation-auc:0.95870 validation-aucpr:0.96345
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.52170 validation-auc:0.95860 validation-aucpr:0.96339
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.51253 validation-auc:0.95885 validation-aucpr:0.96378
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.50548 validation-auc:0.95868 validation-aucpr:0.96377
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.49808 validation-auc:0.95918 validation-aucpr:0.96411
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.49152 validation-auc:0.95945 validation-aucpr:0.96434
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.48438 validation-auc:0.95969 validation-aucpr:0.96456
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.47654 validation-auc:0.95990 validation-aucpr:0.96490
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.47021 validation-auc:0.95999 validation-aucpr:0.96501
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.46361 validation-auc:0.96033 validation-aucpr:0.96527
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.45619 validation-auc:0.96050 validation-aucpr:0.96567
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.45080 validation-auc:0.96038 validation-aucpr:0.96554
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.44537 validation-auc:0.96043 validation-aucpr:0.96557
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.43849 validation-auc:0.96087 validation-aucpr:0.96594
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.43303 validation-auc:0.96089 validation-aucpr:0.96599
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.42862 validation-auc:0.96081 validation-aucpr:0.96592
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.42411 validation-auc:0.96068 validation-aucpr:0.96584
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.41980 validation-auc:0.96054 validation-aucpr:0.96567
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.41370 validation-auc:0.96066 validation-aucpr:0.96585
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.40974 validation-auc:0.96062 validation-aucpr:0.96581
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.40565 validation-auc:0.96061 validation-aucpr:0.96589
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.40119 validation-auc:0.96065 validation-aucpr:0.96593
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.39766 validation-auc:0.96040 validation-aucpr:0.96572
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.39401 validation-auc:0.96045 validation-aucpr:0.96578
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.39070 validation-auc:0.96040 validation-aucpr:0.96570
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.38692 validation-auc:0.96045 validation-aucpr:0.96573
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.38161 validation-auc:0.96070 validation-aucpr:0.96599
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.37827 validation-auc:0.96078 validation-aucpr:0.96604
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.37398 validation-auc:0.96092 validation-aucpr:0.96616
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.37112 validation-auc:0.96068 validation-aucpr:0.96596
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.36786 validation-auc:0.96080 validation-aucpr:0.96605
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.36469 validation-auc:0.96098 validation-aucpr:0.96619
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.36149 validation-auc:0.96097 validation-aucpr:0.96619
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.35907 validation-auc:0.96079 validation-aucpr:0.96607
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.35617 validation-auc:0.96087 validation-aucpr:0.96611
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.35340 validation-auc:0.96092 validation-aucpr:0.96617
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.35059 validation-auc:0.96095 validation-aucpr:0.96622
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.34763 validation-auc:0.96104 validation-aucpr:0.96641
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.34500 validation-auc:0.96108 validation-aucpr:0.96643
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.34175 validation-auc:0.96118 validation-aucpr:0.96658
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.33803 validation-auc:0.96130 validation-aucpr:0.96673
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.33534 validation-auc:0.96151 validation-aucpr:0.96686
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.33164 validation-auc:0.96166 validation-aucpr:0.96701
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.32941 validation-auc:0.96168 validation-aucpr:0.96702
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.32649 validation-auc:0.96170 validation-aucpr:0.96703
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.32462 validation-auc:0.96166 validation-aucpr:0.96698
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.32209 validation-auc:0.96181 validation-aucpr:0.96712
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.31982 validation-auc:0.96193 validation-aucpr:0.96721
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.31801 validation-auc:0.96185 validation-aucpr:0.96714
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.31593 validation-auc:0.96196 validation-aucpr:0.96716
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.31410 validation-auc:0.96204 validation-aucpr:0.96722
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.31233 validation-auc:0.96199 validation-aucpr:0.96717
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.31051 validation-auc:0.96223 validation-aucpr:0.96728
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.30865 validation-auc:0.96229 validation-aucpr:0.96730
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.30694 validation-auc:0.96232 validation-aucpr:0.96726
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.30400 validation-auc:0.96243 validation-aucpr:0.96737
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.30262 validation-auc:0.96242 validation-aucpr:0.96738
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.30106 validation-auc:0.96245 validation-aucpr:0.96739
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.29962 validation-auc:0.96248 validation-aucpr:0.96739
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.29716 validation-auc:0.96261 validation-aucpr:0.96757
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.29508 validation-auc:0.96268 validation-aucpr:0.96767
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.29362 validation-auc:0.96276 validation-aucpr:0.96781
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.29144 validation-auc:0.96285 validation-aucpr:0.96787
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.28988 validation-auc:0.96290 validation-aucpr:0.96792
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.28845 validation-auc:0.96295 validation-aucpr:0.96792
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.28673 validation-auc:0.96313 validation-aucpr:0.96806
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.28573 validation-auc:0.96316 validation-aucpr:0.96805
[20:56:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.28438 validation-auc:0.96326 validation-aucpr:0.96813
[20:56:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.28345 validation-auc:0.96326 validation-aucpr:0.96816
[20:56:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.28233 validation-auc:0.96326 validation-aucpr:0.96816
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.28020 validation-auc:0.96339 validation-aucpr:0.96830
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.27894 validation-auc:0.96344 validation-aucpr:0.96832
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.27785 validation-auc:0.96349 validation-aucpr:0.96835
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.27575 validation-auc:0.96361 validation-aucpr:0.96853
[20:56:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.27360 validation-auc:0.96380 validation-aucpr:0.96869
[20:56:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89] validation-logloss:0.27167 validation-auc:0.96388 validation-aucpr:0.96878
[20:56:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90] validation-logloss:0.27079 validation-auc:0.96384 validation-aucpr:0.96871
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91] validation-logloss:0.26862 validation-auc:0.96401 validation-aucpr:0.96888
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92] validation-logloss:0.26775 validation-auc:0.96405 validation-aucpr:0.96891
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93] validation-logloss:0.26668 validation-auc:0.96410 validation-aucpr:0.96894
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94] validation-logloss:0.26497 validation-auc:0.96420 validation-aucpr:0.96900
{'best_iteration': '94', 'best_score': '0.9689992016802114'}
Trial 59, Fold 5: Log loss = 0.2649650333683201, Average precision = 0.9690049278285596, ROC-AUC = 0.9642040020065772, Elapsed Time = 16.963513800001238 seconds
Optimization Progress: 60%|###### | 60/100 [2:58:08<41:47, 62.69s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 60, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66602 validation-auc:0.94155 validation-aucpr:0.94613
[1] validation-logloss:0.64043 validation-auc:0.94913 validation-aucpr:0.94290
[2] validation-logloss:0.61669 validation-auc:0.95412 validation-aucpr:0.94777
[3] validation-logloss:0.59698 validation-auc:0.95686 validation-aucpr:0.95489
[4] validation-logloss:0.57847 validation-auc:0.95856 validation-aucpr:0.95584
[5] validation-logloss:0.55960 validation-auc:0.95962 validation-aucpr:0.95884
[6] validation-logloss:0.54102 validation-auc:0.96212 validation-aucpr:0.96556
[7] validation-logloss:0.52451 validation-auc:0.96234 validation-aucpr:0.96771
[8] validation-logloss:0.50834 validation-auc:0.96280 validation-aucpr:0.96736
[9] validation-logloss:0.49254 validation-auc:0.96374 validation-aucpr:0.96812
[10] validation-logloss:0.47963 validation-auc:0.96449 validation-aucpr:0.96957
[11] validation-logloss:0.46616 validation-auc:0.96517 validation-aucpr:0.97014
[12] validation-logloss:0.45302 validation-auc:0.96530 validation-aucpr:0.97030
[13] validation-logloss:0.44041 validation-auc:0.96576 validation-aucpr:0.97060
[14] validation-logloss:0.42894 validation-auc:0.96632 validation-aucpr:0.97117
[15] validation-logloss:0.41764 validation-auc:0.96641 validation-aucpr:0.97132
[16] validation-logloss:0.40736 validation-auc:0.96672 validation-aucpr:0.97152
[17] validation-logloss:0.39725 validation-auc:0.96707 validation-aucpr:0.97178
[18] validation-logloss:0.38758 validation-auc:0.96736 validation-aucpr:0.97199
[19] validation-logloss:0.37838 validation-auc:0.96769 validation-aucpr:0.97221
[20] validation-logloss:0.37076 validation-auc:0.96777 validation-aucpr:0.97163
[21] validation-logloss:0.36304 validation-auc:0.96763 validation-aucpr:0.97154
[22] validation-logloss:0.35561 validation-auc:0.96803 validation-aucpr:0.97183
[23] validation-logloss:0.34850 validation-auc:0.96802 validation-aucpr:0.97184
[24] validation-logloss:0.34157 validation-auc:0.96816 validation-aucpr:0.97192
[25] validation-logloss:0.33467 validation-auc:0.96829 validation-aucpr:0.97193
[26] validation-logloss:0.32837 validation-auc:0.96821 validation-aucpr:0.97189
[27] validation-logloss:0.32228 validation-auc:0.96836 validation-aucpr:0.97206
[28] validation-logloss:0.31662 validation-auc:0.96847 validation-aucpr:0.97213
[29] validation-logloss:0.31191 validation-auc:0.96850 validation-aucpr:0.97208
[30] validation-logloss:0.30716 validation-auc:0.96865 validation-aucpr:0.97215
[31] validation-logloss:0.30284 validation-auc:0.96878 validation-aucpr:0.97218
[32] validation-logloss:0.29827 validation-auc:0.96878 validation-aucpr:0.97183
[33] validation-logloss:0.29351 validation-auc:0.96894 validation-aucpr:0.97196
[34] validation-logloss:0.28971 validation-auc:0.96899 validation-aucpr:0.97173
[35] validation-logloss:0.28620 validation-auc:0.96904 validation-aucpr:0.97193
[36] validation-logloss:0.28261 validation-auc:0.96915 validation-aucpr:0.97210
[37] validation-logloss:0.27921 validation-auc:0.96930 validation-aucpr:0.97288
[38] validation-logloss:0.27563 validation-auc:0.96930 validation-aucpr:0.97323
[39] validation-logloss:0.27205 validation-auc:0.96935 validation-aucpr:0.97301
[40] validation-logloss:0.26890 validation-auc:0.96935 validation-aucpr:0.97301
[41] validation-logloss:0.26544 validation-auc:0.96958 validation-aucpr:0.97324
[42] validation-logloss:0.26219 validation-auc:0.96977 validation-aucpr:0.97321
[43] validation-logloss:0.25921 validation-auc:0.96988 validation-aucpr:0.97328
[44] validation-logloss:0.25691 validation-auc:0.96988 validation-aucpr:0.97326
[45] validation-logloss:0.25387 validation-auc:0.97009 validation-aucpr:0.97339
[46] validation-logloss:0.25169 validation-auc:0.97010 validation-aucpr:0.97351
[47] validation-logloss:0.24962 validation-auc:0.96992 validation-aucpr:0.97340
[48] validation-logloss:0.24742 validation-auc:0.96999 validation-aucpr:0.97342
[49] validation-logloss:0.24520 validation-auc:0.96991 validation-aucpr:0.97340
[50] validation-logloss:0.24336 validation-auc:0.96989 validation-aucpr:0.97345
[51] validation-logloss:0.24134 validation-auc:0.96989 validation-aucpr:0.97349
[52] validation-logloss:0.23971 validation-auc:0.96985 validation-aucpr:0.97343
[53] validation-logloss:0.23794 validation-auc:0.96987 validation-aucpr:0.97347
[54] validation-logloss:0.23627 validation-auc:0.96988 validation-aucpr:0.97353
[55] validation-logloss:0.23466 validation-auc:0.97000 validation-aucpr:0.97365
{'best_iteration': '55', 'best_score': '0.9736481198598432'}
Trial 60, Fold 1: Log loss = 0.23465558197815578, Average precision = 0.9737553055902464, ROC-AUC = 0.9700006093976283, Elapsed Time = 5.350716500000999 seconds
Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 60, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66561 validation-auc:0.93663 validation-aucpr:0.92142
[1] validation-logloss:0.64060 validation-auc:0.94660 validation-aucpr:0.92801
[2] validation-logloss:0.61747 validation-auc:0.95261 validation-aucpr:0.95056
[3] validation-logloss:0.59669 validation-auc:0.95770 validation-aucpr:0.95105
[4] validation-logloss:0.57806 validation-auc:0.96111 validation-aucpr:0.95966
[5] validation-logloss:0.55781 validation-auc:0.96360 validation-aucpr:0.96312
[6] validation-logloss:0.53901 validation-auc:0.96522 validation-aucpr:0.96826
[7] validation-logloss:0.52318 validation-auc:0.96641 validation-aucpr:0.97076
[8] validation-logloss:0.50918 validation-auc:0.96654 validation-aucpr:0.97068
[9] validation-logloss:0.49540 validation-auc:0.96657 validation-aucpr:0.97070
[10] validation-logloss:0.48099 validation-auc:0.96642 validation-aucpr:0.97062
[11] validation-logloss:0.46780 validation-auc:0.96652 validation-aucpr:0.97085
[12] validation-logloss:0.45468 validation-auc:0.96678 validation-aucpr:0.97108
[13] validation-logloss:0.44218 validation-auc:0.96716 validation-aucpr:0.97136
[14] validation-logloss:0.43172 validation-auc:0.96740 validation-aucpr:0.97122
[15] validation-logloss:0.42178 validation-auc:0.96769 validation-aucpr:0.97146
[16] validation-logloss:0.41139 validation-auc:0.96790 validation-aucpr:0.97165
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[20] validation-logloss:0.37450 validation-auc:0.96856 validation-aucpr:0.97233
[21] validation-logloss:0.36672 validation-auc:0.96828 validation-aucpr:0.97210
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[25] validation-logloss:0.33732 validation-auc:0.96882 validation-aucpr:0.97286
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[27] validation-logloss:0.32438 validation-auc:0.96913 validation-aucpr:0.97317
[28] validation-logloss:0.31857 validation-auc:0.96903 validation-aucpr:0.97312
[29] validation-logloss:0.31384 validation-auc:0.96905 validation-aucpr:0.97304
[30] validation-logloss:0.30892 validation-auc:0.96912 validation-aucpr:0.97303
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[34] validation-logloss:0.29041 validation-auc:0.96925 validation-aucpr:0.97309
[35] validation-logloss:0.28668 validation-auc:0.96903 validation-aucpr:0.97297
[36] validation-logloss:0.28262 validation-auc:0.96890 validation-aucpr:0.97296
[37] validation-logloss:0.27844 validation-auc:0.96903 validation-aucpr:0.97306
[38] validation-logloss:0.27492 validation-auc:0.96904 validation-aucpr:0.97290
[39] validation-logloss:0.27126 validation-auc:0.96902 validation-aucpr:0.97295
[40] validation-logloss:0.26835 validation-auc:0.96893 validation-aucpr:0.97290
[41] validation-logloss:0.26546 validation-auc:0.96898 validation-aucpr:0.97287
[42] validation-logloss:0.26294 validation-auc:0.96898 validation-aucpr:0.97285
[43] validation-logloss:0.25972 validation-auc:0.96911 validation-aucpr:0.97288
[44] validation-logloss:0.25719 validation-auc:0.96924 validation-aucpr:0.97297
[45] validation-logloss:0.25417 validation-auc:0.96933 validation-aucpr:0.97304
[46] validation-logloss:0.25177 validation-auc:0.96940 validation-aucpr:0.97303
[47] validation-logloss:0.24914 validation-auc:0.96948 validation-aucpr:0.97300
[48] validation-logloss:0.24661 validation-auc:0.96961 validation-aucpr:0.97299
[49] validation-logloss:0.24405 validation-auc:0.96974 validation-aucpr:0.97306
[50] validation-logloss:0.24190 validation-auc:0.96994 validation-aucpr:0.97412
[51] validation-logloss:0.23982 validation-auc:0.96976 validation-aucpr:0.97363
[52] validation-logloss:0.23787 validation-auc:0.96978 validation-aucpr:0.97361
[53] validation-logloss:0.23585 validation-auc:0.96969 validation-aucpr:0.97357
[54] validation-logloss:0.23375 validation-auc:0.96979 validation-aucpr:0.97366
[55] validation-logloss:0.23195 validation-auc:0.96973 validation-aucpr:0.97365
{'best_iteration': '50', 'best_score': '0.9741150097823695'}
Trial 60, Fold 2: Log loss = 0.23194843281151947, Average precision = 0.9736809321240751, ROC-AUC = 0.9697323867104145, Elapsed Time = 5.66577529999995 seconds
Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 60, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66522 validation-auc:0.94317 validation-aucpr:0.94647
[1] validation-logloss:0.64209 validation-auc:0.94919 validation-aucpr:0.93701
[2] validation-logloss:0.61928 validation-auc:0.95353 validation-aucpr:0.94277
[3] validation-logloss:0.59677 validation-auc:0.95891 validation-aucpr:0.95364
[4] validation-logloss:0.57658 validation-auc:0.96108 validation-aucpr:0.96035
[5] validation-logloss:0.55671 validation-auc:0.96269 validation-aucpr:0.96311
[6] validation-logloss:0.53791 validation-auc:0.96383 validation-aucpr:0.96428
[7] validation-logloss:0.52076 validation-auc:0.96461 validation-aucpr:0.96546
[8] validation-logloss:0.50462 validation-auc:0.96527 validation-aucpr:0.96433
[9] validation-logloss:0.49015 validation-auc:0.96675 validation-aucpr:0.96605
[10] validation-logloss:0.47716 validation-auc:0.96678 validation-aucpr:0.96615
[11] validation-logloss:0.46338 validation-auc:0.96728 validation-aucpr:0.96657
[12] validation-logloss:0.45089 validation-auc:0.96739 validation-aucpr:0.96646
[13] validation-logloss:0.43987 validation-auc:0.96735 validation-aucpr:0.96628
[14] validation-logloss:0.42804 validation-auc:0.96745 validation-aucpr:0.96624
[15] validation-logloss:0.41738 validation-auc:0.96739 validation-aucpr:0.96618
[16] validation-logloss:0.40822 validation-auc:0.96717 validation-aucpr:0.96592
[17] validation-logloss:0.39864 validation-auc:0.96718 validation-aucpr:0.96795
[18] validation-logloss:0.38888 validation-auc:0.96747 validation-aucpr:0.96802
[19] validation-logloss:0.37975 validation-auc:0.96756 validation-aucpr:0.96811
[20] validation-logloss:0.37138 validation-auc:0.96784 validation-aucpr:0.96829
[21] validation-logloss:0.36320 validation-auc:0.96775 validation-aucpr:0.96818
[22] validation-logloss:0.35540 validation-auc:0.96773 validation-aucpr:0.96803
[23] validation-logloss:0.34861 validation-auc:0.96787 validation-aucpr:0.96811
[24] validation-logloss:0.34227 validation-auc:0.96780 validation-aucpr:0.96819
[25] validation-logloss:0.33566 validation-auc:0.96777 validation-aucpr:0.96818
[26] validation-logloss:0.32929 validation-auc:0.96780 validation-aucpr:0.96847
[27] validation-logloss:0.32323 validation-auc:0.96849 validation-aucpr:0.97160
[28] validation-logloss:0.31742 validation-auc:0.96847 validation-aucpr:0.97161
[29] validation-logloss:0.31177 validation-auc:0.96866 validation-aucpr:0.97178
[30] validation-logloss:0.30632 validation-auc:0.96871 validation-aucpr:0.97191
[31] validation-logloss:0.30106 validation-auc:0.96897 validation-aucpr:0.97208
[32] validation-logloss:0.29623 validation-auc:0.96907 validation-aucpr:0.97211
[33] validation-logloss:0.29170 validation-auc:0.96918 validation-aucpr:0.97228
[34] validation-logloss:0.28712 validation-auc:0.96917 validation-aucpr:0.97228
[35] validation-logloss:0.28276 validation-auc:0.96925 validation-aucpr:0.97238
[36] validation-logloss:0.27926 validation-auc:0.96940 validation-aucpr:0.97262
[37] validation-logloss:0.27583 validation-auc:0.96927 validation-aucpr:0.97266
[38] validation-logloss:0.27220 validation-auc:0.96932 validation-aucpr:0.97271
[39] validation-logloss:0.26866 validation-auc:0.96953 validation-aucpr:0.97298
[40] validation-logloss:0.26518 validation-auc:0.96974 validation-aucpr:0.97316
[41] validation-logloss:0.26213 validation-auc:0.96985 validation-aucpr:0.97324
[42] validation-logloss:0.25921 validation-auc:0.96988 validation-aucpr:0.97323
[43] validation-logloss:0.25680 validation-auc:0.96988 validation-aucpr:0.97323
[44] validation-logloss:0.25459 validation-auc:0.96982 validation-aucpr:0.97309
[45] validation-logloss:0.25223 validation-auc:0.96981 validation-aucpr:0.97309
[46] validation-logloss:0.24942 validation-auc:0.97003 validation-aucpr:0.97315
[47] validation-logloss:0.24701 validation-auc:0.97010 validation-aucpr:0.97319
[48] validation-logloss:0.24445 validation-auc:0.97015 validation-aucpr:0.97331
[49] validation-logloss:0.24209 validation-auc:0.97033 validation-aucpr:0.97343
[50] validation-logloss:0.23993 validation-auc:0.97025 validation-aucpr:0.97319
[51] validation-logloss:0.23779 validation-auc:0.97033 validation-aucpr:0.97324
[52] validation-logloss:0.23548 validation-auc:0.97049 validation-aucpr:0.97339
[53] validation-logloss:0.23350 validation-auc:0.97061 validation-aucpr:0.97352
[54] validation-logloss:0.23147 validation-auc:0.97071 validation-aucpr:0.97383
[55] validation-logloss:0.22979 validation-auc:0.97069 validation-aucpr:0.97381
{'best_iteration': '54', 'best_score': '0.9738305377640103'}
Trial 60, Fold 3: Log loss = 0.22978920308210152, Average precision = 0.9740861129702912, ROC-AUC = 0.9706929860783389, Elapsed Time = 5.672290900001826 seconds
Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 60, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66510 validation-auc:0.94943 validation-aucpr:0.94812
[1] validation-logloss:0.63930 validation-auc:0.95330 validation-aucpr:0.94564
[2] validation-logloss:0.61620 validation-auc:0.95717 validation-aucpr:0.96030
[3] validation-logloss:0.59405 validation-auc:0.95934 validation-aucpr:0.96372
[4] validation-logloss:0.57315 validation-auc:0.96181 validation-aucpr:0.96657
[5] validation-logloss:0.55415 validation-auc:0.96287 validation-aucpr:0.96595
[6] validation-logloss:0.53756 validation-auc:0.96301 validation-aucpr:0.96733
[7] validation-logloss:0.52092 validation-auc:0.96342 validation-aucpr:0.96702
[8] validation-logloss:0.50642 validation-auc:0.96408 validation-aucpr:0.96827
[9] validation-logloss:0.49113 validation-auc:0.96434 validation-aucpr:0.96850
[10] validation-logloss:0.47701 validation-auc:0.96447 validation-aucpr:0.96860
[11] validation-logloss:0.46296 validation-auc:0.96511 validation-aucpr:0.96905
[12] validation-logloss:0.45030 validation-auc:0.96521 validation-aucpr:0.96906
[13] validation-logloss:0.43967 validation-auc:0.96509 validation-aucpr:0.96922
[14] validation-logloss:0.42836 validation-auc:0.96514 validation-aucpr:0.96914
[15] validation-logloss:0.41764 validation-auc:0.96556 validation-aucpr:0.96976
[16] validation-logloss:0.40724 validation-auc:0.96593 validation-aucpr:0.96970
[17] validation-logloss:0.39746 validation-auc:0.96622 validation-aucpr:0.97002
[18] validation-logloss:0.38772 validation-auc:0.96652 validation-aucpr:0.97002
[19] validation-logloss:0.37854 validation-auc:0.96721 validation-aucpr:0.97040
[20] validation-logloss:0.37137 validation-auc:0.96743 validation-aucpr:0.97211
[21] validation-logloss:0.36399 validation-auc:0.96763 validation-aucpr:0.97230
[22] validation-logloss:0.35625 validation-auc:0.96788 validation-aucpr:0.97319
[23] validation-logloss:0.34877 validation-auc:0.96815 validation-aucpr:0.97342
[24] validation-logloss:0.34236 validation-auc:0.96846 validation-aucpr:0.97364
[25] validation-logloss:0.33695 validation-auc:0.96832 validation-aucpr:0.97352
[26] validation-logloss:0.33104 validation-auc:0.96814 validation-aucpr:0.97338
[27] validation-logloss:0.32491 validation-auc:0.96824 validation-aucpr:0.97345
[28] validation-logloss:0.31903 validation-auc:0.96827 validation-aucpr:0.97346
[29] validation-logloss:0.31432 validation-auc:0.96816 validation-aucpr:0.97340
[30] validation-logloss:0.30910 validation-auc:0.96805 validation-aucpr:0.97339
[31] validation-logloss:0.30434 validation-auc:0.96821 validation-aucpr:0.97342
[32] validation-logloss:0.29931 validation-auc:0.96843 validation-aucpr:0.97359
[33] validation-logloss:0.29476 validation-auc:0.96838 validation-aucpr:0.97359
[34] validation-logloss:0.29066 validation-auc:0.96830 validation-aucpr:0.97355
[35] validation-logloss:0.28643 validation-auc:0.96830 validation-aucpr:0.97351
[36] validation-logloss:0.28240 validation-auc:0.96833 validation-aucpr:0.97353
[37] validation-logloss:0.27847 validation-auc:0.96831 validation-aucpr:0.97353
[38] validation-logloss:0.27511 validation-auc:0.96827 validation-aucpr:0.97350
[39] validation-logloss:0.27146 validation-auc:0.96851 validation-aucpr:0.97360
[40] validation-logloss:0.26809 validation-auc:0.96857 validation-aucpr:0.97362
[41] validation-logloss:0.26531 validation-auc:0.96828 validation-aucpr:0.97341
[42] validation-logloss:0.26203 validation-auc:0.96850 validation-aucpr:0.97351
[43] validation-logloss:0.25869 validation-auc:0.96872 validation-aucpr:0.97367
[44] validation-logloss:0.25629 validation-auc:0.96871 validation-aucpr:0.97366
[45] validation-logloss:0.25356 validation-auc:0.96881 validation-aucpr:0.97373
[46] validation-logloss:0.25121 validation-auc:0.96871 validation-aucpr:0.97366
[47] validation-logloss:0.24909 validation-auc:0.96875 validation-aucpr:0.97368
[48] validation-logloss:0.24698 validation-auc:0.96891 validation-aucpr:0.97379
[49] validation-logloss:0.24454 validation-auc:0.96897 validation-aucpr:0.97383
[50] validation-logloss:0.24210 validation-auc:0.96903 validation-aucpr:0.97391
[51] validation-logloss:0.24009 validation-auc:0.96908 validation-aucpr:0.97395
[52] validation-logloss:0.23808 validation-auc:0.96913 validation-aucpr:0.97400
[53] validation-logloss:0.23595 validation-auc:0.96924 validation-aucpr:0.97407
[54] validation-logloss:0.23471 validation-auc:0.96908 validation-aucpr:0.97394
[55] validation-logloss:0.23294 validation-auc:0.96906 validation-aucpr:0.97395
{'best_iteration': '53', 'best_score': '0.9740728410816708'}
Trial 60, Fold 4: Log loss = 0.23294060156659158, Average precision = 0.9739539172089092, ROC-AUC = 0.9690645185519003, Elapsed Time = 5.714658599998074 seconds
Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 60, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66639 validation-auc:0.92341 validation-aucpr:0.93476
[1] validation-logloss:0.64094 validation-auc:0.94721 validation-aucpr:0.94052
[2] validation-logloss:0.61763 validation-auc:0.95311 validation-aucpr:0.95444
[3] validation-logloss:0.59823 validation-auc:0.95373 validation-aucpr:0.95447
[4] validation-logloss:0.58049 validation-auc:0.95562 validation-aucpr:0.95451
[5] validation-logloss:0.56068 validation-auc:0.95938 validation-aucpr:0.96368
[6] validation-logloss:0.54314 validation-auc:0.95980 validation-aucpr:0.96286
[7] validation-logloss:0.52596 validation-auc:0.96113 validation-aucpr:0.96660
[8] validation-logloss:0.50941 validation-auc:0.96209 validation-aucpr:0.96764
[9] validation-logloss:0.49560 validation-auc:0.96316 validation-aucpr:0.96867
[10] validation-logloss:0.48136 validation-auc:0.96400 validation-aucpr:0.96968
[11] validation-logloss:0.46763 validation-auc:0.96486 validation-aucpr:0.97038
[12] validation-logloss:0.45464 validation-auc:0.96486 validation-aucpr:0.97032
[13] validation-logloss:0.44380 validation-auc:0.96491 validation-aucpr:0.97037
[14] validation-logloss:0.43310 validation-auc:0.96531 validation-aucpr:0.97080
[15] validation-logloss:0.42201 validation-auc:0.96621 validation-aucpr:0.97119
[16] validation-logloss:0.41174 validation-auc:0.96612 validation-aucpr:0.97053
[17] validation-logloss:0.40310 validation-auc:0.96612 validation-aucpr:0.97048
[18] validation-logloss:0.39342 validation-auc:0.96617 validation-aucpr:0.97070
[19] validation-logloss:0.38530 validation-auc:0.96630 validation-aucpr:0.97073
[20] validation-logloss:0.37844 validation-auc:0.96627 validation-aucpr:0.97064
[21] validation-logloss:0.37027 validation-auc:0.96620 validation-aucpr:0.97057
[22] validation-logloss:0.36372 validation-auc:0.96633 validation-aucpr:0.97061
[23] validation-logloss:0.35636 validation-auc:0.96636 validation-aucpr:0.97077
[24] validation-logloss:0.34939 validation-auc:0.96636 validation-aucpr:0.97074
[25] validation-logloss:0.34263 validation-auc:0.96660 validation-aucpr:0.97093
[26] validation-logloss:0.33739 validation-auc:0.96654 validation-aucpr:0.97082
[27] validation-logloss:0.33133 validation-auc:0.96664 validation-aucpr:0.97088
[28] validation-logloss:0.32646 validation-auc:0.96640 validation-aucpr:0.97073
[29] validation-logloss:0.32130 validation-auc:0.96660 validation-aucpr:0.97078
[30] validation-logloss:0.31659 validation-auc:0.96640 validation-aucpr:0.96989
[31] validation-logloss:0.31110 validation-auc:0.96663 validation-aucpr:0.97008
[32] validation-logloss:0.30572 validation-auc:0.96691 validation-aucpr:0.97030
[33] validation-logloss:0.30075 validation-auc:0.96703 validation-aucpr:0.97039
[34] validation-logloss:0.29701 validation-auc:0.96691 validation-aucpr:0.97026
[35] validation-logloss:0.29364 validation-auc:0.96689 validation-aucpr:0.97080
[36] validation-logloss:0.28939 validation-auc:0.96699 validation-aucpr:0.97109
[37] validation-logloss:0.28612 validation-auc:0.96709 validation-aucpr:0.97115
[38] validation-logloss:0.28241 validation-auc:0.96738 validation-aucpr:0.97149
[39] validation-logloss:0.27933 validation-auc:0.96736 validation-aucpr:0.97146
[40] validation-logloss:0.27617 validation-auc:0.96743 validation-aucpr:0.97151
[41] validation-logloss:0.27367 validation-auc:0.96729 validation-aucpr:0.97146
[42] validation-logloss:0.27105 validation-auc:0.96721 validation-aucpr:0.97139
[43] validation-logloss:0.26782 validation-auc:0.96721 validation-aucpr:0.97144
[44] validation-logloss:0.26475 validation-auc:0.96736 validation-aucpr:0.97153
[45] validation-logloss:0.26218 validation-auc:0.96736 validation-aucpr:0.97150
[46] validation-logloss:0.26007 validation-auc:0.96731 validation-aucpr:0.97142
[47] validation-logloss:0.25814 validation-auc:0.96726 validation-aucpr:0.97135
[48] validation-logloss:0.25536 validation-auc:0.96738 validation-aucpr:0.97151
[49] validation-logloss:0.25323 validation-auc:0.96755 validation-aucpr:0.97204
[50] validation-logloss:0.25095 validation-auc:0.96757 validation-aucpr:0.97207
[51] validation-logloss:0.24922 validation-auc:0.96766 validation-aucpr:0.97204
[52] validation-logloss:0.24653 validation-auc:0.96796 validation-aucpr:0.97225
[53] validation-logloss:0.24457 validation-auc:0.96792 validation-aucpr:0.97219
[54] validation-logloss:0.24258 validation-auc:0.96801 validation-aucpr:0.97223
[55] validation-logloss:0.24105 validation-auc:0.96801 validation-aucpr:0.97223
{'best_iteration': '52', 'best_score': '0.97225150400306'}
Trial 60, Fold 5: Log loss = 0.24104923104785034, Average precision = 0.9722273856003052, ROC-AUC = 0.9680141203574679, Elapsed Time = 5.812461699999403 seconds
Optimization Progress: 61%|######1 | 61/100 [2:58:43<35:29, 54.61s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 61, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65332 validation-auc:0.91488 validation-aucpr:0.93009
[1] validation-logloss:0.61684 validation-auc:0.94953 validation-aucpr:0.93989
[2] validation-logloss:0.58403 validation-auc:0.95482 validation-aucpr:0.94498
[3] validation-logloss:0.55500 validation-auc:0.96038 validation-aucpr:0.95724
[4] validation-logloss:0.52896 validation-auc:0.96244 validation-aucpr:0.96479
[5] validation-logloss:0.50474 validation-auc:0.96346 validation-aucpr:0.96611
[6] validation-logloss:0.48500 validation-auc:0.96421 validation-aucpr:0.96559
[7] validation-logloss:0.46407 validation-auc:0.96601 validation-aucpr:0.96882
[8] validation-logloss:0.44688 validation-auc:0.96662 validation-aucpr:0.97142
[9] validation-logloss:0.43114 validation-auc:0.96671 validation-aucpr:0.97134
[10] validation-logloss:0.41494 validation-auc:0.96762 validation-aucpr:0.97197
[11] validation-logloss:0.39993 validation-auc:0.96779 validation-aucpr:0.97195
[12] validation-logloss:0.38622 validation-auc:0.96782 validation-aucpr:0.97202
[13] validation-logloss:0.37336 validation-auc:0.96786 validation-aucpr:0.97202
[14] validation-logloss:0.36163 validation-auc:0.96816 validation-aucpr:0.97230
[15] validation-logloss:0.35062 validation-auc:0.96845 validation-aucpr:0.97250
[16] validation-logloss:0.34023 validation-auc:0.96860 validation-aucpr:0.97260
[17] validation-logloss:0.33075 validation-auc:0.96864 validation-aucpr:0.97255
[18] validation-logloss:0.32176 validation-auc:0.96872 validation-aucpr:0.97263
[19] validation-logloss:0.31345 validation-auc:0.96877 validation-aucpr:0.97294
[20] validation-logloss:0.30527 validation-auc:0.96909 validation-aucpr:0.97320
[21] validation-logloss:0.29781 validation-auc:0.96925 validation-aucpr:0.97334
[22] validation-logloss:0.29128 validation-auc:0.96938 validation-aucpr:0.97340
[23] validation-logloss:0.28519 validation-auc:0.96938 validation-aucpr:0.97335
[24] validation-logloss:0.27902 validation-auc:0.96965 validation-aucpr:0.97357
[25] validation-logloss:0.27351 validation-auc:0.96976 validation-aucpr:0.97367
[26] validation-logloss:0.26846 validation-auc:0.96978 validation-aucpr:0.97341
[27] validation-logloss:0.26355 validation-auc:0.96996 validation-aucpr:0.97355
[28] validation-logloss:0.25912 validation-auc:0.96993 validation-aucpr:0.97346
[29] validation-logloss:0.25517 validation-auc:0.97000 validation-aucpr:0.97351
[30] validation-logloss:0.25173 validation-auc:0.96991 validation-aucpr:0.97309
[31] validation-logloss:0.24779 validation-auc:0.97014 validation-aucpr:0.97326
[32] validation-logloss:0.24504 validation-auc:0.96989 validation-aucpr:0.97265
[33] validation-logloss:0.24225 validation-auc:0.96982 validation-aucpr:0.97259
[34] validation-logloss:0.23934 validation-auc:0.96984 validation-aucpr:0.97261
[35] validation-logloss:0.23641 validation-auc:0.96992 validation-aucpr:0.97263
[36] validation-logloss:0.23344 validation-auc:0.97023 validation-aucpr:0.97284
[37] validation-logloss:0.23078 validation-auc:0.97031 validation-aucpr:0.97274
[38] validation-logloss:0.22854 validation-auc:0.97038 validation-aucpr:0.97277
[39] validation-logloss:0.22607 validation-auc:0.97055 validation-aucpr:0.97288
[40] validation-logloss:0.22433 validation-auc:0.97044 validation-aucpr:0.97270
[41] validation-logloss:0.22252 validation-auc:0.97045 validation-aucpr:0.97261
[42] validation-logloss:0.22057 validation-auc:0.97058 validation-aucpr:0.97274
[43] validation-logloss:0.21911 validation-auc:0.97062 validation-aucpr:0.97285
[44] validation-logloss:0.21724 validation-auc:0.97092 validation-aucpr:0.97482
[45] validation-logloss:0.21565 validation-auc:0.97105 validation-aucpr:0.97485
[46] validation-logloss:0.21436 validation-auc:0.97101 validation-aucpr:0.97481
[47] validation-logloss:0.21298 validation-auc:0.97113 validation-aucpr:0.97486
[48] validation-logloss:0.21149 validation-auc:0.97126 validation-aucpr:0.97495
[49] validation-logloss:0.21013 validation-auc:0.97136 validation-aucpr:0.97508
[50] validation-logloss:0.20874 validation-auc:0.97149 validation-aucpr:0.97515
[51] validation-logloss:0.20788 validation-auc:0.97144 validation-aucpr:0.97511
[52] validation-logloss:0.20680 validation-auc:0.97153 validation-aucpr:0.97521
[53] validation-logloss:0.20599 validation-auc:0.97154 validation-aucpr:0.97514
[54] validation-logloss:0.20541 validation-auc:0.97138 validation-aucpr:0.97499
[55] validation-logloss:0.20457 validation-auc:0.97155 validation-aucpr:0.97519
[56] validation-logloss:0.20390 validation-auc:0.97151 validation-aucpr:0.97491
[57] validation-logloss:0.20329 validation-auc:0.97155 validation-aucpr:0.97507
[58] validation-logloss:0.20266 validation-auc:0.97155 validation-aucpr:0.97506
[59] validation-logloss:0.20194 validation-auc:0.97162 validation-aucpr:0.97512
[60] validation-logloss:0.20127 validation-auc:0.97175 validation-aucpr:0.97516
[61] validation-logloss:0.20084 validation-auc:0.97183 validation-aucpr:0.97543
[62] validation-logloss:0.20066 validation-auc:0.97173 validation-aucpr:0.97534
[63] validation-logloss:0.20027 validation-auc:0.97173 validation-aucpr:0.97524
[64] validation-logloss:0.19984 validation-auc:0.97171 validation-aucpr:0.97541
[65] validation-logloss:0.19946 validation-auc:0.97175 validation-aucpr:0.97539
[66] validation-logloss:0.19906 validation-auc:0.97173 validation-aucpr:0.97537
[67] validation-logloss:0.19871 validation-auc:0.97179 validation-aucpr:0.97538
[68] validation-logloss:0.19836 validation-auc:0.97183 validation-aucpr:0.97542
[69] validation-logloss:0.19838 validation-auc:0.97169 validation-aucpr:0.97529
[70] validation-logloss:0.19822 validation-auc:0.97165 validation-aucpr:0.97527
[71] validation-logloss:0.19808 validation-auc:0.97167 validation-aucpr:0.97522
[72] validation-logloss:0.19769 validation-auc:0.97178 validation-aucpr:0.97529
[73] validation-logloss:0.19793 validation-auc:0.97157 validation-aucpr:0.97512
[74] validation-logloss:0.19803 validation-auc:0.97143 validation-aucpr:0.97485
[75] validation-logloss:0.19784 validation-auc:0.97153 validation-aucpr:0.97536
[76] validation-logloss:0.19751 validation-auc:0.97156 validation-aucpr:0.97534
[77] validation-logloss:0.19741 validation-auc:0.97152 validation-aucpr:0.97512
[78] validation-logloss:0.19689 validation-auc:0.97177 validation-aucpr:0.97515
[79] validation-logloss:0.19663 validation-auc:0.97178 validation-aucpr:0.97527
[80] validation-logloss:0.19658 validation-auc:0.97177 validation-aucpr:0.97524
[81] validation-logloss:0.19633 validation-auc:0.97182 validation-aucpr:0.97503
[82] validation-logloss:0.19641 validation-auc:0.97176 validation-aucpr:0.97485
[83] validation-logloss:0.19635 validation-auc:0.97179 validation-aucpr:0.97484
[84] validation-logloss:0.19622 validation-auc:0.97185 validation-aucpr:0.97482
[85] validation-logloss:0.19602 validation-auc:0.97198 validation-aucpr:0.97538
[86] validation-logloss:0.19597 validation-auc:0.97202 validation-aucpr:0.97567
[87] validation-logloss:0.19619 validation-auc:0.97196 validation-aucpr:0.97560
[88] validation-logloss:0.19598 validation-auc:0.97201 validation-aucpr:0.97556
{'best_iteration': '86', 'best_score': '0.9756681810404803'}
Trial 61, Fold 1: Log loss = 0.19597705106820937, Average precision = 0.975568210512143, ROC-AUC = 0.9720051989981981, Elapsed Time = 2.902240300001722 seconds
Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 61, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65238 validation-auc:0.94185 validation-aucpr:0.94565
[1] validation-logloss:0.61617 validation-auc:0.95448 validation-aucpr:0.94587
[2] validation-logloss:0.58301 validation-auc:0.96415 validation-aucpr:0.96661
[3] validation-logloss:0.55338 validation-auc:0.96547 validation-aucpr:0.97008
[4] validation-logloss:0.52638 validation-auc:0.96581 validation-aucpr:0.97070
[5] validation-logloss:0.50198 validation-auc:0.96644 validation-aucpr:0.97105
[6] validation-logloss:0.47883 validation-auc:0.96833 validation-aucpr:0.97262
[7] validation-logloss:0.46054 validation-auc:0.96905 validation-aucpr:0.97324
[8] validation-logloss:0.44160 validation-auc:0.96932 validation-aucpr:0.97329
[9] validation-logloss:0.42422 validation-auc:0.96970 validation-aucpr:0.97371
[10] validation-logloss:0.40843 validation-auc:0.96937 validation-aucpr:0.97342
[11] validation-logloss:0.39328 validation-auc:0.96954 validation-aucpr:0.97350
[12] validation-logloss:0.37979 validation-auc:0.96953 validation-aucpr:0.97355
[13] validation-logloss:0.36731 validation-auc:0.96924 validation-aucpr:0.97317
[14] validation-logloss:0.35570 validation-auc:0.96914 validation-aucpr:0.97303
[15] validation-logloss:0.34499 validation-auc:0.96938 validation-aucpr:0.97331
[16] validation-logloss:0.33448 validation-auc:0.96975 validation-aucpr:0.97348
[17] validation-logloss:0.32497 validation-auc:0.96981 validation-aucpr:0.97350
[18] validation-logloss:0.31608 validation-auc:0.96988 validation-aucpr:0.97357
[19] validation-logloss:0.30788 validation-auc:0.96982 validation-aucpr:0.97354
[20] validation-logloss:0.30070 validation-auc:0.97028 validation-aucpr:0.97401
[21] validation-logloss:0.29340 validation-auc:0.97054 validation-aucpr:0.97412
[22] validation-logloss:0.28669 validation-auc:0.97075 validation-aucpr:0.97412
[23] validation-logloss:0.28032 validation-auc:0.97092 validation-aucpr:0.97434
[24] validation-logloss:0.27450 validation-auc:0.97090 validation-aucpr:0.97426
[25] validation-logloss:0.26895 validation-auc:0.97110 validation-aucpr:0.97450
[26] validation-logloss:0.26377 validation-auc:0.97113 validation-aucpr:0.97472
[27] validation-logloss:0.25858 validation-auc:0.97145 validation-aucpr:0.97491
[28] validation-logloss:0.25400 validation-auc:0.97145 validation-aucpr:0.97490
[29] validation-logloss:0.24940 validation-auc:0.97162 validation-aucpr:0.97503
[30] validation-logloss:0.24517 validation-auc:0.97170 validation-aucpr:0.97506
[31] validation-logloss:0.24110 validation-auc:0.97196 validation-aucpr:0.97520
[32] validation-logloss:0.23739 validation-auc:0.97206 validation-aucpr:0.97546
[33] validation-logloss:0.23407 validation-auc:0.97208 validation-aucpr:0.97546
[34] validation-logloss:0.23090 validation-auc:0.97202 validation-aucpr:0.97543
[35] validation-logloss:0.22799 validation-auc:0.97212 validation-aucpr:0.97544
[36] validation-logloss:0.22501 validation-auc:0.97222 validation-aucpr:0.97554
[37] validation-logloss:0.22257 validation-auc:0.97221 validation-aucpr:0.97551
[38] validation-logloss:0.22025 validation-auc:0.97250 validation-aucpr:0.97573
[39] validation-logloss:0.21789 validation-auc:0.97259 validation-aucpr:0.97578
[40] validation-logloss:0.21589 validation-auc:0.97251 validation-aucpr:0.97573
[41] validation-logloss:0.21382 validation-auc:0.97255 validation-aucpr:0.97572
[42] validation-logloss:0.21200 validation-auc:0.97254 validation-aucpr:0.97572
[43] validation-logloss:0.21016 validation-auc:0.97261 validation-aucpr:0.97574
[44] validation-logloss:0.20837 validation-auc:0.97246 validation-aucpr:0.97565
[45] validation-logloss:0.20672 validation-auc:0.97267 validation-aucpr:0.97582
[46] validation-logloss:0.20511 validation-auc:0.97270 validation-aucpr:0.97584
[47] validation-logloss:0.20359 validation-auc:0.97277 validation-aucpr:0.97589
[48] validation-logloss:0.20216 validation-auc:0.97285 validation-aucpr:0.97593
[49] validation-logloss:0.20083 validation-auc:0.97294 validation-aucpr:0.97601
[50] validation-logloss:0.19981 validation-auc:0.97288 validation-aucpr:0.97595
[51] validation-logloss:0.19848 validation-auc:0.97289 validation-aucpr:0.97569
[52] validation-logloss:0.19776 validation-auc:0.97269 validation-aucpr:0.97549
[53] validation-logloss:0.19689 validation-auc:0.97277 validation-aucpr:0.97552
[54] validation-logloss:0.19584 validation-auc:0.97284 validation-aucpr:0.97553
[55] validation-logloss:0.19487 validation-auc:0.97292 validation-aucpr:0.97558
[56] validation-logloss:0.19407 validation-auc:0.97298 validation-aucpr:0.97559
[57] validation-logloss:0.19321 validation-auc:0.97304 validation-aucpr:0.97565
[58] validation-logloss:0.19217 validation-auc:0.97307 validation-aucpr:0.97555
[59] validation-logloss:0.19151 validation-auc:0.97306 validation-aucpr:0.97557
[60] validation-logloss:0.19094 validation-auc:0.97305 validation-aucpr:0.97537
[61] validation-logloss:0.19051 validation-auc:0.97296 validation-aucpr:0.97526
[62] validation-logloss:0.18976 validation-auc:0.97307 validation-aucpr:0.97543
[63] validation-logloss:0.18864 validation-auc:0.97327 validation-aucpr:0.97584
[64] validation-logloss:0.18789 validation-auc:0.97337 validation-aucpr:0.97610
[65] validation-logloss:0.18748 validation-auc:0.97335 validation-aucpr:0.97636
[66] validation-logloss:0.18714 validation-auc:0.97334 validation-aucpr:0.97610
[67] validation-logloss:0.18688 validation-auc:0.97331 validation-aucpr:0.97605
[68] validation-logloss:0.18662 validation-auc:0.97341 validation-aucpr:0.97650
[69] validation-logloss:0.18634 validation-auc:0.97340 validation-aucpr:0.97640
[70] validation-logloss:0.18595 validation-auc:0.97336 validation-aucpr:0.97635
[71] validation-logloss:0.18577 validation-auc:0.97329 validation-aucpr:0.97628
[72] validation-logloss:0.18563 validation-auc:0.97330 validation-aucpr:0.97624
[73] validation-logloss:0.18532 validation-auc:0.97334 validation-aucpr:0.97639
[74] validation-logloss:0.18528 validation-auc:0.97329 validation-aucpr:0.97635
[75] validation-logloss:0.18521 validation-auc:0.97323 validation-aucpr:0.97628
[76] validation-logloss:0.18501 validation-auc:0.97331 validation-aucpr:0.97640
[77] validation-logloss:0.18489 validation-auc:0.97328 validation-aucpr:0.97639
[78] validation-logloss:0.18469 validation-auc:0.97335 validation-aucpr:0.97644
[79] validation-logloss:0.18467 validation-auc:0.97325 validation-aucpr:0.97635
[80] validation-logloss:0.18461 validation-auc:0.97331 validation-aucpr:0.97635
[81] validation-logloss:0.18419 validation-auc:0.97345 validation-aucpr:0.97639
[82] validation-logloss:0.18396 validation-auc:0.97345 validation-aucpr:0.97638
[83] validation-logloss:0.18392 validation-auc:0.97345 validation-aucpr:0.97631
[84] validation-logloss:0.18395 validation-auc:0.97347 validation-aucpr:0.97628
[85] validation-logloss:0.18400 validation-auc:0.97345 validation-aucpr:0.97630
[86] validation-logloss:0.18371 validation-auc:0.97350 validation-aucpr:0.97629
[87] validation-logloss:0.18373 validation-auc:0.97348 validation-aucpr:0.97627
[88] validation-logloss:0.18354 validation-auc:0.97343 validation-aucpr:0.97625
{'best_iteration': '68', 'best_score': '0.9764972411007973'}
Trial 61, Fold 2: Log loss = 0.1835398357728264, Average precision = 0.9762499405488697, ROC-AUC = 0.973432908206854, Elapsed Time = 3.3019077999997535 seconds
Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 61, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65257 validation-auc:0.94874 validation-aucpr:0.95522
[1] validation-logloss:0.61608 validation-auc:0.95651 validation-aucpr:0.94847
[2] validation-logloss:0.58239 validation-auc:0.96551 validation-aucpr:0.96145
[3] validation-logloss:0.55286 validation-auc:0.96633 validation-aucpr:0.96346
[4] validation-logloss:0.52647 validation-auc:0.96743 validation-aucpr:0.97130
[5] validation-logloss:0.50173 validation-auc:0.96789 validation-aucpr:0.97175
[6] validation-logloss:0.47979 validation-auc:0.96831 validation-aucpr:0.97334
[7] validation-logloss:0.45929 validation-auc:0.96828 validation-aucpr:0.97278
[8] validation-logloss:0.44136 validation-auc:0.96839 validation-aucpr:0.97268
[9] validation-logloss:0.42410 validation-auc:0.96848 validation-aucpr:0.97269
[10] validation-logloss:0.40778 validation-auc:0.96858 validation-aucpr:0.97279
[11] validation-logloss:0.39318 validation-auc:0.96899 validation-aucpr:0.97324
[12] validation-logloss:0.37912 validation-auc:0.96938 validation-aucpr:0.97365
[13] validation-logloss:0.36658 validation-auc:0.96961 validation-aucpr:0.97377
[14] validation-logloss:0.35465 validation-auc:0.96963 validation-aucpr:0.97381
[15] validation-logloss:0.34340 validation-auc:0.96986 validation-aucpr:0.97370
[16] validation-logloss:0.33300 validation-auc:0.96997 validation-aucpr:0.97377
[17] validation-logloss:0.32425 validation-auc:0.97034 validation-aucpr:0.97392
[18] validation-logloss:0.31523 validation-auc:0.97055 validation-aucpr:0.97423
[19] validation-logloss:0.30669 validation-auc:0.97088 validation-aucpr:0.97513
[20] validation-logloss:0.29900 validation-auc:0.97083 validation-aucpr:0.97505
[21] validation-logloss:0.29155 validation-auc:0.97105 validation-aucpr:0.97518
[22] validation-logloss:0.28463 validation-auc:0.97113 validation-aucpr:0.97528
[23] validation-logloss:0.27818 validation-auc:0.97135 validation-aucpr:0.97539
[24] validation-logloss:0.27244 validation-auc:0.97122 validation-aucpr:0.97529
[25] validation-logloss:0.26697 validation-auc:0.97151 validation-aucpr:0.97545
[26] validation-logloss:0.26203 validation-auc:0.97135 validation-aucpr:0.97531
[27] validation-logloss:0.25734 validation-auc:0.97133 validation-aucpr:0.97525
[28] validation-logloss:0.25267 validation-auc:0.97146 validation-aucpr:0.97491
[29] validation-logloss:0.24822 validation-auc:0.97173 validation-aucpr:0.97514
[30] validation-logloss:0.24434 validation-auc:0.97163 validation-aucpr:0.97500
[31] validation-logloss:0.24081 validation-auc:0.97155 validation-aucpr:0.97492
[32] validation-logloss:0.23729 validation-auc:0.97158 validation-aucpr:0.97493
[33] validation-logloss:0.23417 validation-auc:0.97176 validation-aucpr:0.97524
[34] validation-logloss:0.23112 validation-auc:0.97172 validation-aucpr:0.97513
[35] validation-logloss:0.22804 validation-auc:0.97180 validation-aucpr:0.97518
[36] validation-logloss:0.22568 validation-auc:0.97187 validation-aucpr:0.97524
[37] validation-logloss:0.22320 validation-auc:0.97200 validation-aucpr:0.97588
[38] validation-logloss:0.22087 validation-auc:0.97192 validation-aucpr:0.97583
[39] validation-logloss:0.21860 validation-auc:0.97192 validation-aucpr:0.97575
[40] validation-logloss:0.21615 validation-auc:0.97226 validation-aucpr:0.97600
[41] validation-logloss:0.21434 validation-auc:0.97217 validation-aucpr:0.97593
[42] validation-logloss:0.21256 validation-auc:0.97212 validation-aucpr:0.97582
[43] validation-logloss:0.21087 validation-auc:0.97217 validation-aucpr:0.97580
[44] validation-logloss:0.20925 validation-auc:0.97222 validation-aucpr:0.97579
[45] validation-logloss:0.20741 validation-auc:0.97242 validation-aucpr:0.97598
[46] validation-logloss:0.20594 validation-auc:0.97244 validation-aucpr:0.97589
[47] validation-logloss:0.20450 validation-auc:0.97256 validation-aucpr:0.97594
[48] validation-logloss:0.20297 validation-auc:0.97262 validation-aucpr:0.97602
[49] validation-logloss:0.20179 validation-auc:0.97266 validation-aucpr:0.97601
[50] validation-logloss:0.20065 validation-auc:0.97272 validation-aucpr:0.97602
[51] validation-logloss:0.19954 validation-auc:0.97274 validation-aucpr:0.97600
[52] validation-logloss:0.19838 validation-auc:0.97290 validation-aucpr:0.97613
[53] validation-logloss:0.19767 validation-auc:0.97284 validation-aucpr:0.97604
[54] validation-logloss:0.19640 validation-auc:0.97305 validation-aucpr:0.97628
[55] validation-logloss:0.19558 validation-auc:0.97309 validation-aucpr:0.97629
[56] validation-logloss:0.19456 validation-auc:0.97324 validation-aucpr:0.97637
[57] validation-logloss:0.19397 validation-auc:0.97327 validation-aucpr:0.97636
[58] validation-logloss:0.19325 validation-auc:0.97329 validation-aucpr:0.97641
[59] validation-logloss:0.19279 validation-auc:0.97340 validation-aucpr:0.97653
[60] validation-logloss:0.19204 validation-auc:0.97351 validation-aucpr:0.97653
[61] validation-logloss:0.19146 validation-auc:0.97360 validation-aucpr:0.97709
[62] validation-logloss:0.19109 validation-auc:0.97357 validation-aucpr:0.97704
[63] validation-logloss:0.19060 validation-auc:0.97354 validation-aucpr:0.97704
[64] validation-logloss:0.19012 validation-auc:0.97359 validation-aucpr:0.97705
[65] validation-logloss:0.18966 validation-auc:0.97362 validation-aucpr:0.97707
[66] validation-logloss:0.18949 validation-auc:0.97356 validation-aucpr:0.97690
[67] validation-logloss:0.18917 validation-auc:0.97354 validation-aucpr:0.97684
[68] validation-logloss:0.18883 validation-auc:0.97356 validation-aucpr:0.97680
[69] validation-logloss:0.18842 validation-auc:0.97362 validation-aucpr:0.97687
[70] validation-logloss:0.18827 validation-auc:0.97361 validation-aucpr:0.97696
[71] validation-logloss:0.18832 validation-auc:0.97351 validation-aucpr:0.97683
[72] validation-logloss:0.18821 validation-auc:0.97347 validation-aucpr:0.97687
[73] validation-logloss:0.18787 validation-auc:0.97355 validation-aucpr:0.97693
[74] validation-logloss:0.18738 validation-auc:0.97365 validation-aucpr:0.97709
[75] validation-logloss:0.18723 validation-auc:0.97366 validation-aucpr:0.97708
[76] validation-logloss:0.18715 validation-auc:0.97367 validation-aucpr:0.97710
[77] validation-logloss:0.18675 validation-auc:0.97380 validation-aucpr:0.97721
[78] validation-logloss:0.18647 validation-auc:0.97394 validation-aucpr:0.97739
[79] validation-logloss:0.18630 validation-auc:0.97394 validation-aucpr:0.97736
[80] validation-logloss:0.18624 validation-auc:0.97387 validation-aucpr:0.97728
[81] validation-logloss:0.18613 validation-auc:0.97388 validation-aucpr:0.97726
[82] validation-logloss:0.18605 validation-auc:0.97395 validation-aucpr:0.97741
[83] validation-logloss:0.18584 validation-auc:0.97398 validation-aucpr:0.97743
[84] validation-logloss:0.18593 validation-auc:0.97394 validation-aucpr:0.97739
[85] validation-logloss:0.18595 validation-auc:0.97392 validation-aucpr:0.97730
[86] validation-logloss:0.18594 validation-auc:0.97392 validation-aucpr:0.97726
[87] validation-logloss:0.18599 validation-auc:0.97390 validation-aucpr:0.97735
[88] validation-logloss:0.18598 validation-auc:0.97386 validation-aucpr:0.97730
{'best_iteration': '83', 'best_score': '0.9774279649471089'}
Trial 61, Fold 3: Log loss = 0.1859758454152286, Average precision = 0.9773062201854303, ROC-AUC = 0.9738628004882935, Elapsed Time = 3.588831900000514 seconds
Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 61, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65218 validation-auc:0.93116 validation-aucpr:0.92204
[1] validation-logloss:0.61553 validation-auc:0.95476 validation-aucpr:0.94696
[2] validation-logloss:0.58266 validation-auc:0.96118 validation-aucpr:0.95811
[3] validation-logloss:0.55270 validation-auc:0.96466 validation-aucpr:0.96351
[4] validation-logloss:0.52872 validation-auc:0.96615 validation-aucpr:0.96626
[5] validation-logloss:0.50357 validation-auc:0.96793 validation-aucpr:0.96916
[6] validation-logloss:0.48082 validation-auc:0.96891 validation-aucpr:0.97161
[7] validation-logloss:0.46213 validation-auc:0.96899 validation-aucpr:0.97168
[8] validation-logloss:0.44325 validation-auc:0.96925 validation-aucpr:0.97187
[9] validation-logloss:0.42587 validation-auc:0.96905 validation-aucpr:0.97179
[10] validation-logloss:0.40992 validation-auc:0.96888 validation-aucpr:0.97183
[11] validation-logloss:0.39536 validation-auc:0.96865 validation-aucpr:0.97183
[12] validation-logloss:0.38131 validation-auc:0.96906 validation-aucpr:0.97213
[13] validation-logloss:0.36810 validation-auc:0.96936 validation-aucpr:0.97232
[14] validation-logloss:0.35637 validation-auc:0.96954 validation-aucpr:0.97250
[15] validation-logloss:0.34524 validation-auc:0.96998 validation-aucpr:0.97454
[16] validation-logloss:0.33494 validation-auc:0.97021 validation-aucpr:0.97474
[17] validation-logloss:0.32643 validation-auc:0.97042 validation-aucpr:0.97491
[18] validation-logloss:0.31792 validation-auc:0.97034 validation-aucpr:0.97492
[19] validation-logloss:0.30972 validation-auc:0.97027 validation-aucpr:0.97484
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[60] validation-logloss:0.19859 validation-auc:0.97063 validation-aucpr:0.97531
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[65] validation-logloss:0.19600 validation-auc:0.97086 validation-aucpr:0.97547
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[69] validation-logloss:0.19417 validation-auc:0.97104 validation-aucpr:0.97559
[70] validation-logloss:0.19402 validation-auc:0.97105 validation-aucpr:0.97561
[71] validation-logloss:0.19404 validation-auc:0.97091 validation-aucpr:0.97552
[72] validation-logloss:0.19376 validation-auc:0.97092 validation-aucpr:0.97555
[73] validation-logloss:0.19366 validation-auc:0.97094 validation-aucpr:0.97557
[74] validation-logloss:0.19360 validation-auc:0.97085 validation-aucpr:0.97550
[75] validation-logloss:0.19356 validation-auc:0.97084 validation-aucpr:0.97548
[76] validation-logloss:0.19329 validation-auc:0.97081 validation-aucpr:0.97547
[77] validation-logloss:0.19324 validation-auc:0.97080 validation-aucpr:0.97545
[78] validation-logloss:0.19342 validation-auc:0.97080 validation-aucpr:0.97542
[79] validation-logloss:0.19323 validation-auc:0.97085 validation-aucpr:0.97549
[80] validation-logloss:0.19299 validation-auc:0.97098 validation-aucpr:0.97555
[81] validation-logloss:0.19290 validation-auc:0.97099 validation-aucpr:0.97554
[82] validation-logloss:0.19286 validation-auc:0.97101 validation-aucpr:0.97557
[83] validation-logloss:0.19315 validation-auc:0.97088 validation-aucpr:0.97549
[84] validation-logloss:0.19323 validation-auc:0.97091 validation-aucpr:0.97550
{'best_iteration': '34', 'best_score': '0.9757358168229795'}
Trial 61, Fold 4: Log loss = 0.19322964262513223, Average precision = 0.9755070062750073, ROC-AUC = 0.9709110203843192, Elapsed Time = 3.3171443000028376 seconds
Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 61, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65263 validation-auc:0.94417 validation-aucpr:0.94844
[1] validation-logloss:0.62087 validation-auc:0.94755 validation-aucpr:0.93609
[2] validation-logloss:0.58831 validation-auc:0.95691 validation-aucpr:0.95541
[3] validation-logloss:0.55887 validation-auc:0.96060 validation-aucpr:0.96631
[4] validation-logloss:0.53202 validation-auc:0.96238 validation-aucpr:0.96769
[5] validation-logloss:0.50769 validation-auc:0.96314 validation-aucpr:0.96876
[6] validation-logloss:0.48687 validation-auc:0.96369 validation-aucpr:0.96956
[7] validation-logloss:0.46624 validation-auc:0.96542 validation-aucpr:0.97056
[8] validation-logloss:0.44754 validation-auc:0.96591 validation-aucpr:0.97090
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[10] validation-logloss:0.41362 validation-auc:0.96748 validation-aucpr:0.97209
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[15] validation-logloss:0.34911 validation-auc:0.96892 validation-aucpr:0.97286
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[75] validation-logloss:0.19532 validation-auc:0.97239 validation-aucpr:0.97539
[76] validation-logloss:0.19542 validation-auc:0.97230 validation-aucpr:0.97529
[77] validation-logloss:0.19511 validation-auc:0.97235 validation-aucpr:0.97531
[78] validation-logloss:0.19495 validation-auc:0.97238 validation-aucpr:0.97552
[79] validation-logloss:0.19446 validation-auc:0.97254 validation-aucpr:0.97557
[80] validation-logloss:0.19431 validation-auc:0.97258 validation-aucpr:0.97573
[81] validation-logloss:0.19428 validation-auc:0.97258 validation-aucpr:0.97569
[82] validation-logloss:0.19407 validation-auc:0.97267 validation-aucpr:0.97596
[83] validation-logloss:0.19422 validation-auc:0.97259 validation-aucpr:0.97578
[84] validation-logloss:0.19458 validation-auc:0.97247 validation-aucpr:0.97572
[85] validation-logloss:0.19459 validation-auc:0.97254 validation-aucpr:0.97588
[86] validation-logloss:0.19454 validation-auc:0.97258 validation-aucpr:0.97586
[87] validation-logloss:0.19444 validation-auc:0.97260 validation-aucpr:0.97598
[88] validation-logloss:0.19437 validation-auc:0.97266 validation-aucpr:0.97610
{'best_iteration': '88', 'best_score': '0.9761023838603088'}
Trial 61, Fold 5: Log loss = 0.1943696178039605, Average precision = 0.9761065560095816, ROC-AUC = 0.9726645486130465, Elapsed Time = 3.414511799997854 seconds
Optimization Progress: 62%|######2 | 62/100 [2:59:08<28:50, 45.54s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 62, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68248 validation-auc:0.93953 validation-aucpr:0.94317
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67097 validation-auc:0.95882 validation-aucpr:0.96404
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66079 validation-auc:0.96151 validation-aucpr:0.96615
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65115 validation-auc:0.96150 validation-aucpr:0.96613
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64136 validation-auc:0.96256 validation-aucpr:0.96751
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63242 validation-auc:0.96148 validation-aucpr:0.96649
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.62264 validation-auc:0.96329 validation-aucpr:0.96833
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.61280 validation-auc:0.96421 validation-aucpr:0.96947
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60480 validation-auc:0.96407 validation-aucpr:0.96972
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.59684 validation-auc:0.96406 validation-aucpr:0.96953
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.58889 validation-auc:0.96398 validation-aucpr:0.96947
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.57973 validation-auc:0.96465 validation-aucpr:0.97011
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.57087 validation-auc:0.96537 validation-aucpr:0.97079
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.56328 validation-auc:0.96577 validation-aucpr:0.97109
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.55630 validation-auc:0.96551 validation-aucpr:0.97082
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.54950 validation-auc:0.96561 validation-aucpr:0.97080
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.54178 validation-auc:0.96585 validation-aucpr:0.97105
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.53503 validation-auc:0.96595 validation-aucpr:0.97107
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.52864 validation-auc:0.96609 validation-aucpr:0.97112
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.52218 validation-auc:0.96617 validation-aucpr:0.97112
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.51597 validation-auc:0.96612 validation-aucpr:0.97111
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.51002 validation-auc:0.96608 validation-aucpr:0.97106
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.50317 validation-auc:0.96633 validation-aucpr:0.97135
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.49738 validation-auc:0.96652 validation-aucpr:0.97145
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.49094 validation-auc:0.96674 validation-aucpr:0.97167
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.48535 validation-auc:0.96687 validation-aucpr:0.97172
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.47946 validation-auc:0.96701 validation-aucpr:0.97186
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.47364 validation-auc:0.96714 validation-aucpr:0.97202
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.46876 validation-auc:0.96702 validation-aucpr:0.97195
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.46309 validation-auc:0.96711 validation-aucpr:0.97208
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.45733 validation-auc:0.96723 validation-aucpr:0.97222
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.45260 validation-auc:0.96733 validation-aucpr:0.97227
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.44813 validation-auc:0.96724 validation-aucpr:0.97221
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.44359 validation-auc:0.96726 validation-aucpr:0.97221
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.43844 validation-auc:0.96733 validation-aucpr:0.97232
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.43332 validation-auc:0.96747 validation-aucpr:0.97247
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.42844 validation-auc:0.96765 validation-aucpr:0.97268
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.42352 validation-auc:0.96782 validation-aucpr:0.97281
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.41962 validation-auc:0.96784 validation-aucpr:0.97281
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.41585 validation-auc:0.96773 validation-aucpr:0.97271
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.41224 validation-auc:0.96769 validation-aucpr:0.97268
[20:58:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.40781 validation-auc:0.96787 validation-aucpr:0.97281
{'best_iteration': '37', 'best_score': '0.972812172962304'}
Trial 62, Fold 1: Log loss = 0.40781070738928166, Average precision = 0.9728138503826426, ROC-AUC = 0.9678679417415867, Elapsed Time = 7.990083500000765 seconds
Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 62, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68270 validation-auc:0.94135 validation-aucpr:0.94169
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67085 validation-auc:0.96259 validation-aucpr:0.96625
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66083 validation-auc:0.96391 validation-aucpr:0.96757
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65096 validation-auc:0.96396 validation-aucpr:0.96731
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64171 validation-auc:0.96327 validation-aucpr:0.96660
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63239 validation-auc:0.96365 validation-aucpr:0.96694
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.62233 validation-auc:0.96613 validation-aucpr:0.96964
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.61335 validation-auc:0.96625 validation-aucpr:0.96969
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60353 validation-auc:0.96734 validation-aucpr:0.97093
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.59392 validation-auc:0.96815 validation-aucpr:0.97172
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.58475 validation-auc:0.96896 validation-aucpr:0.97239
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.57716 validation-auc:0.96863 validation-aucpr:0.97207
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56929 validation-auc:0.96871 validation-aucpr:0.97206
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.56201 validation-auc:0.96871 validation-aucpr:0.97201
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.55467 validation-auc:0.96861 validation-aucpr:0.97188
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.54778 validation-auc:0.96849 validation-aucpr:0.97208
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.54077 validation-auc:0.96853 validation-aucpr:0.97213
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.53408 validation-auc:0.96833 validation-aucpr:0.97187
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.52759 validation-auc:0.96835 validation-aucpr:0.97185
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.52120 validation-auc:0.96850 validation-aucpr:0.97191
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.51501 validation-auc:0.96850 validation-aucpr:0.97183
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.50793 validation-auc:0.96881 validation-aucpr:0.97218
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.50102 validation-auc:0.96931 validation-aucpr:0.97262
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.49549 validation-auc:0.96919 validation-aucpr:0.97246
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.48889 validation-auc:0.96945 validation-aucpr:0.97314
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.48321 validation-auc:0.96956 validation-aucpr:0.97321
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.47814 validation-auc:0.96943 validation-aucpr:0.97308
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.47221 validation-auc:0.96960 validation-aucpr:0.97321
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.46615 validation-auc:0.96989 validation-aucpr:0.97346
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.46125 validation-auc:0.96987 validation-aucpr:0.97340
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.45654 validation-auc:0.96982 validation-aucpr:0.97337
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.45094 validation-auc:0.96992 validation-aucpr:0.97351
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.44649 validation-auc:0.96983 validation-aucpr:0.97345
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.44216 validation-auc:0.96969 validation-aucpr:0.97333
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.43782 validation-auc:0.96959 validation-aucpr:0.97322
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.43360 validation-auc:0.96959 validation-aucpr:0.97323
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.42858 validation-auc:0.96986 validation-aucpr:0.97343
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.42388 validation-auc:0.96998 validation-aucpr:0.97354
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.41982 validation-auc:0.96993 validation-aucpr:0.97348
[20:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.41507 validation-auc:0.97026 validation-aucpr:0.97374
[20:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.41032 validation-auc:0.97051 validation-aucpr:0.97393
[20:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.40678 validation-auc:0.97042 validation-aucpr:0.97379
{'best_iteration': '40', 'best_score': '0.9739341330303594'}
Trial 62, Fold 2: Log loss = 0.40677707386682616, Average precision = 0.9737911873049275, ROC-AUC = 0.9704198041785878, Elapsed Time = 7.774721900001168 seconds
Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 62, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68239 validation-auc:0.94132 validation-aucpr:0.94100
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67076 validation-auc:0.96059 validation-aucpr:0.96419
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66041 validation-auc:0.96454 validation-aucpr:0.96768
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65058 validation-auc:0.96531 validation-aucpr:0.96720
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64083 validation-auc:0.96618 validation-aucpr:0.96969
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63020 validation-auc:0.96822 validation-aucpr:0.97200
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.62098 validation-auc:0.96790 validation-aucpr:0.97178
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.61215 validation-auc:0.96784 validation-aucpr:0.97170
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60426 validation-auc:0.96772 validation-aucpr:0.97171
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.59491 validation-auc:0.96818 validation-aucpr:0.97235
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.58589 validation-auc:0.96839 validation-aucpr:0.97262
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.57680 validation-auc:0.96896 validation-aucpr:0.97313
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.56939 validation-auc:0.96891 validation-aucpr:0.97309
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.56173 validation-auc:0.96904 validation-aucpr:0.97321
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.55469 validation-auc:0.96877 validation-aucpr:0.97299
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.54760 validation-auc:0.96892 validation-aucpr:0.97306
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.53952 validation-auc:0.96927 validation-aucpr:0.97346
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.53311 validation-auc:0.96917 validation-aucpr:0.97334
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.52664 validation-auc:0.96916 validation-aucpr:0.97330
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.51923 validation-auc:0.96941 validation-aucpr:0.97355
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.51289 validation-auc:0.96945 validation-aucpr:0.97355
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.50691 validation-auc:0.96940 validation-aucpr:0.97348
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.50005 validation-auc:0.96965 validation-aucpr:0.97375
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.49444 validation-auc:0.96958 validation-aucpr:0.97369
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.48802 validation-auc:0.96963 validation-aucpr:0.97388
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.48160 validation-auc:0.96981 validation-aucpr:0.97404
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.47546 validation-auc:0.96993 validation-aucpr:0.97418
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.47016 validation-auc:0.97000 validation-aucpr:0.97423
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.46418 validation-auc:0.97016 validation-aucpr:0.97440
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.45944 validation-auc:0.97003 validation-aucpr:0.97431
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.45470 validation-auc:0.97000 validation-aucpr:0.97426
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.44993 validation-auc:0.97003 validation-aucpr:0.97424
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.44527 validation-auc:0.96999 validation-aucpr:0.97420
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.44061 validation-auc:0.97005 validation-aucpr:0.97423
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.43540 validation-auc:0.97031 validation-aucpr:0.97444
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.43107 validation-auc:0.97027 validation-aucpr:0.97440
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.42620 validation-auc:0.97027 validation-aucpr:0.97444
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.42224 validation-auc:0.97028 validation-aucpr:0.97444
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.41740 validation-auc:0.97041 validation-aucpr:0.97455
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.41257 validation-auc:0.97057 validation-aucpr:0.97472
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.40791 validation-auc:0.97076 validation-aucpr:0.97489
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.40408 validation-auc:0.97074 validation-aucpr:0.97486
{'best_iteration': '40', 'best_score': '0.9748941392910312'}
Trial 62, Fold 3: Log loss = 0.40408457793104074, Average precision = 0.974869227836466, ROC-AUC = 0.9707354980689774, Elapsed Time = 7.972263800002111 seconds
Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 62, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68261 validation-auc:0.93563 validation-aucpr:0.93838
[20:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67076 validation-auc:0.96144 validation-aucpr:0.96397
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66066 validation-auc:0.96174 validation-aucpr:0.96735
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65087 validation-auc:0.96233 validation-aucpr:0.96758
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64137 validation-auc:0.96294 validation-aucpr:0.96809
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63251 validation-auc:0.96198 validation-aucpr:0.96723
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.62268 validation-auc:0.96329 validation-aucpr:0.96891
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.61389 validation-auc:0.96352 validation-aucpr:0.96902
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60416 validation-auc:0.96509 validation-aucpr:0.97058
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.59576 validation-auc:0.96532 validation-aucpr:0.97073
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.58771 validation-auc:0.96526 validation-aucpr:0.97066
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.58023 validation-auc:0.96482 validation-aucpr:0.97030
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.57225 validation-auc:0.96510 validation-aucpr:0.97040
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.56483 validation-auc:0.96499 validation-aucpr:0.97027
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.55772 validation-auc:0.96503 validation-aucpr:0.97025
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.55052 validation-auc:0.96504 validation-aucpr:0.97026
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.54364 validation-auc:0.96500 validation-aucpr:0.97019
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.53687 validation-auc:0.96475 validation-aucpr:0.97001
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.52930 validation-auc:0.96538 validation-aucpr:0.97067
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.52310 validation-auc:0.96530 validation-aucpr:0.97059
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.51587 validation-auc:0.96566 validation-aucpr:0.97100
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.50982 validation-auc:0.96559 validation-aucpr:0.97094
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.50433 validation-auc:0.96556 validation-aucpr:0.97089
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.49876 validation-auc:0.96537 validation-aucpr:0.97068
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.49223 validation-auc:0.96571 validation-aucpr:0.97104
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.48563 validation-auc:0.96609 validation-aucpr:0.97143
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.48064 validation-auc:0.96604 validation-aucpr:0.97136
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.47573 validation-auc:0.96599 validation-aucpr:0.97131
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.46976 validation-auc:0.96613 validation-aucpr:0.97145
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.46513 validation-auc:0.96607 validation-aucpr:0.97140
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.45941 validation-auc:0.96629 validation-aucpr:0.97161
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.45462 validation-auc:0.96634 validation-aucpr:0.97163
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.45013 validation-auc:0.96629 validation-aucpr:0.97158
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.44565 validation-auc:0.96622 validation-aucpr:0.97151
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.44137 validation-auc:0.96624 validation-aucpr:0.97151
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.43612 validation-auc:0.96653 validation-aucpr:0.97179
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.43112 validation-auc:0.96671 validation-aucpr:0.97196
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.42700 validation-auc:0.96666 validation-aucpr:0.97189
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.42302 validation-auc:0.96668 validation-aucpr:0.97189
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.41922 validation-auc:0.96656 validation-aucpr:0.97182
[20:58:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.41542 validation-auc:0.96653 validation-aucpr:0.97179
[20:58:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.41174 validation-auc:0.96649 validation-aucpr:0.97177
{'best_iteration': '36', 'best_score': '0.9719608080914132'}
Trial 62, Fold 4: Log loss = 0.4117373799923235, Average precision = 0.9717651888435617, ROC-AUC = 0.9664940503015086, Elapsed Time = 7.8552474999996775 seconds
Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 62, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68271 validation-auc:0.93592 validation-aucpr:0.93858
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67102 validation-auc:0.95941 validation-aucpr:0.96306
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.65949 validation-auc:0.96464 validation-aucpr:0.96849
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.64987 validation-auc:0.96459 validation-aucpr:0.96829
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64025 validation-auc:0.96497 validation-aucpr:0.96857
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63030 validation-auc:0.96546 validation-aucpr:0.96957
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.62159 validation-auc:0.96472 validation-aucpr:0.96857
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.61179 validation-auc:0.96538 validation-aucpr:0.96880
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.60329 validation-auc:0.96554 validation-aucpr:0.96941
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.59502 validation-auc:0.96564 validation-aucpr:0.96938
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.58633 validation-auc:0.96551 validation-aucpr:0.96936
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.57923 validation-auc:0.96502 validation-aucpr:0.96880
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.57144 validation-auc:0.96503 validation-aucpr:0.96884
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.56416 validation-auc:0.96516 validation-aucpr:0.96963
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.55714 validation-auc:0.96498 validation-aucpr:0.96942
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.54911 validation-auc:0.96528 validation-aucpr:0.96974
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.54262 validation-auc:0.96527 validation-aucpr:0.96988
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.53480 validation-auc:0.96582 validation-aucpr:0.97038
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.52843 validation-auc:0.96585 validation-aucpr:0.97044
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.52225 validation-auc:0.96588 validation-aucpr:0.97043
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.51624 validation-auc:0.96565 validation-aucpr:0.97026
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.51029 validation-auc:0.96561 validation-aucpr:0.97018
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.50349 validation-auc:0.96593 validation-aucpr:0.97045
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.49768 validation-auc:0.96596 validation-aucpr:0.97046
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.49146 validation-auc:0.96606 validation-aucpr:0.97057
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.48512 validation-auc:0.96637 validation-aucpr:0.97084
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.47986 validation-auc:0.96632 validation-aucpr:0.97082
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.47374 validation-auc:0.96656 validation-aucpr:0.97107
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.46888 validation-auc:0.96650 validation-aucpr:0.97099
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.46289 validation-auc:0.96690 validation-aucpr:0.97130
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.45827 validation-auc:0.96671 validation-aucpr:0.97117
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.45364 validation-auc:0.96674 validation-aucpr:0.97123
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.44826 validation-auc:0.96683 validation-aucpr:0.97135
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.44305 validation-auc:0.96696 validation-aucpr:0.97148
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.43877 validation-auc:0.96691 validation-aucpr:0.97141
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.43360 validation-auc:0.96731 validation-aucpr:0.97175
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.42864 validation-auc:0.96752 validation-aucpr:0.97193
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.42471 validation-auc:0.96738 validation-aucpr:0.97181
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.42069 validation-auc:0.96752 validation-aucpr:0.97187
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.41602 validation-auc:0.96763 validation-aucpr:0.97201
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.41150 validation-auc:0.96780 validation-aucpr:0.97217
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.40700 validation-auc:0.96799 validation-aucpr:0.97233
{'best_iteration': '41', 'best_score': '0.9723345784298949'}
Trial 62, Fold 5: Log loss = 0.40699792927955714, Average precision = 0.9723394565659912, ROC-AUC = 0.9679921966445572, Elapsed Time = 8.108416099999886 seconds
Optimization Progress: 63%|######3 | 63/100 [2:59:56<28:40, 46.50s/it]
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 63, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67832 validation-auc:0.92353 validation-aucpr:0.92326
[1] validation-logloss:0.66323 validation-auc:0.94121 validation-aucpr:0.94346
[2] validation-logloss:0.65079 validation-auc:0.93858 validation-aucpr:0.94112
[3] validation-logloss:0.63928 validation-auc:0.93992 validation-aucpr:0.94359
[4] validation-logloss:0.62799 validation-auc:0.93817 validation-aucpr:0.94204
[5] validation-logloss:0.61553 validation-auc:0.94174 validation-aucpr:0.94691
[6] validation-logloss:0.60391 validation-auc:0.94419 validation-aucpr:0.95006
[7] validation-logloss:0.59353 validation-auc:0.94477 validation-aucpr:0.95064
[8] validation-logloss:0.58297 validation-auc:0.94535 validation-aucpr:0.95151
[9] validation-logloss:0.57233 validation-auc:0.94666 validation-aucpr:0.95298
[10] validation-logloss:0.56316 validation-auc:0.94682 validation-aucpr:0.95318
[11] validation-logloss:0.55484 validation-auc:0.94779 validation-aucpr:0.95413
[12] validation-logloss:0.54587 validation-auc:0.94813 validation-aucpr:0.95433
[13] validation-logloss:0.53970 validation-auc:0.94854 validation-aucpr:0.95472
[14] validation-logloss:0.53207 validation-auc:0.94887 validation-aucpr:0.95516
[15] validation-logloss:0.52443 validation-auc:0.94913 validation-aucpr:0.95527
[16] validation-logloss:0.51673 validation-auc:0.94898 validation-aucpr:0.95504
[17] validation-logloss:0.50996 validation-auc:0.94873 validation-aucpr:0.95471
[18] validation-logloss:0.50331 validation-auc:0.94898 validation-aucpr:0.95487
[19] validation-logloss:0.49572 validation-auc:0.94982 validation-aucpr:0.95593
[20] validation-logloss:0.48951 validation-auc:0.95006 validation-aucpr:0.95639
[21] validation-logloss:0.48352 validation-auc:0.94987 validation-aucpr:0.95621
[22] validation-logloss:0.47802 validation-auc:0.94975 validation-aucpr:0.95600
[23] validation-logloss:0.47252 validation-auc:0.94986 validation-aucpr:0.95602
[24] validation-logloss:0.46703 validation-auc:0.94978 validation-aucpr:0.95587
[25] validation-logloss:0.46219 validation-auc:0.94964 validation-aucpr:0.95571
[26] validation-logloss:0.45673 validation-auc:0.95018 validation-aucpr:0.95624
{'best_iteration': '20', 'best_score': '0.9563913163849957'}
Trial 63, Fold 1: Log loss = 0.45672835759562763, Average precision = 0.9562278721538539, ROC-AUC = 0.9501815198376852, Elapsed Time = 0.45719150000149966 seconds
Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 63, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67858 validation-auc:0.91963 validation-aucpr:0.91597
[1] validation-logloss:0.66428 validation-auc:0.93085 validation-aucpr:0.93238
[2] validation-logloss:0.65073 validation-auc:0.93382 validation-aucpr:0.93639
[3] validation-logloss:0.63874 validation-auc:0.93356 validation-aucpr:0.93664
[4] validation-logloss:0.62726 validation-auc:0.93563 validation-aucpr:0.93603
[5] validation-logloss:0.61539 validation-auc:0.93695 validation-aucpr:0.93969
[6] validation-logloss:0.60487 validation-auc:0.93791 validation-aucpr:0.94148
[7] validation-logloss:0.59497 validation-auc:0.93961 validation-aucpr:0.94268
[8] validation-logloss:0.58472 validation-auc:0.94065 validation-aucpr:0.94421
[9] validation-logloss:0.57620 validation-auc:0.94037 validation-aucpr:0.94378
[10] validation-logloss:0.56508 validation-auc:0.95059 validation-aucpr:0.95576
[11] validation-logloss:0.55497 validation-auc:0.95074 validation-aucpr:0.95535
[12] validation-logloss:0.54663 validation-auc:0.95085 validation-aucpr:0.95556
[13] validation-logloss:0.53993 validation-auc:0.95195 validation-aucpr:0.95678
[14] validation-logloss:0.53338 validation-auc:0.95228 validation-aucpr:0.95693
[15] validation-logloss:0.52579 validation-auc:0.95219 validation-aucpr:0.95670
[16] validation-logloss:0.51818 validation-auc:0.95189 validation-aucpr:0.95659
[17] validation-logloss:0.51148 validation-auc:0.95148 validation-aucpr:0.95608
[18] validation-logloss:0.50464 validation-auc:0.95173 validation-aucpr:0.95635
[19] validation-logloss:0.49716 validation-auc:0.95292 validation-aucpr:0.95752
[20] validation-logloss:0.48818 validation-auc:0.95498 validation-aucpr:0.96019
[21] validation-logloss:0.48203 validation-auc:0.95477 validation-aucpr:0.95997
[22] validation-logloss:0.47622 validation-auc:0.95474 validation-aucpr:0.95981
[23] validation-logloss:0.47082 validation-auc:0.95453 validation-aucpr:0.95949
[24] validation-logloss:0.46308 validation-auc:0.95553 validation-aucpr:0.96049
[25] validation-logloss:0.45774 validation-auc:0.95569 validation-aucpr:0.96049
[26] validation-logloss:0.45242 validation-auc:0.95583 validation-aucpr:0.96054
{'best_iteration': '26', 'best_score': '0.9605407520010589'}
Trial 63, Fold 2: Log loss = 0.4524220961549884, Average precision = 0.9604365271065654, ROC-AUC = 0.9558308479575204, Elapsed Time = 0.4858105000021169 seconds
Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 63, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67808 validation-auc:0.92802 validation-aucpr:0.91740
[1] validation-logloss:0.66489 validation-auc:0.93125 validation-aucpr:0.92869
[2] validation-logloss:0.65182 validation-auc:0.93639 validation-aucpr:0.93817
[3] validation-logloss:0.63689 validation-auc:0.94992 validation-aucpr:0.95469
[4] validation-logloss:0.62565 validation-auc:0.95382 validation-aucpr:0.95862
[5] validation-logloss:0.61371 validation-auc:0.95547 validation-aucpr:0.96133
[6] validation-logloss:0.60241 validation-auc:0.95594 validation-aucpr:0.96229
[7] validation-logloss:0.59327 validation-auc:0.95601 validation-aucpr:0.96199
[8] validation-logloss:0.58300 validation-auc:0.95635 validation-aucpr:0.96216
[9] validation-logloss:0.57206 validation-auc:0.95709 validation-aucpr:0.96290
[10] validation-logloss:0.56253 validation-auc:0.95677 validation-aucpr:0.96287
[11] validation-logloss:0.55348 validation-auc:0.95712 validation-aucpr:0.96283
[12] validation-logloss:0.54514 validation-auc:0.95743 validation-aucpr:0.96304
[13] validation-logloss:0.53661 validation-auc:0.95839 validation-aucpr:0.96421
[14] validation-logloss:0.52893 validation-auc:0.95898 validation-aucpr:0.96462
[15] validation-logloss:0.52120 validation-auc:0.95897 validation-aucpr:0.96466
[16] validation-logloss:0.51400 validation-auc:0.95858 validation-aucpr:0.96437
[17] validation-logloss:0.50722 validation-auc:0.95835 validation-aucpr:0.96412
[18] validation-logloss:0.50061 validation-auc:0.95839 validation-aucpr:0.96416
[19] validation-logloss:0.49371 validation-auc:0.95826 validation-aucpr:0.96400
[20] validation-logloss:0.48723 validation-auc:0.95826 validation-aucpr:0.96399
[21] validation-logloss:0.48111 validation-auc:0.95806 validation-aucpr:0.96378
[22] validation-logloss:0.47444 validation-auc:0.95856 validation-aucpr:0.96416
[23] validation-logloss:0.46832 validation-auc:0.95830 validation-aucpr:0.96392
[24] validation-logloss:0.46284 validation-auc:0.95812 validation-aucpr:0.96368
[25] validation-logloss:0.45699 validation-auc:0.95841 validation-aucpr:0.96389
[26] validation-logloss:0.45128 validation-auc:0.95816 validation-aucpr:0.96361
{'best_iteration': '15', 'best_score': '0.9646621250956404'}
Trial 63, Fold 3: Log loss = 0.4512801008490302, Average precision = 0.9636091254630796, ROC-AUC = 0.9581633856940606, Elapsed Time = 0.5337794000006397 seconds
Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 63, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67815 validation-auc:0.92384 validation-aucpr:0.91387
[1] validation-logloss:0.66368 validation-auc:0.93671 validation-aucpr:0.93977
[2] validation-logloss:0.64849 validation-auc:0.95031 validation-aucpr:0.95568
[3] validation-logloss:0.63388 validation-auc:0.95210 validation-aucpr:0.95842
[4] validation-logloss:0.62180 validation-auc:0.95293 validation-aucpr:0.95851
[5] validation-logloss:0.61078 validation-auc:0.95317 validation-aucpr:0.95918
[6] validation-logloss:0.59838 validation-auc:0.95474 validation-aucpr:0.96043
[7] validation-logloss:0.58663 validation-auc:0.95574 validation-aucpr:0.96225
[8] validation-logloss:0.57625 validation-auc:0.95639 validation-aucpr:0.96303
[9] validation-logloss:0.56822 validation-auc:0.95654 validation-aucpr:0.96318
[10] validation-logloss:0.55823 validation-auc:0.95722 validation-aucpr:0.96393
[11] validation-logloss:0.54818 validation-auc:0.95711 validation-aucpr:0.96403
[12] validation-logloss:0.53973 validation-auc:0.95669 validation-aucpr:0.96361
[13] validation-logloss:0.53232 validation-auc:0.95731 validation-aucpr:0.96433
[14] validation-logloss:0.52495 validation-auc:0.95758 validation-aucpr:0.96463
[15] validation-logloss:0.51639 validation-auc:0.95789 validation-aucpr:0.96497
[16] validation-logloss:0.50923 validation-auc:0.95758 validation-aucpr:0.96453
[17] validation-logloss:0.50267 validation-auc:0.95704 validation-aucpr:0.96406
[18] validation-logloss:0.49579 validation-auc:0.95712 validation-aucpr:0.96419
[19] validation-logloss:0.48830 validation-auc:0.95758 validation-aucpr:0.96460
[20] validation-logloss:0.48195 validation-auc:0.95778 validation-aucpr:0.96478
[21] validation-logloss:0.47566 validation-auc:0.95755 validation-aucpr:0.96452
[22] validation-logloss:0.46990 validation-auc:0.95742 validation-aucpr:0.96435
[23] validation-logloss:0.46449 validation-auc:0.95745 validation-aucpr:0.96428
[24] validation-logloss:0.45901 validation-auc:0.95758 validation-aucpr:0.96439
[25] validation-logloss:0.45467 validation-auc:0.95769 validation-aucpr:0.96442
[26] validation-logloss:0.44921 validation-auc:0.95776 validation-aucpr:0.96452
{'best_iteration': '15', 'best_score': '0.9649736225770621'}
Trial 63, Fold 4: Log loss = 0.4492070739008998, Average precision = 0.9645035016118194, ROC-AUC = 0.9577632358586617, Elapsed Time = 0.5850017999982811 seconds
Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 63, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67909 validation-auc:0.90844 validation-aucpr:0.89348
[1] validation-logloss:0.66187 validation-auc:0.94828 validation-aucpr:0.95390
[2] validation-logloss:0.64937 validation-auc:0.94753 validation-aucpr:0.95366
[3] validation-logloss:0.63876 validation-auc:0.94667 validation-aucpr:0.95276
[4] validation-logloss:0.62707 validation-auc:0.94690 validation-aucpr:0.95194
[5] validation-logloss:0.61460 validation-auc:0.95048 validation-aucpr:0.95669
[6] validation-logloss:0.60402 validation-auc:0.95076 validation-aucpr:0.95645
[7] validation-logloss:0.59320 validation-auc:0.95066 validation-aucpr:0.95615
[8] validation-logloss:0.58295 validation-auc:0.94991 validation-aucpr:0.95531
[9] validation-logloss:0.57265 validation-auc:0.94996 validation-aucpr:0.95521
[10] validation-logloss:0.56375 validation-auc:0.94980 validation-aucpr:0.95456
[11] validation-logloss:0.55517 validation-auc:0.95042 validation-aucpr:0.95580
[12] validation-logloss:0.54707 validation-auc:0.95069 validation-aucpr:0.95599
[13] validation-logloss:0.53702 validation-auc:0.95196 validation-aucpr:0.95761
[14] validation-logloss:0.53108 validation-auc:0.95211 validation-aucpr:0.95812
[15] validation-logloss:0.52338 validation-auc:0.95234 validation-aucpr:0.95847
[16] validation-logloss:0.51671 validation-auc:0.95211 validation-aucpr:0.95836
[17] validation-logloss:0.51018 validation-auc:0.95165 validation-aucpr:0.95798
[18] validation-logloss:0.50264 validation-auc:0.95232 validation-aucpr:0.95865
[19] validation-logloss:0.49641 validation-auc:0.95235 validation-aucpr:0.95866
[20] validation-logloss:0.49069 validation-auc:0.95236 validation-aucpr:0.95843
[21] validation-logloss:0.48386 validation-auc:0.95275 validation-aucpr:0.95877
[22] validation-logloss:0.47912 validation-auc:0.95247 validation-aucpr:0.95858
[23] validation-logloss:0.47315 validation-auc:0.95256 validation-aucpr:0.95856
[24] validation-logloss:0.46778 validation-auc:0.95288 validation-aucpr:0.95877
[25] validation-logloss:0.46302 validation-auc:0.95264 validation-aucpr:0.95852
[26] validation-logloss:0.45551 validation-auc:0.95342 validation-aucpr:0.95948
{'best_iteration': '26', 'best_score': '0.9594836374150355'}
Trial 63, Fold 5: Log loss = 0.4555060647716498, Average precision = 0.9594378534684262, ROC-AUC = 0.9534234806681159, Elapsed Time = 0.5480397999999695 seconds
Optimization Progress: 64%|######4 | 64/100 [3:00:09<21:43, 36.20s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 64, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65272 validation-auc:0.90122 validation-aucpr:0.85272
[1] validation-logloss:0.61729 validation-auc:0.94674 validation-aucpr:0.92387
[2] validation-logloss:0.58508 validation-auc:0.95753 validation-aucpr:0.95646
[3] validation-logloss:0.55693 validation-auc:0.96058 validation-aucpr:0.96534
[4] validation-logloss:0.53309 validation-auc:0.96161 validation-aucpr:0.96567
[5] validation-logloss:0.50919 validation-auc:0.96258 validation-aucpr:0.96642
[6] validation-logloss:0.48774 validation-auc:0.96286 validation-aucpr:0.96647
[7] validation-logloss:0.46849 validation-auc:0.96315 validation-aucpr:0.96667
[8] validation-logloss:0.45033 validation-auc:0.96312 validation-aucpr:0.96462
[9] validation-logloss:0.43244 validation-auc:0.96418 validation-aucpr:0.96547
[10] validation-logloss:0.41632 validation-auc:0.96453 validation-aucpr:0.96633
[11] validation-logloss:0.40188 validation-auc:0.96432 validation-aucpr:0.96758
[12] validation-logloss:0.38823 validation-auc:0.96487 validation-aucpr:0.96843
[13] validation-logloss:0.37705 validation-auc:0.96503 validation-aucpr:0.96927
[14] validation-logloss:0.36508 validation-auc:0.96576 validation-aucpr:0.96974
[15] validation-logloss:0.35442 validation-auc:0.96596 validation-aucpr:0.97002
[16] validation-logloss:0.34373 validation-auc:0.96655 validation-aucpr:0.97049
[17] validation-logloss:0.33450 validation-auc:0.96678 validation-aucpr:0.97080
[18] validation-logloss:0.32610 validation-auc:0.96722 validation-aucpr:0.97123
[19] validation-logloss:0.31797 validation-auc:0.96733 validation-aucpr:0.97130
[20] validation-logloss:0.31022 validation-auc:0.96746 validation-aucpr:0.97150
[21] validation-logloss:0.30324 validation-auc:0.96740 validation-aucpr:0.97149
[22] validation-logloss:0.29614 validation-auc:0.96772 validation-aucpr:0.97164
[23] validation-logloss:0.29029 validation-auc:0.96790 validation-aucpr:0.97179
[24] validation-logloss:0.28509 validation-auc:0.96813 validation-aucpr:0.97185
[25] validation-logloss:0.27919 validation-auc:0.96829 validation-aucpr:0.97203
[26] validation-logloss:0.27403 validation-auc:0.96842 validation-aucpr:0.97219
[27] validation-logloss:0.26835 validation-auc:0.96882 validation-aucpr:0.97252
[28] validation-logloss:0.26343 validation-auc:0.96904 validation-aucpr:0.97271
[29] validation-logloss:0.25970 validation-auc:0.96911 validation-aucpr:0.97407
[30] validation-logloss:0.25514 validation-auc:0.96931 validation-aucpr:0.97352
[31] validation-logloss:0.25133 validation-auc:0.96940 validation-aucpr:0.97355
[32] validation-logloss:0.24805 validation-auc:0.96933 validation-aucpr:0.97357
[33] validation-logloss:0.24461 validation-auc:0.96953 validation-aucpr:0.97373
[34] validation-logloss:0.24125 validation-auc:0.96960 validation-aucpr:0.97376
[35] validation-logloss:0.23843 validation-auc:0.96962 validation-aucpr:0.97379
[36] validation-logloss:0.23609 validation-auc:0.96970 validation-aucpr:0.97387
[37] validation-logloss:0.23353 validation-auc:0.96975 validation-aucpr:0.97395
[38] validation-logloss:0.23141 validation-auc:0.96973 validation-aucpr:0.97389
[39] validation-logloss:0.22888 validation-auc:0.96992 validation-aucpr:0.97413
[40] validation-logloss:0.22665 validation-auc:0.96992 validation-aucpr:0.97415
[41] validation-logloss:0.22429 validation-auc:0.97015 validation-aucpr:0.97430
[42] validation-logloss:0.22236 validation-auc:0.97020 validation-aucpr:0.97435
[43] validation-logloss:0.22071 validation-auc:0.97023 validation-aucpr:0.97440
[44] validation-logloss:0.21921 validation-auc:0.97019 validation-aucpr:0.97438
[45] validation-logloss:0.21761 validation-auc:0.97039 validation-aucpr:0.97475
[46] validation-logloss:0.21630 validation-auc:0.97045 validation-aucpr:0.97475
[47] validation-logloss:0.21519 validation-auc:0.97035 validation-aucpr:0.97470
[48] validation-logloss:0.21371 validation-auc:0.97048 validation-aucpr:0.97481
[49] validation-logloss:0.21251 validation-auc:0.97047 validation-aucpr:0.97481
[50] validation-logloss:0.21147 validation-auc:0.97047 validation-aucpr:0.97487
[51] validation-logloss:0.21076 validation-auc:0.97036 validation-aucpr:0.97476
[52] validation-logloss:0.21028 validation-auc:0.97017 validation-aucpr:0.97459
[53] validation-logloss:0.20962 validation-auc:0.97014 validation-aucpr:0.97451
[54] validation-logloss:0.20907 validation-auc:0.97009 validation-aucpr:0.97445
[55] validation-logloss:0.20829 validation-auc:0.97017 validation-aucpr:0.97448
[56] validation-logloss:0.20755 validation-auc:0.97016 validation-aucpr:0.97448
[57] validation-logloss:0.20713 validation-auc:0.97018 validation-aucpr:0.97452
[58] validation-logloss:0.20647 validation-auc:0.97025 validation-aucpr:0.97485
[59] validation-logloss:0.20588 validation-auc:0.97031 validation-aucpr:0.97493
[60] validation-logloss:0.20538 validation-auc:0.97032 validation-aucpr:0.97500
[61] validation-logloss:0.20487 validation-auc:0.97033 validation-aucpr:0.97460
[62] validation-logloss:0.20460 validation-auc:0.97019 validation-aucpr:0.97441
[63] validation-logloss:0.20447 validation-auc:0.97021 validation-aucpr:0.97495
[64] validation-logloss:0.20389 validation-auc:0.97038 validation-aucpr:0.97509
[65] validation-logloss:0.20362 validation-auc:0.97038 validation-aucpr:0.97507
[66] validation-logloss:0.20347 validation-auc:0.97040 validation-aucpr:0.97507
[67] validation-logloss:0.20335 validation-auc:0.97035 validation-aucpr:0.97491
[68] validation-logloss:0.20294 validation-auc:0.97045 validation-aucpr:0.97496
[69] validation-logloss:0.20305 validation-auc:0.97035 validation-aucpr:0.97489
[70] validation-logloss:0.20296 validation-auc:0.97032 validation-aucpr:0.97487
[71] validation-logloss:0.20305 validation-auc:0.97033 validation-aucpr:0.97487
[72] validation-logloss:0.20293 validation-auc:0.97042 validation-aucpr:0.97510
[73] validation-logloss:0.20320 validation-auc:0.97036 validation-aucpr:0.97503
[74] validation-logloss:0.20326 validation-auc:0.97039 validation-aucpr:0.97505
[75] validation-logloss:0.20289 validation-auc:0.97056 validation-aucpr:0.97514
[76] validation-logloss:0.20265 validation-auc:0.97066 validation-aucpr:0.97523
[77] validation-logloss:0.20267 validation-auc:0.97067 validation-aucpr:0.97522
[78] validation-logloss:0.20250 validation-auc:0.97076 validation-aucpr:0.97532
[79] validation-logloss:0.20239 validation-auc:0.97089 validation-aucpr:0.97536
[80] validation-logloss:0.20232 validation-auc:0.97093 validation-aucpr:0.97539
[81] validation-logloss:0.20230 validation-auc:0.97093 validation-aucpr:0.97540
[82] validation-logloss:0.20227 validation-auc:0.97087 validation-aucpr:0.97533
[83] validation-logloss:0.20229 validation-auc:0.97087 validation-aucpr:0.97532
[84] validation-logloss:0.20244 validation-auc:0.97082 validation-aucpr:0.97522
[85] validation-logloss:0.20264 validation-auc:0.97082 validation-aucpr:0.97517
[86] validation-logloss:0.20264 validation-auc:0.97089 validation-aucpr:0.97529
[87] validation-logloss:0.20267 validation-auc:0.97093 validation-aucpr:0.97538
[88] validation-logloss:0.20275 validation-auc:0.97098 validation-aucpr:0.97539
{'best_iteration': '81', 'best_score': '0.9754009204043076'}
Trial 64, Fold 1: Log loss = 0.20275462881741438, Average precision = 0.9753897889959595, ROC-AUC = 0.9709829643490437, Elapsed Time = 16.874604999997246 seconds
Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 64, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65288 validation-auc:0.90207 validation-aucpr:0.83301
[1] validation-logloss:0.61960 validation-auc:0.94802 validation-aucpr:0.92119
[2] validation-logloss:0.59022 validation-auc:0.95707 validation-aucpr:0.95389
[3] validation-logloss:0.56197 validation-auc:0.96178 validation-aucpr:0.96109
[4] validation-logloss:0.53460 validation-auc:0.96408 validation-aucpr:0.96791
[5] validation-logloss:0.51022 validation-auc:0.96425 validation-aucpr:0.96822
[6] validation-logloss:0.48796 validation-auc:0.96531 validation-aucpr:0.96985
[7] validation-logloss:0.46778 validation-auc:0.96583 validation-aucpr:0.97035
[8] validation-logloss:0.44942 validation-auc:0.96616 validation-aucpr:0.97072
[9] validation-logloss:0.43197 validation-auc:0.96656 validation-aucpr:0.97106
[10] validation-logloss:0.41559 validation-auc:0.96698 validation-aucpr:0.97153
[11] validation-logloss:0.40121 validation-auc:0.96758 validation-aucpr:0.97188
[12] validation-logloss:0.38786 validation-auc:0.96806 validation-aucpr:0.97222
[13] validation-logloss:0.37545 validation-auc:0.96822 validation-aucpr:0.97232
[14] validation-logloss:0.36304 validation-auc:0.96845 validation-aucpr:0.97264
[15] validation-logloss:0.35170 validation-auc:0.96875 validation-aucpr:0.97283
[16] validation-logloss:0.34060 validation-auc:0.96925 validation-aucpr:0.97324
[17] validation-logloss:0.33106 validation-auc:0.96906 validation-aucpr:0.97313
[18] validation-logloss:0.32198 validation-auc:0.96919 validation-aucpr:0.97317
[19] validation-logloss:0.31417 validation-auc:0.96928 validation-aucpr:0.97323
[20] validation-logloss:0.30606 validation-auc:0.96934 validation-aucpr:0.97330
[21] validation-logloss:0.29868 validation-auc:0.96942 validation-aucpr:0.97333
[22] validation-logloss:0.29164 validation-auc:0.96973 validation-aucpr:0.97356
[23] validation-logloss:0.28539 validation-auc:0.96977 validation-aucpr:0.97361
[24] validation-logloss:0.27926 validation-auc:0.97003 validation-aucpr:0.97389
[25] validation-logloss:0.27329 validation-auc:0.97017 validation-aucpr:0.97404
[26] validation-logloss:0.26775 validation-auc:0.97020 validation-aucpr:0.97408
[27] validation-logloss:0.26283 validation-auc:0.97041 validation-aucpr:0.97422
[28] validation-logloss:0.25888 validation-auc:0.97026 validation-aucpr:0.97413
[29] validation-logloss:0.25442 validation-auc:0.97034 validation-aucpr:0.97416
[30] validation-logloss:0.25051 validation-auc:0.97045 validation-aucpr:0.97425
[31] validation-logloss:0.24686 validation-auc:0.97046 validation-aucpr:0.97412
[32] validation-logloss:0.24367 validation-auc:0.97053 validation-aucpr:0.97421
[33] validation-logloss:0.24045 validation-auc:0.97055 validation-aucpr:0.97417
[34] validation-logloss:0.23724 validation-auc:0.97056 validation-aucpr:0.97417
[35] validation-logloss:0.23402 validation-auc:0.97077 validation-aucpr:0.97435
[36] validation-logloss:0.23114 validation-auc:0.97067 validation-aucpr:0.97433
[37] validation-logloss:0.22831 validation-auc:0.97080 validation-aucpr:0.97411
[38] validation-logloss:0.22585 validation-auc:0.97087 validation-aucpr:0.97416
[39] validation-logloss:0.22354 validation-auc:0.97092 validation-aucpr:0.97421
[40] validation-logloss:0.22124 validation-auc:0.97103 validation-aucpr:0.97430
[41] validation-logloss:0.21914 validation-auc:0.97112 validation-aucpr:0.97425
[42] validation-logloss:0.21717 validation-auc:0.97124 validation-aucpr:0.97430
[43] validation-logloss:0.21581 validation-auc:0.97117 validation-aucpr:0.97423
[44] validation-logloss:0.21415 validation-auc:0.97139 validation-aucpr:0.97431
[45] validation-logloss:0.21282 validation-auc:0.97133 validation-aucpr:0.97409
[46] validation-logloss:0.21081 validation-auc:0.97157 validation-aucpr:0.97427
[47] validation-logloss:0.20946 validation-auc:0.97156 validation-aucpr:0.97385
[48] validation-logloss:0.20810 validation-auc:0.97162 validation-aucpr:0.97392
[49] validation-logloss:0.20700 validation-auc:0.97153 validation-aucpr:0.97387
[50] validation-logloss:0.20628 validation-auc:0.97133 validation-aucpr:0.97349
[51] validation-logloss:0.20538 validation-auc:0.97127 validation-aucpr:0.97338
[52] validation-logloss:0.20462 validation-auc:0.97116 validation-aucpr:0.97333
[53] validation-logloss:0.20366 validation-auc:0.97119 validation-aucpr:0.97335
[54] validation-logloss:0.20224 validation-auc:0.97134 validation-aucpr:0.97328
[55] validation-logloss:0.20124 validation-auc:0.97133 validation-aucpr:0.97344
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[57] validation-logloss:0.19968 validation-auc:0.97134 validation-aucpr:0.97351
[58] validation-logloss:0.19880 validation-auc:0.97137 validation-aucpr:0.97351
[59] validation-logloss:0.19793 validation-auc:0.97148 validation-aucpr:0.97355
[60] validation-logloss:0.19726 validation-auc:0.97154 validation-aucpr:0.97363
[61] validation-logloss:0.19667 validation-auc:0.97168 validation-aucpr:0.97409
[62] validation-logloss:0.19635 validation-auc:0.97149 validation-aucpr:0.97390
[63] validation-logloss:0.19564 validation-auc:0.97171 validation-aucpr:0.97400
[64] validation-logloss:0.19488 validation-auc:0.97184 validation-aucpr:0.97406
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[66] validation-logloss:0.19395 validation-auc:0.97196 validation-aucpr:0.97438
[67] validation-logloss:0.19377 validation-auc:0.97193 validation-aucpr:0.97495
[68] validation-logloss:0.19355 validation-auc:0.97193 validation-aucpr:0.97469
[69] validation-logloss:0.19326 validation-auc:0.97192 validation-aucpr:0.97482
[70] validation-logloss:0.19311 validation-auc:0.97188 validation-aucpr:0.97483
[71] validation-logloss:0.19306 validation-auc:0.97179 validation-aucpr:0.97418
[72] validation-logloss:0.19303 validation-auc:0.97174 validation-aucpr:0.97412
[73] validation-logloss:0.19275 validation-auc:0.97178 validation-aucpr:0.97420
[74] validation-logloss:0.19258 validation-auc:0.97180 validation-aucpr:0.97457
[75] validation-logloss:0.19260 validation-auc:0.97171 validation-aucpr:0.97441
[76] validation-logloss:0.19244 validation-auc:0.97170 validation-aucpr:0.97439
[77] validation-logloss:0.19257 validation-auc:0.97160 validation-aucpr:0.97416
[78] validation-logloss:0.19226 validation-auc:0.97178 validation-aucpr:0.97422
[79] validation-logloss:0.19223 validation-auc:0.97177 validation-aucpr:0.97400
[80] validation-logloss:0.19229 validation-auc:0.97172 validation-aucpr:0.97396
[81] validation-logloss:0.19192 validation-auc:0.97180 validation-aucpr:0.97392
[82] validation-logloss:0.19184 validation-auc:0.97190 validation-aucpr:0.97439
[83] validation-logloss:0.19139 validation-auc:0.97203 validation-aucpr:0.97468
[84] validation-logloss:0.19147 validation-auc:0.97204 validation-aucpr:0.97467
[85] validation-logloss:0.19145 validation-auc:0.97207 validation-aucpr:0.97454
[86] validation-logloss:0.19148 validation-auc:0.97199 validation-aucpr:0.97464
[87] validation-logloss:0.19151 validation-auc:0.97207 validation-aucpr:0.97509
[88] validation-logloss:0.19154 validation-auc:0.97201 validation-aucpr:0.97501
{'best_iteration': '87', 'best_score': '0.9750940179143601'}
Trial 64, Fold 2: Log loss = 0.19154041852961148, Average precision = 0.9750110784779089, ROC-AUC = 0.9720083758707668, Elapsed Time = 16.697156499998528 seconds
Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 64, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65331 validation-auc:0.89847 validation-aucpr:0.84646
[1] validation-logloss:0.61695 validation-auc:0.94909 validation-aucpr:0.92788
[2] validation-logloss:0.58392 validation-auc:0.96071 validation-aucpr:0.95286
[3] validation-logloss:0.55601 validation-auc:0.96329 validation-aucpr:0.96052
[4] validation-logloss:0.52938 validation-auc:0.96526 validation-aucpr:0.96460
[5] validation-logloss:0.50616 validation-auc:0.96546 validation-aucpr:0.96502
[6] validation-logloss:0.48629 validation-auc:0.96584 validation-aucpr:0.96477
[7] validation-logloss:0.46516 validation-auc:0.96681 validation-aucpr:0.96734
[8] validation-logloss:0.44571 validation-auc:0.96761 validation-aucpr:0.96779
[9] validation-logloss:0.42918 validation-auc:0.96838 validation-aucpr:0.97060
[10] validation-logloss:0.41306 validation-auc:0.96820 validation-aucpr:0.97067
[11] validation-logloss:0.39846 validation-auc:0.96850 validation-aucpr:0.97091
[12] validation-logloss:0.38571 validation-auc:0.96871 validation-aucpr:0.97102
[13] validation-logloss:0.37270 validation-auc:0.96888 validation-aucpr:0.97128
[14] validation-logloss:0.36208 validation-auc:0.96864 validation-aucpr:0.97114
[15] validation-logloss:0.35034 validation-auc:0.96882 validation-aucpr:0.97131
[16] validation-logloss:0.33996 validation-auc:0.96969 validation-aucpr:0.97320
[17] validation-logloss:0.33039 validation-auc:0.97000 validation-aucpr:0.97330
[18] validation-logloss:0.32179 validation-auc:0.96988 validation-aucpr:0.97212
[19] validation-logloss:0.31366 validation-auc:0.96979 validation-aucpr:0.97212
[20] validation-logloss:0.30602 validation-auc:0.97003 validation-aucpr:0.97215
[21] validation-logloss:0.29967 validation-auc:0.97019 validation-aucpr:0.97254
[22] validation-logloss:0.29316 validation-auc:0.97023 validation-aucpr:0.97253
[23] validation-logloss:0.28675 validation-auc:0.97057 validation-aucpr:0.97283
[24] validation-logloss:0.28142 validation-auc:0.97058 validation-aucpr:0.97279
[25] validation-logloss:0.27567 validation-auc:0.97065 validation-aucpr:0.97277
[26] validation-logloss:0.27007 validation-auc:0.97086 validation-aucpr:0.97393
[27] validation-logloss:0.26448 validation-auc:0.97116 validation-aucpr:0.97422
[28] validation-logloss:0.25971 validation-auc:0.97115 validation-aucpr:0.97419
[29] validation-logloss:0.25546 validation-auc:0.97129 validation-aucpr:0.97447
[30] validation-logloss:0.25119 validation-auc:0.97125 validation-aucpr:0.97441
[31] validation-logloss:0.24723 validation-auc:0.97131 validation-aucpr:0.97444
[32] validation-logloss:0.24330 validation-auc:0.97135 validation-aucpr:0.97448
[33] validation-logloss:0.24078 validation-auc:0.97113 validation-aucpr:0.97423
[34] validation-logloss:0.23783 validation-auc:0.97109 validation-aucpr:0.97418
[35] validation-logloss:0.23457 validation-auc:0.97132 validation-aucpr:0.97423
[36] validation-logloss:0.23193 validation-auc:0.97137 validation-aucpr:0.97425
[37] validation-logloss:0.22923 validation-auc:0.97159 validation-aucpr:0.97562
[38] validation-logloss:0.22708 validation-auc:0.97158 validation-aucpr:0.97566
[39] validation-logloss:0.22466 validation-auc:0.97157 validation-aucpr:0.97565
[40] validation-logloss:0.22287 validation-auc:0.97145 validation-aucpr:0.97553
[41] validation-logloss:0.22096 validation-auc:0.97144 validation-aucpr:0.97552
[42] validation-logloss:0.21896 validation-auc:0.97145 validation-aucpr:0.97548
[43] validation-logloss:0.21699 validation-auc:0.97143 validation-aucpr:0.97545
[44] validation-logloss:0.21522 validation-auc:0.97151 validation-aucpr:0.97552
[45] validation-logloss:0.21355 validation-auc:0.97153 validation-aucpr:0.97549
[46] validation-logloss:0.21198 validation-auc:0.97146 validation-aucpr:0.97546
[47] validation-logloss:0.21075 validation-auc:0.97147 validation-aucpr:0.97542
[48] validation-logloss:0.20939 validation-auc:0.97150 validation-aucpr:0.97545
[49] validation-logloss:0.20806 validation-auc:0.97158 validation-aucpr:0.97548
[50] validation-logloss:0.20684 validation-auc:0.97149 validation-aucpr:0.97533
[51] validation-logloss:0.20569 validation-auc:0.97153 validation-aucpr:0.97540
[52] validation-logloss:0.20464 validation-auc:0.97157 validation-aucpr:0.97541
[53] validation-logloss:0.20369 validation-auc:0.97155 validation-aucpr:0.97538
[54] validation-logloss:0.20333 validation-auc:0.97142 validation-aucpr:0.97519
[55] validation-logloss:0.20253 validation-auc:0.97151 validation-aucpr:0.97527
[56] validation-logloss:0.20164 validation-auc:0.97156 validation-aucpr:0.97528
[57] validation-logloss:0.20092 validation-auc:0.97155 validation-aucpr:0.97525
[58] validation-logloss:0.19997 validation-auc:0.97163 validation-aucpr:0.97526
[59] validation-logloss:0.19959 validation-auc:0.97158 validation-aucpr:0.97525
[60] validation-logloss:0.19903 validation-auc:0.97163 validation-aucpr:0.97531
[61] validation-logloss:0.19860 validation-auc:0.97158 validation-aucpr:0.97511
[62] validation-logloss:0.19817 validation-auc:0.97163 validation-aucpr:0.97503
[63] validation-logloss:0.19810 validation-auc:0.97160 validation-aucpr:0.97515
[64] validation-logloss:0.19802 validation-auc:0.97151 validation-aucpr:0.97508
[65] validation-logloss:0.19714 validation-auc:0.97163 validation-aucpr:0.97522
[66] validation-logloss:0.19698 validation-auc:0.97155 validation-aucpr:0.97490
[67] validation-logloss:0.19680 validation-auc:0.97159 validation-aucpr:0.97529
[68] validation-logloss:0.19638 validation-auc:0.97169 validation-aucpr:0.97543
[69] validation-logloss:0.19626 validation-auc:0.97164 validation-aucpr:0.97539
[70] validation-logloss:0.19597 validation-auc:0.97161 validation-aucpr:0.97524
[71] validation-logloss:0.19576 validation-auc:0.97162 validation-aucpr:0.97524
[72] validation-logloss:0.19542 validation-auc:0.97172 validation-aucpr:0.97537
[73] validation-logloss:0.19535 validation-auc:0.97170 validation-aucpr:0.97534
[74] validation-logloss:0.19511 validation-auc:0.97171 validation-aucpr:0.97531
[75] validation-logloss:0.19473 validation-auc:0.97181 validation-aucpr:0.97542
[76] validation-logloss:0.19477 validation-auc:0.97178 validation-aucpr:0.97451
[77] validation-logloss:0.19444 validation-auc:0.97200 validation-aucpr:0.97472
[78] validation-logloss:0.19464 validation-auc:0.97199 validation-aucpr:0.97444
[79] validation-logloss:0.19482 validation-auc:0.97191 validation-aucpr:0.97353
[80] validation-logloss:0.19448 validation-auc:0.97197 validation-aucpr:0.97357
[81] validation-logloss:0.19441 validation-auc:0.97199 validation-aucpr:0.97360
[82] validation-logloss:0.19437 validation-auc:0.97200 validation-aucpr:0.97359
[83] validation-logloss:0.19437 validation-auc:0.97197 validation-aucpr:0.97384
[84] validation-logloss:0.19415 validation-auc:0.97208 validation-aucpr:0.97392
[85] validation-logloss:0.19430 validation-auc:0.97213 validation-aucpr:0.97396
[86] validation-logloss:0.19389 validation-auc:0.97225 validation-aucpr:0.97406
[87] validation-logloss:0.19400 validation-auc:0.97225 validation-aucpr:0.97407
{'best_iteration': '38', 'best_score': '0.9756599265861333'}
Trial 64, Fold 3: Log loss = 0.19373711385512057, Average precision = 0.9743362243542404, ROC-AUC = 0.972316567908428, Elapsed Time = 17.79897510000228 seconds
Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 64, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65334 validation-auc:0.89940 validation-aucpr:0.85789
[1] validation-logloss:0.61820 validation-auc:0.94311 validation-aucpr:0.93094
[2] validation-logloss:0.58539 validation-auc:0.95680 validation-aucpr:0.95333
[3] validation-logloss:0.55727 validation-auc:0.96054 validation-aucpr:0.96180
[4] validation-logloss:0.53129 validation-auc:0.96219 validation-aucpr:0.96861
[5] validation-logloss:0.50723 validation-auc:0.96344 validation-aucpr:0.96921
[6] validation-logloss:0.48595 validation-auc:0.96447 validation-aucpr:0.96928
[7] validation-logloss:0.46603 validation-auc:0.96454 validation-aucpr:0.96890
[8] validation-logloss:0.44722 validation-auc:0.96471 validation-aucpr:0.96862
[9] validation-logloss:0.43001 validation-auc:0.96492 validation-aucpr:0.96878
[10] validation-logloss:0.41409 validation-auc:0.96563 validation-aucpr:0.96961
[11] validation-logloss:0.39901 validation-auc:0.96594 validation-aucpr:0.97058
[12] validation-logloss:0.38595 validation-auc:0.96680 validation-aucpr:0.97222
[13] validation-logloss:0.37397 validation-auc:0.96700 validation-aucpr:0.97236
[14] validation-logloss:0.36354 validation-auc:0.96700 validation-aucpr:0.97235
[15] validation-logloss:0.35173 validation-auc:0.96768 validation-aucpr:0.97286
[16] validation-logloss:0.34185 validation-auc:0.96789 validation-aucpr:0.97324
[17] validation-logloss:0.33328 validation-auc:0.96772 validation-aucpr:0.97320
[18] validation-logloss:0.32482 validation-auc:0.96782 validation-aucpr:0.97331
[19] validation-logloss:0.31626 validation-auc:0.96806 validation-aucpr:0.97346
[20] validation-logloss:0.30833 validation-auc:0.96833 validation-aucpr:0.97370
[21] validation-logloss:0.30098 validation-auc:0.96839 validation-aucpr:0.97367
[22] validation-logloss:0.29417 validation-auc:0.96854 validation-aucpr:0.97378
[23] validation-logloss:0.28755 validation-auc:0.96882 validation-aucpr:0.97396
[24] validation-logloss:0.28192 validation-auc:0.96866 validation-aucpr:0.97389
[25] validation-logloss:0.27655 validation-auc:0.96868 validation-aucpr:0.97391
[26] validation-logloss:0.27149 validation-auc:0.96886 validation-aucpr:0.97405
[27] validation-logloss:0.26614 validation-auc:0.96911 validation-aucpr:0.97423
[28] validation-logloss:0.26176 validation-auc:0.96906 validation-aucpr:0.97419
[29] validation-logloss:0.25736 validation-auc:0.96913 validation-aucpr:0.97428
[30] validation-logloss:0.25350 validation-auc:0.96903 validation-aucpr:0.97424
[31] validation-logloss:0.24958 validation-auc:0.96899 validation-aucpr:0.97422
[32] validation-logloss:0.24644 validation-auc:0.96883 validation-aucpr:0.97411
[33] validation-logloss:0.24288 validation-auc:0.96907 validation-aucpr:0.97425
[34] validation-logloss:0.23972 validation-auc:0.96913 validation-aucpr:0.97425
[35] validation-logloss:0.23689 validation-auc:0.96914 validation-aucpr:0.97426
[36] validation-logloss:0.23444 validation-auc:0.96912 validation-aucpr:0.97422
[37] validation-logloss:0.23223 validation-auc:0.96900 validation-aucpr:0.97412
[38] validation-logloss:0.22996 validation-auc:0.96894 validation-aucpr:0.97408
[39] validation-logloss:0.22737 validation-auc:0.96919 validation-aucpr:0.97424
[40] validation-logloss:0.22519 validation-auc:0.96935 validation-aucpr:0.97436
[41] validation-logloss:0.22343 validation-auc:0.96937 validation-aucpr:0.97435
[42] validation-logloss:0.22155 validation-auc:0.96936 validation-aucpr:0.97436
[43] validation-logloss:0.21969 validation-auc:0.96943 validation-aucpr:0.97439
[44] validation-logloss:0.21791 validation-auc:0.96954 validation-aucpr:0.97447
[45] validation-logloss:0.21650 validation-auc:0.96962 validation-aucpr:0.97449
[46] validation-logloss:0.21479 validation-auc:0.96977 validation-aucpr:0.97461
[47] validation-logloss:0.21353 validation-auc:0.96972 validation-aucpr:0.97458
[48] validation-logloss:0.21226 validation-auc:0.96981 validation-aucpr:0.97464
[49] validation-logloss:0.21107 validation-auc:0.96989 validation-aucpr:0.97472
[50] validation-logloss:0.21022 validation-auc:0.96983 validation-aucpr:0.97467
[51] validation-logloss:0.20946 validation-auc:0.96981 validation-aucpr:0.97467
[52] validation-logloss:0.20873 validation-auc:0.96987 validation-aucpr:0.97473
[53] validation-logloss:0.20784 validation-auc:0.96988 validation-aucpr:0.97474
[54] validation-logloss:0.20662 validation-auc:0.97003 validation-aucpr:0.97484
[55] validation-logloss:0.20557 validation-auc:0.97009 validation-aucpr:0.97491
[56] validation-logloss:0.20454 validation-auc:0.97030 validation-aucpr:0.97506
[57] validation-logloss:0.20410 validation-auc:0.97020 validation-aucpr:0.97500
[58] validation-logloss:0.20354 validation-auc:0.97022 validation-aucpr:0.97500
[59] validation-logloss:0.20300 validation-auc:0.97019 validation-aucpr:0.97499
[60] validation-logloss:0.20256 validation-auc:0.97013 validation-aucpr:0.97495
[61] validation-logloss:0.20210 validation-auc:0.97012 validation-aucpr:0.97492
[62] validation-logloss:0.20187 validation-auc:0.97009 validation-aucpr:0.97488
[63] validation-logloss:0.20146 validation-auc:0.97012 validation-aucpr:0.97491
[64] validation-logloss:0.20100 validation-auc:0.97017 validation-aucpr:0.97493
[65] validation-logloss:0.20041 validation-auc:0.97025 validation-aucpr:0.97502
[66] validation-logloss:0.19996 validation-auc:0.97031 validation-aucpr:0.97507
[67] validation-logloss:0.19954 validation-auc:0.97034 validation-aucpr:0.97509
[68] validation-logloss:0.19920 validation-auc:0.97041 validation-aucpr:0.97513
[69] validation-logloss:0.19928 validation-auc:0.97034 validation-aucpr:0.97510
[70] validation-logloss:0.19918 validation-auc:0.97031 validation-aucpr:0.97507
[71] validation-logloss:0.19880 validation-auc:0.97039 validation-aucpr:0.97513
[72] validation-logloss:0.19895 validation-auc:0.97030 validation-aucpr:0.97506
[73] validation-logloss:0.19891 validation-auc:0.97030 validation-aucpr:0.97504
[74] validation-logloss:0.19858 validation-auc:0.97038 validation-aucpr:0.97509
[75] validation-logloss:0.19833 validation-auc:0.97044 validation-aucpr:0.97510
[76] validation-logloss:0.19823 validation-auc:0.97049 validation-aucpr:0.97513
[77] validation-logloss:0.19814 validation-auc:0.97055 validation-aucpr:0.97518
[78] validation-logloss:0.19796 validation-auc:0.97061 validation-aucpr:0.97520
[79] validation-logloss:0.19796 validation-auc:0.97060 validation-aucpr:0.97519
[80] validation-logloss:0.19795 validation-auc:0.97060 validation-aucpr:0.97519
[81] validation-logloss:0.19770 validation-auc:0.97071 validation-aucpr:0.97525
[82] validation-logloss:0.19755 validation-auc:0.97074 validation-aucpr:0.97527
[83] validation-logloss:0.19776 validation-auc:0.97067 validation-aucpr:0.97519
[84] validation-logloss:0.19800 validation-auc:0.97066 validation-aucpr:0.97516
[85] validation-logloss:0.19820 validation-auc:0.97063 validation-aucpr:0.97514
[86] validation-logloss:0.19806 validation-auc:0.97069 validation-aucpr:0.97516
[87] validation-logloss:0.19797 validation-auc:0.97080 validation-aucpr:0.97524
[88] validation-logloss:0.19805 validation-auc:0.97080 validation-aucpr:0.97523
{'best_iteration': '82', 'best_score': '0.975269879696509'}
Trial 64, Fold 4: Log loss = 0.1980478209605085, Average precision = 0.9752385460516755, ROC-AUC = 0.9707982825096391, Elapsed Time = 18.344916800000647 seconds
Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 64, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65294 validation-auc:0.90143 validation-aucpr:0.85366
[1] validation-logloss:0.61725 validation-auc:0.95167 validation-aucpr:0.93663
[2] validation-logloss:0.58558 validation-auc:0.95664 validation-aucpr:0.95611
[3] validation-logloss:0.55732 validation-auc:0.96091 validation-aucpr:0.96049
[4] validation-logloss:0.53102 validation-auc:0.96253 validation-aucpr:0.96095
[5] validation-logloss:0.50824 validation-auc:0.96397 validation-aucpr:0.96860
[6] validation-logloss:0.48880 validation-auc:0.96429 validation-aucpr:0.96878
[7] validation-logloss:0.47002 validation-auc:0.96447 validation-aucpr:0.96912
[8] validation-logloss:0.45079 validation-auc:0.96512 validation-aucpr:0.96985
[9] validation-logloss:0.43314 validation-auc:0.96567 validation-aucpr:0.97035
[10] validation-logloss:0.41722 validation-auc:0.96582 validation-aucpr:0.97023
[11] validation-logloss:0.40421 validation-auc:0.96583 validation-aucpr:0.97014
[12] validation-logloss:0.39084 validation-auc:0.96578 validation-aucpr:0.97028
[13] validation-logloss:0.37703 validation-auc:0.96661 validation-aucpr:0.97102
[14] validation-logloss:0.36611 validation-auc:0.96678 validation-aucpr:0.97104
[15] validation-logloss:0.35483 validation-auc:0.96714 validation-aucpr:0.97135
[16] validation-logloss:0.34628 validation-auc:0.96665 validation-aucpr:0.97094
[17] validation-logloss:0.33629 validation-auc:0.96691 validation-aucpr:0.97121
[18] validation-logloss:0.32796 validation-auc:0.96686 validation-aucpr:0.97123
[19] validation-logloss:0.32007 validation-auc:0.96692 validation-aucpr:0.97133
[20] validation-logloss:0.31232 validation-auc:0.96705 validation-aucpr:0.97144
[21] validation-logloss:0.30544 validation-auc:0.96698 validation-aucpr:0.97131
[22] validation-logloss:0.29907 validation-auc:0.96683 validation-aucpr:0.97100
[23] validation-logloss:0.29376 validation-auc:0.96679 validation-aucpr:0.97110
[24] validation-logloss:0.28739 validation-auc:0.96694 validation-aucpr:0.97119
[25] validation-logloss:0.28148 validation-auc:0.96726 validation-aucpr:0.97145
[26] validation-logloss:0.27660 validation-auc:0.96709 validation-aucpr:0.97134
[27] validation-logloss:0.27151 validation-auc:0.96710 validation-aucpr:0.97139
[28] validation-logloss:0.26720 validation-auc:0.96720 validation-aucpr:0.97155
[29] validation-logloss:0.26296 validation-auc:0.96727 validation-aucpr:0.97205
[30] validation-logloss:0.25942 validation-auc:0.96772 validation-aucpr:0.97220
[31] validation-logloss:0.25552 validation-auc:0.96773 validation-aucpr:0.97222
[32] validation-logloss:0.25193 validation-auc:0.96805 validation-aucpr:0.97250
[33] validation-logloss:0.24855 validation-auc:0.96816 validation-aucpr:0.97255
[34] validation-logloss:0.24519 validation-auc:0.96841 validation-aucpr:0.97271
[35] validation-logloss:0.24276 validation-auc:0.96834 validation-aucpr:0.97265
[36] validation-logloss:0.23980 validation-auc:0.96840 validation-aucpr:0.97283
[37] validation-logloss:0.23749 validation-auc:0.96845 validation-aucpr:0.97289
[38] validation-logloss:0.23496 validation-auc:0.96861 validation-aucpr:0.97300
[39] validation-logloss:0.23277 validation-auc:0.96863 validation-aucpr:0.97304
[40] validation-logloss:0.23052 validation-auc:0.96869 validation-aucpr:0.97309
[41] validation-logloss:0.22853 validation-auc:0.96876 validation-aucpr:0.97311
[42] validation-logloss:0.22709 validation-auc:0.96856 validation-aucpr:0.97299
[43] validation-logloss:0.22557 validation-auc:0.96856 validation-aucpr:0.97298
[44] validation-logloss:0.22341 validation-auc:0.96893 validation-aucpr:0.97326
[45] validation-logloss:0.22153 validation-auc:0.96907 validation-aucpr:0.97338
[46] validation-logloss:0.21982 validation-auc:0.96926 validation-aucpr:0.97352
[47] validation-logloss:0.21857 validation-auc:0.96931 validation-aucpr:0.97354
[48] validation-logloss:0.21734 validation-auc:0.96931 validation-aucpr:0.97346
[49] validation-logloss:0.21596 validation-auc:0.96943 validation-aucpr:0.97356
[50] validation-logloss:0.21516 validation-auc:0.96930 validation-aucpr:0.97280
[51] validation-logloss:0.21421 validation-auc:0.96942 validation-aucpr:0.97303
[52] validation-logloss:0.21301 validation-auc:0.96955 validation-aucpr:0.97329
[53] validation-logloss:0.21191 validation-auc:0.96966 validation-aucpr:0.97338
[54] validation-logloss:0.21102 validation-auc:0.96968 validation-aucpr:0.97346
[55] validation-logloss:0.21001 validation-auc:0.96985 validation-aucpr:0.97350
[56] validation-logloss:0.20928 validation-auc:0.96989 validation-aucpr:0.97352
[57] validation-logloss:0.20863 validation-auc:0.96998 validation-aucpr:0.97357
[58] validation-logloss:0.20795 validation-auc:0.97005 validation-aucpr:0.97343
[59] validation-logloss:0.20759 validation-auc:0.96998 validation-aucpr:0.97339
[60] validation-logloss:0.20736 validation-auc:0.96993 validation-aucpr:0.97337
[61] validation-logloss:0.20682 validation-auc:0.96993 validation-aucpr:0.97343
[62] validation-logloss:0.20601 validation-auc:0.97006 validation-aucpr:0.97362
[63] validation-logloss:0.20587 validation-auc:0.96999 validation-aucpr:0.97345
[64] validation-logloss:0.20524 validation-auc:0.97005 validation-aucpr:0.97354
[65] validation-logloss:0.20441 validation-auc:0.97027 validation-aucpr:0.97373
[66] validation-logloss:0.20392 validation-auc:0.97036 validation-aucpr:0.97385
[67] validation-logloss:0.20393 validation-auc:0.97032 validation-aucpr:0.97376
[68] validation-logloss:0.20334 validation-auc:0.97047 validation-aucpr:0.97384
[69] validation-logloss:0.20324 validation-auc:0.97045 validation-aucpr:0.97414
[70] validation-logloss:0.20295 validation-auc:0.97055 validation-aucpr:0.97421
[71] validation-logloss:0.20273 validation-auc:0.97053 validation-aucpr:0.97428
[72] validation-logloss:0.20253 validation-auc:0.97055 validation-aucpr:0.97433
[73] validation-logloss:0.20244 validation-auc:0.97056 validation-aucpr:0.97435
[74] validation-logloss:0.20260 validation-auc:0.97056 validation-aucpr:0.97435
[75] validation-logloss:0.20238 validation-auc:0.97058 validation-aucpr:0.97433
[76] validation-logloss:0.20217 validation-auc:0.97063 validation-aucpr:0.97436
[77] validation-logloss:0.20213 validation-auc:0.97064 validation-aucpr:0.97437
[78] validation-logloss:0.20249 validation-auc:0.97051 validation-aucpr:0.97423
[79] validation-logloss:0.20243 validation-auc:0.97055 validation-aucpr:0.97421
[80] validation-logloss:0.20238 validation-auc:0.97054 validation-aucpr:0.97419
[81] validation-logloss:0.20238 validation-auc:0.97066 validation-aucpr:0.97434
[82] validation-logloss:0.20237 validation-auc:0.97075 validation-aucpr:0.97463
[83] validation-logloss:0.20246 validation-auc:0.97072 validation-aucpr:0.97459
[84] validation-logloss:0.20292 validation-auc:0.97056 validation-aucpr:0.97447
[85] validation-logloss:0.20309 validation-auc:0.97053 validation-aucpr:0.97440
[86] validation-logloss:0.20329 validation-auc:0.97045 validation-aucpr:0.97433
[87] validation-logloss:0.20347 validation-auc:0.97043 validation-aucpr:0.97434
[88] validation-logloss:0.20347 validation-auc:0.97041 validation-aucpr:0.97431
{'best_iteration': '82', 'best_score': '0.9746318812315987'}
Trial 64, Fold 5: Log loss = 0.20347111347276622, Average precision = 0.9743139083594021, ROC-AUC = 0.9704094532077365, Elapsed Time = 17.027871199999936 seconds
Optimization Progress: 65%|######5 | 65/100 [3:01:43<31:19, 53.69s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 65, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.63304 validation-auc:0.95532 validation-aucpr:0.95790
[1] validation-logloss:0.58295 validation-auc:0.96014 validation-aucpr:0.96636
[2] validation-logloss:0.53928 validation-auc:0.96217 validation-aucpr:0.96760
[3] validation-logloss:0.50166 validation-auc:0.96332 validation-aucpr:0.96852
[4] validation-logloss:0.47213 validation-auc:0.96449 validation-aucpr:0.96940
[5] validation-logloss:0.44658 validation-auc:0.96425 validation-aucpr:0.96917
[6] validation-logloss:0.42039 validation-auc:0.96533 validation-aucpr:0.96993
[7] validation-logloss:0.39782 validation-auc:0.96551 validation-aucpr:0.97012
[8] validation-logloss:0.37855 validation-auc:0.96556 validation-aucpr:0.97004
[9] validation-logloss:0.36118 validation-auc:0.96567 validation-aucpr:0.97013
[10] validation-logloss:0.34473 validation-auc:0.96616 validation-aucpr:0.97053
[11] validation-logloss:0.33230 validation-auc:0.96659 validation-aucpr:0.97079
[12] validation-logloss:0.31884 validation-auc:0.96672 validation-aucpr:0.97092
[13] validation-logloss:0.30721 validation-auc:0.96720 validation-aucpr:0.97180
[14] validation-logloss:0.29718 validation-auc:0.96725 validation-aucpr:0.97219
[15] validation-logloss:0.28730 validation-auc:0.96778 validation-aucpr:0.97259
[16] validation-logloss:0.27902 validation-auc:0.96807 validation-aucpr:0.97275
[17] validation-logloss:0.27190 validation-auc:0.96849 validation-aucpr:0.97305
[18] validation-logloss:0.26552 validation-auc:0.96875 validation-aucpr:0.97322
[19] validation-logloss:0.25920 validation-auc:0.96913 validation-aucpr:0.97380
[20] validation-logloss:0.25328 validation-auc:0.96945 validation-aucpr:0.97398
[21] validation-logloss:0.24818 validation-auc:0.96938 validation-aucpr:0.97397
[22] validation-logloss:0.24316 validation-auc:0.96966 validation-aucpr:0.97466
[23] validation-logloss:0.23864 validation-auc:0.96997 validation-aucpr:0.97484
[24] validation-logloss:0.23442 validation-auc:0.97016 validation-aucpr:0.97504
[25] validation-logloss:0.23094 validation-auc:0.97047 validation-aucpr:0.97520
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[29] validation-logloss:0.21875 validation-auc:0.97123 validation-aucpr:0.97574
[30] validation-logloss:0.21624 validation-auc:0.97136 validation-aucpr:0.97583
[31] validation-logloss:0.21407 validation-auc:0.97142 validation-aucpr:0.97586
[32] validation-logloss:0.21234 validation-auc:0.97146 validation-aucpr:0.97587
[33] validation-logloss:0.21085 validation-auc:0.97141 validation-aucpr:0.97582
[34] validation-logloss:0.20897 validation-auc:0.97150 validation-aucpr:0.97583
[35] validation-logloss:0.20769 validation-auc:0.97149 validation-aucpr:0.97581
[36] validation-logloss:0.20605 validation-auc:0.97166 validation-aucpr:0.97590
[37] validation-logloss:0.20469 validation-auc:0.97167 validation-aucpr:0.97593
[38] validation-logloss:0.20368 validation-auc:0.97175 validation-aucpr:0.97612
[39] validation-logloss:0.20253 validation-auc:0.97186 validation-aucpr:0.97618
[40] validation-logloss:0.20155 validation-auc:0.97189 validation-aucpr:0.97617
[41] validation-logloss:0.20087 validation-auc:0.97189 validation-aucpr:0.97612
[42] validation-logloss:0.19994 validation-auc:0.97201 validation-aucpr:0.97619
[43] validation-logloss:0.19916 validation-auc:0.97204 validation-aucpr:0.97623
[44] validation-logloss:0.19816 validation-auc:0.97216 validation-aucpr:0.97630
[45] validation-logloss:0.19736 validation-auc:0.97228 validation-aucpr:0.97641
[46] validation-logloss:0.19653 validation-auc:0.97246 validation-aucpr:0.97652
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[48] validation-logloss:0.19567 validation-auc:0.97261 validation-aucpr:0.97660
[49] validation-logloss:0.19528 validation-auc:0.97254 validation-aucpr:0.97660
[50] validation-logloss:0.19497 validation-auc:0.97257 validation-aucpr:0.97658
[51] validation-logloss:0.19454 validation-auc:0.97269 validation-aucpr:0.97660
[52] validation-logloss:0.19374 validation-auc:0.97291 validation-aucpr:0.97678
[53] validation-logloss:0.19344 validation-auc:0.97293 validation-aucpr:0.97677
[54] validation-logloss:0.19324 validation-auc:0.97294 validation-aucpr:0.97682
[55] validation-logloss:0.19306 validation-auc:0.97300 validation-aucpr:0.97688
[56] validation-logloss:0.19268 validation-auc:0.97310 validation-aucpr:0.97700
[57] validation-logloss:0.19271 validation-auc:0.97300 validation-aucpr:0.97690
[58] validation-logloss:0.19248 validation-auc:0.97300 validation-aucpr:0.97690
[59] validation-logloss:0.19215 validation-auc:0.97303 validation-aucpr:0.97695
[60] validation-logloss:0.19195 validation-auc:0.97305 validation-aucpr:0.97693
[61] validation-logloss:0.19187 validation-auc:0.97297 validation-aucpr:0.97686
[62] validation-logloss:0.19179 validation-auc:0.97293 validation-aucpr:0.97683
[63] validation-logloss:0.19179 validation-auc:0.97287 validation-aucpr:0.97682
[64] validation-logloss:0.19179 validation-auc:0.97291 validation-aucpr:0.97687
[65] validation-logloss:0.19143 validation-auc:0.97303 validation-aucpr:0.97694
[66] validation-logloss:0.19134 validation-auc:0.97302 validation-aucpr:0.97697
[67] validation-logloss:0.19126 validation-auc:0.97304 validation-aucpr:0.97702
[68] validation-logloss:0.19126 validation-auc:0.97309 validation-aucpr:0.97705
[69] validation-logloss:0.19128 validation-auc:0.97305 validation-aucpr:0.97698
[70] validation-logloss:0.19132 validation-auc:0.97298 validation-aucpr:0.97690
[71] validation-logloss:0.19122 validation-auc:0.97304 validation-aucpr:0.97693
[72] validation-logloss:0.19110 validation-auc:0.97310 validation-aucpr:0.97697
[73] validation-logloss:0.19099 validation-auc:0.97311 validation-aucpr:0.97697
[74] validation-logloss:0.19093 validation-auc:0.97308 validation-aucpr:0.97699
[75] validation-logloss:0.19072 validation-auc:0.97312 validation-aucpr:0.97699
[76] validation-logloss:0.19047 validation-auc:0.97319 validation-aucpr:0.97702
[77] validation-logloss:0.19035 validation-auc:0.97324 validation-aucpr:0.97706
[78] validation-logloss:0.19042 validation-auc:0.97327 validation-aucpr:0.97710
[79] validation-logloss:0.19051 validation-auc:0.97320 validation-aucpr:0.97705
[80] validation-logloss:0.19051 validation-auc:0.97321 validation-aucpr:0.97708
[81] validation-logloss:0.19039 validation-auc:0.97326 validation-aucpr:0.97714
[82] validation-logloss:0.19030 validation-auc:0.97325 validation-aucpr:0.97714
[83] validation-logloss:0.19029 validation-auc:0.97328 validation-aucpr:0.97718
[84] validation-logloss:0.19020 validation-auc:0.97332 validation-aucpr:0.97722
[85] validation-logloss:0.19029 validation-auc:0.97329 validation-aucpr:0.97718
[86] validation-logloss:0.19044 validation-auc:0.97319 validation-aucpr:0.97710
[87] validation-logloss:0.19080 validation-auc:0.97304 validation-aucpr:0.97696
[88] validation-logloss:0.19075 validation-auc:0.97309 validation-aucpr:0.97700
[89] validation-logloss:0.19067 validation-auc:0.97317 validation-aucpr:0.97709
[90] validation-logloss:0.19056 validation-auc:0.97319 validation-aucpr:0.97709
[91] validation-logloss:0.19071 validation-auc:0.97310 validation-aucpr:0.97699
[92] validation-logloss:0.19077 validation-auc:0.97303 validation-aucpr:0.97695
[93] validation-logloss:0.19089 validation-auc:0.97298 validation-aucpr:0.97689
[94] validation-logloss:0.19094 validation-auc:0.97295 validation-aucpr:0.97685
[95] validation-logloss:0.19072 validation-auc:0.97301 validation-aucpr:0.97690
[96] validation-logloss:0.19096 validation-auc:0.97291 validation-aucpr:0.97684
{'best_iteration': '84', 'best_score': '0.9772210952959658'}
Trial 65, Fold 1: Log loss = 0.19095632367941393, Average precision = 0.9768403685158552, ROC-AUC = 0.9729122007379687, Elapsed Time = 2.4595071999974607 seconds
Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 65, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.63236 validation-auc:0.95699 validation-aucpr:0.95729
[1] validation-logloss:0.58097 validation-auc:0.96491 validation-aucpr:0.96673
[2] validation-logloss:0.53829 validation-auc:0.96638 validation-aucpr:0.97073
[3] validation-logloss:0.50432 validation-auc:0.96749 validation-aucpr:0.97148
[4] validation-logloss:0.47047 validation-auc:0.96843 validation-aucpr:0.97218
[5] validation-logloss:0.44143 validation-auc:0.96867 validation-aucpr:0.97227
[6] validation-logloss:0.41628 validation-auc:0.96894 validation-aucpr:0.97239
[7] validation-logloss:0.39390 validation-auc:0.96907 validation-aucpr:0.97237
[8] validation-logloss:0.37404 validation-auc:0.96921 validation-aucpr:0.97261
[9] validation-logloss:0.35595 validation-auc:0.96964 validation-aucpr:0.97345
[10] validation-logloss:0.33948 validation-auc:0.97002 validation-aucpr:0.97378
[11] validation-logloss:0.32467 validation-auc:0.97021 validation-aucpr:0.97399
[12] validation-logloss:0.31186 validation-auc:0.97022 validation-aucpr:0.97402
[13] validation-logloss:0.30062 validation-auc:0.97016 validation-aucpr:0.97396
[14] validation-logloss:0.29007 validation-auc:0.97024 validation-aucpr:0.97403
[15] validation-logloss:0.28067 validation-auc:0.97049 validation-aucpr:0.97414
[16] validation-logloss:0.27306 validation-auc:0.97073 validation-aucpr:0.97436
[17] validation-logloss:0.26609 validation-auc:0.97089 validation-aucpr:0.97445
[18] validation-logloss:0.25992 validation-auc:0.97104 validation-aucpr:0.97454
[19] validation-logloss:0.25342 validation-auc:0.97104 validation-aucpr:0.97456
[20] validation-logloss:0.24769 validation-auc:0.97099 validation-aucpr:0.97454
[21] validation-logloss:0.24303 validation-auc:0.97106 validation-aucpr:0.97454
[22] validation-logloss:0.23779 validation-auc:0.97119 validation-aucpr:0.97462
[23] validation-logloss:0.23355 validation-auc:0.97127 validation-aucpr:0.97461
[24] validation-logloss:0.22949 validation-auc:0.97129 validation-aucpr:0.97462
[25] validation-logloss:0.22578 validation-auc:0.97124 validation-aucpr:0.97456
[26] validation-logloss:0.22217 validation-auc:0.97132 validation-aucpr:0.97461
[27] validation-logloss:0.21925 validation-auc:0.97119 validation-aucpr:0.97451
[28] validation-logloss:0.21663 validation-auc:0.97104 validation-aucpr:0.97441
[29] validation-logloss:0.21390 validation-auc:0.97115 validation-aucpr:0.97451
[30] validation-logloss:0.21165 validation-auc:0.97125 validation-aucpr:0.97456
[31] validation-logloss:0.20931 validation-auc:0.97140 validation-aucpr:0.97466
[32] validation-logloss:0.20716 validation-auc:0.97166 validation-aucpr:0.97480
[33] validation-logloss:0.20522 validation-auc:0.97166 validation-aucpr:0.97479
[34] validation-logloss:0.20357 validation-auc:0.97171 validation-aucpr:0.97480
[35] validation-logloss:0.20202 validation-auc:0.97183 validation-aucpr:0.97520
[36] validation-logloss:0.20054 validation-auc:0.97192 validation-aucpr:0.97526
[37] validation-logloss:0.19932 validation-auc:0.97196 validation-aucpr:0.97518
[38] validation-logloss:0.19741 validation-auc:0.97224 validation-aucpr:0.97538
[39] validation-logloss:0.19645 validation-auc:0.97217 validation-aucpr:0.97529
[40] validation-logloss:0.19575 validation-auc:0.97207 validation-aucpr:0.97537
[41] validation-logloss:0.19492 validation-auc:0.97202 validation-aucpr:0.97532
[42] validation-logloss:0.19392 validation-auc:0.97214 validation-aucpr:0.97538
[43] validation-logloss:0.19285 validation-auc:0.97229 validation-aucpr:0.97543
[44] validation-logloss:0.19207 validation-auc:0.97235 validation-aucpr:0.97551
[45] validation-logloss:0.19147 validation-auc:0.97237 validation-aucpr:0.97551
[46] validation-logloss:0.19081 validation-auc:0.97235 validation-aucpr:0.97542
[47] validation-logloss:0.19042 validation-auc:0.97240 validation-aucpr:0.97537
[48] validation-logloss:0.18994 validation-auc:0.97247 validation-aucpr:0.97540
[49] validation-logloss:0.18929 validation-auc:0.97260 validation-aucpr:0.97541
[50] validation-logloss:0.18853 validation-auc:0.97265 validation-aucpr:0.97544
[51] validation-logloss:0.18787 validation-auc:0.97282 validation-aucpr:0.97590
[52] validation-logloss:0.18728 validation-auc:0.97302 validation-aucpr:0.97609
[53] validation-logloss:0.18683 validation-auc:0.97300 validation-aucpr:0.97605
[54] validation-logloss:0.18623 validation-auc:0.97314 validation-aucpr:0.97619
[55] validation-logloss:0.18579 validation-auc:0.97318 validation-aucpr:0.97622
[56] validation-logloss:0.18546 validation-auc:0.97326 validation-aucpr:0.97630
[57] validation-logloss:0.18495 validation-auc:0.97343 validation-aucpr:0.97638
[58] validation-logloss:0.18488 validation-auc:0.97340 validation-aucpr:0.97633
[59] validation-logloss:0.18446 validation-auc:0.97343 validation-aucpr:0.97634
[60] validation-logloss:0.18413 validation-auc:0.97345 validation-aucpr:0.97646
[61] validation-logloss:0.18393 validation-auc:0.97346 validation-aucpr:0.97645
[62] validation-logloss:0.18379 validation-auc:0.97350 validation-aucpr:0.97645
[63] validation-logloss:0.18324 validation-auc:0.97374 validation-aucpr:0.97664
[64] validation-logloss:0.18291 validation-auc:0.97392 validation-aucpr:0.97682
[65] validation-logloss:0.18293 validation-auc:0.97387 validation-aucpr:0.97680
[66] validation-logloss:0.18273 validation-auc:0.97391 validation-aucpr:0.97683
[67] validation-logloss:0.18268 validation-auc:0.97392 validation-aucpr:0.97688
[68] validation-logloss:0.18290 validation-auc:0.97386 validation-aucpr:0.97686
[69] validation-logloss:0.18267 validation-auc:0.97395 validation-aucpr:0.97686
[70] validation-logloss:0.18253 validation-auc:0.97395 validation-aucpr:0.97686
[71] validation-logloss:0.18247 validation-auc:0.97395 validation-aucpr:0.97684
[72] validation-logloss:0.18223 validation-auc:0.97400 validation-aucpr:0.97689
[73] validation-logloss:0.18213 validation-auc:0.97402 validation-aucpr:0.97690
[74] validation-logloss:0.18203 validation-auc:0.97404 validation-aucpr:0.97691
[75] validation-logloss:0.18222 validation-auc:0.97391 validation-aucpr:0.97681
[76] validation-logloss:0.18221 validation-auc:0.97387 validation-aucpr:0.97679
[77] validation-logloss:0.18201 validation-auc:0.97395 validation-aucpr:0.97684
[78] validation-logloss:0.18190 validation-auc:0.97395 validation-aucpr:0.97678
[79] validation-logloss:0.18194 validation-auc:0.97397 validation-aucpr:0.97681
[80] validation-logloss:0.18182 validation-auc:0.97395 validation-aucpr:0.97678
[81] validation-logloss:0.18180 validation-auc:0.97390 validation-aucpr:0.97672
[82] validation-logloss:0.18152 validation-auc:0.97403 validation-aucpr:0.97680
[83] validation-logloss:0.18130 validation-auc:0.97405 validation-aucpr:0.97684
[84] validation-logloss:0.18128 validation-auc:0.97410 validation-aucpr:0.97690
[85] validation-logloss:0.18140 validation-auc:0.97407 validation-aucpr:0.97686
[86] validation-logloss:0.18099 validation-auc:0.97421 validation-aucpr:0.97699
[87] validation-logloss:0.18107 validation-auc:0.97415 validation-aucpr:0.97689
[88] validation-logloss:0.18101 validation-auc:0.97417 validation-aucpr:0.97692
[89] validation-logloss:0.18099 validation-auc:0.97416 validation-aucpr:0.97694
[90] validation-logloss:0.18096 validation-auc:0.97419 validation-aucpr:0.97700
[91] validation-logloss:0.18111 validation-auc:0.97414 validation-aucpr:0.97695
[92] validation-logloss:0.18114 validation-auc:0.97406 validation-aucpr:0.97688
[93] validation-logloss:0.18082 validation-auc:0.97414 validation-aucpr:0.97691
[94] validation-logloss:0.18068 validation-auc:0.97422 validation-aucpr:0.97695
[95] validation-logloss:0.18072 validation-auc:0.97417 validation-aucpr:0.97697
[96] validation-logloss:0.18067 validation-auc:0.97416 validation-aucpr:0.97698
{'best_iteration': '90', 'best_score': '0.9769978487826361'}
Trial 65, Fold 2: Log loss = 0.1806736366945864, Average precision = 0.9769836979905047, ROC-AUC = 0.9741555311612561, Elapsed Time = 2.701753800000006 seconds
Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 65, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.63211 validation-auc:0.96084 validation-aucpr:0.96028
[1] validation-logloss:0.58061 validation-auc:0.96425 validation-aucpr:0.96580
[2] validation-logloss:0.53715 validation-auc:0.96485 validation-aucpr:0.96824
[3] validation-logloss:0.49834 validation-auc:0.96757 validation-aucpr:0.97166
[4] validation-logloss:0.46540 validation-auc:0.96855 validation-aucpr:0.97223
[5] validation-logloss:0.44027 validation-auc:0.96884 validation-aucpr:0.97263
[6] validation-logloss:0.41484 validation-auc:0.96813 validation-aucpr:0.97219
[7] validation-logloss:0.39235 validation-auc:0.96884 validation-aucpr:0.97298
[8] validation-logloss:0.37443 validation-auc:0.96879 validation-aucpr:0.97298
[9] validation-logloss:0.35600 validation-auc:0.96889 validation-aucpr:0.97311
[10] validation-logloss:0.33951 validation-auc:0.96917 validation-aucpr:0.97336
[11] validation-logloss:0.32506 validation-auc:0.96931 validation-aucpr:0.97343
[12] validation-logloss:0.31221 validation-auc:0.96947 validation-aucpr:0.97350
[13] validation-logloss:0.30117 validation-auc:0.96930 validation-aucpr:0.97327
[14] validation-logloss:0.29119 validation-auc:0.96934 validation-aucpr:0.97330
[15] validation-logloss:0.28308 validation-auc:0.96966 validation-aucpr:0.97344
[16] validation-logloss:0.27460 validation-auc:0.96978 validation-aucpr:0.97354
[17] validation-logloss:0.26643 validation-auc:0.97007 validation-aucpr:0.97385
[18] validation-logloss:0.25933 validation-auc:0.97013 validation-aucpr:0.97389
[19] validation-logloss:0.25286 validation-auc:0.97020 validation-aucpr:0.97391
[20] validation-logloss:0.24692 validation-auc:0.97047 validation-aucpr:0.97409
[21] validation-logloss:0.24162 validation-auc:0.97063 validation-aucpr:0.97421
[22] validation-logloss:0.23680 validation-auc:0.97057 validation-aucpr:0.97411
[23] validation-logloss:0.23231 validation-auc:0.97077 validation-aucpr:0.97420
[24] validation-logloss:0.22834 validation-auc:0.97082 validation-aucpr:0.97423
[25] validation-logloss:0.22476 validation-auc:0.97112 validation-aucpr:0.97444
[26] validation-logloss:0.22110 validation-auc:0.97147 validation-aucpr:0.97470
[27] validation-logloss:0.21840 validation-auc:0.97152 validation-aucpr:0.97472
[28] validation-logloss:0.21588 validation-auc:0.97153 validation-aucpr:0.97474
[29] validation-logloss:0.21366 validation-auc:0.97166 validation-aucpr:0.97483
[30] validation-logloss:0.21179 validation-auc:0.97159 validation-aucpr:0.97451
[31] validation-logloss:0.20962 validation-auc:0.97178 validation-aucpr:0.97465
[32] validation-logloss:0.20780 validation-auc:0.97166 validation-aucpr:0.97457
[33] validation-logloss:0.20572 validation-auc:0.97179 validation-aucpr:0.97467
[34] validation-logloss:0.20411 validation-auc:0.97195 validation-aucpr:0.97475
[35] validation-logloss:0.20245 validation-auc:0.97203 validation-aucpr:0.97482
[36] validation-logloss:0.20095 validation-auc:0.97214 validation-aucpr:0.97492
[37] validation-logloss:0.19975 validation-auc:0.97212 validation-aucpr:0.97401
[38] validation-logloss:0.19858 validation-auc:0.97216 validation-aucpr:0.97422
[39] validation-logloss:0.19728 validation-auc:0.97234 validation-aucpr:0.97433
[40] validation-logloss:0.19622 validation-auc:0.97247 validation-aucpr:0.97430
[41] validation-logloss:0.19521 validation-auc:0.97259 validation-aucpr:0.97542
[42] validation-logloss:0.19429 validation-auc:0.97271 validation-aucpr:0.97555
[43] validation-logloss:0.19347 validation-auc:0.97278 validation-aucpr:0.97551
[44] validation-logloss:0.19261 validation-auc:0.97284 validation-aucpr:0.97550
[45] validation-logloss:0.19218 validation-auc:0.97274 validation-aucpr:0.97535
[46] validation-logloss:0.19150 validation-auc:0.97275 validation-aucpr:0.97527
[47] validation-logloss:0.19104 validation-auc:0.97285 validation-aucpr:0.97577
[48] validation-logloss:0.19058 validation-auc:0.97295 validation-aucpr:0.97587
[49] validation-logloss:0.18995 validation-auc:0.97305 validation-aucpr:0.97593
[50] validation-logloss:0.18929 validation-auc:0.97315 validation-aucpr:0.97609
[51] validation-logloss:0.18881 validation-auc:0.97319 validation-aucpr:0.97612
[52] validation-logloss:0.18839 validation-auc:0.97328 validation-aucpr:0.97626
[53] validation-logloss:0.18787 validation-auc:0.97339 validation-aucpr:0.97633
[54] validation-logloss:0.18779 validation-auc:0.97337 validation-aucpr:0.97611
[55] validation-logloss:0.18733 validation-auc:0.97346 validation-aucpr:0.97605
[56] validation-logloss:0.18723 validation-auc:0.97341 validation-aucpr:0.97589
[57] validation-logloss:0.18714 validation-auc:0.97340 validation-aucpr:0.97631
[58] validation-logloss:0.18718 validation-auc:0.97334 validation-aucpr:0.97615
[59] validation-logloss:0.18698 validation-auc:0.97337 validation-aucpr:0.97608
[60] validation-logloss:0.18679 validation-auc:0.97343 validation-aucpr:0.97634
[61] validation-logloss:0.18658 validation-auc:0.97343 validation-aucpr:0.97625
[62] validation-logloss:0.18626 validation-auc:0.97357 validation-aucpr:0.97632
[63] validation-logloss:0.18634 validation-auc:0.97351 validation-aucpr:0.97633
[64] validation-logloss:0.18626 validation-auc:0.97349 validation-aucpr:0.97624
[65] validation-logloss:0.18604 validation-auc:0.97351 validation-aucpr:0.97629
[66] validation-logloss:0.18587 validation-auc:0.97352 validation-aucpr:0.97638
[67] validation-logloss:0.18567 validation-auc:0.97361 validation-aucpr:0.97656
[68] validation-logloss:0.18569 validation-auc:0.97358 validation-aucpr:0.97651
[69] validation-logloss:0.18552 validation-auc:0.97361 validation-aucpr:0.97647
[70] validation-logloss:0.18544 validation-auc:0.97367 validation-aucpr:0.97635
[71] validation-logloss:0.18546 validation-auc:0.97362 validation-aucpr:0.97620
[72] validation-logloss:0.18554 validation-auc:0.97363 validation-aucpr:0.97634
[73] validation-logloss:0.18551 validation-auc:0.97359 validation-aucpr:0.97621
[74] validation-logloss:0.18538 validation-auc:0.97366 validation-aucpr:0.97621
[75] validation-logloss:0.18530 validation-auc:0.97375 validation-aucpr:0.97642
[76] validation-logloss:0.18520 validation-auc:0.97378 validation-aucpr:0.97643
[77] validation-logloss:0.18505 validation-auc:0.97387 validation-aucpr:0.97652
[78] validation-logloss:0.18460 validation-auc:0.97401 validation-aucpr:0.97661
[79] validation-logloss:0.18469 validation-auc:0.97396 validation-aucpr:0.97653
[80] validation-logloss:0.18489 validation-auc:0.97386 validation-aucpr:0.97633
[81] validation-logloss:0.18491 validation-auc:0.97385 validation-aucpr:0.97637
[82] validation-logloss:0.18490 validation-auc:0.97385 validation-aucpr:0.97630
[83] validation-logloss:0.18480 validation-auc:0.97388 validation-aucpr:0.97626
[84] validation-logloss:0.18472 validation-auc:0.97392 validation-aucpr:0.97627
[85] validation-logloss:0.18476 validation-auc:0.97391 validation-aucpr:0.97618
[86] validation-logloss:0.18458 validation-auc:0.97396 validation-aucpr:0.97638
[87] validation-logloss:0.18466 validation-auc:0.97394 validation-aucpr:0.97651
[88] validation-logloss:0.18457 validation-auc:0.97398 validation-aucpr:0.97657
[89] validation-logloss:0.18455 validation-auc:0.97398 validation-aucpr:0.97658
[90] validation-logloss:0.18464 validation-auc:0.97401 validation-aucpr:0.97655
[91] validation-logloss:0.18460 validation-auc:0.97398 validation-aucpr:0.97659
[92] validation-logloss:0.18471 validation-auc:0.97394 validation-aucpr:0.97659
[93] validation-logloss:0.18472 validation-auc:0.97396 validation-aucpr:0.97664
[94] validation-logloss:0.18468 validation-auc:0.97395 validation-aucpr:0.97660
[95] validation-logloss:0.18470 validation-auc:0.97395 validation-aucpr:0.97648
[96] validation-logloss:0.18468 validation-auc:0.97396 validation-aucpr:0.97645
{'best_iteration': '93', 'best_score': '0.9766434736304546'}
Trial 65, Fold 3: Log loss = 0.18468022857613342, Average precision = 0.9764594632367195, ROC-AUC = 0.9739638511138292, Elapsed Time = 2.7284695000016654 seconds
Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 65, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.63227 validation-auc:0.96018 validation-aucpr:0.96040
[1] validation-logloss:0.58161 validation-auc:0.96473 validation-aucpr:0.96583
[2] validation-logloss:0.53782 validation-auc:0.96663 validation-aucpr:0.97084
[3] validation-logloss:0.49989 validation-auc:0.96676 validation-aucpr:0.97111
[4] validation-logloss:0.46721 validation-auc:0.96840 validation-aucpr:0.97319
[5] validation-logloss:0.43827 validation-auc:0.96872 validation-aucpr:0.97358
[6] validation-logloss:0.41584 validation-auc:0.96886 validation-aucpr:0.97373
[7] validation-logloss:0.39655 validation-auc:0.96891 validation-aucpr:0.97373
[8] validation-logloss:0.37627 validation-auc:0.96904 validation-aucpr:0.97394
[9] validation-logloss:0.35840 validation-auc:0.96901 validation-aucpr:0.97386
[10] validation-logloss:0.34248 validation-auc:0.96906 validation-aucpr:0.97391
[11] validation-logloss:0.32792 validation-auc:0.96919 validation-aucpr:0.97403
[12] validation-logloss:0.31502 validation-auc:0.96931 validation-aucpr:0.97410
[13] validation-logloss:0.30360 validation-auc:0.96976 validation-aucpr:0.97447
[14] validation-logloss:0.29488 validation-auc:0.96940 validation-aucpr:0.97426
[15] validation-logloss:0.28512 validation-auc:0.96960 validation-aucpr:0.97440
[16] validation-logloss:0.27761 validation-auc:0.96956 validation-aucpr:0.97437
[17] validation-logloss:0.26995 validation-auc:0.96971 validation-aucpr:0.97448
[18] validation-logloss:0.26285 validation-auc:0.96988 validation-aucpr:0.97460
[19] validation-logloss:0.25631 validation-auc:0.96994 validation-aucpr:0.97467
[20] validation-logloss:0.25049 validation-auc:0.97010 validation-aucpr:0.97480
[21] validation-logloss:0.24515 validation-auc:0.97021 validation-aucpr:0.97485
[22] validation-logloss:0.24042 validation-auc:0.97032 validation-aucpr:0.97490
[23] validation-logloss:0.23670 validation-auc:0.97024 validation-aucpr:0.97484
[24] validation-logloss:0.23264 validation-auc:0.97040 validation-aucpr:0.97497
[25] validation-logloss:0.22943 validation-auc:0.97051 validation-aucpr:0.97504
[26] validation-logloss:0.22607 validation-auc:0.97047 validation-aucpr:0.97500
[27] validation-logloss:0.22288 validation-auc:0.97069 validation-aucpr:0.97514
[28] validation-logloss:0.21974 validation-auc:0.97089 validation-aucpr:0.97529
[29] validation-logloss:0.21710 validation-auc:0.97090 validation-aucpr:0.97532
[30] validation-logloss:0.21470 validation-auc:0.97089 validation-aucpr:0.97532
[31] validation-logloss:0.21251 validation-auc:0.97085 validation-aucpr:0.97530
[32] validation-logloss:0.21026 validation-auc:0.97103 validation-aucpr:0.97543
[33] validation-logloss:0.20813 validation-auc:0.97123 validation-aucpr:0.97556
[34] validation-logloss:0.20656 validation-auc:0.97132 validation-aucpr:0.97562
[35] validation-logloss:0.20500 validation-auc:0.97136 validation-aucpr:0.97564
[36] validation-logloss:0.20364 validation-auc:0.97149 validation-aucpr:0.97574
[37] validation-logloss:0.20216 validation-auc:0.97158 validation-aucpr:0.97581
[38] validation-logloss:0.20112 validation-auc:0.97146 validation-aucpr:0.97574
[39] validation-logloss:0.19975 validation-auc:0.97165 validation-aucpr:0.97587
[40] validation-logloss:0.19847 validation-auc:0.97180 validation-aucpr:0.97600
[41] validation-logloss:0.19773 validation-auc:0.97172 validation-aucpr:0.97595
[42] validation-logloss:0.19717 validation-auc:0.97171 validation-aucpr:0.97593
[43] validation-logloss:0.19649 validation-auc:0.97179 validation-aucpr:0.97599
[44] validation-logloss:0.19586 validation-auc:0.97180 validation-aucpr:0.97600
[45] validation-logloss:0.19535 validation-auc:0.97167 validation-aucpr:0.97590
[46] validation-logloss:0.19470 validation-auc:0.97177 validation-aucpr:0.97597
[47] validation-logloss:0.19400 validation-auc:0.97184 validation-aucpr:0.97600
[48] validation-logloss:0.19364 validation-auc:0.97178 validation-aucpr:0.97596
[49] validation-logloss:0.19342 validation-auc:0.97167 validation-aucpr:0.97587
[50] validation-logloss:0.19294 validation-auc:0.97177 validation-aucpr:0.97595
[51] validation-logloss:0.19275 validation-auc:0.97168 validation-aucpr:0.97588
[52] validation-logloss:0.19264 validation-auc:0.97164 validation-aucpr:0.97585
[53] validation-logloss:0.19236 validation-auc:0.97161 validation-aucpr:0.97584
[54] validation-logloss:0.19163 validation-auc:0.97182 validation-aucpr:0.97599
[55] validation-logloss:0.19138 validation-auc:0.97193 validation-aucpr:0.97607
[56] validation-logloss:0.19114 validation-auc:0.97190 validation-aucpr:0.97605
[57] validation-logloss:0.19087 validation-auc:0.97195 validation-aucpr:0.97608
[58] validation-logloss:0.19075 validation-auc:0.97195 validation-aucpr:0.97606
[59] validation-logloss:0.19073 validation-auc:0.97188 validation-aucpr:0.97600
[60] validation-logloss:0.19066 validation-auc:0.97183 validation-aucpr:0.97597
[61] validation-logloss:0.19070 validation-auc:0.97165 validation-aucpr:0.97583
[62] validation-logloss:0.19038 validation-auc:0.97175 validation-aucpr:0.97590
[63] validation-logloss:0.19043 validation-auc:0.97165 validation-aucpr:0.97581
[64] validation-logloss:0.19044 validation-auc:0.97159 validation-aucpr:0.97576
[65] validation-logloss:0.19035 validation-auc:0.97150 validation-aucpr:0.97569
[66] validation-logloss:0.19022 validation-auc:0.97153 validation-aucpr:0.97573
[67] validation-logloss:0.19004 validation-auc:0.97159 validation-aucpr:0.97577
[68] validation-logloss:0.19007 validation-auc:0.97156 validation-aucpr:0.97573
[69] validation-logloss:0.18996 validation-auc:0.97159 validation-aucpr:0.97575
[70] validation-logloss:0.18990 validation-auc:0.97159 validation-aucpr:0.97576
[71] validation-logloss:0.18973 validation-auc:0.97161 validation-aucpr:0.97577
[72] validation-logloss:0.18975 validation-auc:0.97160 validation-aucpr:0.97575
[73] validation-logloss:0.18992 validation-auc:0.97153 validation-aucpr:0.97569
[74] validation-logloss:0.18989 validation-auc:0.97155 validation-aucpr:0.97571
[75] validation-logloss:0.18975 validation-auc:0.97158 validation-aucpr:0.97571
[76] validation-logloss:0.18982 validation-auc:0.97151 validation-aucpr:0.97565
[77] validation-logloss:0.18979 validation-auc:0.97157 validation-aucpr:0.97571
[78] validation-logloss:0.18979 validation-auc:0.97158 validation-aucpr:0.97571
[79] validation-logloss:0.18972 validation-auc:0.97159 validation-aucpr:0.97572
[80] validation-logloss:0.18981 validation-auc:0.97155 validation-aucpr:0.97566
[81] validation-logloss:0.18966 validation-auc:0.97162 validation-aucpr:0.97571
[82] validation-logloss:0.18952 validation-auc:0.97164 validation-aucpr:0.97572
[83] validation-logloss:0.18950 validation-auc:0.97160 validation-aucpr:0.97568
[84] validation-logloss:0.18959 validation-auc:0.97155 validation-aucpr:0.97565
[85] validation-logloss:0.18955 validation-auc:0.97157 validation-aucpr:0.97566
[86] validation-logloss:0.18958 validation-auc:0.97159 validation-aucpr:0.97568
[87] validation-logloss:0.18941 validation-auc:0.97163 validation-aucpr:0.97573
[88] validation-logloss:0.18915 validation-auc:0.97178 validation-aucpr:0.97584
[89] validation-logloss:0.18912 validation-auc:0.97180 validation-aucpr:0.97585
[90] validation-logloss:0.18908 validation-auc:0.97175 validation-aucpr:0.97582
[91] validation-logloss:0.18928 validation-auc:0.97169 validation-aucpr:0.97578
[92] validation-logloss:0.18934 validation-auc:0.97171 validation-aucpr:0.97579
[93] validation-logloss:0.18944 validation-auc:0.97164 validation-aucpr:0.97571
[94] validation-logloss:0.18938 validation-auc:0.97167 validation-aucpr:0.97573
[95] validation-logloss:0.18942 validation-auc:0.97168 validation-aucpr:0.97574
[96] validation-logloss:0.18949 validation-auc:0.97170 validation-aucpr:0.97574
{'best_iteration': '57', 'best_score': '0.976076300519751'}
Trial 65, Fold 4: Log loss = 0.18949136138858005, Average precision = 0.9757470371557592, ROC-AUC = 0.9716955657131412, Elapsed Time = 2.6781521999982942 seconds
Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 65, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.63258 validation-auc:0.95470 validation-aucpr:0.95545
[1] validation-logloss:0.58266 validation-auc:0.96421 validation-aucpr:0.96888
[2] validation-logloss:0.53907 validation-auc:0.96466 validation-aucpr:0.96913
[3] validation-logloss:0.50150 validation-auc:0.96582 validation-aucpr:0.97037
[4] validation-logloss:0.46936 validation-auc:0.96576 validation-aucpr:0.96989
[5] validation-logloss:0.44095 validation-auc:0.96590 validation-aucpr:0.97013
[6] validation-logloss:0.41574 validation-auc:0.96643 validation-aucpr:0.97058
[7] validation-logloss:0.39553 validation-auc:0.96724 validation-aucpr:0.97152
[8] validation-logloss:0.37544 validation-auc:0.96744 validation-aucpr:0.97164
[9] validation-logloss:0.35970 validation-auc:0.96804 validation-aucpr:0.97219
[10] validation-logloss:0.34453 validation-auc:0.96812 validation-aucpr:0.97228
[11] validation-logloss:0.33042 validation-auc:0.96808 validation-aucpr:0.97227
[12] validation-logloss:0.31769 validation-auc:0.96848 validation-aucpr:0.97253
[13] validation-logloss:0.30611 validation-auc:0.96893 validation-aucpr:0.97285
[14] validation-logloss:0.29561 validation-auc:0.96925 validation-aucpr:0.97308
[15] validation-logloss:0.28742 validation-auc:0.96924 validation-aucpr:0.97307
[16] validation-logloss:0.27974 validation-auc:0.96966 validation-aucpr:0.97337
[17] validation-logloss:0.27196 validation-auc:0.96998 validation-aucpr:0.97363
[18] validation-logloss:0.26470 validation-auc:0.97008 validation-aucpr:0.97371
[19] validation-logloss:0.25944 validation-auc:0.97009 validation-aucpr:0.97366
[20] validation-logloss:0.25361 validation-auc:0.97044 validation-aucpr:0.97384
[21] validation-logloss:0.24829 validation-auc:0.97052 validation-aucpr:0.97386
[22] validation-logloss:0.24353 validation-auc:0.97040 validation-aucpr:0.97384
[23] validation-logloss:0.23926 validation-auc:0.97049 validation-aucpr:0.97391
[24] validation-logloss:0.23548 validation-auc:0.97064 validation-aucpr:0.97398
[25] validation-logloss:0.23296 validation-auc:0.97057 validation-aucpr:0.97382
[26] validation-logloss:0.22978 validation-auc:0.97062 validation-aucpr:0.97383
[27] validation-logloss:0.22677 validation-auc:0.97071 validation-aucpr:0.97386
[28] validation-logloss:0.22376 validation-auc:0.97098 validation-aucpr:0.97397
[29] validation-logloss:0.22122 validation-auc:0.97105 validation-aucpr:0.97401
[30] validation-logloss:0.21878 validation-auc:0.97121 validation-aucpr:0.97412
[31] validation-logloss:0.21660 validation-auc:0.97127 validation-aucpr:0.97417
[32] validation-logloss:0.21452 validation-auc:0.97130 validation-aucpr:0.97419
[33] validation-logloss:0.21288 validation-auc:0.97127 validation-aucpr:0.97433
[34] validation-logloss:0.21144 validation-auc:0.97130 validation-aucpr:0.97442
[35] validation-logloss:0.20970 validation-auc:0.97135 validation-aucpr:0.97428
[36] validation-logloss:0.20832 validation-auc:0.97138 validation-aucpr:0.97429
[37] validation-logloss:0.20693 validation-auc:0.97153 validation-aucpr:0.97436
[38] validation-logloss:0.20555 validation-auc:0.97179 validation-aucpr:0.97457
[39] validation-logloss:0.20431 validation-auc:0.97199 validation-aucpr:0.97462
[40] validation-logloss:0.20317 validation-auc:0.97212 validation-aucpr:0.97485
[41] validation-logloss:0.20220 validation-auc:0.97222 validation-aucpr:0.97493
[42] validation-logloss:0.20140 validation-auc:0.97216 validation-aucpr:0.97484
[43] validation-logloss:0.20067 validation-auc:0.97223 validation-aucpr:0.97492
[44] validation-logloss:0.19989 validation-auc:0.97229 validation-aucpr:0.97499
[45] validation-logloss:0.19936 validation-auc:0.97238 validation-aucpr:0.97498
[46] validation-logloss:0.19845 validation-auc:0.97251 validation-aucpr:0.97507
[47] validation-logloss:0.19780 validation-auc:0.97258 validation-aucpr:0.97513
[48] validation-logloss:0.19706 validation-auc:0.97274 validation-aucpr:0.97523
[49] validation-logloss:0.19656 validation-auc:0.97277 validation-aucpr:0.97517
[50] validation-logloss:0.19612 validation-auc:0.97282 validation-aucpr:0.97517
[51] validation-logloss:0.19579 validation-auc:0.97284 validation-aucpr:0.97514
[52] validation-logloss:0.19520 validation-auc:0.97291 validation-aucpr:0.97554
[53] validation-logloss:0.19494 validation-auc:0.97282 validation-aucpr:0.97563
[54] validation-logloss:0.19450 validation-auc:0.97289 validation-aucpr:0.97561
[55] validation-logloss:0.19435 validation-auc:0.97289 validation-aucpr:0.97552
[56] validation-logloss:0.19403 validation-auc:0.97298 validation-aucpr:0.97551
[57] validation-logloss:0.19375 validation-auc:0.97303 validation-aucpr:0.97541
[58] validation-logloss:0.19369 validation-auc:0.97305 validation-aucpr:0.97548
[59] validation-logloss:0.19334 validation-auc:0.97311 validation-aucpr:0.97569
[60] validation-logloss:0.19333 validation-auc:0.97309 validation-aucpr:0.97581
[61] validation-logloss:0.19319 validation-auc:0.97302 validation-aucpr:0.97560
[62] validation-logloss:0.19285 validation-auc:0.97304 validation-aucpr:0.97557
[63] validation-logloss:0.19237 validation-auc:0.97321 validation-aucpr:0.97560
[64] validation-logloss:0.19214 validation-auc:0.97330 validation-aucpr:0.97588
[65] validation-logloss:0.19225 validation-auc:0.97324 validation-aucpr:0.97602
[66] validation-logloss:0.19196 validation-auc:0.97335 validation-aucpr:0.97621
[67] validation-logloss:0.19174 validation-auc:0.97345 validation-aucpr:0.97640
[68] validation-logloss:0.19175 validation-auc:0.97341 validation-aucpr:0.97639
[69] validation-logloss:0.19170 validation-auc:0.97338 validation-aucpr:0.97633
[70] validation-logloss:0.19131 validation-auc:0.97347 validation-aucpr:0.97636
[71] validation-logloss:0.19114 validation-auc:0.97347 validation-aucpr:0.97632
[72] validation-logloss:0.19100 validation-auc:0.97351 validation-aucpr:0.97630
[73] validation-logloss:0.19088 validation-auc:0.97349 validation-aucpr:0.97627
[74] validation-logloss:0.19083 validation-auc:0.97350 validation-aucpr:0.97616
[75] validation-logloss:0.19053 validation-auc:0.97353 validation-aucpr:0.97618
[76] validation-logloss:0.19035 validation-auc:0.97351 validation-aucpr:0.97617
[77] validation-logloss:0.19038 validation-auc:0.97356 validation-aucpr:0.97631
[78] validation-logloss:0.19034 validation-auc:0.97357 validation-aucpr:0.97645
[79] validation-logloss:0.19020 validation-auc:0.97361 validation-aucpr:0.97642
[80] validation-logloss:0.19019 validation-auc:0.97364 validation-aucpr:0.97646
[81] validation-logloss:0.19037 validation-auc:0.97353 validation-aucpr:0.97626
[82] validation-logloss:0.19042 validation-auc:0.97356 validation-aucpr:0.97644
[83] validation-logloss:0.19032 validation-auc:0.97354 validation-aucpr:0.97643
[84] validation-logloss:0.19038 validation-auc:0.97354 validation-aucpr:0.97648
[85] validation-logloss:0.19052 validation-auc:0.97343 validation-aucpr:0.97634
[86] validation-logloss:0.19054 validation-auc:0.97335 validation-aucpr:0.97616
[87] validation-logloss:0.19040 validation-auc:0.97335 validation-aucpr:0.97615
[88] validation-logloss:0.19033 validation-auc:0.97335 validation-aucpr:0.97614
[89] validation-logloss:0.19054 validation-auc:0.97324 validation-aucpr:0.97599
[90] validation-logloss:0.19057 validation-auc:0.97322 validation-aucpr:0.97596
[91] validation-logloss:0.19064 validation-auc:0.97317 validation-aucpr:0.97588
[92] validation-logloss:0.19067 validation-auc:0.97313 validation-aucpr:0.97576
[93] validation-logloss:0.19051 validation-auc:0.97318 validation-aucpr:0.97580
[94] validation-logloss:0.19071 validation-auc:0.97311 validation-aucpr:0.97585
[95] validation-logloss:0.19087 validation-auc:0.97303 validation-aucpr:0.97584
[96] validation-logloss:0.19068 validation-auc:0.97310 validation-aucpr:0.97600
{'best_iteration': '84', 'best_score': '0.9764759330080807'}
Trial 65, Fold 5: Log loss = 0.19067964677015295, Average precision = 0.9760026014555567, ROC-AUC = 0.9731033944596176, Elapsed Time = 2.712488900000608 seconds
Optimization Progress: 66%|######6 | 66/100 [3:02:04<24:52, 43.90s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 66, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66326 validation-auc:0.94744 validation-aucpr:0.92501
[1] validation-logloss:0.63903 validation-auc:0.95849 validation-aucpr:0.95428
[2] validation-logloss:0.61594 validation-auc:0.96031 validation-aucpr:0.95947
[3] validation-logloss:0.59520 validation-auc:0.96201 validation-aucpr:0.96459
[4] validation-logloss:0.57298 validation-auc:0.96370 validation-aucpr:0.96639
[5] validation-logloss:0.55201 validation-auc:0.96421 validation-aucpr:0.96559
[6] validation-logloss:0.53231 validation-auc:0.96527 validation-aucpr:0.96632
[7] validation-logloss:0.51598 validation-auc:0.96536 validation-aucpr:0.96836
[8] validation-logloss:0.50123 validation-auc:0.96552 validation-aucpr:0.96853
[9] validation-logloss:0.48724 validation-auc:0.96581 validation-aucpr:0.96871
[10] validation-logloss:0.47435 validation-auc:0.96579 validation-aucpr:0.96892
[11] validation-logloss:0.46179 validation-auc:0.96582 validation-aucpr:0.96894
[12] validation-logloss:0.45031 validation-auc:0.96557 validation-aucpr:0.96872
[13] validation-logloss:0.43720 validation-auc:0.96600 validation-aucpr:0.96895
[14] validation-logloss:0.42659 validation-auc:0.96612 validation-aucpr:0.96887
[15] validation-logloss:0.41684 validation-auc:0.96616 validation-aucpr:0.96885
[16] validation-logloss:0.40516 validation-auc:0.96671 validation-aucpr:0.96946
[17] validation-logloss:0.39645 validation-auc:0.96671 validation-aucpr:0.96940
[18] validation-logloss:0.38626 validation-auc:0.96694 validation-aucpr:0.96966
[19] validation-logloss:0.37653 validation-auc:0.96723 validation-aucpr:0.96995
[20] validation-logloss:0.36720 validation-auc:0.96762 validation-aucpr:0.97034
[21] validation-logloss:0.35843 validation-auc:0.96787 validation-aucpr:0.97059
[22] validation-logloss:0.35028 validation-auc:0.96815 validation-aucpr:0.97076
[23] validation-logloss:0.34376 validation-auc:0.96799 validation-aucpr:0.97063
[24] validation-logloss:0.33632 validation-auc:0.96830 validation-aucpr:0.97090
[25] validation-logloss:0.32913 validation-auc:0.96852 validation-aucpr:0.97112
[26] validation-logloss:0.32349 validation-auc:0.96871 validation-aucpr:0.97127
[27] validation-logloss:0.31806 validation-auc:0.96883 validation-aucpr:0.97133
[28] validation-logloss:0.31185 validation-auc:0.96917 validation-aucpr:0.97168
[29] validation-logloss:0.30733 validation-auc:0.96898 validation-aucpr:0.96962
[30] validation-logloss:0.30172 validation-auc:0.96922 validation-aucpr:0.96983
[31] validation-logloss:0.29652 validation-auc:0.96961 validation-aucpr:0.97195
[32] validation-logloss:0.29283 validation-auc:0.96949 validation-aucpr:0.97205
[33] validation-logloss:0.28928 validation-auc:0.96940 validation-aucpr:0.97182
[34] validation-logloss:0.28461 validation-auc:0.96951 validation-aucpr:0.97194
[35] validation-logloss:0.28118 validation-auc:0.96950 validation-aucpr:0.97193
[36] validation-logloss:0.27680 validation-auc:0.96981 validation-aucpr:0.97191
[37] validation-logloss:0.27248 validation-auc:0.97015 validation-aucpr:0.97217
[38] validation-logloss:0.26967 validation-auc:0.97007 validation-aucpr:0.97213
[39] validation-logloss:0.26683 validation-auc:0.97006 validation-aucpr:0.97212
[40] validation-logloss:0.26311 validation-auc:0.97036 validation-aucpr:0.97265
[41] validation-logloss:0.26075 validation-auc:0.97018 validation-aucpr:0.97250
[42] validation-logloss:0.25758 validation-auc:0.97021 validation-aucpr:0.97254
[43] validation-logloss:0.25507 validation-auc:0.97027 validation-aucpr:0.97229
[44] validation-logloss:0.25285 validation-auc:0.97024 validation-aucpr:0.97253
[45] validation-logloss:0.25040 validation-auc:0.97037 validation-aucpr:0.97265
[46] validation-logloss:0.24749 validation-auc:0.97048 validation-aucpr:0.97289
[47] validation-logloss:0.24492 validation-auc:0.97054 validation-aucpr:0.97291
[48] validation-logloss:0.24312 validation-auc:0.97052 validation-aucpr:0.97290
[49] validation-logloss:0.24046 validation-auc:0.97065 validation-aucpr:0.97287
[50] validation-logloss:0.23870 validation-auc:0.97068 validation-aucpr:0.97287
[51] validation-logloss:0.23643 validation-auc:0.97084 validation-aucpr:0.97301
[52] validation-logloss:0.23492 validation-auc:0.97082 validation-aucpr:0.97299
[53] validation-logloss:0.23276 validation-auc:0.97089 validation-aucpr:0.97305
[54] validation-logloss:0.23135 validation-auc:0.97081 validation-aucpr:0.97296
[55] validation-logloss:0.22992 validation-auc:0.97086 validation-aucpr:0.97299
[56] validation-logloss:0.22782 validation-auc:0.97103 validation-aucpr:0.97330
[57] validation-logloss:0.22604 validation-auc:0.97112 validation-aucpr:0.97341
[58] validation-logloss:0.22478 validation-auc:0.97118 validation-aucpr:0.97344
[59] validation-logloss:0.22296 validation-auc:0.97134 validation-aucpr:0.97341
[60] validation-logloss:0.22122 validation-auc:0.97151 validation-aucpr:0.97356
{'best_iteration': '60', 'best_score': '0.973559109648379'}
Trial 66, Fold 1: Log loss = 0.22121907431323887, Average precision = 0.9740674502888876, ROC-AUC = 0.9715058066034805, Elapsed Time = 1.7955819999988307 seconds
Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 66, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66278 validation-auc:0.94974 validation-aucpr:0.94237
[1] validation-logloss:0.63510 validation-auc:0.96293 validation-aucpr:0.96337
[2] validation-logloss:0.61009 validation-auc:0.96484 validation-aucpr:0.96897
[3] validation-logloss:0.58614 validation-auc:0.96721 validation-aucpr:0.97118
[4] validation-logloss:0.56672 validation-auc:0.96730 validation-aucpr:0.97108
[5] validation-logloss:0.54815 validation-auc:0.96765 validation-aucpr:0.97136
[6] validation-logloss:0.52861 validation-auc:0.96826 validation-aucpr:0.97188
[7] validation-logloss:0.51075 validation-auc:0.96822 validation-aucpr:0.97189
[8] validation-logloss:0.49538 validation-auc:0.96805 validation-aucpr:0.97171
[9] validation-logloss:0.47921 validation-auc:0.96850 validation-aucpr:0.97208
[10] validation-logloss:0.46583 validation-auc:0.96853 validation-aucpr:0.97203
[11] validation-logloss:0.45127 validation-auc:0.96906 validation-aucpr:0.97244
[12] validation-logloss:0.43776 validation-auc:0.96940 validation-aucpr:0.97270
[13] validation-logloss:0.42483 validation-auc:0.96991 validation-aucpr:0.97309
[14] validation-logloss:0.41278 validation-auc:0.97008 validation-aucpr:0.97314
[15] validation-logloss:0.40147 validation-auc:0.97034 validation-aucpr:0.97336
[16] validation-logloss:0.39220 validation-auc:0.97033 validation-aucpr:0.97336
[17] validation-logloss:0.38199 validation-auc:0.97054 validation-aucpr:0.97360
[18] validation-logloss:0.37274 validation-auc:0.97063 validation-aucpr:0.97367
[19] validation-logloss:0.36466 validation-auc:0.97070 validation-aucpr:0.97366
[20] validation-logloss:0.35618 validation-auc:0.97068 validation-aucpr:0.97363
[21] validation-logloss:0.34799 validation-auc:0.97084 validation-aucpr:0.97376
[22] validation-logloss:0.34013 validation-auc:0.97097 validation-aucpr:0.97388
[23] validation-logloss:0.33263 validation-auc:0.97118 validation-aucpr:0.97401
[24] validation-logloss:0.32647 validation-auc:0.97116 validation-aucpr:0.97401
[25] validation-logloss:0.31957 validation-auc:0.97141 validation-aucpr:0.97418
[26] validation-logloss:0.31328 validation-auc:0.97134 validation-aucpr:0.97414
[27] validation-logloss:0.30741 validation-auc:0.97135 validation-aucpr:0.97416
[28] validation-logloss:0.30175 validation-auc:0.97132 validation-aucpr:0.97414
[29] validation-logloss:0.29695 validation-auc:0.97132 validation-aucpr:0.97413
[30] validation-logloss:0.29243 validation-auc:0.97142 validation-aucpr:0.97418
[31] validation-logloss:0.28778 validation-auc:0.97142 validation-aucpr:0.97417
[32] validation-logloss:0.28349 validation-auc:0.97157 validation-aucpr:0.97432
[33] validation-logloss:0.27946 validation-auc:0.97160 validation-aucpr:0.97434
[34] validation-logloss:0.27492 validation-auc:0.97168 validation-aucpr:0.97441
[35] validation-logloss:0.27081 validation-auc:0.97182 validation-aucpr:0.97488
[36] validation-logloss:0.26662 validation-auc:0.97191 validation-aucpr:0.97496
[37] validation-logloss:0.26344 validation-auc:0.97186 validation-aucpr:0.97490
[38] validation-logloss:0.25972 validation-auc:0.97203 validation-aucpr:0.97501
[39] validation-logloss:0.25626 validation-auc:0.97203 validation-aucpr:0.97491
[40] validation-logloss:0.25307 validation-auc:0.97208 validation-aucpr:0.97495
[41] validation-logloss:0.24992 validation-auc:0.97213 validation-aucpr:0.97499
[42] validation-logloss:0.24684 validation-auc:0.97225 validation-aucpr:0.97510
[43] validation-logloss:0.24433 validation-auc:0.97226 validation-aucpr:0.97510
[44] validation-logloss:0.24144 validation-auc:0.97235 validation-aucpr:0.97516
[45] validation-logloss:0.23895 validation-auc:0.97228 validation-aucpr:0.97512
[46] validation-logloss:0.23632 validation-auc:0.97227 validation-aucpr:0.97507
[47] validation-logloss:0.23421 validation-auc:0.97242 validation-aucpr:0.97516
[48] validation-logloss:0.23203 validation-auc:0.97252 validation-aucpr:0.97524
[49] validation-logloss:0.22985 validation-auc:0.97245 validation-aucpr:0.97522
[50] validation-logloss:0.22794 validation-auc:0.97255 validation-aucpr:0.97530
[51] validation-logloss:0.22592 validation-auc:0.97251 validation-aucpr:0.97528
[52] validation-logloss:0.22395 validation-auc:0.97255 validation-aucpr:0.97528
[53] validation-logloss:0.22205 validation-auc:0.97264 validation-aucpr:0.97528
[54] validation-logloss:0.22018 validation-auc:0.97270 validation-aucpr:0.97533
[55] validation-logloss:0.21841 validation-auc:0.97280 validation-aucpr:0.97540
[56] validation-logloss:0.21675 validation-auc:0.97279 validation-aucpr:0.97539
[57] validation-logloss:0.21491 validation-auc:0.97291 validation-aucpr:0.97546
[58] validation-logloss:0.21357 validation-auc:0.97295 validation-aucpr:0.97548
[59] validation-logloss:0.21187 validation-auc:0.97304 validation-aucpr:0.97556
[60] validation-logloss:0.21044 validation-auc:0.97316 validation-aucpr:0.97561
{'best_iteration': '60', 'best_score': '0.9756137684891409'}
Trial 66, Fold 2: Log loss = 0.2104350181056542, Average precision = 0.9755512232417399, ROC-AUC = 0.9731561961200518, Elapsed Time = 1.952999400000408 seconds
Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 66, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66268 validation-auc:0.95210 validation-aucpr:0.95295
[1] validation-logloss:0.63570 validation-auc:0.96114 validation-aucpr:0.96550
[2] validation-logloss:0.61340 validation-auc:0.96345 validation-aucpr:0.96666
[3] validation-logloss:0.59172 validation-auc:0.96439 validation-aucpr:0.96763
[4] validation-logloss:0.56913 validation-auc:0.96544 validation-aucpr:0.96915
[5] validation-logloss:0.54809 validation-auc:0.96597 validation-aucpr:0.96983
[6] validation-logloss:0.53156 validation-auc:0.96662 validation-aucpr:0.97098
[7] validation-logloss:0.51295 validation-auc:0.96777 validation-aucpr:0.97216
[8] validation-logloss:0.49552 validation-auc:0.96809 validation-aucpr:0.97253
[9] validation-logloss:0.48132 validation-auc:0.96821 validation-aucpr:0.97234
[10] validation-logloss:0.46554 validation-auc:0.96850 validation-aucpr:0.97257
[11] validation-logloss:0.45330 validation-auc:0.96839 validation-aucpr:0.97312
[12] validation-logloss:0.44156 validation-auc:0.96875 validation-aucpr:0.97340
[13] validation-logloss:0.42889 validation-auc:0.96894 validation-aucpr:0.97358
[14] validation-logloss:0.41655 validation-auc:0.96931 validation-aucpr:0.97388
[15] validation-logloss:0.40508 validation-auc:0.96949 validation-aucpr:0.97407
[16] validation-logloss:0.39463 validation-auc:0.96950 validation-aucpr:0.97409
[17] validation-logloss:0.38596 validation-auc:0.96937 validation-aucpr:0.97402
[18] validation-logloss:0.37743 validation-auc:0.96943 validation-aucpr:0.97402
[19] validation-logloss:0.36811 validation-auc:0.96964 validation-aucpr:0.97416
[20] validation-logloss:0.35891 validation-auc:0.96999 validation-aucpr:0.97444
[21] validation-logloss:0.35066 validation-auc:0.97034 validation-aucpr:0.97473
[22] validation-logloss:0.34269 validation-auc:0.97038 validation-aucpr:0.97480
[23] validation-logloss:0.33504 validation-auc:0.97053 validation-aucpr:0.97492
[24] validation-logloss:0.32876 validation-auc:0.97056 validation-aucpr:0.97495
[25] validation-logloss:0.32205 validation-auc:0.97058 validation-aucpr:0.97495
[26] validation-logloss:0.31554 validation-auc:0.97067 validation-aucpr:0.97502
[27] validation-logloss:0.31021 validation-auc:0.97069 validation-aucpr:0.97503
[28] validation-logloss:0.30430 validation-auc:0.97076 validation-aucpr:0.97509
[29] validation-logloss:0.29881 validation-auc:0.97084 validation-aucpr:0.97513
[30] validation-logloss:0.29345 validation-auc:0.97092 validation-aucpr:0.97519
[31] validation-logloss:0.28911 validation-auc:0.97094 validation-aucpr:0.97519
[32] validation-logloss:0.28530 validation-auc:0.97080 validation-aucpr:0.97504
[33] validation-logloss:0.28143 validation-auc:0.97072 validation-aucpr:0.97496
[34] validation-logloss:0.27762 validation-auc:0.97082 validation-aucpr:0.97497
[35] validation-logloss:0.27382 validation-auc:0.97091 validation-aucpr:0.97501
[36] validation-logloss:0.26960 validation-auc:0.97103 validation-aucpr:0.97509
[37] validation-logloss:0.26565 validation-auc:0.97120 validation-aucpr:0.97522
[38] validation-logloss:0.26248 validation-auc:0.97134 validation-aucpr:0.97535
[39] validation-logloss:0.25954 validation-auc:0.97137 validation-aucpr:0.97539
[40] validation-logloss:0.25609 validation-auc:0.97148 validation-aucpr:0.97548
[41] validation-logloss:0.25273 validation-auc:0.97155 validation-aucpr:0.97554
[42] validation-logloss:0.25017 validation-auc:0.97165 validation-aucpr:0.97569
[43] validation-logloss:0.24697 validation-auc:0.97180 validation-aucpr:0.97583
[44] validation-logloss:0.24410 validation-auc:0.97194 validation-aucpr:0.97594
[45] validation-logloss:0.24124 validation-auc:0.97197 validation-aucpr:0.97598
[46] validation-logloss:0.23874 validation-auc:0.97197 validation-aucpr:0.97599
[47] validation-logloss:0.23613 validation-auc:0.97209 validation-aucpr:0.97608
[48] validation-logloss:0.23380 validation-auc:0.97214 validation-aucpr:0.97612
[49] validation-logloss:0.23159 validation-auc:0.97214 validation-aucpr:0.97615
[50] validation-logloss:0.22934 validation-auc:0.97221 validation-aucpr:0.97621
[51] validation-logloss:0.22729 validation-auc:0.97224 validation-aucpr:0.97624
[52] validation-logloss:0.22544 validation-auc:0.97220 validation-aucpr:0.97621
[53] validation-logloss:0.22340 validation-auc:0.97220 validation-aucpr:0.97620
[54] validation-logloss:0.22168 validation-auc:0.97216 validation-aucpr:0.97617
[55] validation-logloss:0.22050 validation-auc:0.97210 validation-aucpr:0.97609
[56] validation-logloss:0.21922 validation-auc:0.97210 validation-aucpr:0.97606
[57] validation-logloss:0.21764 validation-auc:0.97215 validation-aucpr:0.97610
[58] validation-logloss:0.21587 validation-auc:0.97229 validation-aucpr:0.97622
[59] validation-logloss:0.21453 validation-auc:0.97230 validation-aucpr:0.97622
[60] validation-logloss:0.21306 validation-auc:0.97238 validation-aucpr:0.97629
{'best_iteration': '60', 'best_score': '0.9762925367757626'}
Trial 66, Fold 3: Log loss = 0.2130604057966504, Average precision = 0.9762961959857607, ROC-AUC = 0.9723778905143936, Elapsed Time = 2.1198683000002347 seconds
Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 66, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66279 validation-auc:0.94747 validation-aucpr:0.92289
[1] validation-logloss:0.63758 validation-auc:0.96398 validation-aucpr:0.96642
[2] validation-logloss:0.61157 validation-auc:0.96684 validation-aucpr:0.97024
[3] validation-logloss:0.58741 validation-auc:0.96806 validation-aucpr:0.97142
[4] validation-logloss:0.56524 validation-auc:0.96855 validation-aucpr:0.97155
[5] validation-logloss:0.54712 validation-auc:0.96722 validation-aucpr:0.97034
[6] validation-logloss:0.52758 validation-auc:0.96724 validation-aucpr:0.97028
[7] validation-logloss:0.51174 validation-auc:0.96695 validation-aucpr:0.97024
[8] validation-logloss:0.49663 validation-auc:0.96697 validation-aucpr:0.97022
[9] validation-logloss:0.48034 validation-auc:0.96761 validation-aucpr:0.97080
[10] validation-logloss:0.46539 validation-auc:0.96827 validation-aucpr:0.97328
[11] validation-logloss:0.45201 validation-auc:0.96816 validation-aucpr:0.97317
[12] validation-logloss:0.44130 validation-auc:0.96763 validation-aucpr:0.97282
[13] validation-logloss:0.43029 validation-auc:0.96772 validation-aucpr:0.97293
[14] validation-logloss:0.41830 validation-auc:0.96798 validation-aucpr:0.97316
[15] validation-logloss:0.40696 validation-auc:0.96795 validation-aucpr:0.97319
[16] validation-logloss:0.39594 validation-auc:0.96827 validation-aucpr:0.97349
[17] validation-logloss:0.38568 validation-auc:0.96855 validation-aucpr:0.97369
[18] validation-logloss:0.37765 validation-auc:0.96838 validation-aucpr:0.97354
[19] validation-logloss:0.36979 validation-auc:0.96831 validation-aucpr:0.97342
[20] validation-logloss:0.36112 validation-auc:0.96860 validation-aucpr:0.97371
[21] validation-logloss:0.35269 validation-auc:0.96885 validation-aucpr:0.97391
[22] validation-logloss:0.34596 validation-auc:0.96916 validation-aucpr:0.97405
[23] validation-logloss:0.33948 validation-auc:0.96905 validation-aucpr:0.97395
[24] validation-logloss:0.33326 validation-auc:0.96915 validation-aucpr:0.97399
[25] validation-logloss:0.32740 validation-auc:0.96925 validation-aucpr:0.97408
[26] validation-logloss:0.32057 validation-auc:0.96944 validation-aucpr:0.97424
[27] validation-logloss:0.31421 validation-auc:0.96974 validation-aucpr:0.97449
[28] validation-logloss:0.30820 validation-auc:0.96998 validation-aucpr:0.97469
[29] validation-logloss:0.30345 validation-auc:0.96997 validation-aucpr:0.97467
[30] validation-logloss:0.29787 validation-auc:0.97016 validation-aucpr:0.97483
[31] validation-logloss:0.29386 validation-auc:0.97006 validation-aucpr:0.97475
[32] validation-logloss:0.28948 validation-auc:0.97013 validation-aucpr:0.97477
[33] validation-logloss:0.28566 validation-auc:0.97003 validation-aucpr:0.97472
[34] validation-logloss:0.28105 validation-auc:0.97017 validation-aucpr:0.97487
[35] validation-logloss:0.27677 validation-auc:0.97017 validation-aucpr:0.97490
[36] validation-logloss:0.27360 validation-auc:0.97021 validation-aucpr:0.97491
[37] validation-logloss:0.26931 validation-auc:0.97039 validation-aucpr:0.97507
[38] validation-logloss:0.26628 validation-auc:0.97035 validation-aucpr:0.97503
[39] validation-logloss:0.26254 validation-auc:0.97047 validation-aucpr:0.97514
[40] validation-logloss:0.25897 validation-auc:0.97060 validation-aucpr:0.97527
[41] validation-logloss:0.25562 validation-auc:0.97071 validation-aucpr:0.97535
[42] validation-logloss:0.25217 validation-auc:0.97101 validation-aucpr:0.97560
[43] validation-logloss:0.24992 validation-auc:0.97092 validation-aucpr:0.97553
[44] validation-logloss:0.24738 validation-auc:0.97106 validation-aucpr:0.97563
[45] validation-logloss:0.24446 validation-auc:0.97116 validation-aucpr:0.97570
[46] validation-logloss:0.24213 validation-auc:0.97136 validation-aucpr:0.97582
[47] validation-logloss:0.24013 validation-auc:0.97134 validation-aucpr:0.97580
[48] validation-logloss:0.23765 validation-auc:0.97145 validation-aucpr:0.97591
[49] validation-logloss:0.23501 validation-auc:0.97159 validation-aucpr:0.97603
[50] validation-logloss:0.23267 validation-auc:0.97172 validation-aucpr:0.97612
[51] validation-logloss:0.23050 validation-auc:0.97179 validation-aucpr:0.97620
[52] validation-logloss:0.22872 validation-auc:0.97171 validation-aucpr:0.97613
[53] validation-logloss:0.22685 validation-auc:0.97177 validation-aucpr:0.97617
[54] validation-logloss:0.22477 validation-auc:0.97198 validation-aucpr:0.97634
[55] validation-logloss:0.22336 validation-auc:0.97198 validation-aucpr:0.97634
[56] validation-logloss:0.22201 validation-auc:0.97196 validation-aucpr:0.97630
[57] validation-logloss:0.22000 validation-auc:0.97212 validation-aucpr:0.97643
[58] validation-logloss:0.21838 validation-auc:0.97215 validation-aucpr:0.97647
[59] validation-logloss:0.21718 validation-auc:0.97207 validation-aucpr:0.97642
[60] validation-logloss:0.21548 validation-auc:0.97218 validation-aucpr:0.97651
{'best_iteration': '60', 'best_score': '0.9765077996628351'}
Trial 66, Fold 4: Log loss = 0.2154846902786677, Average precision = 0.976506590273033, ROC-AUC = 0.9721760242828291, Elapsed Time = 2.0137092000004486 seconds
Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 66, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66316 validation-auc:0.95118 validation-aucpr:0.94310
[1] validation-logloss:0.63899 validation-auc:0.95792 validation-aucpr:0.96211
[2] validation-logloss:0.61668 validation-auc:0.95951 validation-aucpr:0.96374
[3] validation-logloss:0.59250 validation-auc:0.96209 validation-aucpr:0.96623
[4] validation-logloss:0.57003 validation-auc:0.96449 validation-aucpr:0.96846
[5] validation-logloss:0.54925 validation-auc:0.96476 validation-aucpr:0.96865
[6] validation-logloss:0.53042 validation-auc:0.96586 validation-aucpr:0.97049
[7] validation-logloss:0.51210 validation-auc:0.96628 validation-aucpr:0.97083
[8] validation-logloss:0.49565 validation-auc:0.96602 validation-aucpr:0.97063
[9] validation-logloss:0.47992 validation-auc:0.96680 validation-aucpr:0.97132
[10] validation-logloss:0.46640 validation-auc:0.96639 validation-aucpr:0.97103
[11] validation-logloss:0.45441 validation-auc:0.96608 validation-aucpr:0.97075
[12] validation-logloss:0.44309 validation-auc:0.96611 validation-aucpr:0.97069
[13] validation-logloss:0.43034 validation-auc:0.96671 validation-aucpr:0.97112
[14] validation-logloss:0.41829 validation-auc:0.96729 validation-aucpr:0.97191
[15] validation-logloss:0.40694 validation-auc:0.96763 validation-aucpr:0.97218
[16] validation-logloss:0.39647 validation-auc:0.96780 validation-aucpr:0.97229
[17] validation-logloss:0.38670 validation-auc:0.96783 validation-aucpr:0.97229
[18] validation-logloss:0.37724 validation-auc:0.96811 validation-aucpr:0.97254
[19] validation-logloss:0.36811 validation-auc:0.96864 validation-aucpr:0.97291
[20] validation-logloss:0.36023 validation-auc:0.96869 validation-aucpr:0.97294
[21] validation-logloss:0.35236 validation-auc:0.96889 validation-aucpr:0.97310
[22] validation-logloss:0.34451 validation-auc:0.96918 validation-aucpr:0.97336
[23] validation-logloss:0.33746 validation-auc:0.96914 validation-aucpr:0.97339
[24] validation-logloss:0.33179 validation-auc:0.96897 validation-aucpr:0.97323
[25] validation-logloss:0.32547 validation-auc:0.96898 validation-aucpr:0.97326
[26] validation-logloss:0.31902 validation-auc:0.96932 validation-aucpr:0.97353
[27] validation-logloss:0.31315 validation-auc:0.96945 validation-aucpr:0.97364
[28] validation-logloss:0.30807 validation-auc:0.96951 validation-aucpr:0.97370
[29] validation-logloss:0.30356 validation-auc:0.96954 validation-aucpr:0.97370
[30] validation-logloss:0.29927 validation-auc:0.96954 validation-aucpr:0.97364
[31] validation-logloss:0.29424 validation-auc:0.96972 validation-aucpr:0.97378
[32] validation-logloss:0.28943 validation-auc:0.96983 validation-aucpr:0.97386
[33] validation-logloss:0.28500 validation-auc:0.96985 validation-aucpr:0.97386
[34] validation-logloss:0.28153 validation-auc:0.96985 validation-aucpr:0.97390
[35] validation-logloss:0.27722 validation-auc:0.97009 validation-aucpr:0.97409
[36] validation-logloss:0.27395 validation-auc:0.97011 validation-aucpr:0.97409
[37] validation-logloss:0.27092 validation-auc:0.96996 validation-aucpr:0.97398
[38] validation-logloss:0.26802 validation-auc:0.96985 validation-aucpr:0.97389
[39] validation-logloss:0.26507 validation-auc:0.96998 validation-aucpr:0.97401
[40] validation-logloss:0.26236 validation-auc:0.96993 validation-aucpr:0.97395
[41] validation-logloss:0.25912 validation-auc:0.97007 validation-aucpr:0.97408
[42] validation-logloss:0.25610 validation-auc:0.97014 validation-aucpr:0.97417
[43] validation-logloss:0.25363 validation-auc:0.97022 validation-aucpr:0.97424
[44] validation-logloss:0.25085 validation-auc:0.97026 validation-aucpr:0.97428
[45] validation-logloss:0.24862 validation-auc:0.97033 validation-aucpr:0.97432
[46] validation-logloss:0.24588 validation-auc:0.97041 validation-aucpr:0.97437
[47] validation-logloss:0.24375 validation-auc:0.97040 validation-aucpr:0.97437
[48] validation-logloss:0.24167 validation-auc:0.97055 validation-aucpr:0.97445
[49] validation-logloss:0.23955 validation-auc:0.97064 validation-aucpr:0.97450
[50] validation-logloss:0.23784 validation-auc:0.97065 validation-aucpr:0.97449
[51] validation-logloss:0.23618 validation-auc:0.97057 validation-aucpr:0.97442
[52] validation-logloss:0.23442 validation-auc:0.97059 validation-aucpr:0.97444
[53] validation-logloss:0.23224 validation-auc:0.97081 validation-aucpr:0.97461
[54] validation-logloss:0.23025 validation-auc:0.97094 validation-aucpr:0.97474
[55] validation-logloss:0.22894 validation-auc:0.97090 validation-aucpr:0.97469
[56] validation-logloss:0.22710 validation-auc:0.97105 validation-aucpr:0.97482
[57] validation-logloss:0.22557 validation-auc:0.97108 validation-aucpr:0.97484
[58] validation-logloss:0.22391 validation-auc:0.97117 validation-aucpr:0.97493
[59] validation-logloss:0.22217 validation-auc:0.97139 validation-aucpr:0.97511
[60] validation-logloss:0.22073 validation-auc:0.97145 validation-aucpr:0.97512
{'best_iteration': '60', 'best_score': '0.975122968769573'}
Trial 66, Fold 5: Log loss = 0.2207293657428227, Average precision = 0.975127238399295, ROC-AUC = 0.9714478197053305, Elapsed Time = 2.1293003000027966 seconds
Optimization Progress: 67%|######7 | 67/100 [3:02:22<19:51, 36.11s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 67, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67790 validation-auc:0.95553 validation-aucpr:0.96092
[1] validation-logloss:0.66503 validation-auc:0.95859 validation-aucpr:0.96439
[2] validation-logloss:0.65245 validation-auc:0.95961 validation-aucpr:0.96443
[3] validation-logloss:0.64023 validation-auc:0.96095 validation-aucpr:0.96598
[4] validation-logloss:0.62862 validation-auc:0.96154 validation-aucpr:0.96621
[5] validation-logloss:0.61621 validation-auc:0.96280 validation-aucpr:0.96709
[6] validation-logloss:0.60377 validation-auc:0.96375 validation-aucpr:0.96800
[7] validation-logloss:0.59216 validation-auc:0.96451 validation-aucpr:0.96998
[8] validation-logloss:0.58076 validation-auc:0.96534 validation-aucpr:0.97092
[9] validation-logloss:0.57128 validation-auc:0.96557 validation-aucpr:0.97103
[10] validation-logloss:0.56224 validation-auc:0.96568 validation-aucpr:0.97098
[11] validation-logloss:0.55305 validation-auc:0.96598 validation-aucpr:0.97114
[12] validation-logloss:0.54469 validation-auc:0.96576 validation-aucpr:0.97092
[13] validation-logloss:0.53612 validation-auc:0.96562 validation-aucpr:0.97079
[14] validation-logloss:0.52686 validation-auc:0.96552 validation-aucpr:0.97076
[15] validation-logloss:0.51927 validation-auc:0.96533 validation-aucpr:0.97058
[16] validation-logloss:0.51172 validation-auc:0.96523 validation-aucpr:0.97046
[17] validation-logloss:0.50450 validation-auc:0.96513 validation-aucpr:0.97036
[18] validation-logloss:0.49774 validation-auc:0.96502 validation-aucpr:0.97035
[19] validation-logloss:0.49092 validation-auc:0.96510 validation-aucpr:0.97040
[20] validation-logloss:0.48288 validation-auc:0.96534 validation-aucpr:0.97064
[21] validation-logloss:0.47516 validation-auc:0.96564 validation-aucpr:0.97093
[22] validation-logloss:0.46888 validation-auc:0.96566 validation-aucpr:0.97089
[23] validation-logloss:0.46154 validation-auc:0.96584 validation-aucpr:0.97108
[24] validation-logloss:0.45452 validation-auc:0.96614 validation-aucpr:0.97139
[25] validation-logloss:0.44906 validation-auc:0.96585 validation-aucpr:0.97110
[26] validation-logloss:0.44358 validation-auc:0.96596 validation-aucpr:0.97122
[27] validation-logloss:0.43696 validation-auc:0.96620 validation-aucpr:0.97145
[28] validation-logloss:0.43182 validation-auc:0.96607 validation-aucpr:0.97139
[29] validation-logloss:0.42615 validation-auc:0.96607 validation-aucpr:0.97140
[30] validation-logloss:0.42044 validation-auc:0.96625 validation-aucpr:0.97159
[31] validation-logloss:0.41479 validation-auc:0.96627 validation-aucpr:0.97166
[32] validation-logloss:0.41016 validation-auc:0.96623 validation-aucpr:0.97160
[33] validation-logloss:0.40555 validation-auc:0.96626 validation-aucpr:0.97163
[34] validation-logloss:0.40121 validation-auc:0.96630 validation-aucpr:0.97160
[35] validation-logloss:0.39598 validation-auc:0.96644 validation-aucpr:0.97172
[36] validation-logloss:0.39184 validation-auc:0.96645 validation-aucpr:0.97170
[37] validation-logloss:0.38787 validation-auc:0.96649 validation-aucpr:0.97173
[38] validation-logloss:0.38321 validation-auc:0.96645 validation-aucpr:0.97172
[39] validation-logloss:0.37852 validation-auc:0.96658 validation-aucpr:0.97188
[40] validation-logloss:0.37513 validation-auc:0.96652 validation-aucpr:0.97182
[41] validation-logloss:0.37082 validation-auc:0.96662 validation-aucpr:0.97194
[42] validation-logloss:0.36719 validation-auc:0.96670 validation-aucpr:0.97196
[43] validation-logloss:0.36289 validation-auc:0.96675 validation-aucpr:0.97203
[44] validation-logloss:0.35947 validation-auc:0.96676 validation-aucpr:0.97204
[45] validation-logloss:0.35625 validation-auc:0.96676 validation-aucpr:0.97201
[46] validation-logloss:0.35315 validation-auc:0.96670 validation-aucpr:0.97194
[47] validation-logloss:0.35009 validation-auc:0.96671 validation-aucpr:0.97193
[48] validation-logloss:0.34688 validation-auc:0.96674 validation-aucpr:0.97193
[49] validation-logloss:0.34341 validation-auc:0.96678 validation-aucpr:0.97195
[50] validation-logloss:0.33964 validation-auc:0.96697 validation-aucpr:0.97210
[51] validation-logloss:0.33605 validation-auc:0.96706 validation-aucpr:0.97217
[52] validation-logloss:0.33335 validation-auc:0.96703 validation-aucpr:0.97212
[53] validation-logloss:0.33064 validation-auc:0.96701 validation-aucpr:0.97208
[54] validation-logloss:0.32790 validation-auc:0.96704 validation-aucpr:0.97209
[55] validation-logloss:0.32474 validation-auc:0.96719 validation-aucpr:0.97222
[56] validation-logloss:0.32225 validation-auc:0.96722 validation-aucpr:0.97225
[57] validation-logloss:0.31938 validation-auc:0.96723 validation-aucpr:0.97230
[58] validation-logloss:0.31725 validation-auc:0.96717 validation-aucpr:0.97224
[59] validation-logloss:0.31414 validation-auc:0.96730 validation-aucpr:0.97237
[60] validation-logloss:0.31202 validation-auc:0.96727 validation-aucpr:0.97234
{'best_iteration': '59', 'best_score': '0.9723680795391926'}
Trial 67, Fold 1: Log loss = 0.31202037811636174, Average precision = 0.9723428888836552, ROC-AUC = 0.9672663856268192, Elapsed Time = 1.4499145000008866 seconds
Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 67, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67734 validation-auc:0.95774 validation-aucpr:0.96414
[1] validation-logloss:0.66279 validation-auc:0.96283 validation-aucpr:0.96703
[2] validation-logloss:0.64856 validation-auc:0.96545 validation-aucpr:0.96898
[3] validation-logloss:0.63506 validation-auc:0.96584 validation-aucpr:0.96995
[4] validation-logloss:0.62224 validation-auc:0.96586 validation-aucpr:0.97016
[5] validation-logloss:0.60985 validation-auc:0.96734 validation-aucpr:0.97134
[6] validation-logloss:0.59958 validation-auc:0.96733 validation-aucpr:0.97056
[7] validation-logloss:0.58905 validation-auc:0.96671 validation-aucpr:0.97011
[8] validation-logloss:0.57791 validation-auc:0.96692 validation-aucpr:0.97025
[9] validation-logloss:0.56677 validation-auc:0.96753 validation-aucpr:0.97081
[10] validation-logloss:0.55610 validation-auc:0.96803 validation-aucpr:0.97107
[11] validation-logloss:0.54698 validation-auc:0.96814 validation-aucpr:0.97108
[12] validation-logloss:0.53699 validation-auc:0.96855 validation-aucpr:0.97136
[13] validation-logloss:0.52857 validation-auc:0.96860 validation-aucpr:0.97135
[14] validation-logloss:0.51941 validation-auc:0.96860 validation-aucpr:0.97126
[15] validation-logloss:0.51163 validation-auc:0.96850 validation-aucpr:0.97117
[16] validation-logloss:0.50410 validation-auc:0.96829 validation-aucpr:0.97124
[17] validation-logloss:0.49579 validation-auc:0.96849 validation-aucpr:0.97144
[18] validation-logloss:0.48889 validation-auc:0.96848 validation-aucpr:0.97197
[19] validation-logloss:0.48161 validation-auc:0.96861 validation-aucpr:0.97203
[20] validation-logloss:0.47509 validation-auc:0.96866 validation-aucpr:0.97259
[21] validation-logloss:0.46855 validation-auc:0.96868 validation-aucpr:0.97254
[22] validation-logloss:0.46113 validation-auc:0.96894 validation-aucpr:0.97275
[23] validation-logloss:0.45398 validation-auc:0.96917 validation-aucpr:0.97297
[24] validation-logloss:0.44831 validation-auc:0.96901 validation-aucpr:0.97280
[25] validation-logloss:0.44163 validation-auc:0.96901 validation-aucpr:0.97279
[26] validation-logloss:0.43622 validation-auc:0.96897 validation-aucpr:0.97271
[27] validation-logloss:0.42998 validation-auc:0.96917 validation-aucpr:0.97290
[28] validation-logloss:0.42470 validation-auc:0.96900 validation-aucpr:0.97276
[29] validation-logloss:0.41978 validation-auc:0.96896 validation-aucpr:0.97272
[30] validation-logloss:0.41470 validation-auc:0.96896 validation-aucpr:0.97273
[31] validation-logloss:0.41013 validation-auc:0.96889 validation-aucpr:0.97263
[32] validation-logloss:0.40457 validation-auc:0.96902 validation-aucpr:0.97273
[33] validation-logloss:0.40003 validation-auc:0.96898 validation-aucpr:0.97269
[34] validation-logloss:0.39589 validation-auc:0.96886 validation-aucpr:0.97260
[35] validation-logloss:0.39071 validation-auc:0.96891 validation-aucpr:0.97268
[36] validation-logloss:0.38664 validation-auc:0.96900 validation-aucpr:0.97274
[37] validation-logloss:0.38159 validation-auc:0.96917 validation-aucpr:0.97290
[38] validation-logloss:0.37790 validation-auc:0.96917 validation-aucpr:0.97289
[39] validation-logloss:0.37312 validation-auc:0.96923 validation-aucpr:0.97295
[40] validation-logloss:0.36934 validation-auc:0.96918 validation-aucpr:0.97290
[41] validation-logloss:0.36472 validation-auc:0.96938 validation-aucpr:0.97303
[42] validation-logloss:0.36042 validation-auc:0.96945 validation-aucpr:0.97310
[43] validation-logloss:0.35615 validation-auc:0.96955 validation-aucpr:0.97320
[44] validation-logloss:0.35274 validation-auc:0.96951 validation-aucpr:0.97317
[45] validation-logloss:0.34947 validation-auc:0.96949 validation-aucpr:0.97314
[46] validation-logloss:0.34557 validation-auc:0.96947 validation-aucpr:0.97312
[47] validation-logloss:0.34196 validation-auc:0.96943 validation-aucpr:0.97310
[48] validation-logloss:0.33907 validation-auc:0.96936 validation-aucpr:0.97304
[49] validation-logloss:0.33610 validation-auc:0.96940 validation-aucpr:0.97306
[50] validation-logloss:0.33311 validation-auc:0.96936 validation-aucpr:0.97293
[51] validation-logloss:0.32951 validation-auc:0.96943 validation-aucpr:0.97298
[52] validation-logloss:0.32664 validation-auc:0.96945 validation-aucpr:0.97299
[53] validation-logloss:0.32330 validation-auc:0.96953 validation-aucpr:0.97304
[54] validation-logloss:0.32064 validation-auc:0.96953 validation-aucpr:0.97303
[55] validation-logloss:0.31733 validation-auc:0.96968 validation-aucpr:0.97317
[56] validation-logloss:0.31485 validation-auc:0.96972 validation-aucpr:0.97319
[57] validation-logloss:0.31239 validation-auc:0.96975 validation-aucpr:0.97319
[58] validation-logloss:0.30924 validation-auc:0.96986 validation-aucpr:0.97328
[59] validation-logloss:0.30623 validation-auc:0.97004 validation-aucpr:0.97344
[60] validation-logloss:0.30382 validation-auc:0.97008 validation-aucpr:0.97338
{'best_iteration': '59', 'best_score': '0.9734417834944555'}
Trial 67, Fold 2: Log loss = 0.3038166756126164, Average precision = 0.9733877376478585, ROC-AUC = 0.9700820136081343, Elapsed Time = 1.8838468999965698 seconds
Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 67, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67764 validation-auc:0.95744 validation-aucpr:0.96229
[1] validation-logloss:0.66499 validation-auc:0.95839 validation-aucpr:0.96064
[2] validation-logloss:0.65268 validation-auc:0.96027 validation-aucpr:0.96414
[3] validation-logloss:0.64100 validation-auc:0.96113 validation-aucpr:0.96504
[4] validation-logloss:0.62797 validation-auc:0.96349 validation-aucpr:0.96657
[5] validation-logloss:0.61668 validation-auc:0.96525 validation-aucpr:0.97016
[6] validation-logloss:0.60609 validation-auc:0.96553 validation-aucpr:0.97001
[7] validation-logloss:0.59395 validation-auc:0.96700 validation-aucpr:0.97146
[8] validation-logloss:0.58272 validation-auc:0.96729 validation-aucpr:0.97175
[9] validation-logloss:0.57319 validation-auc:0.96732 validation-aucpr:0.97200
[10] validation-logloss:0.56416 validation-auc:0.96685 validation-aucpr:0.97015
[11] validation-logloss:0.55511 validation-auc:0.96694 validation-aucpr:0.97167
[12] validation-logloss:0.54659 validation-auc:0.96710 validation-aucpr:0.97166
[13] validation-logloss:0.53805 validation-auc:0.96719 validation-aucpr:0.97168
[14] validation-logloss:0.53014 validation-auc:0.96702 validation-aucpr:0.97152
[15] validation-logloss:0.52067 validation-auc:0.96741 validation-aucpr:0.97184
[16] validation-logloss:0.51162 validation-auc:0.96777 validation-aucpr:0.97215
[17] validation-logloss:0.50294 validation-auc:0.96806 validation-aucpr:0.97251
[18] validation-logloss:0.49605 validation-auc:0.96803 validation-aucpr:0.97246
[19] validation-logloss:0.48766 validation-auc:0.96815 validation-aucpr:0.97258
[20] validation-logloss:0.48120 validation-auc:0.96814 validation-aucpr:0.97256
[21] validation-logloss:0.47343 validation-auc:0.96849 validation-aucpr:0.97290
[22] validation-logloss:0.46718 validation-auc:0.96837 validation-aucpr:0.97280
[23] validation-logloss:0.45972 validation-auc:0.96865 validation-aucpr:0.97306
[24] validation-logloss:0.45383 validation-auc:0.96867 validation-aucpr:0.97306
[25] validation-logloss:0.44694 validation-auc:0.96883 validation-aucpr:0.97320
[26] validation-logloss:0.44121 validation-auc:0.96888 validation-aucpr:0.97323
[27] validation-logloss:0.43553 validation-auc:0.96900 validation-aucpr:0.97331
[28] validation-logloss:0.43031 validation-auc:0.96897 validation-aucpr:0.97327
[29] validation-logloss:0.42440 validation-auc:0.96906 validation-aucpr:0.97334
[30] validation-logloss:0.41843 validation-auc:0.96913 validation-aucpr:0.97348
[31] validation-logloss:0.41363 validation-auc:0.96922 validation-aucpr:0.97354
[32] validation-logloss:0.40916 validation-auc:0.96911 validation-aucpr:0.97344
[33] validation-logloss:0.40425 validation-auc:0.96913 validation-aucpr:0.97342
[34] validation-logloss:0.39970 validation-auc:0.96922 validation-aucpr:0.97349
[35] validation-logloss:0.39436 validation-auc:0.96935 validation-aucpr:0.97361
[36] validation-logloss:0.38913 validation-auc:0.96945 validation-aucpr:0.97370
[37] validation-logloss:0.38415 validation-auc:0.96948 validation-aucpr:0.97374
[38] validation-logloss:0.37917 validation-auc:0.96959 validation-aucpr:0.97384
[39] validation-logloss:0.37446 validation-auc:0.96965 validation-aucpr:0.97388
[40] validation-logloss:0.37067 validation-auc:0.96967 validation-aucpr:0.97389
[41] validation-logloss:0.36608 validation-auc:0.96976 validation-aucpr:0.97396
[42] validation-logloss:0.36261 validation-auc:0.96981 validation-aucpr:0.97399
[43] validation-logloss:0.35890 validation-auc:0.96989 validation-aucpr:0.97404
[44] validation-logloss:0.35529 validation-auc:0.96999 validation-aucpr:0.97416
[45] validation-logloss:0.35196 validation-auc:0.97001 validation-aucpr:0.97416
[46] validation-logloss:0.34789 validation-auc:0.97008 validation-aucpr:0.97422
[47] validation-logloss:0.34463 validation-auc:0.97016 validation-aucpr:0.97429
[48] validation-logloss:0.34173 validation-auc:0.97008 validation-aucpr:0.97422
[49] validation-logloss:0.33805 validation-auc:0.97012 validation-aucpr:0.97431
[50] validation-logloss:0.33487 validation-auc:0.97015 validation-aucpr:0.97433
[51] validation-logloss:0.33196 validation-auc:0.97011 validation-aucpr:0.97429
[52] validation-logloss:0.32836 validation-auc:0.97016 validation-aucpr:0.97433
[53] validation-logloss:0.32548 validation-auc:0.97023 validation-aucpr:0.97436
[54] validation-logloss:0.32207 validation-auc:0.97026 validation-aucpr:0.97442
[55] validation-logloss:0.31958 validation-auc:0.97023 validation-aucpr:0.97439
[56] validation-logloss:0.31726 validation-auc:0.97017 validation-aucpr:0.97432
[57] validation-logloss:0.31405 validation-auc:0.97024 validation-aucpr:0.97438
[58] validation-logloss:0.31093 validation-auc:0.97031 validation-aucpr:0.97444
[59] validation-logloss:0.30784 validation-auc:0.97040 validation-aucpr:0.97453
[60] validation-logloss:0.30496 validation-auc:0.97042 validation-aucpr:0.97456
{'best_iteration': '60', 'best_score': '0.9745641931920642'}
Trial 67, Fold 3: Log loss = 0.30495799683777036, Average precision = 0.9745684705321301, ROC-AUC = 0.9704159433358053, Elapsed Time = 1.829988699999376 seconds
Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 67, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67757 validation-auc:0.95496 validation-aucpr:0.95939
[1] validation-logloss:0.66306 validation-auc:0.96062 validation-aucpr:0.96533
[2] validation-logloss:0.64938 validation-auc:0.96258 validation-aucpr:0.96685
[3] validation-logloss:0.63606 validation-auc:0.96429 validation-aucpr:0.97027
[4] validation-logloss:0.62445 validation-auc:0.96421 validation-aucpr:0.97008
[5] validation-logloss:0.61199 validation-auc:0.96530 validation-aucpr:0.97089
[6] validation-logloss:0.59982 validation-auc:0.96530 validation-aucpr:0.97093
[7] validation-logloss:0.58815 validation-auc:0.96611 validation-aucpr:0.97158
[8] validation-logloss:0.57673 validation-auc:0.96724 validation-aucpr:0.97248
[9] validation-logloss:0.56693 validation-auc:0.96727 validation-aucpr:0.97250
[10] validation-logloss:0.55785 validation-auc:0.96723 validation-aucpr:0.97251
[11] validation-logloss:0.54906 validation-auc:0.96683 validation-aucpr:0.97215
[12] validation-logloss:0.54060 validation-auc:0.96679 validation-aucpr:0.97209
[13] validation-logloss:0.53118 validation-auc:0.96685 validation-aucpr:0.97219
[14] validation-logloss:0.52181 validation-auc:0.96703 validation-aucpr:0.97238
[15] validation-logloss:0.51307 validation-auc:0.96710 validation-aucpr:0.97247
[16] validation-logloss:0.50531 validation-auc:0.96741 validation-aucpr:0.97275
[17] validation-logloss:0.49807 validation-auc:0.96748 validation-aucpr:0.97276
[18] validation-logloss:0.49097 validation-auc:0.96742 validation-aucpr:0.97270
[19] validation-logloss:0.48416 validation-auc:0.96731 validation-aucpr:0.97258
[20] validation-logloss:0.47726 validation-auc:0.96727 validation-aucpr:0.97251
[21] validation-logloss:0.46976 validation-auc:0.96736 validation-aucpr:0.97263
[22] validation-logloss:0.46357 validation-auc:0.96742 validation-aucpr:0.97261
[23] validation-logloss:0.45741 validation-auc:0.96732 validation-aucpr:0.97255
[24] validation-logloss:0.45181 validation-auc:0.96703 validation-aucpr:0.97232
[25] validation-logloss:0.44489 validation-auc:0.96717 validation-aucpr:0.97246
[26] validation-logloss:0.43840 validation-auc:0.96719 validation-aucpr:0.97250
[27] validation-logloss:0.43204 validation-auc:0.96736 validation-aucpr:0.97262
[28] validation-logloss:0.42684 validation-auc:0.96730 validation-aucpr:0.97254
[29] validation-logloss:0.42179 validation-auc:0.96730 validation-aucpr:0.97252
[30] validation-logloss:0.41675 validation-auc:0.96745 validation-aucpr:0.97261
[31] validation-logloss:0.41211 validation-auc:0.96735 validation-aucpr:0.97249
[32] validation-logloss:0.40740 validation-auc:0.96732 validation-aucpr:0.97244
[33] validation-logloss:0.40193 validation-auc:0.96746 validation-aucpr:0.97258
[34] validation-logloss:0.39748 validation-auc:0.96753 validation-aucpr:0.97266
[35] validation-logloss:0.39316 validation-auc:0.96753 validation-aucpr:0.97263
[36] validation-logloss:0.38915 validation-auc:0.96753 validation-aucpr:0.97259
[37] validation-logloss:0.38435 validation-auc:0.96756 validation-aucpr:0.97267
[38] validation-logloss:0.38037 validation-auc:0.96761 validation-aucpr:0.97273
[39] validation-logloss:0.37568 validation-auc:0.96766 validation-aucpr:0.97280
[40] validation-logloss:0.37201 validation-auc:0.96757 validation-aucpr:0.97274
[41] validation-logloss:0.36832 validation-auc:0.96758 validation-aucpr:0.97271
[42] validation-logloss:0.36451 validation-auc:0.96752 validation-aucpr:0.97271
[43] validation-logloss:0.36109 validation-auc:0.96754 validation-aucpr:0.97274
[44] validation-logloss:0.35773 validation-auc:0.96754 validation-aucpr:0.97272
[45] validation-logloss:0.35445 validation-auc:0.96762 validation-aucpr:0.97276
[46] validation-logloss:0.35144 validation-auc:0.96757 validation-aucpr:0.97271
[47] validation-logloss:0.34826 validation-auc:0.96758 validation-aucpr:0.97270
[48] validation-logloss:0.34499 validation-auc:0.96767 validation-aucpr:0.97276
[49] validation-logloss:0.34107 validation-auc:0.96777 validation-aucpr:0.97288
[50] validation-logloss:0.33840 validation-auc:0.96768 validation-aucpr:0.97281
[51] validation-logloss:0.33481 validation-auc:0.96775 validation-aucpr:0.97289
[52] validation-logloss:0.33145 validation-auc:0.96776 validation-aucpr:0.97293
[53] validation-logloss:0.32786 validation-auc:0.96790 validation-aucpr:0.97306
[54] validation-logloss:0.32531 validation-auc:0.96790 validation-aucpr:0.97305
[55] validation-logloss:0.32291 validation-auc:0.96787 validation-aucpr:0.97303
[56] validation-logloss:0.31963 validation-auc:0.96790 validation-aucpr:0.97309
[57] validation-logloss:0.31729 validation-auc:0.96789 validation-aucpr:0.97306
[58] validation-logloss:0.31484 validation-auc:0.96793 validation-aucpr:0.97308
[59] validation-logloss:0.31261 validation-auc:0.96791 validation-aucpr:0.97305
[60] validation-logloss:0.30951 validation-auc:0.96800 validation-aucpr:0.97316
{'best_iteration': '60', 'best_score': '0.973162413525967'}
Trial 67, Fold 4: Log loss = 0.3095125853597949, Average precision = 0.9731652736050046, ROC-AUC = 0.9680012953306103, Elapsed Time = 1.7693674999973155 seconds
Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 67, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67778 validation-auc:0.95233 validation-aucpr:0.94662
[1] validation-logloss:0.66519 validation-auc:0.95641 validation-aucpr:0.95980
[2] validation-logloss:0.65176 validation-auc:0.95921 validation-aucpr:0.96469
[3] validation-logloss:0.63841 validation-auc:0.96148 validation-aucpr:0.96672
[4] validation-logloss:0.62749 validation-auc:0.96130 validation-aucpr:0.96454
[5] validation-logloss:0.61654 validation-auc:0.96128 validation-aucpr:0.96399
[6] validation-logloss:0.60449 validation-auc:0.96262 validation-aucpr:0.96727
[7] validation-logloss:0.59433 validation-auc:0.96241 validation-aucpr:0.96695
[8] validation-logloss:0.58470 validation-auc:0.96183 validation-aucpr:0.96658
[9] validation-logloss:0.57403 validation-auc:0.96261 validation-aucpr:0.96779
[10] validation-logloss:0.56505 validation-auc:0.96257 validation-aucpr:0.96766
[11] validation-logloss:0.55493 validation-auc:0.96299 validation-aucpr:0.96804
[12] validation-logloss:0.54536 validation-auc:0.96287 validation-aucpr:0.96811
[13] validation-logloss:0.53602 validation-auc:0.96317 validation-aucpr:0.96838
[14] validation-logloss:0.52718 validation-auc:0.96346 validation-aucpr:0.96848
[15] validation-logloss:0.51805 validation-auc:0.96377 validation-aucpr:0.96878
[16] validation-logloss:0.50944 validation-auc:0.96411 validation-aucpr:0.96910
[17] validation-logloss:0.50215 validation-auc:0.96407 validation-aucpr:0.96905
[18] validation-logloss:0.49389 validation-auc:0.96439 validation-aucpr:0.96929
[19] validation-logloss:0.48625 validation-auc:0.96439 validation-aucpr:0.96932
[20] validation-logloss:0.47948 validation-auc:0.96439 validation-aucpr:0.96934
[21] validation-logloss:0.47332 validation-auc:0.96432 validation-aucpr:0.96932
[22] validation-logloss:0.46714 validation-auc:0.96431 validation-aucpr:0.96932
[23] validation-logloss:0.46117 validation-auc:0.96429 validation-aucpr:0.96926
[24] validation-logloss:0.45547 validation-auc:0.96415 validation-aucpr:0.96903
[25] validation-logloss:0.44895 validation-auc:0.96429 validation-aucpr:0.96922
[26] validation-logloss:0.44355 validation-auc:0.96422 validation-aucpr:0.96909
[27] validation-logloss:0.43807 validation-auc:0.96430 validation-aucpr:0.96919
[28] validation-logloss:0.43289 validation-auc:0.96442 validation-aucpr:0.96933
[29] validation-logloss:0.42702 validation-auc:0.96449 validation-aucpr:0.96955
[30] validation-logloss:0.42219 validation-auc:0.96460 validation-aucpr:0.96958
[31] validation-logloss:0.41743 validation-auc:0.96464 validation-aucpr:0.96958
[32] validation-logloss:0.41158 validation-auc:0.96483 validation-aucpr:0.96975
[33] validation-logloss:0.40693 validation-auc:0.96488 validation-aucpr:0.96979
[34] validation-logloss:0.40250 validation-auc:0.96490 validation-aucpr:0.96977
[35] validation-logloss:0.39846 validation-auc:0.96490 validation-aucpr:0.96976
[36] validation-logloss:0.39429 validation-auc:0.96495 validation-aucpr:0.96975
[37] validation-logloss:0.39031 validation-auc:0.96499 validation-aucpr:0.96978
[38] validation-logloss:0.38533 validation-auc:0.96517 validation-aucpr:0.96995
[39] validation-logloss:0.38162 validation-auc:0.96511 validation-aucpr:0.96990
[40] validation-logloss:0.37792 validation-auc:0.96504 validation-aucpr:0.96985
[41] validation-logloss:0.37434 validation-auc:0.96505 validation-aucpr:0.96983
[42] validation-logloss:0.36979 validation-auc:0.96519 validation-aucpr:0.96996
[43] validation-logloss:0.36572 validation-auc:0.96532 validation-aucpr:0.97006
[44] validation-logloss:0.36266 validation-auc:0.96526 validation-aucpr:0.97001
[45] validation-logloss:0.35940 validation-auc:0.96528 validation-aucpr:0.97003
[46] validation-logloss:0.35533 validation-auc:0.96547 validation-aucpr:0.97019
[47] validation-logloss:0.35212 validation-auc:0.96552 validation-aucpr:0.97020
[48] validation-logloss:0.34907 validation-auc:0.96555 validation-aucpr:0.97021
[49] validation-logloss:0.34531 validation-auc:0.96568 validation-aucpr:0.97031
[50] validation-logloss:0.34247 validation-auc:0.96561 validation-aucpr:0.97027
[51] validation-logloss:0.33961 validation-auc:0.96564 validation-aucpr:0.97027
[52] validation-logloss:0.33617 validation-auc:0.96572 validation-aucpr:0.97039
[53] validation-logloss:0.33334 validation-auc:0.96578 validation-aucpr:0.97044
[54] validation-logloss:0.33000 validation-auc:0.96594 validation-aucpr:0.97056
[55] validation-logloss:0.32669 validation-auc:0.96616 validation-aucpr:0.97075
[56] validation-logloss:0.32369 validation-auc:0.96624 validation-aucpr:0.97087
[57] validation-logloss:0.32123 validation-auc:0.96626 validation-aucpr:0.97088
[58] validation-logloss:0.31879 validation-auc:0.96631 validation-aucpr:0.97091
[59] validation-logloss:0.31655 validation-auc:0.96631 validation-aucpr:0.97093
[60] validation-logloss:0.31453 validation-auc:0.96627 validation-aucpr:0.97087
{'best_iteration': '59', 'best_score': '0.9709289988232364'}
Trial 67, Fold 5: Log loss = 0.31453136502821993, Average precision = 0.9708783903919904, ROC-AUC = 0.9662654998792338, Elapsed Time = 1.8825285999992047 seconds
Optimization Progress: 68%|######8 | 68/100 [3:02:39<16:08, 30.27s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 68, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68643 validation-auc:0.93634 validation-aucpr:0.92382
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67955 validation-auc:0.95075 validation-aucpr:0.95354
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67197 validation-auc:0.96136 validation-aucpr:0.96432
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66458 validation-auc:0.96420 validation-aucpr:0.96792
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65817 validation-auc:0.96400 validation-aucpr:0.96774
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65122 validation-auc:0.96518 validation-aucpr:0.96927
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64440 validation-auc:0.96529 validation-aucpr:0.96981
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63837 validation-auc:0.96549 validation-aucpr:0.97009
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63246 validation-auc:0.96565 validation-aucpr:0.97025
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62587 validation-auc:0.96619 validation-aucpr:0.97074
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62027 validation-auc:0.96605 validation-aucpr:0.97063
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61491 validation-auc:0.96615 validation-aucpr:0.97098
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60966 validation-auc:0.96584 validation-aucpr:0.97063
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60360 validation-auc:0.96613 validation-aucpr:0.97083
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59817 validation-auc:0.96625 validation-aucpr:0.97099
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59299 validation-auc:0.96619 validation-aucpr:0.97126
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58802 validation-auc:0.96595 validation-aucpr:0.97105
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58288 validation-auc:0.96607 validation-aucpr:0.97115
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57784 validation-auc:0.96617 validation-aucpr:0.97119
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57238 validation-auc:0.96641 validation-aucpr:0.97141
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56763 validation-auc:0.96634 validation-aucpr:0.97130
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56304 validation-auc:0.96638 validation-aucpr:0.97128
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55770 validation-auc:0.96661 validation-aucpr:0.97156
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55317 validation-auc:0.96658 validation-aucpr:0.97154
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54886 validation-auc:0.96654 validation-aucpr:0.97156
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54391 validation-auc:0.96670 validation-aucpr:0.97173
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53888 validation-auc:0.96695 validation-aucpr:0.97197
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53436 validation-auc:0.96701 validation-aucpr:0.97205
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52955 validation-auc:0.96725 validation-aucpr:0.97229
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52496 validation-auc:0.96734 validation-aucpr:0.97234
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52040 validation-auc:0.96734 validation-aucpr:0.97237
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51643 validation-auc:0.96727 validation-aucpr:0.97229
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51259 validation-auc:0.96725 validation-aucpr:0.97225
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50820 validation-auc:0.96743 validation-aucpr:0.97239
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50399 validation-auc:0.96754 validation-aucpr:0.97247
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49977 validation-auc:0.96767 validation-aucpr:0.97259
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49556 validation-auc:0.96775 validation-aucpr:0.97268
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49180 validation-auc:0.96770 validation-aucpr:0.97265
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48782 validation-auc:0.96772 validation-aucpr:0.97265
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48388 validation-auc:0.96779 validation-aucpr:0.97271
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48042 validation-auc:0.96783 validation-aucpr:0.97271
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47707 validation-auc:0.96787 validation-aucpr:0.97272
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47352 validation-auc:0.96792 validation-aucpr:0.97275
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46981 validation-auc:0.96787 validation-aucpr:0.97274
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46617 validation-auc:0.96789 validation-aucpr:0.97276
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46295 validation-auc:0.96792 validation-aucpr:0.97277
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.45980 validation-auc:0.96796 validation-aucpr:0.97278
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45621 validation-auc:0.96800 validation-aucpr:0.97283
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45315 validation-auc:0.96802 validation-aucpr:0.97284
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45015 validation-auc:0.96798 validation-aucpr:0.97279
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44729 validation-auc:0.96787 validation-aucpr:0.97269
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44443 validation-auc:0.96776 validation-aucpr:0.97260
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44106 validation-auc:0.96792 validation-aucpr:0.97274
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.43840 validation-auc:0.96791 validation-aucpr:0.97279
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43565 validation-auc:0.96788 validation-aucpr:0.97274
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43241 validation-auc:0.96791 validation-aucpr:0.97278
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.42971 validation-auc:0.96788 validation-aucpr:0.97275
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42714 validation-auc:0.96782 validation-aucpr:0.97268
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42414 validation-auc:0.96789 validation-aucpr:0.97274
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42154 validation-auc:0.96791 validation-aucpr:0.97277
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.41885 validation-auc:0.96793 validation-aucpr:0.97280
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41589 validation-auc:0.96797 validation-aucpr:0.97284
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41363 validation-auc:0.96792 validation-aucpr:0.97279
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41117 validation-auc:0.96795 validation-aucpr:0.97280
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40885 validation-auc:0.96794 validation-aucpr:0.97276
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40655 validation-auc:0.96788 validation-aucpr:0.97271
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40418 validation-auc:0.96789 validation-aucpr:0.97272
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40192 validation-auc:0.96789 validation-aucpr:0.97271
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.39976 validation-auc:0.96786 validation-aucpr:0.97267
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39712 validation-auc:0.96792 validation-aucpr:0.97274
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39457 validation-auc:0.96789 validation-aucpr:0.97274
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39198 validation-auc:0.96798 validation-aucpr:0.97281
[21:01:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.38990 validation-auc:0.96793 validation-aucpr:0.97275
[21:01:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38772 validation-auc:0.96794 validation-aucpr:0.97276
[21:01:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38536 validation-auc:0.96799 validation-aucpr:0.97281
[21:01:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38328 validation-auc:0.96796 validation-aucpr:0.97278
[21:01:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38117 validation-auc:0.96800 validation-aucpr:0.97281
[21:01:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37916 validation-auc:0.96797 validation-aucpr:0.97278
[21:01:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37723 validation-auc:0.96795 validation-aucpr:0.97276
[21:01:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37531 validation-auc:0.96796 validation-aucpr:0.97276
{'best_iteration': '61', 'best_score': '0.9728415399042182'}
Trial 68, Fold 1: Log loss = 0.375314446571651, Average precision = 0.9727612448617955, ROC-AUC = 0.96795927670476, Elapsed Time = 21.340347599998495 seconds
Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 68, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68585 validation-auc:0.94018 validation-aucpr:0.92886
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67924 validation-auc:0.95496 validation-aucpr:0.95415
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67287 validation-auc:0.95648 validation-aucpr:0.95858
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66572 validation-auc:0.96356 validation-aucpr:0.96661
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65871 validation-auc:0.96475 validation-aucpr:0.96835
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65247 validation-auc:0.96501 validation-aucpr:0.96876
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64641 validation-auc:0.96517 validation-aucpr:0.96893
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64049 validation-auc:0.96497 validation-aucpr:0.96863
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63470 validation-auc:0.96491 validation-aucpr:0.96839
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62821 validation-auc:0.96618 validation-aucpr:0.96964
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62270 validation-auc:0.96555 validation-aucpr:0.96901
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61712 validation-auc:0.96548 validation-aucpr:0.96892
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61173 validation-auc:0.96526 validation-aucpr:0.96870
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60566 validation-auc:0.96592 validation-aucpr:0.96904
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.60029 validation-auc:0.96619 validation-aucpr:0.96922
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59502 validation-auc:0.96631 validation-aucpr:0.96930
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58994 validation-auc:0.96609 validation-aucpr:0.96899
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58488 validation-auc:0.96626 validation-aucpr:0.96906
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.58003 validation-auc:0.96635 validation-aucpr:0.96903
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57499 validation-auc:0.96649 validation-aucpr:0.96910
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56932 validation-auc:0.96739 validation-aucpr:0.97000
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56390 validation-auc:0.96790 validation-aucpr:0.97093
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55941 validation-auc:0.96782 validation-aucpr:0.97080
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55421 validation-auc:0.96820 validation-aucpr:0.97119
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54981 validation-auc:0.96805 validation-aucpr:0.97106
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54544 validation-auc:0.96805 validation-aucpr:0.97101
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54035 validation-auc:0.96839 validation-aucpr:0.97166
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53552 validation-auc:0.96857 validation-aucpr:0.97187
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53068 validation-auc:0.96883 validation-aucpr:0.97212
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52663 validation-auc:0.96890 validation-aucpr:0.97239
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52248 validation-auc:0.96893 validation-aucpr:0.97240
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51801 validation-auc:0.96903 validation-aucpr:0.97250
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51421 validation-auc:0.96897 validation-aucpr:0.97243
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51029 validation-auc:0.96893 validation-aucpr:0.97238
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50595 validation-auc:0.96907 validation-aucpr:0.97252
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50151 validation-auc:0.96922 validation-aucpr:0.97268
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49719 validation-auc:0.96937 validation-aucpr:0.97283
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49299 validation-auc:0.96953 validation-aucpr:0.97299
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48876 validation-auc:0.96973 validation-aucpr:0.97317
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48465 validation-auc:0.96984 validation-aucpr:0.97328
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48079 validation-auc:0.96987 validation-aucpr:0.97330
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47745 validation-auc:0.96973 validation-aucpr:0.97318
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47368 validation-auc:0.96976 validation-aucpr:0.97320
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47010 validation-auc:0.96984 validation-aucpr:0.97325
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46629 validation-auc:0.96992 validation-aucpr:0.97335
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46309 validation-auc:0.96999 validation-aucpr:0.97341
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.45994 validation-auc:0.97000 validation-aucpr:0.97346
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45643 validation-auc:0.97010 validation-aucpr:0.97354
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45339 validation-auc:0.97010 validation-aucpr:0.97353
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45029 validation-auc:0.97017 validation-aucpr:0.97356
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44666 validation-auc:0.97034 validation-aucpr:0.97374
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44320 validation-auc:0.97039 validation-aucpr:0.97378
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44045 validation-auc:0.97037 validation-aucpr:0.97374
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.43728 validation-auc:0.97040 validation-aucpr:0.97376
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43448 validation-auc:0.97038 validation-aucpr:0.97375
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43176 validation-auc:0.97037 validation-aucpr:0.97372
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.42857 validation-auc:0.97046 validation-aucpr:0.97381
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42593 validation-auc:0.97038 validation-aucpr:0.97374
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42281 validation-auc:0.97051 validation-aucpr:0.97386
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.41975 validation-auc:0.97058 validation-aucpr:0.97392
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.41688 validation-auc:0.97064 validation-aucpr:0.97393
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41397 validation-auc:0.97061 validation-aucpr:0.97393
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41103 validation-auc:0.97066 validation-aucpr:0.97398
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.40804 validation-auc:0.97082 validation-aucpr:0.97412
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40532 validation-auc:0.97085 validation-aucpr:0.97410
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40301 validation-auc:0.97084 validation-aucpr:0.97408
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40069 validation-auc:0.97088 validation-aucpr:0.97410
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.39790 validation-auc:0.97092 validation-aucpr:0.97416
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.39520 validation-auc:0.97095 validation-aucpr:0.97419
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39255 validation-auc:0.97099 validation-aucpr:0.97422
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39039 validation-auc:0.97096 validation-aucpr:0.97413
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.38783 validation-auc:0.97102 validation-aucpr:0.97419
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.38525 validation-auc:0.97109 validation-aucpr:0.97427
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38296 validation-auc:0.97111 validation-aucpr:0.97428
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38046 validation-auc:0.97114 validation-aucpr:0.97431
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.37843 validation-auc:0.97114 validation-aucpr:0.97429
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.37634 validation-auc:0.97119 validation-aucpr:0.97433
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37389 validation-auc:0.97122 validation-aucpr:0.97437
[21:02:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37190 validation-auc:0.97120 validation-aucpr:0.97435
[21:02:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.36992 validation-auc:0.97119 validation-aucpr:0.97435
{'best_iteration': '77', 'best_score': '0.9743684100818301'}
Trial 68, Fold 2: Log loss = 0.36992255065135127, Average precision = 0.9743500422679949, ROC-AUC = 0.9711869651080246, Elapsed Time = 21.50118659999862 seconds
Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 68, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:02:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68606 validation-auc:0.94324 validation-aucpr:0.92817
[21:02:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67925 validation-auc:0.95504 validation-aucpr:0.95683
[21:02:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67178 validation-auc:0.96425 validation-aucpr:0.96810
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66504 validation-auc:0.96577 validation-aucpr:0.96976
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65868 validation-auc:0.96595 validation-aucpr:0.96956
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65238 validation-auc:0.96531 validation-aucpr:0.96883
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64546 validation-auc:0.96727 validation-aucpr:0.97124
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63946 validation-auc:0.96694 validation-aucpr:0.97104
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63349 validation-auc:0.96724 validation-aucpr:0.97106
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62763 validation-auc:0.96742 validation-aucpr:0.97118
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62185 validation-auc:0.96741 validation-aucpr:0.97108
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61548 validation-auc:0.96845 validation-aucpr:0.97222
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60921 validation-auc:0.96895 validation-aucpr:0.97276
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60389 validation-auc:0.96877 validation-aucpr:0.97253
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59806 validation-auc:0.96916 validation-aucpr:0.97302
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59283 validation-auc:0.96894 validation-aucpr:0.97283
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58692 validation-auc:0.96936 validation-aucpr:0.97329
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58185 validation-auc:0.96927 validation-aucpr:0.97321
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57707 validation-auc:0.96911 validation-aucpr:0.97308
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57243 validation-auc:0.96884 validation-aucpr:0.97282
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56709 validation-auc:0.96908 validation-aucpr:0.97306
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56247 validation-auc:0.96896 validation-aucpr:0.97292
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55714 validation-auc:0.96923 validation-aucpr:0.97326
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55254 validation-auc:0.96921 validation-aucpr:0.97328
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54819 validation-auc:0.96904 validation-aucpr:0.97311
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54316 validation-auc:0.96924 validation-aucpr:0.97333
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53889 validation-auc:0.96925 validation-aucpr:0.97338
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53392 validation-auc:0.96948 validation-aucpr:0.97358
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52975 validation-auc:0.96950 validation-aucpr:0.97356
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52501 validation-auc:0.96968 validation-aucpr:0.97375
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52102 validation-auc:0.96967 validation-aucpr:0.97373
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51647 validation-auc:0.96979 validation-aucpr:0.97386
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51190 validation-auc:0.96992 validation-aucpr:0.97398
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50796 validation-auc:0.96993 validation-aucpr:0.97399
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50418 validation-auc:0.96986 validation-aucpr:0.97394
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49990 validation-auc:0.96987 validation-aucpr:0.97397
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49569 validation-auc:0.96992 validation-aucpr:0.97403
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49145 validation-auc:0.97010 validation-aucpr:0.97418
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48782 validation-auc:0.97001 validation-aucpr:0.97411
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48372 validation-auc:0.97009 validation-aucpr:0.97420
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47983 validation-auc:0.97016 validation-aucpr:0.97425
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47647 validation-auc:0.97009 validation-aucpr:0.97419
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47263 validation-auc:0.97014 validation-aucpr:0.97425
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46882 validation-auc:0.97023 validation-aucpr:0.97435
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46560 validation-auc:0.97023 validation-aucpr:0.97433
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46241 validation-auc:0.97025 validation-aucpr:0.97434
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.45860 validation-auc:0.97042 validation-aucpr:0.97449
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45528 validation-auc:0.97044 validation-aucpr:0.97450
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45233 validation-auc:0.97035 validation-aucpr:0.97444
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.44875 validation-auc:0.97048 validation-aucpr:0.97453
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44568 validation-auc:0.97050 validation-aucpr:0.97454
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44223 validation-auc:0.97059 validation-aucpr:0.97463
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.43890 validation-auc:0.97059 validation-aucpr:0.97463
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.43605 validation-auc:0.97060 validation-aucpr:0.97464
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43300 validation-auc:0.97060 validation-aucpr:0.97465
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43024 validation-auc:0.97063 validation-aucpr:0.97467
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.42694 validation-auc:0.97070 validation-aucpr:0.97475
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42425 validation-auc:0.97068 validation-aucpr:0.97472
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42155 validation-auc:0.97070 validation-aucpr:0.97474
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.41898 validation-auc:0.97067 validation-aucpr:0.97471
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.41594 validation-auc:0.97070 validation-aucpr:0.97473
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41292 validation-auc:0.97075 validation-aucpr:0.97479
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41018 validation-auc:0.97075 validation-aucpr:0.97480
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.40774 validation-auc:0.97075 validation-aucpr:0.97478
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40523 validation-auc:0.97079 validation-aucpr:0.97482
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40234 validation-auc:0.97084 validation-aucpr:0.97487
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40002 validation-auc:0.97082 validation-aucpr:0.97484
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.39774 validation-auc:0.97080 validation-aucpr:0.97485
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.39507 validation-auc:0.97083 validation-aucpr:0.97488
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39241 validation-auc:0.97086 validation-aucpr:0.97491
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.38984 validation-auc:0.97091 validation-aucpr:0.97496
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.38778 validation-auc:0.97090 validation-aucpr:0.97495
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.38522 validation-auc:0.97094 validation-aucpr:0.97498
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38323 validation-auc:0.97089 validation-aucpr:0.97493
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38125 validation-auc:0.97086 validation-aucpr:0.97489
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.37876 validation-auc:0.97088 validation-aucpr:0.97492
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.37672 validation-auc:0.97091 validation-aucpr:0.97492
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37427 validation-auc:0.97095 validation-aucpr:0.97496
[21:02:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37222 validation-auc:0.97096 validation-aucpr:0.97496
[21:02:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37027 validation-auc:0.97096 validation-aucpr:0.97494
{'best_iteration': '72', 'best_score': '0.9749780107157653'}
Trial 68, Fold 3: Log loss = 0.37027450129392103, Average precision = 0.9749421781509551, ROC-AUC = 0.970958667209215, Elapsed Time = 20.445873399999982 seconds
Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 68, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:02:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68598 validation-auc:0.93902 validation-aucpr:0.91408
[21:02:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67910 validation-auc:0.95301 validation-aucpr:0.95750
[21:02:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67157 validation-auc:0.96192 validation-aucpr:0.96755
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66491 validation-auc:0.96249 validation-aucpr:0.96810
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65867 validation-auc:0.96173 validation-aucpr:0.96762
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65177 validation-auc:0.96410 validation-aucpr:0.96977
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64494 validation-auc:0.96510 validation-aucpr:0.97077
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63875 validation-auc:0.96570 validation-aucpr:0.97109
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63288 validation-auc:0.96528 validation-aucpr:0.97072
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62712 validation-auc:0.96564 validation-aucpr:0.97100
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62140 validation-auc:0.96561 validation-aucpr:0.97091
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61507 validation-auc:0.96625 validation-aucpr:0.97152
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60954 validation-auc:0.96652 validation-aucpr:0.97164
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60349 validation-auc:0.96683 validation-aucpr:0.97209
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59758 validation-auc:0.96695 validation-aucpr:0.97225
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59155 validation-auc:0.96741 validation-aucpr:0.97271
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58654 validation-auc:0.96728 validation-aucpr:0.97257
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58163 validation-auc:0.96728 validation-aucpr:0.97254
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57594 validation-auc:0.96753 validation-aucpr:0.97281
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57048 validation-auc:0.96773 validation-aucpr:0.97300
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56550 validation-auc:0.96766 validation-aucpr:0.97295
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56075 validation-auc:0.96751 validation-aucpr:0.97285
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55609 validation-auc:0.96760 validation-aucpr:0.97287
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55155 validation-auc:0.96742 validation-aucpr:0.97271
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54650 validation-auc:0.96759 validation-aucpr:0.97285
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54242 validation-auc:0.96760 validation-aucpr:0.97284
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53743 validation-auc:0.96784 validation-aucpr:0.97306
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53316 validation-auc:0.96775 validation-aucpr:0.97299
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52915 validation-auc:0.96756 validation-aucpr:0.97281
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52512 validation-auc:0.96759 validation-aucpr:0.97279
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52111 validation-auc:0.96761 validation-aucpr:0.97278
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51714 validation-auc:0.96756 validation-aucpr:0.97272
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51312 validation-auc:0.96765 validation-aucpr:0.97278
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50942 validation-auc:0.96756 validation-aucpr:0.97268
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50498 validation-auc:0.96763 validation-aucpr:0.97277
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50129 validation-auc:0.96766 validation-aucpr:0.97276
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49754 validation-auc:0.96765 validation-aucpr:0.97273
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49350 validation-auc:0.96780 validation-aucpr:0.97284
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48929 validation-auc:0.96796 validation-aucpr:0.97298
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48546 validation-auc:0.96800 validation-aucpr:0.97303
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48198 validation-auc:0.96794 validation-aucpr:0.97299
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47875 validation-auc:0.96790 validation-aucpr:0.97295
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47550 validation-auc:0.96779 validation-aucpr:0.97285
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47220 validation-auc:0.96777 validation-aucpr:0.97283
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46900 validation-auc:0.96785 validation-aucpr:0.97289
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46597 validation-auc:0.96785 validation-aucpr:0.97288
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46268 validation-auc:0.96795 validation-aucpr:0.97294
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45913 validation-auc:0.96793 validation-aucpr:0.97296
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45562 validation-auc:0.96801 validation-aucpr:0.97303
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45206 validation-auc:0.96809 validation-aucpr:0.97311
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44903 validation-auc:0.96808 validation-aucpr:0.97310
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44612 validation-auc:0.96799 validation-aucpr:0.97303
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44316 validation-auc:0.96798 validation-aucpr:0.97302
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.43975 validation-auc:0.96805 validation-aucpr:0.97310
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43654 validation-auc:0.96810 validation-aucpr:0.97315
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43386 validation-auc:0.96805 validation-aucpr:0.97312
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43119 validation-auc:0.96806 validation-aucpr:0.97311
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42861 validation-auc:0.96801 validation-aucpr:0.97307
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42541 validation-auc:0.96810 validation-aucpr:0.97316
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42280 validation-auc:0.96808 validation-aucpr:0.97315
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42036 validation-auc:0.96799 validation-aucpr:0.97308
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41730 validation-auc:0.96805 validation-aucpr:0.97316
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41497 validation-auc:0.96798 validation-aucpr:0.97311
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41206 validation-auc:0.96805 validation-aucpr:0.97319
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40975 validation-auc:0.96800 validation-aucpr:0.97314
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40689 validation-auc:0.96809 validation-aucpr:0.97323
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40405 validation-auc:0.96819 validation-aucpr:0.97332
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40177 validation-auc:0.96814 validation-aucpr:0.97328
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.39900 validation-auc:0.96821 validation-aucpr:0.97335
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39695 validation-auc:0.96816 validation-aucpr:0.97329
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39472 validation-auc:0.96813 validation-aucpr:0.97327
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39212 validation-auc:0.96818 validation-aucpr:0.97331
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.38994 validation-auc:0.96823 validation-aucpr:0.97336
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38789 validation-auc:0.96823 validation-aucpr:0.97335
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38574 validation-auc:0.96827 validation-aucpr:0.97336
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38380 validation-auc:0.96826 validation-aucpr:0.97336
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38132 validation-auc:0.96834 validation-aucpr:0.97343
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37931 validation-auc:0.96833 validation-aucpr:0.97341
[21:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37731 validation-auc:0.96831 validation-aucpr:0.97339
[21:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37494 validation-auc:0.96840 validation-aucpr:0.97347
{'best_iteration': '79', 'best_score': '0.9734720863134078'}
Trial 68, Fold 4: Log loss = 0.37494391354788814, Average precision = 0.9734713875983636, ROC-AUC = 0.9684023977624698, Elapsed Time = 20.46157889999813 seconds
Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 68, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68626 validation-auc:0.93212 validation-aucpr:0.90712
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67984 validation-auc:0.94790 validation-aucpr:0.94679
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67253 validation-auc:0.95790 validation-aucpr:0.95955
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66593 validation-auc:0.95991 validation-aucpr:0.96408
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65908 validation-auc:0.96234 validation-aucpr:0.96654
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65302 validation-auc:0.96251 validation-aucpr:0.96687
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64701 validation-auc:0.96232 validation-aucpr:0.96676
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64105 validation-auc:0.96234 validation-aucpr:0.96659
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63436 validation-auc:0.96408 validation-aucpr:0.96848
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62802 validation-auc:0.96443 validation-aucpr:0.96890
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62226 validation-auc:0.96468 validation-aucpr:0.96895
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61601 validation-auc:0.96523 validation-aucpr:0.96953
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60993 validation-auc:0.96542 validation-aucpr:0.96909
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60460 validation-auc:0.96549 validation-aucpr:0.96918
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59876 validation-auc:0.96558 validation-aucpr:0.96929
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59288 validation-auc:0.96574 validation-aucpr:0.96944
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58732 validation-auc:0.96565 validation-aucpr:0.96923
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58236 validation-auc:0.96559 validation-aucpr:0.96916
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57752 validation-auc:0.96571 validation-aucpr:0.96911
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57202 validation-auc:0.96594 validation-aucpr:0.96934
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56720 validation-auc:0.96599 validation-aucpr:0.96933
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56273 validation-auc:0.96605 validation-aucpr:0.97055
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55763 validation-auc:0.96608 validation-aucpr:0.97061
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55254 validation-auc:0.96627 validation-aucpr:0.97083
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54770 validation-auc:0.96634 validation-aucpr:0.97090
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54271 validation-auc:0.96662 validation-aucpr:0.97113
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53779 validation-auc:0.96695 validation-aucpr:0.97139
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53358 validation-auc:0.96693 validation-aucpr:0.97133
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52943 validation-auc:0.96698 validation-aucpr:0.97134
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52477 validation-auc:0.96706 validation-aucpr:0.97144
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52087 validation-auc:0.96713 validation-aucpr:0.97146
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51688 validation-auc:0.96710 validation-aucpr:0.97142
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51252 validation-auc:0.96714 validation-aucpr:0.97146
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50807 validation-auc:0.96724 validation-aucpr:0.97147
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50383 validation-auc:0.96736 validation-aucpr:0.97158
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49960 validation-auc:0.96747 validation-aucpr:0.97176
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49597 validation-auc:0.96741 validation-aucpr:0.97171
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49244 validation-auc:0.96733 validation-aucpr:0.97162
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48907 validation-auc:0.96722 validation-aucpr:0.97153
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48555 validation-auc:0.96721 validation-aucpr:0.97152
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48221 validation-auc:0.96719 validation-aucpr:0.97149
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47892 validation-auc:0.96705 validation-aucpr:0.97034
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47504 validation-auc:0.96710 validation-aucpr:0.97037
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47134 validation-auc:0.96718 validation-aucpr:0.97047
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46757 validation-auc:0.96724 validation-aucpr:0.97053
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46451 validation-auc:0.96726 validation-aucpr:0.97053
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46120 validation-auc:0.96721 validation-aucpr:0.97052
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45826 validation-auc:0.96716 validation-aucpr:0.97046
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45537 validation-auc:0.96708 validation-aucpr:0.97038
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45190 validation-auc:0.96718 validation-aucpr:0.97046
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44860 validation-auc:0.96730 validation-aucpr:0.97188
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44541 validation-auc:0.96736 validation-aucpr:0.97195
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44204 validation-auc:0.96749 validation-aucpr:0.97206
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.43928 validation-auc:0.96747 validation-aucpr:0.97204
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43600 validation-auc:0.96756 validation-aucpr:0.97212
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43282 validation-auc:0.96759 validation-aucpr:0.97217
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43003 validation-auc:0.96762 validation-aucpr:0.97219
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42742 validation-auc:0.96760 validation-aucpr:0.97216
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42445 validation-auc:0.96764 validation-aucpr:0.97220
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42182 validation-auc:0.96761 validation-aucpr:0.97216
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.41935 validation-auc:0.96756 validation-aucpr:0.97211
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41680 validation-auc:0.96758 validation-aucpr:0.97212
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41390 validation-auc:0.96764 validation-aucpr:0.97216
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41158 validation-auc:0.96757 validation-aucpr:0.97211
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.40918 validation-auc:0.96756 validation-aucpr:0.97208
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40676 validation-auc:0.96759 validation-aucpr:0.97211
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40403 validation-auc:0.96765 validation-aucpr:0.97216
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40197 validation-auc:0.96755 validation-aucpr:0.97204
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.39975 validation-auc:0.96756 validation-aucpr:0.97203
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39749 validation-auc:0.96760 validation-aucpr:0.97207
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39523 validation-auc:0.96766 validation-aucpr:0.97210
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39312 validation-auc:0.96770 validation-aucpr:0.97212
[21:03:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39102 validation-auc:0.96766 validation-aucpr:0.97209
[21:03:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38856 validation-auc:0.96772 validation-aucpr:0.97215
[21:03:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38605 validation-auc:0.96777 validation-aucpr:0.97221
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38359 validation-auc:0.96786 validation-aucpr:0.97228
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38163 validation-auc:0.96783 validation-aucpr:0.97225
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37916 validation-auc:0.96798 validation-aucpr:0.97236
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37683 validation-auc:0.96807 validation-aucpr:0.97244
[21:03:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37495 validation-auc:0.96801 validation-aucpr:0.97239
{'best_iteration': '78', 'best_score': '0.9724375478366029'}
Trial 68, Fold 5: Log loss = 0.3749512595837695, Average precision = 0.9723950680655613, ROC-AUC = 0.968007729037772, Elapsed Time = 20.992388499998924 seconds
Optimization Progress: 69%|######9 | 69/100 [3:04:32<28:27, 55.10s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 69, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68494 validation-auc:0.94993 validation-aucpr:0.95429
[1] validation-logloss:0.67660 validation-auc:0.96044 validation-aucpr:0.96753
[2] validation-logloss:0.66935 validation-auc:0.96113 validation-aucpr:0.96789
[3] validation-logloss:0.66162 validation-auc:0.96248 validation-aucpr:0.96906
[4] validation-logloss:0.65393 validation-auc:0.96286 validation-aucpr:0.96944
[5] validation-logloss:0.64613 validation-auc:0.96437 validation-aucpr:0.97053
[6] validation-logloss:0.63846 validation-auc:0.96489 validation-aucpr:0.97098
[7] validation-logloss:0.63111 validation-auc:0.96482 validation-aucpr:0.97094
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[10] validation-logloss:0.61073 validation-auc:0.96544 validation-aucpr:0.97130
[11] validation-logloss:0.60435 validation-auc:0.96559 validation-aucpr:0.97146
[12] validation-logloss:0.59850 validation-auc:0.96545 validation-aucpr:0.97138
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[81] validation-logloss:0.34987 validation-auc:0.96716 validation-aucpr:0.97280
[82] validation-logloss:0.34788 validation-auc:0.96716 validation-aucpr:0.97280
[83] validation-logloss:0.34615 validation-auc:0.96720 validation-aucpr:0.97282
[84] validation-logloss:0.34419 validation-auc:0.96723 validation-aucpr:0.97285
[85] validation-logloss:0.34231 validation-auc:0.96722 validation-aucpr:0.97283
[86] validation-logloss:0.34046 validation-auc:0.96725 validation-aucpr:0.97286
[87] validation-logloss:0.33885 validation-auc:0.96721 validation-aucpr:0.97283
[88] validation-logloss:0.33715 validation-auc:0.96720 validation-aucpr:0.97282
[89] validation-logloss:0.33550 validation-auc:0.96725 validation-aucpr:0.97285
[90] validation-logloss:0.33369 validation-auc:0.96728 validation-aucpr:0.97289
[91] validation-logloss:0.33189 validation-auc:0.96731 validation-aucpr:0.97292
[92] validation-logloss:0.33006 validation-auc:0.96733 validation-aucpr:0.97294
[93] validation-logloss:0.32858 validation-auc:0.96733 validation-aucpr:0.97294
[94] validation-logloss:0.32682 validation-auc:0.96733 validation-aucpr:0.97293
{'best_iteration': '92', 'best_score': '0.9729422850626687'}
Trial 69, Fold 1: Log loss = 0.3268189989634485, Average precision = 0.9729359479843955, ROC-AUC = 0.9673291177356218, Elapsed Time = 225.05377579999913 seconds
Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 69, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68561 validation-auc:0.94421 validation-aucpr:0.94823
[1] validation-logloss:0.67842 validation-auc:0.95073 validation-aucpr:0.95487
[2] validation-logloss:0.67043 validation-auc:0.96058 validation-aucpr:0.96496
[3] validation-logloss:0.66257 validation-auc:0.96352 validation-aucpr:0.96778
[4] validation-logloss:0.65561 validation-auc:0.96301 validation-aucpr:0.96716
[5] validation-logloss:0.64796 validation-auc:0.96437 validation-aucpr:0.96852
[6] validation-logloss:0.64048 validation-auc:0.96521 validation-aucpr:0.96932
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[55] validation-logloss:0.41379 validation-auc:0.96854 validation-aucpr:0.97219
[56] validation-logloss:0.41085 validation-auc:0.96856 validation-aucpr:0.97223
[57] validation-logloss:0.40817 validation-auc:0.96864 validation-aucpr:0.97230
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[59] validation-logloss:0.40219 validation-auc:0.96863 validation-aucpr:0.97231
[60] validation-logloss:0.39939 validation-auc:0.96868 validation-aucpr:0.97235
[61] validation-logloss:0.39654 validation-auc:0.96873 validation-aucpr:0.97239
[62] validation-logloss:0.39413 validation-auc:0.96877 validation-aucpr:0.97237
[63] validation-logloss:0.39183 validation-auc:0.96873 validation-aucpr:0.97236
[64] validation-logloss:0.38915 validation-auc:0.96873 validation-aucpr:0.97236
[65] validation-logloss:0.38649 validation-auc:0.96877 validation-aucpr:0.97239
[66] validation-logloss:0.38388 validation-auc:0.96876 validation-aucpr:0.97237
[67] validation-logloss:0.38162 validation-auc:0.96878 validation-aucpr:0.97238
[68] validation-logloss:0.37930 validation-auc:0.96883 validation-aucpr:0.97241
[69] validation-logloss:0.37690 validation-auc:0.96886 validation-aucpr:0.97247
[70] validation-logloss:0.37485 validation-auc:0.96885 validation-aucpr:0.97247
[71] validation-logloss:0.37228 validation-auc:0.96894 validation-aucpr:0.97268
[72] validation-logloss:0.36996 validation-auc:0.96892 validation-aucpr:0.97265
[73] validation-logloss:0.36750 validation-auc:0.96902 validation-aucpr:0.97272
[74] validation-logloss:0.36517 validation-auc:0.96904 validation-aucpr:0.97271
[75] validation-logloss:0.36289 validation-auc:0.96908 validation-aucpr:0.97275
[76] validation-logloss:0.36083 validation-auc:0.96902 validation-aucpr:0.97270
[77] validation-logloss:0.35893 validation-auc:0.96905 validation-aucpr:0.97272
[78] validation-logloss:0.35683 validation-auc:0.96905 validation-aucpr:0.97273
[79] validation-logloss:0.35486 validation-auc:0.96907 validation-aucpr:0.97273
[80] validation-logloss:0.35274 validation-auc:0.96914 validation-aucpr:0.97278
[81] validation-logloss:0.35074 validation-auc:0.96915 validation-aucpr:0.97278
[82] validation-logloss:0.34911 validation-auc:0.96911 validation-aucpr:0.97275
[83] validation-logloss:0.34711 validation-auc:0.96907 validation-aucpr:0.97273
[84] validation-logloss:0.34509 validation-auc:0.96910 validation-aucpr:0.97275
[85] validation-logloss:0.34296 validation-auc:0.96915 validation-aucpr:0.97279
[86] validation-logloss:0.34092 validation-auc:0.96914 validation-aucpr:0.97278
[87] validation-logloss:0.33909 validation-auc:0.96919 validation-aucpr:0.97281
[88] validation-logloss:0.33724 validation-auc:0.96918 validation-aucpr:0.97282
[89] validation-logloss:0.33542 validation-auc:0.96913 validation-aucpr:0.97279
[90] validation-logloss:0.33359 validation-auc:0.96919 validation-aucpr:0.97283
[91] validation-logloss:0.33178 validation-auc:0.96916 validation-aucpr:0.97281
[92] validation-logloss:0.32998 validation-auc:0.96918 validation-aucpr:0.97284
[93] validation-logloss:0.32836 validation-auc:0.96916 validation-aucpr:0.97282
[94] validation-logloss:0.32659 validation-auc:0.96920 validation-aucpr:0.97282
{'best_iteration': '92', 'best_score': '0.9728419835631521'}
Trial 69, Fold 2: Log loss = 0.32658608862626937, Average precision = 0.9728280145197388, ROC-AUC = 0.9691953512976407, Elapsed Time = 241.70224149999922 seconds
Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 69, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68478 validation-auc:0.95556 validation-aucpr:0.95743
[1] validation-logloss:0.67744 validation-auc:0.95982 validation-aucpr:0.96365
[2] validation-logloss:0.66920 validation-auc:0.96338 validation-aucpr:0.96775
[3] validation-logloss:0.66125 validation-auc:0.96505 validation-aucpr:0.97031
[4] validation-logloss:0.65337 validation-auc:0.96585 validation-aucpr:0.97118
[5] validation-logloss:0.64588 validation-auc:0.96634 validation-aucpr:0.97159
[6] validation-logloss:0.63863 validation-auc:0.96692 validation-aucpr:0.97206
[7] validation-logloss:0.63219 validation-auc:0.96703 validation-aucpr:0.97199
[8] validation-logloss:0.62536 validation-auc:0.96675 validation-aucpr:0.97179
[9] validation-logloss:0.61903 validation-auc:0.96691 validation-aucpr:0.97196
[10] validation-logloss:0.61233 validation-auc:0.96685 validation-aucpr:0.97188
[11] validation-logloss:0.60578 validation-auc:0.96700 validation-aucpr:0.97205
[12] validation-logloss:0.59993 validation-auc:0.96694 validation-aucpr:0.97197
[13] validation-logloss:0.59354 validation-auc:0.96707 validation-aucpr:0.97206
[14] validation-logloss:0.58764 validation-auc:0.96695 validation-aucpr:0.97197
[15] validation-logloss:0.58139 validation-auc:0.96713 validation-aucpr:0.97214
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[31] validation-logloss:0.50080 validation-auc:0.96771 validation-aucpr:0.97274
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[38] validation-logloss:0.47139 validation-auc:0.96785 validation-aucpr:0.97276
[39] validation-logloss:0.46742 validation-auc:0.96787 validation-aucpr:0.97279
[40] validation-logloss:0.46338 validation-auc:0.96788 validation-aucpr:0.97279
[41] validation-logloss:0.45957 validation-auc:0.96785 validation-aucpr:0.97275
[42] validation-logloss:0.45572 validation-auc:0.96794 validation-aucpr:0.97282
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[44] validation-logloss:0.44899 validation-auc:0.96783 validation-aucpr:0.97273
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[50] validation-logloss:0.42851 validation-auc:0.96799 validation-aucpr:0.97281
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[60] validation-logloss:0.39847 validation-auc:0.96820 validation-aucpr:0.97303
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[63] validation-logloss:0.39056 validation-auc:0.96817 validation-aucpr:0.97306
[64] validation-logloss:0.38795 validation-auc:0.96818 validation-aucpr:0.97311
[65] validation-logloss:0.38532 validation-auc:0.96818 validation-aucpr:0.97310
[66] validation-logloss:0.38278 validation-auc:0.96818 validation-aucpr:0.97311
[67] validation-logloss:0.38021 validation-auc:0.96818 validation-aucpr:0.97312
[68] validation-logloss:0.37769 validation-auc:0.96815 validation-aucpr:0.97309
[69] validation-logloss:0.37552 validation-auc:0.96818 validation-aucpr:0.97310
[70] validation-logloss:0.37327 validation-auc:0.96822 validation-aucpr:0.97315
[71] validation-logloss:0.37082 validation-auc:0.96820 validation-aucpr:0.97312
[72] validation-logloss:0.36878 validation-auc:0.96821 validation-aucpr:0.97311
[73] validation-logloss:0.36662 validation-auc:0.96822 validation-aucpr:0.97312
[74] validation-logloss:0.36460 validation-auc:0.96826 validation-aucpr:0.97314
[75] validation-logloss:0.36261 validation-auc:0.96826 validation-aucpr:0.97314
[76] validation-logloss:0.36049 validation-auc:0.96829 validation-aucpr:0.97317
[77] validation-logloss:0.35832 validation-auc:0.96828 validation-aucpr:0.97317
[78] validation-logloss:0.35622 validation-auc:0.96829 validation-aucpr:0.97319
[79] validation-logloss:0.35412 validation-auc:0.96828 validation-aucpr:0.97320
[80] validation-logloss:0.35194 validation-auc:0.96829 validation-aucpr:0.97319
[81] validation-logloss:0.34980 validation-auc:0.96832 validation-aucpr:0.97324
[82] validation-logloss:0.34772 validation-auc:0.96834 validation-aucpr:0.97327
[83] validation-logloss:0.34588 validation-auc:0.96839 validation-aucpr:0.97331
[84] validation-logloss:0.34381 validation-auc:0.96841 validation-aucpr:0.97332
[85] validation-logloss:0.34215 validation-auc:0.96836 validation-aucpr:0.97328
[86] validation-logloss:0.34027 validation-auc:0.96837 validation-aucpr:0.97328
[87] validation-logloss:0.33843 validation-auc:0.96839 validation-aucpr:0.97329
[88] validation-logloss:0.33662 validation-auc:0.96837 validation-aucpr:0.97325
[89] validation-logloss:0.33471 validation-auc:0.96846 validation-aucpr:0.97331
[90] validation-logloss:0.33318 validation-auc:0.96843 validation-aucpr:0.97331
[91] validation-logloss:0.33140 validation-auc:0.96845 validation-aucpr:0.97332
[92] validation-logloss:0.32956 validation-auc:0.96846 validation-aucpr:0.97333
[93] validation-logloss:0.32801 validation-auc:0.96845 validation-aucpr:0.97331
[94] validation-logloss:0.32630 validation-auc:0.96847 validation-aucpr:0.97336
{'best_iteration': '94', 'best_score': '0.9733583680595139'}
Trial 69, Fold 3: Log loss = 0.3263034784487951, Average precision = 0.9733629369980912, ROC-AUC = 0.9684713019233178, Elapsed Time = 236.4137340999987 seconds
Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 69, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68452 validation-auc:0.95758 validation-aucpr:0.96321
[1] validation-logloss:0.67640 validation-auc:0.96182 validation-aucpr:0.96821
[2] validation-logloss:0.66914 validation-auc:0.96363 validation-aucpr:0.96952
[3] validation-logloss:0.66134 validation-auc:0.96442 validation-aucpr:0.97021
[4] validation-logloss:0.65399 validation-auc:0.96564 validation-aucpr:0.97100
[5] validation-logloss:0.64650 validation-auc:0.96615 validation-aucpr:0.97148
[6] validation-logloss:0.63961 validation-auc:0.96606 validation-aucpr:0.97145
[7] validation-logloss:0.63302 validation-auc:0.96570 validation-aucpr:0.97114
[8] validation-logloss:0.62576 validation-auc:0.96606 validation-aucpr:0.97146
[9] validation-logloss:0.61873 validation-auc:0.96616 validation-aucpr:0.97157
[10] validation-logloss:0.61255 validation-auc:0.96591 validation-aucpr:0.97140
[11] validation-logloss:0.60637 validation-auc:0.96552 validation-aucpr:0.97106
[12] validation-logloss:0.60045 validation-auc:0.96559 validation-aucpr:0.97108
[13] validation-logloss:0.59454 validation-auc:0.96563 validation-aucpr:0.97111
[14] validation-logloss:0.58813 validation-auc:0.96600 validation-aucpr:0.97142
[15] validation-logloss:0.58211 validation-auc:0.96608 validation-aucpr:0.97149
[16] validation-logloss:0.57596 validation-auc:0.96631 validation-aucpr:0.97170
[17] validation-logloss:0.57065 validation-auc:0.96631 validation-aucpr:0.97170
[18] validation-logloss:0.56469 validation-auc:0.96647 validation-aucpr:0.97184
[19] validation-logloss:0.55907 validation-auc:0.96656 validation-aucpr:0.97194
[20] validation-logloss:0.55338 validation-auc:0.96684 validation-aucpr:0.97211
[21] validation-logloss:0.54792 validation-auc:0.96700 validation-aucpr:0.97226
[22] validation-logloss:0.54317 validation-auc:0.96689 validation-aucpr:0.97218
[23] validation-logloss:0.53779 validation-auc:0.96698 validation-aucpr:0.97229
[24] validation-logloss:0.53263 validation-auc:0.96700 validation-aucpr:0.97235
[25] validation-logloss:0.52735 validation-auc:0.96719 validation-aucpr:0.97249
[26] validation-logloss:0.52237 validation-auc:0.96729 validation-aucpr:0.97255
[27] validation-logloss:0.51794 validation-auc:0.96719 validation-aucpr:0.97247
[28] validation-logloss:0.51329 validation-auc:0.96722 validation-aucpr:0.97247
[29] validation-logloss:0.50862 validation-auc:0.96730 validation-aucpr:0.97253
[30] validation-logloss:0.50467 validation-auc:0.96714 validation-aucpr:0.97242
[31] validation-logloss:0.50051 validation-auc:0.96704 validation-aucpr:0.97234
[32] validation-logloss:0.49631 validation-auc:0.96702 validation-aucpr:0.97232
[33] validation-logloss:0.49186 validation-auc:0.96706 validation-aucpr:0.97236
[34] validation-logloss:0.48751 validation-auc:0.96701 validation-aucpr:0.97233
[35] validation-logloss:0.48323 validation-auc:0.96701 validation-aucpr:0.97235
[36] validation-logloss:0.47897 validation-auc:0.96706 validation-aucpr:0.97237
[37] validation-logloss:0.47481 validation-auc:0.96708 validation-aucpr:0.97240
[38] validation-logloss:0.47113 validation-auc:0.96708 validation-aucpr:0.97242
[39] validation-logloss:0.46767 validation-auc:0.96708 validation-aucpr:0.97241
[40] validation-logloss:0.46401 validation-auc:0.96710 validation-aucpr:0.97241
[41] validation-logloss:0.46064 validation-auc:0.96698 validation-aucpr:0.97234
[42] validation-logloss:0.45676 validation-auc:0.96710 validation-aucpr:0.97244
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[44] validation-logloss:0.44988 validation-auc:0.96704 validation-aucpr:0.97239
[45] validation-logloss:0.44606 validation-auc:0.96716 validation-aucpr:0.97248
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[48] validation-logloss:0.43570 validation-auc:0.96718 validation-aucpr:0.97250
[49] validation-logloss:0.43247 validation-auc:0.96716 validation-aucpr:0.97249
[50] validation-logloss:0.42909 validation-auc:0.96719 validation-aucpr:0.97252
[51] validation-logloss:0.42565 validation-auc:0.96722 validation-aucpr:0.97255
[52] validation-logloss:0.42268 validation-auc:0.96718 validation-aucpr:0.97251
[53] validation-logloss:0.41992 validation-auc:0.96711 validation-aucpr:0.97246
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[62] validation-logloss:0.39443 validation-auc:0.96712 validation-aucpr:0.97250
[63] validation-logloss:0.39194 validation-auc:0.96708 validation-aucpr:0.97248
[64] validation-logloss:0.38917 validation-auc:0.96720 validation-aucpr:0.97255
[65] validation-logloss:0.38651 validation-auc:0.96729 validation-aucpr:0.97262
[66] validation-logloss:0.38382 validation-auc:0.96734 validation-aucpr:0.97267
[67] validation-logloss:0.38119 validation-auc:0.96744 validation-aucpr:0.97274
[68] validation-logloss:0.37895 validation-auc:0.96744 validation-aucpr:0.97274
[69] validation-logloss:0.37639 validation-auc:0.96751 validation-aucpr:0.97279
[70] validation-logloss:0.37431 validation-auc:0.96748 validation-aucpr:0.97276
[71] validation-logloss:0.37188 validation-auc:0.96750 validation-aucpr:0.97279
[72] validation-logloss:0.36964 validation-auc:0.96750 validation-aucpr:0.97278
[73] validation-logloss:0.36731 validation-auc:0.96754 validation-aucpr:0.97282
[74] validation-logloss:0.36508 validation-auc:0.96758 validation-aucpr:0.97286
[75] validation-logloss:0.36278 validation-auc:0.96765 validation-aucpr:0.97292
[76] validation-logloss:0.36053 validation-auc:0.96771 validation-aucpr:0.97295
[77] validation-logloss:0.35854 validation-auc:0.96770 validation-aucpr:0.97294
[78] validation-logloss:0.35635 validation-auc:0.96766 validation-aucpr:0.97292
[79] validation-logloss:0.35453 validation-auc:0.96762 validation-aucpr:0.97290
[80] validation-logloss:0.35250 validation-auc:0.96763 validation-aucpr:0.97290
[81] validation-logloss:0.35063 validation-auc:0.96761 validation-aucpr:0.97289
[82] validation-logloss:0.34857 validation-auc:0.96765 validation-aucpr:0.97291
[83] validation-logloss:0.34649 validation-auc:0.96771 validation-aucpr:0.97296
[84] validation-logloss:0.34451 validation-auc:0.96773 validation-aucpr:0.97297
[85] validation-logloss:0.34293 validation-auc:0.96769 validation-aucpr:0.97295
[86] validation-logloss:0.34126 validation-auc:0.96770 validation-aucpr:0.97296
[87] validation-logloss:0.33943 validation-auc:0.96771 validation-aucpr:0.97297
[88] validation-logloss:0.33794 validation-auc:0.96765 validation-aucpr:0.97292
[89] validation-logloss:0.33604 validation-auc:0.96772 validation-aucpr:0.97297
[90] validation-logloss:0.33416 validation-auc:0.96774 validation-aucpr:0.97299
[91] validation-logloss:0.33269 validation-auc:0.96772 validation-aucpr:0.97299
[92] validation-logloss:0.33092 validation-auc:0.96773 validation-aucpr:0.97300
[93] validation-logloss:0.32919 validation-auc:0.96776 validation-aucpr:0.97302
[94] validation-logloss:0.32744 validation-auc:0.96775 validation-aucpr:0.97301
{'best_iteration': '93', 'best_score': '0.9730163723177887'}
Trial 69, Fold 4: Log loss = 0.3274444627638355, Average precision = 0.9730170377555121, ROC-AUC = 0.9677517519450822, Elapsed Time = 237.86633739999888 seconds
Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 69, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68493 validation-auc:0.95046 validation-aucpr:0.95399
[1] validation-logloss:0.67694 validation-auc:0.95990 validation-aucpr:0.96461
[2] validation-logloss:0.66914 validation-auc:0.96105 validation-aucpr:0.96499
[3] validation-logloss:0.66165 validation-auc:0.96195 validation-aucpr:0.96693
[4] validation-logloss:0.65396 validation-auc:0.96267 validation-aucpr:0.96765
[5] validation-logloss:0.64658 validation-auc:0.96307 validation-aucpr:0.96790
[6] validation-logloss:0.63891 validation-auc:0.96409 validation-aucpr:0.96888
[7] validation-logloss:0.63213 validation-auc:0.96397 validation-aucpr:0.96866
[8] validation-logloss:0.62531 validation-auc:0.96398 validation-aucpr:0.96874
[9] validation-logloss:0.61839 validation-auc:0.96379 validation-aucpr:0.96873
[10] validation-logloss:0.61217 validation-auc:0.96334 validation-aucpr:0.96830
[11] validation-logloss:0.60558 validation-auc:0.96357 validation-aucpr:0.96842
[12] validation-logloss:0.59874 validation-auc:0.96420 validation-aucpr:0.96899
[13] validation-logloss:0.59292 validation-auc:0.96441 validation-aucpr:0.96913
[14] validation-logloss:0.58738 validation-auc:0.96409 validation-aucpr:0.96885
[15] validation-logloss:0.58129 validation-auc:0.96420 validation-aucpr:0.96902
[16] validation-logloss:0.57541 validation-auc:0.96426 validation-aucpr:0.96905
[17] validation-logloss:0.56948 validation-auc:0.96426 validation-aucpr:0.96911
[18] validation-logloss:0.56387 validation-auc:0.96429 validation-aucpr:0.96911
[19] validation-logloss:0.55828 validation-auc:0.96445 validation-aucpr:0.96984
[20] validation-logloss:0.55308 validation-auc:0.96446 validation-aucpr:0.96981
[21] validation-logloss:0.54753 validation-auc:0.96475 validation-aucpr:0.97003
[22] validation-logloss:0.54246 validation-auc:0.96482 validation-aucpr:0.97010
[23] validation-logloss:0.53751 validation-auc:0.96474 validation-aucpr:0.97002
[24] validation-logloss:0.53250 validation-auc:0.96470 validation-aucpr:0.96998
[25] validation-logloss:0.52740 validation-auc:0.96477 validation-aucpr:0.96992
[26] validation-logloss:0.52294 validation-auc:0.96469 validation-aucpr:0.96988
[27] validation-logloss:0.51824 validation-auc:0.96464 validation-aucpr:0.96984
[28] validation-logloss:0.51332 validation-auc:0.96482 validation-aucpr:0.96998
[29] validation-logloss:0.50853 validation-auc:0.96488 validation-aucpr:0.97001
[30] validation-logloss:0.50417 validation-auc:0.96470 validation-aucpr:0.96984
[31] validation-logloss:0.50006 validation-auc:0.96473 validation-aucpr:0.96984
[32] validation-logloss:0.49612 validation-auc:0.96455 validation-aucpr:0.96972
[33] validation-logloss:0.49167 validation-auc:0.96460 validation-aucpr:0.96976
[34] validation-logloss:0.48801 validation-auc:0.96458 validation-aucpr:0.96973
[35] validation-logloss:0.48381 validation-auc:0.96464 validation-aucpr:0.96977
[36] validation-logloss:0.47958 validation-auc:0.96474 validation-aucpr:0.96986
[37] validation-logloss:0.47541 validation-auc:0.96489 validation-aucpr:0.96999
[38] validation-logloss:0.47202 validation-auc:0.96487 validation-aucpr:0.96996
[39] validation-logloss:0.46872 validation-auc:0.96491 validation-aucpr:0.97000
[40] validation-logloss:0.46496 validation-auc:0.96482 validation-aucpr:0.96994
[41] validation-logloss:0.46153 validation-auc:0.96470 validation-aucpr:0.96985
[42] validation-logloss:0.45785 validation-auc:0.96477 validation-aucpr:0.96991
[43] validation-logloss:0.45456 validation-auc:0.96469 validation-aucpr:0.97012
[44] validation-logloss:0.45077 validation-auc:0.96477 validation-aucpr:0.97020
[45] validation-logloss:0.44710 validation-auc:0.96490 validation-aucpr:0.97032
[46] validation-logloss:0.44408 validation-auc:0.96485 validation-aucpr:0.97026
[47] validation-logloss:0.44092 validation-auc:0.96477 validation-aucpr:0.97019
[48] validation-logloss:0.43762 validation-auc:0.96485 validation-aucpr:0.97022
[49] validation-logloss:0.43417 validation-auc:0.96503 validation-aucpr:0.97037
[50] validation-logloss:0.43138 validation-auc:0.96496 validation-aucpr:0.97029
[51] validation-logloss:0.42814 validation-auc:0.96499 validation-aucpr:0.97031
[52] validation-logloss:0.42542 validation-auc:0.96496 validation-aucpr:0.97028
[53] validation-logloss:0.42229 validation-auc:0.96492 validation-aucpr:0.97025
[54] validation-logloss:0.41907 validation-auc:0.96506 validation-aucpr:0.97036
[55] validation-logloss:0.41616 validation-auc:0.96511 validation-aucpr:0.97042
[56] validation-logloss:0.41330 validation-auc:0.96513 validation-aucpr:0.97042
[57] validation-logloss:0.41071 validation-auc:0.96511 validation-aucpr:0.97040
[58] validation-logloss:0.40823 validation-auc:0.96506 validation-aucpr:0.97035
[59] validation-logloss:0.40569 validation-auc:0.96504 validation-aucpr:0.97032
[60] validation-logloss:0.40293 validation-auc:0.96503 validation-aucpr:0.97031
[61] validation-logloss:0.40006 validation-auc:0.96506 validation-aucpr:0.97035
[62] validation-logloss:0.39768 validation-auc:0.96502 validation-aucpr:0.97032
[63] validation-logloss:0.39522 validation-auc:0.96505 validation-aucpr:0.97034
[64] validation-logloss:0.39298 validation-auc:0.96497 validation-aucpr:0.97028
[65] validation-logloss:0.39063 validation-auc:0.96502 validation-aucpr:0.97032
[66] validation-logloss:0.38803 validation-auc:0.96505 validation-aucpr:0.97036
[67] validation-logloss:0.38546 validation-auc:0.96511 validation-aucpr:0.97038
[68] validation-logloss:0.38287 validation-auc:0.96522 validation-aucpr:0.97047
[69] validation-logloss:0.38055 validation-auc:0.96527 validation-aucpr:0.97051
[70] validation-logloss:0.37836 validation-auc:0.96528 validation-aucpr:0.97052
[71] validation-logloss:0.37625 validation-auc:0.96528 validation-aucpr:0.97051
[72] validation-logloss:0.37369 validation-auc:0.96535 validation-aucpr:0.97055
[73] validation-logloss:0.37119 validation-auc:0.96544 validation-aucpr:0.97064
[74] validation-logloss:0.36919 validation-auc:0.96541 validation-aucpr:0.97060
[75] validation-logloss:0.36697 validation-auc:0.96544 validation-aucpr:0.97061
[76] validation-logloss:0.36490 validation-auc:0.96545 validation-aucpr:0.97062
[77] validation-logloss:0.36263 validation-auc:0.96550 validation-aucpr:0.97065
[78] validation-logloss:0.36040 validation-auc:0.96547 validation-aucpr:0.97064
[79] validation-logloss:0.35812 validation-auc:0.96554 validation-aucpr:0.97069
[80] validation-logloss:0.35614 validation-auc:0.96560 validation-aucpr:0.97075
[81] validation-logloss:0.35434 validation-auc:0.96563 validation-aucpr:0.97078
[82] validation-logloss:0.35230 validation-auc:0.96567 validation-aucpr:0.97081
[83] validation-logloss:0.35058 validation-auc:0.96565 validation-aucpr:0.97079
[84] validation-logloss:0.34858 validation-auc:0.96562 validation-aucpr:0.97078
[85] validation-logloss:0.34677 validation-auc:0.96568 validation-aucpr:0.97081
[86] validation-logloss:0.34478 validation-auc:0.96569 validation-aucpr:0.97082
[87] validation-logloss:0.34317 validation-auc:0.96567 validation-aucpr:0.97080
[88] validation-logloss:0.34135 validation-auc:0.96572 validation-aucpr:0.97084
[89] validation-logloss:0.33957 validation-auc:0.96574 validation-aucpr:0.97085
[90] validation-logloss:0.33773 validation-auc:0.96576 validation-aucpr:0.97085
[91] validation-logloss:0.33624 validation-auc:0.96572 validation-aucpr:0.97080
[92] validation-logloss:0.33443 validation-auc:0.96579 validation-aucpr:0.97086
[93] validation-logloss:0.33281 validation-auc:0.96582 validation-aucpr:0.97087
[94] validation-logloss:0.33098 validation-auc:0.96591 validation-aucpr:0.97094
{'best_iteration': '94', 'best_score': '0.9709447835729923'}
Trial 69, Fold 5: Log loss = 0.3309771129872184, Average precision = 0.9709498977967147, ROC-AUC = 0.9659115248128123, Elapsed Time = 239.0036919000013 seconds
Optimization Progress: 70%|####### | 70/100 [3:24:19<3:17:27, 394.91s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 70, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67291 validation-auc:0.94441 validation-aucpr:0.94450
[1] validation-logloss:0.65387 validation-auc:0.95691 validation-aucpr:0.96256
[2] validation-logloss:0.63586 validation-auc:0.96020 validation-aucpr:0.96376
[3] validation-logloss:0.61769 validation-auc:0.96293 validation-aucpr:0.96850
[4] validation-logloss:0.60076 validation-auc:0.96394 validation-aucpr:0.96921
[5] validation-logloss:0.58723 validation-auc:0.96332 validation-aucpr:0.96823
[6] validation-logloss:0.57224 validation-auc:0.96408 validation-aucpr:0.96918
[7] validation-logloss:0.55889 validation-auc:0.96397 validation-aucpr:0.96910
[8] validation-logloss:0.54658 validation-auc:0.96418 validation-aucpr:0.96924
[9] validation-logloss:0.53292 validation-auc:0.96483 validation-aucpr:0.96994
[10] validation-logloss:0.52180 validation-auc:0.96490 validation-aucpr:0.96996
[11] validation-logloss:0.50941 validation-auc:0.96516 validation-aucpr:0.97043
[12] validation-logloss:0.49834 validation-auc:0.96540 validation-aucpr:0.97074
[13] validation-logloss:0.48728 validation-auc:0.96566 validation-aucpr:0.97120
[14] validation-logloss:0.47779 validation-auc:0.96546 validation-aucpr:0.97096
[15] validation-logloss:0.46747 validation-auc:0.96574 validation-aucpr:0.97117
[16] validation-logloss:0.45875 validation-auc:0.96578 validation-aucpr:0.97124
[17] validation-logloss:0.44974 validation-auc:0.96594 validation-aucpr:0.97139
[18] validation-logloss:0.44066 validation-auc:0.96607 validation-aucpr:0.97160
[19] validation-logloss:0.43277 validation-auc:0.96617 validation-aucpr:0.97168
[20] validation-logloss:0.42529 validation-auc:0.96644 validation-aucpr:0.97186
[21] validation-logloss:0.41727 validation-auc:0.96663 validation-aucpr:0.97197
[22] validation-logloss:0.41021 validation-auc:0.96679 validation-aucpr:0.97210
[23] validation-logloss:0.40394 validation-auc:0.96694 validation-aucpr:0.97215
[24] validation-logloss:0.39655 validation-auc:0.96709 validation-aucpr:0.97228
[25] validation-logloss:0.39057 validation-auc:0.96711 validation-aucpr:0.97230
[26] validation-logloss:0.38485 validation-auc:0.96710 validation-aucpr:0.97230
[27] validation-logloss:0.37832 validation-auc:0.96731 validation-aucpr:0.97260
[28] validation-logloss:0.37198 validation-auc:0.96762 validation-aucpr:0.97282
[29] validation-logloss:0.36582 validation-auc:0.96785 validation-aucpr:0.97302
[30] validation-logloss:0.36099 validation-auc:0.96803 validation-aucpr:0.97311
[31] validation-logloss:0.35578 validation-auc:0.96814 validation-aucpr:0.97320
[32] validation-logloss:0.35058 validation-auc:0.96816 validation-aucpr:0.97322
[33] validation-logloss:0.34594 validation-auc:0.96811 validation-aucpr:0.97314
[34] validation-logloss:0.34170 validation-auc:0.96801 validation-aucpr:0.97304
[35] validation-logloss:0.33696 validation-auc:0.96801 validation-aucpr:0.97313
[36] validation-logloss:0.33270 validation-auc:0.96811 validation-aucpr:0.97318
[37] validation-logloss:0.32847 validation-auc:0.96819 validation-aucpr:0.97324
[38] validation-logloss:0.32489 validation-auc:0.96816 validation-aucpr:0.97321
[39] validation-logloss:0.32016 validation-auc:0.96835 validation-aucpr:0.97338
[40] validation-logloss:0.31690 validation-auc:0.96838 validation-aucpr:0.97335
[41] validation-logloss:0.31364 validation-auc:0.96827 validation-aucpr:0.97328
[42] validation-logloss:0.31050 validation-auc:0.96820 validation-aucpr:0.97321
[43] validation-logloss:0.30775 validation-auc:0.96798 validation-aucpr:0.97303
[44] validation-logloss:0.30455 validation-auc:0.96808 validation-aucpr:0.97308
[45] validation-logloss:0.30127 validation-auc:0.96800 validation-aucpr:0.97305
[46] validation-logloss:0.29862 validation-auc:0.96796 validation-aucpr:0.97302
[47] validation-logloss:0.29582 validation-auc:0.96800 validation-aucpr:0.97299
[48] validation-logloss:0.29328 validation-auc:0.96803 validation-aucpr:0.97300
[49] validation-logloss:0.29100 validation-auc:0.96788 validation-aucpr:0.97292
[50] validation-logloss:0.28807 validation-auc:0.96798 validation-aucpr:0.97301
[51] validation-logloss:0.28487 validation-auc:0.96820 validation-aucpr:0.97319
[52] validation-logloss:0.28176 validation-auc:0.96824 validation-aucpr:0.97325
[53] validation-logloss:0.27945 validation-auc:0.96832 validation-aucpr:0.97329
[54] validation-logloss:0.27657 validation-auc:0.96843 validation-aucpr:0.97338
[55] validation-logloss:0.27445 validation-auc:0.96843 validation-aucpr:0.97342
[56] validation-logloss:0.27254 validation-auc:0.96837 validation-aucpr:0.97337
[57] validation-logloss:0.27021 validation-auc:0.96854 validation-aucpr:0.97350
[58] validation-logloss:0.26805 validation-auc:0.96849 validation-aucpr:0.97347
[59] validation-logloss:0.26532 validation-auc:0.96864 validation-aucpr:0.97361
[60] validation-logloss:0.26365 validation-auc:0.96863 validation-aucpr:0.97360
[61] validation-logloss:0.26168 validation-auc:0.96863 validation-aucpr:0.97361
[62] validation-logloss:0.25997 validation-auc:0.96864 validation-aucpr:0.97361
[63] validation-logloss:0.25774 validation-auc:0.96878 validation-aucpr:0.97372
[64] validation-logloss:0.25565 validation-auc:0.96878 validation-aucpr:0.97374
[65] validation-logloss:0.25349 validation-auc:0.96896 validation-aucpr:0.97388
[66] validation-logloss:0.25148 validation-auc:0.96904 validation-aucpr:0.97394
[67] validation-logloss:0.25009 validation-auc:0.96899 validation-aucpr:0.97389
[68] validation-logloss:0.24812 validation-auc:0.96909 validation-aucpr:0.97396
[69] validation-logloss:0.24680 validation-auc:0.96912 validation-aucpr:0.97396
[70] validation-logloss:0.24557 validation-auc:0.96908 validation-aucpr:0.97393
[71] validation-logloss:0.24441 validation-auc:0.96910 validation-aucpr:0.97392
[72] validation-logloss:0.24313 validation-auc:0.96907 validation-aucpr:0.97391
[73] validation-logloss:0.24190 validation-auc:0.96913 validation-aucpr:0.97396
[74] validation-logloss:0.24076 validation-auc:0.96918 validation-aucpr:0.97398
[75] validation-logloss:0.23922 validation-auc:0.96921 validation-aucpr:0.97403
[76] validation-logloss:0.23815 validation-auc:0.96920 validation-aucpr:0.97402
[77] validation-logloss:0.23657 validation-auc:0.96934 validation-aucpr:0.97413
[78] validation-logloss:0.23511 validation-auc:0.96943 validation-aucpr:0.97420
[79] validation-logloss:0.23367 validation-auc:0.96951 validation-aucpr:0.97427
[80] validation-logloss:0.23218 validation-auc:0.96956 validation-aucpr:0.97431
[81] validation-logloss:0.23149 validation-auc:0.96950 validation-aucpr:0.97427
[82] validation-logloss:0.23019 validation-auc:0.96960 validation-aucpr:0.97437
[83] validation-logloss:0.22928 validation-auc:0.96964 validation-aucpr:0.97443
[84] validation-logloss:0.22815 validation-auc:0.96968 validation-aucpr:0.97447
[85] validation-logloss:0.22729 validation-auc:0.96974 validation-aucpr:0.97453
[86] validation-logloss:0.22620 validation-auc:0.96981 validation-aucpr:0.97459
[87] validation-logloss:0.22560 validation-auc:0.96972 validation-aucpr:0.97452
[88] validation-logloss:0.22441 validation-auc:0.96982 validation-aucpr:0.97459
[89] validation-logloss:0.22382 validation-auc:0.96980 validation-aucpr:0.97458
[90] validation-logloss:0.22299 validation-auc:0.96987 validation-aucpr:0.97462
[91] validation-logloss:0.22204 validation-auc:0.96993 validation-aucpr:0.97465
[92] validation-logloss:0.22136 validation-auc:0.96997 validation-aucpr:0.97468
[93] validation-logloss:0.22070 validation-auc:0.96996 validation-aucpr:0.97467
[94] validation-logloss:0.21973 validation-auc:0.97002 validation-aucpr:0.97473
[95] validation-logloss:0.21885 validation-auc:0.97008 validation-aucpr:0.97478
[96] validation-logloss:0.21840 validation-auc:0.97003 validation-aucpr:0.97475
[97] validation-logloss:0.21745 validation-auc:0.97011 validation-aucpr:0.97480
{'best_iteration': '97', 'best_score': '0.9747997415825582'}
Trial 70, Fold 1: Log loss = 0.21744990998351488, Average precision = 0.9748039295492716, ROC-AUC = 0.970106283128528, Elapsed Time = 17.291013699999894 seconds
Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 70, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67321 validation-auc:0.94151 validation-aucpr:0.94429
[1] validation-logloss:0.65366 validation-auc:0.95852 validation-aucpr:0.96174
[2] validation-logloss:0.63629 validation-auc:0.96110 validation-aucpr:0.96444
[3] validation-logloss:0.61961 validation-auc:0.96219 validation-aucpr:0.96562
[4] validation-logloss:0.60220 validation-auc:0.96457 validation-aucpr:0.96845
[5] validation-logloss:0.58604 validation-auc:0.96492 validation-aucpr:0.96916
[6] validation-logloss:0.57080 validation-auc:0.96607 validation-aucpr:0.96979
[7] validation-logloss:0.55641 validation-auc:0.96640 validation-aucpr:0.97011
[8] validation-logloss:0.54462 validation-auc:0.96626 validation-aucpr:0.96975
[9] validation-logloss:0.53322 validation-auc:0.96656 validation-aucpr:0.96977
[10] validation-logloss:0.52063 validation-auc:0.96718 validation-aucpr:0.97040
[11] validation-logloss:0.50965 validation-auc:0.96710 validation-aucpr:0.97031
[12] validation-logloss:0.49931 validation-auc:0.96724 validation-aucpr:0.97092
[13] validation-logloss:0.48798 validation-auc:0.96732 validation-aucpr:0.97109
[14] validation-logloss:0.47811 validation-auc:0.96762 validation-aucpr:0.97130
[15] validation-logloss:0.46748 validation-auc:0.96792 validation-aucpr:0.97162
[16] validation-logloss:0.45746 validation-auc:0.96820 validation-aucpr:0.97189
[17] validation-logloss:0.44738 validation-auc:0.96880 validation-aucpr:0.97236
[18] validation-logloss:0.43781 validation-auc:0.96907 validation-aucpr:0.97260
[19] validation-logloss:0.43003 validation-auc:0.96916 validation-aucpr:0.97275
[20] validation-logloss:0.42278 validation-auc:0.96914 validation-aucpr:0.97270
[21] validation-logloss:0.41535 validation-auc:0.96928 validation-aucpr:0.97279
[22] validation-logloss:0.40868 validation-auc:0.96921 validation-aucpr:0.97274
[23] validation-logloss:0.40106 validation-auc:0.96940 validation-aucpr:0.97306
[24] validation-logloss:0.39447 validation-auc:0.96953 validation-aucpr:0.97315
[25] validation-logloss:0.38765 validation-auc:0.96962 validation-aucpr:0.97315
[26] validation-logloss:0.38170 validation-auc:0.96953 validation-aucpr:0.97309
[27] validation-logloss:0.37637 validation-auc:0.96945 validation-aucpr:0.97303
[28] validation-logloss:0.37099 validation-auc:0.96938 validation-aucpr:0.97294
[29] validation-logloss:0.36543 validation-auc:0.96944 validation-aucpr:0.97295
[30] validation-logloss:0.35958 validation-auc:0.96939 validation-aucpr:0.97289
[31] validation-logloss:0.35504 validation-auc:0.96937 validation-aucpr:0.97282
[32] validation-logloss:0.35043 validation-auc:0.96931 validation-aucpr:0.97280
[33] validation-logloss:0.34448 validation-auc:0.96962 validation-aucpr:0.97307
[34] validation-logloss:0.33920 validation-auc:0.96967 validation-aucpr:0.97314
[35] validation-logloss:0.33511 validation-auc:0.96971 validation-aucpr:0.97317
[36] validation-logloss:0.33092 validation-auc:0.96998 validation-aucpr:0.97336
[37] validation-logloss:0.32620 validation-auc:0.97017 validation-aucpr:0.97375
[38] validation-logloss:0.32246 validation-auc:0.97016 validation-aucpr:0.97373
[39] validation-logloss:0.31833 validation-auc:0.97010 validation-aucpr:0.97369
[40] validation-logloss:0.31371 validation-auc:0.97030 validation-aucpr:0.97383
[41] validation-logloss:0.30923 validation-auc:0.97050 validation-aucpr:0.97397
[42] validation-logloss:0.30498 validation-auc:0.97070 validation-aucpr:0.97413
[43] validation-logloss:0.30177 validation-auc:0.97079 validation-aucpr:0.97429
[44] validation-logloss:0.29858 validation-auc:0.97079 validation-aucpr:0.97429
[45] validation-logloss:0.29511 validation-auc:0.97086 validation-aucpr:0.97434
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[47] validation-logloss:0.28901 validation-auc:0.97088 validation-aucpr:0.97434
[48] validation-logloss:0.28546 validation-auc:0.97100 validation-aucpr:0.97442
[49] validation-logloss:0.28284 validation-auc:0.97108 validation-aucpr:0.97449
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[51] validation-logloss:0.27723 validation-auc:0.97115 validation-aucpr:0.97452
[52] validation-logloss:0.27409 validation-auc:0.97123 validation-aucpr:0.97457
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[54] validation-logloss:0.26881 validation-auc:0.97136 validation-aucpr:0.97467
[55] validation-logloss:0.26669 validation-auc:0.97137 validation-aucpr:0.97468
[56] validation-logloss:0.26481 validation-auc:0.97133 validation-aucpr:0.97465
[57] validation-logloss:0.26286 validation-auc:0.97127 validation-aucpr:0.97461
[58] validation-logloss:0.26088 validation-auc:0.97115 validation-aucpr:0.97447
[59] validation-logloss:0.25897 validation-auc:0.97122 validation-aucpr:0.97450
[60] validation-logloss:0.25632 validation-auc:0.97134 validation-aucpr:0.97458
[61] validation-logloss:0.25405 validation-auc:0.97137 validation-aucpr:0.97458
[62] validation-logloss:0.25195 validation-auc:0.97136 validation-aucpr:0.97459
[63] validation-logloss:0.25046 validation-auc:0.97128 validation-aucpr:0.97449
[64] validation-logloss:0.24889 validation-auc:0.97133 validation-aucpr:0.97451
[65] validation-logloss:0.24668 validation-auc:0.97140 validation-aucpr:0.97451
[66] validation-logloss:0.24493 validation-auc:0.97150 validation-aucpr:0.97458
[67] validation-logloss:0.24288 validation-auc:0.97157 validation-aucpr:0.97464
[68] validation-logloss:0.24154 validation-auc:0.97147 validation-aucpr:0.97458
[69] validation-logloss:0.24014 validation-auc:0.97149 validation-aucpr:0.97460
[70] validation-logloss:0.23853 validation-auc:0.97151 validation-aucpr:0.97461
[71] validation-logloss:0.23663 validation-auc:0.97161 validation-aucpr:0.97467
[72] validation-logloss:0.23474 validation-auc:0.97167 validation-aucpr:0.97473
[73] validation-logloss:0.23311 validation-auc:0.97168 validation-aucpr:0.97475
[74] validation-logloss:0.23163 validation-auc:0.97160 validation-aucpr:0.97468
[75] validation-logloss:0.23015 validation-auc:0.97158 validation-aucpr:0.97466
[76] validation-logloss:0.22905 validation-auc:0.97156 validation-aucpr:0.97465
[77] validation-logloss:0.22751 validation-auc:0.97164 validation-aucpr:0.97472
[78] validation-logloss:0.22641 validation-auc:0.97170 validation-aucpr:0.97476
[79] validation-logloss:0.22505 validation-auc:0.97174 validation-aucpr:0.97480
[80] validation-logloss:0.22403 validation-auc:0.97176 validation-aucpr:0.97482
[81] validation-logloss:0.22317 validation-auc:0.97172 validation-aucpr:0.97477
[82] validation-logloss:0.22206 validation-auc:0.97181 validation-aucpr:0.97483
[83] validation-logloss:0.22077 validation-auc:0.97183 validation-aucpr:0.97484
[84] validation-logloss:0.21990 validation-auc:0.97187 validation-aucpr:0.97487
[85] validation-logloss:0.21904 validation-auc:0.97186 validation-aucpr:0.97491
[86] validation-logloss:0.21798 validation-auc:0.97192 validation-aucpr:0.97495
[87] validation-logloss:0.21688 validation-auc:0.97187 validation-aucpr:0.97494
[88] validation-logloss:0.21557 validation-auc:0.97199 validation-aucpr:0.97504
[89] validation-logloss:0.21445 validation-auc:0.97201 validation-aucpr:0.97509
[90] validation-logloss:0.21369 validation-auc:0.97201 validation-aucpr:0.97508
[91] validation-logloss:0.21259 validation-auc:0.97204 validation-aucpr:0.97509
[92] validation-logloss:0.21165 validation-auc:0.97204 validation-aucpr:0.97526
[93] validation-logloss:0.21064 validation-auc:0.97207 validation-aucpr:0.97529
[94] validation-logloss:0.20978 validation-auc:0.97205 validation-aucpr:0.97528
[95] validation-logloss:0.20922 validation-auc:0.97205 validation-aucpr:0.97523
[96] validation-logloss:0.20853 validation-auc:0.97208 validation-aucpr:0.97524
[97] validation-logloss:0.20795 validation-auc:0.97204 validation-aucpr:0.97515
{'best_iteration': '93', 'best_score': '0.9752944596780706'}
Trial 70, Fold 2: Log loss = 0.2079469238659796, Average precision = 0.9751581080665546, ROC-AUC = 0.972035993967722, Elapsed Time = 17.38810250000097 seconds
Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 70, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67257 validation-auc:0.94873 validation-aucpr:0.94502
[1] validation-logloss:0.65257 validation-auc:0.96102 validation-aucpr:0.95878
[2] validation-logloss:0.63460 validation-auc:0.96466 validation-aucpr:0.96501
[3] validation-logloss:0.61683 validation-auc:0.96580 validation-aucpr:0.96614
[4] validation-logloss:0.60173 validation-auc:0.96600 validation-aucpr:0.96583
[5] validation-logloss:0.58757 validation-auc:0.96655 validation-aucpr:0.96787
[6] validation-logloss:0.57243 validation-auc:0.96720 validation-aucpr:0.97052
[7] validation-logloss:0.55745 validation-auc:0.96826 validation-aucpr:0.97138
[8] validation-logloss:0.54348 validation-auc:0.96842 validation-aucpr:0.97208
[9] validation-logloss:0.53019 validation-auc:0.96889 validation-aucpr:0.97284
[10] validation-logloss:0.51868 validation-auc:0.96910 validation-aucpr:0.97300
[11] validation-logloss:0.50653 validation-auc:0.96927 validation-aucpr:0.97300
[12] validation-logloss:0.49464 validation-auc:0.96904 validation-aucpr:0.97293
[13] validation-logloss:0.48383 validation-auc:0.96880 validation-aucpr:0.97347
[14] validation-logloss:0.47338 validation-auc:0.96892 validation-aucpr:0.97348
[15] validation-logloss:0.46491 validation-auc:0.96882 validation-aucpr:0.97354
[16] validation-logloss:0.45628 validation-auc:0.96893 validation-aucpr:0.97354
[17] validation-logloss:0.44672 validation-auc:0.96896 validation-aucpr:0.97356
[18] validation-logloss:0.43753 validation-auc:0.96909 validation-aucpr:0.97374
[19] validation-logloss:0.42862 validation-auc:0.96923 validation-aucpr:0.97387
[20] validation-logloss:0.42099 validation-auc:0.96920 validation-aucpr:0.97381
[21] validation-logloss:0.41277 validation-auc:0.96935 validation-aucpr:0.97389
[22] validation-logloss:0.40581 validation-auc:0.96937 validation-aucpr:0.97387
[23] validation-logloss:0.39968 validation-auc:0.96932 validation-aucpr:0.97380
[24] validation-logloss:0.39329 validation-auc:0.96917 validation-aucpr:0.97368
[25] validation-logloss:0.38640 validation-auc:0.96908 validation-aucpr:0.97361
[26] validation-logloss:0.38110 validation-auc:0.96911 validation-aucpr:0.97353
[27] validation-logloss:0.37569 validation-auc:0.96914 validation-aucpr:0.97365
[28] validation-logloss:0.36945 validation-auc:0.96920 validation-aucpr:0.97369
[29] validation-logloss:0.36357 validation-auc:0.96924 validation-aucpr:0.97367
[30] validation-logloss:0.35755 validation-auc:0.96929 validation-aucpr:0.97377
[31] validation-logloss:0.35289 validation-auc:0.96919 validation-aucpr:0.97366
[32] validation-logloss:0.34710 validation-auc:0.96936 validation-aucpr:0.97381
[33] validation-logloss:0.34257 validation-auc:0.96932 validation-aucpr:0.97372
[34] validation-logloss:0.33740 validation-auc:0.96941 validation-aucpr:0.97378
[35] validation-logloss:0.33314 validation-auc:0.96954 validation-aucpr:0.97386
[36] validation-logloss:0.32821 validation-auc:0.96979 validation-aucpr:0.97404
[37] validation-logloss:0.32434 validation-auc:0.96983 validation-aucpr:0.97406
[38] validation-logloss:0.32055 validation-auc:0.96992 validation-aucpr:0.97411
[39] validation-logloss:0.31685 validation-auc:0.96998 validation-aucpr:0.97412
[40] validation-logloss:0.31285 validation-auc:0.97011 validation-aucpr:0.97425
[41] validation-logloss:0.30953 validation-auc:0.97013 validation-aucpr:0.97425
[42] validation-logloss:0.30673 validation-auc:0.97006 validation-aucpr:0.97418
[43] validation-logloss:0.30341 validation-auc:0.97001 validation-aucpr:0.97413
[44] validation-logloss:0.29991 validation-auc:0.97001 validation-aucpr:0.97414
[45] validation-logloss:0.29696 validation-auc:0.97000 validation-aucpr:0.97415
[46] validation-logloss:0.29391 validation-auc:0.97010 validation-aucpr:0.97422
[47] validation-logloss:0.29122 validation-auc:0.97009 validation-aucpr:0.97418
[48] validation-logloss:0.28859 validation-auc:0.96998 validation-aucpr:0.97412
[49] validation-logloss:0.28536 validation-auc:0.97008 validation-aucpr:0.97436
[50] validation-logloss:0.28233 validation-auc:0.97005 validation-aucpr:0.97432
[51] validation-logloss:0.27906 validation-auc:0.97022 validation-aucpr:0.97444
[52] validation-logloss:0.27666 validation-auc:0.97025 validation-aucpr:0.97444
[53] validation-logloss:0.27444 validation-auc:0.97021 validation-aucpr:0.97441
[54] validation-logloss:0.27205 validation-auc:0.97029 validation-aucpr:0.97446
[55] validation-logloss:0.26929 validation-auc:0.97036 validation-aucpr:0.97451
[56] validation-logloss:0.26661 validation-auc:0.97049 validation-aucpr:0.97462
[57] validation-logloss:0.26424 validation-auc:0.97058 validation-aucpr:0.97467
[58] validation-logloss:0.26222 validation-auc:0.97065 validation-aucpr:0.97471
[59] validation-logloss:0.26021 validation-auc:0.97066 validation-aucpr:0.97471
[60] validation-logloss:0.25788 validation-auc:0.97068 validation-aucpr:0.97473
[61] validation-logloss:0.25622 validation-auc:0.97071 validation-aucpr:0.97475
[62] validation-logloss:0.25444 validation-auc:0.97078 validation-aucpr:0.97480
[63] validation-logloss:0.25258 validation-auc:0.97081 validation-aucpr:0.97484
[64] validation-logloss:0.25072 validation-auc:0.97083 validation-aucpr:0.97487
[65] validation-logloss:0.24859 validation-auc:0.97082 validation-aucpr:0.97488
[66] validation-logloss:0.24655 validation-auc:0.97090 validation-aucpr:0.97497
[67] validation-logloss:0.24517 validation-auc:0.97090 validation-aucpr:0.97495
[68] validation-logloss:0.24390 validation-auc:0.97091 validation-aucpr:0.97498
[69] validation-logloss:0.24221 validation-auc:0.97093 validation-aucpr:0.97499
[70] validation-logloss:0.24113 validation-auc:0.97085 validation-aucpr:0.97494
[71] validation-logloss:0.23934 validation-auc:0.97089 validation-aucpr:0.97498
[72] validation-logloss:0.23787 validation-auc:0.97092 validation-aucpr:0.97500
[73] validation-logloss:0.23666 validation-auc:0.97098 validation-aucpr:0.97502
[74] validation-logloss:0.23509 validation-auc:0.97098 validation-aucpr:0.97504
[75] validation-logloss:0.23394 validation-auc:0.97099 validation-aucpr:0.97505
[76] validation-logloss:0.23254 validation-auc:0.97099 validation-aucpr:0.97505
[77] validation-logloss:0.23138 validation-auc:0.97103 validation-aucpr:0.97508
[78] validation-logloss:0.22975 validation-auc:0.97111 validation-aucpr:0.97514
[79] validation-logloss:0.22869 validation-auc:0.97112 validation-aucpr:0.97516
[80] validation-logloss:0.22746 validation-auc:0.97122 validation-aucpr:0.97526
[81] validation-logloss:0.22615 validation-auc:0.97120 validation-aucpr:0.97525
[82] validation-logloss:0.22528 validation-auc:0.97119 validation-aucpr:0.97524
[83] validation-logloss:0.22413 validation-auc:0.97125 validation-aucpr:0.97528
[84] validation-logloss:0.22300 validation-auc:0.97131 validation-aucpr:0.97533
[85] validation-logloss:0.22202 validation-auc:0.97139 validation-aucpr:0.97540
[86] validation-logloss:0.22089 validation-auc:0.97144 validation-aucpr:0.97544
[87] validation-logloss:0.22005 validation-auc:0.97146 validation-aucpr:0.97545
[88] validation-logloss:0.21903 validation-auc:0.97143 validation-aucpr:0.97542
[89] validation-logloss:0.21780 validation-auc:0.97151 validation-aucpr:0.97550
[90] validation-logloss:0.21732 validation-auc:0.97144 validation-aucpr:0.97544
[91] validation-logloss:0.21679 validation-auc:0.97138 validation-aucpr:0.97539
[92] validation-logloss:0.21582 validation-auc:0.97144 validation-aucpr:0.97545
[93] validation-logloss:0.21502 validation-auc:0.97146 validation-aucpr:0.97547
[94] validation-logloss:0.21411 validation-auc:0.97153 validation-aucpr:0.97553
[95] validation-logloss:0.21321 validation-auc:0.97156 validation-aucpr:0.97555
[96] validation-logloss:0.21245 validation-auc:0.97160 validation-aucpr:0.97559
[97] validation-logloss:0.21155 validation-auc:0.97165 validation-aucpr:0.97564
{'best_iteration': '97', 'best_score': '0.9756388331758201'}
Trial 70, Fold 3: Log loss = 0.21154853985304786, Average precision = 0.9756430835055213, ROC-AUC = 0.9716489415492483, Elapsed Time = 17.922511900000245 seconds
Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 70, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67400 validation-auc:0.93355 validation-aucpr:0.93296
[1] validation-logloss:0.65611 validation-auc:0.94846 validation-aucpr:0.95293
[2] validation-logloss:0.63952 validation-auc:0.95213 validation-aucpr:0.95769
[3] validation-logloss:0.62193 validation-auc:0.95913 validation-aucpr:0.96460
[4] validation-logloss:0.60666 validation-auc:0.95981 validation-aucpr:0.96526
[5] validation-logloss:0.59201 validation-auc:0.96034 validation-aucpr:0.96573
[6] validation-logloss:0.57629 validation-auc:0.96247 validation-aucpr:0.96798
[7] validation-logloss:0.56330 validation-auc:0.96270 validation-aucpr:0.96824
[8] validation-logloss:0.55092 validation-auc:0.96321 validation-aucpr:0.96868
[9] validation-logloss:0.53897 validation-auc:0.96340 validation-aucpr:0.96876
[10] validation-logloss:0.52795 validation-auc:0.96376 validation-aucpr:0.96889
[11] validation-logloss:0.51504 validation-auc:0.96494 validation-aucpr:0.96998
[12] validation-logloss:0.50458 validation-auc:0.96510 validation-aucpr:0.97007
[13] validation-logloss:0.49466 validation-auc:0.96526 validation-aucpr:0.97031
[14] validation-logloss:0.48392 validation-auc:0.96563 validation-aucpr:0.97076
[15] validation-logloss:0.47482 validation-auc:0.96545 validation-aucpr:0.97054
[16] validation-logloss:0.46462 validation-auc:0.96575 validation-aucpr:0.97094
[17] validation-logloss:0.45514 validation-auc:0.96605 validation-aucpr:0.97118
[18] validation-logloss:0.44597 validation-auc:0.96644 validation-aucpr:0.97156
[19] validation-logloss:0.43850 validation-auc:0.96633 validation-aucpr:0.97145
[20] validation-logloss:0.43108 validation-auc:0.96631 validation-aucpr:0.97141
[21] validation-logloss:0.42439 validation-auc:0.96614 validation-aucpr:0.97124
[22] validation-logloss:0.41650 validation-auc:0.96639 validation-aucpr:0.97157
[23] validation-logloss:0.40839 validation-auc:0.96673 validation-aucpr:0.97192
[24] validation-logloss:0.40230 validation-auc:0.96665 validation-aucpr:0.97185
[25] validation-logloss:0.39507 validation-auc:0.96671 validation-aucpr:0.97196
[26] validation-logloss:0.38779 validation-auc:0.96692 validation-aucpr:0.97217
[27] validation-logloss:0.38113 validation-auc:0.96715 validation-aucpr:0.97236
[28] validation-logloss:0.37580 validation-auc:0.96701 validation-aucpr:0.97225
[29] validation-logloss:0.36945 validation-auc:0.96713 validation-aucpr:0.97236
[30] validation-logloss:0.36325 validation-auc:0.96732 validation-aucpr:0.97257
[31] validation-logloss:0.35832 validation-auc:0.96753 validation-aucpr:0.97266
[32] validation-logloss:0.35286 validation-auc:0.96766 validation-aucpr:0.97279
[33] validation-logloss:0.34840 validation-auc:0.96767 validation-aucpr:0.97275
[34] validation-logloss:0.34425 validation-auc:0.96768 validation-aucpr:0.97274
[35] validation-logloss:0.33910 validation-auc:0.96783 validation-aucpr:0.97290
[36] validation-logloss:0.33414 validation-auc:0.96800 validation-aucpr:0.97305
[37] validation-logloss:0.33013 validation-auc:0.96809 validation-aucpr:0.97311
[38] validation-logloss:0.32658 validation-auc:0.96794 validation-aucpr:0.97299
[39] validation-logloss:0.32216 validation-auc:0.96802 validation-aucpr:0.97310
[40] validation-logloss:0.31757 validation-auc:0.96819 validation-aucpr:0.97326
[41] validation-logloss:0.31426 validation-auc:0.96809 validation-aucpr:0.97318
[42] validation-logloss:0.31002 validation-auc:0.96828 validation-aucpr:0.97335
[43] validation-logloss:0.30589 validation-auc:0.96840 validation-aucpr:0.97347
[44] validation-logloss:0.30295 validation-auc:0.96842 validation-aucpr:0.97347
[45] validation-logloss:0.29966 validation-auc:0.96854 validation-aucpr:0.97354
[46] validation-logloss:0.29599 validation-auc:0.96865 validation-aucpr:0.97369
[47] validation-logloss:0.29255 validation-auc:0.96884 validation-aucpr:0.97385
[48] validation-logloss:0.28982 validation-auc:0.96900 validation-aucpr:0.97394
[49] validation-logloss:0.28644 validation-auc:0.96910 validation-aucpr:0.97402
[50] validation-logloss:0.28402 validation-auc:0.96910 validation-aucpr:0.97401
[51] validation-logloss:0.28085 validation-auc:0.96917 validation-aucpr:0.97408
[52] validation-logloss:0.27794 validation-auc:0.96919 validation-aucpr:0.97409
[53] validation-logloss:0.27500 validation-auc:0.96926 validation-aucpr:0.97420
[54] validation-logloss:0.27273 validation-auc:0.96928 validation-aucpr:0.97420
[55] validation-logloss:0.27011 validation-auc:0.96934 validation-aucpr:0.97428
[56] validation-logloss:0.26792 validation-auc:0.96931 validation-aucpr:0.97424
[57] validation-logloss:0.26534 validation-auc:0.96944 validation-aucpr:0.97435
[58] validation-logloss:0.26287 validation-auc:0.96948 validation-aucpr:0.97441
[59] validation-logloss:0.26100 validation-auc:0.96946 validation-aucpr:0.97440
[60] validation-logloss:0.25892 validation-auc:0.96951 validation-aucpr:0.97444
[61] validation-logloss:0.25640 validation-auc:0.96972 validation-aucpr:0.97462
[62] validation-logloss:0.25496 validation-auc:0.96964 validation-aucpr:0.97456
[63] validation-logloss:0.25324 validation-auc:0.96964 validation-aucpr:0.97452
[64] validation-logloss:0.25169 validation-auc:0.96965 validation-aucpr:0.97451
[65] validation-logloss:0.24983 validation-auc:0.96975 validation-aucpr:0.97458
[66] validation-logloss:0.24782 validation-auc:0.96981 validation-aucpr:0.97464
[67] validation-logloss:0.24647 validation-auc:0.96979 validation-aucpr:0.97461
[68] validation-logloss:0.24515 validation-auc:0.96974 validation-aucpr:0.97457
[69] validation-logloss:0.24329 validation-auc:0.96974 validation-aucpr:0.97458
[70] validation-logloss:0.24195 validation-auc:0.96975 validation-aucpr:0.97459
[71] validation-logloss:0.24062 validation-auc:0.96971 validation-aucpr:0.97457
[72] validation-logloss:0.23930 validation-auc:0.96971 validation-aucpr:0.97457
[73] validation-logloss:0.23780 validation-auc:0.96974 validation-aucpr:0.97458
[74] validation-logloss:0.23605 validation-auc:0.96984 validation-aucpr:0.97467
[75] validation-logloss:0.23488 validation-auc:0.96985 validation-aucpr:0.97466
[76] validation-logloss:0.23330 validation-auc:0.96990 validation-aucpr:0.97471
[77] validation-logloss:0.23197 validation-auc:0.96994 validation-aucpr:0.97474
[78] validation-logloss:0.23035 validation-auc:0.97007 validation-aucpr:0.97484
[79] validation-logloss:0.22873 validation-auc:0.97017 validation-aucpr:0.97492
[80] validation-logloss:0.22767 validation-auc:0.97016 validation-aucpr:0.97490
[81] validation-logloss:0.22667 validation-auc:0.97017 validation-aucpr:0.97491
[82] validation-logloss:0.22515 validation-auc:0.97031 validation-aucpr:0.97502
[83] validation-logloss:0.22420 validation-auc:0.97037 validation-aucpr:0.97507
[84] validation-logloss:0.22318 validation-auc:0.97033 validation-aucpr:0.97504
[85] validation-logloss:0.22244 validation-auc:0.97030 validation-aucpr:0.97501
[86] validation-logloss:0.22136 validation-auc:0.97035 validation-aucpr:0.97506
[87] validation-logloss:0.22048 validation-auc:0.97038 validation-aucpr:0.97509
[88] validation-logloss:0.21959 validation-auc:0.97041 validation-aucpr:0.97510
[89] validation-logloss:0.21855 validation-auc:0.97048 validation-aucpr:0.97515
[90] validation-logloss:0.21755 validation-auc:0.97050 validation-aucpr:0.97519
[91] validation-logloss:0.21687 validation-auc:0.97050 validation-aucpr:0.97520
[92] validation-logloss:0.21580 validation-auc:0.97057 validation-aucpr:0.97525
[93] validation-logloss:0.21515 validation-auc:0.97055 validation-aucpr:0.97523
[94] validation-logloss:0.21446 validation-auc:0.97049 validation-aucpr:0.97519
[95] validation-logloss:0.21348 validation-auc:0.97055 validation-aucpr:0.97522
[96] validation-logloss:0.21263 validation-auc:0.97058 validation-aucpr:0.97524
[97] validation-logloss:0.21186 validation-auc:0.97055 validation-aucpr:0.97522
{'best_iteration': '92', 'best_score': '0.9752471491651576'}
Trial 70, Fold 4: Log loss = 0.2118629863121309, Average precision = 0.9752193204989157, ROC-AUC = 0.9705480685088616, Elapsed Time = 17.842629599999782 seconds
Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 70, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67302 validation-auc:0.93436 validation-aucpr:0.93150
[1] validation-logloss:0.65307 validation-auc:0.95469 validation-aucpr:0.95539
[2] validation-logloss:0.63662 validation-auc:0.95763 validation-aucpr:0.96329
[3] validation-logloss:0.61845 validation-auc:0.96103 validation-aucpr:0.96637
[4] validation-logloss:0.60163 validation-auc:0.96285 validation-aucpr:0.96784
[5] validation-logloss:0.58611 validation-auc:0.96243 validation-aucpr:0.96737
[6] validation-logloss:0.57224 validation-auc:0.96350 validation-aucpr:0.96805
[7] validation-logloss:0.55781 validation-auc:0.96369 validation-aucpr:0.96827
[8] validation-logloss:0.54535 validation-auc:0.96445 validation-aucpr:0.96883
[9] validation-logloss:0.53413 validation-auc:0.96404 validation-aucpr:0.96848
[10] validation-logloss:0.52284 validation-auc:0.96410 validation-aucpr:0.96879
[11] validation-logloss:0.51080 validation-auc:0.96477 validation-aucpr:0.96934
[12] validation-logloss:0.50012 validation-auc:0.96525 validation-aucpr:0.96969
[13] validation-logloss:0.49004 validation-auc:0.96519 validation-aucpr:0.96968
[14] validation-logloss:0.48079 validation-auc:0.96525 validation-aucpr:0.96957
[15] validation-logloss:0.47022 validation-auc:0.96564 validation-aucpr:0.96993
[16] validation-logloss:0.46173 validation-auc:0.96552 validation-aucpr:0.96971
[17] validation-logloss:0.45217 validation-auc:0.96586 validation-aucpr:0.97009
[18] validation-logloss:0.44306 validation-auc:0.96619 validation-aucpr:0.97035
[19] validation-logloss:0.43522 validation-auc:0.96630 validation-aucpr:0.97041
[20] validation-logloss:0.42786 validation-auc:0.96629 validation-aucpr:0.97038
[21] validation-logloss:0.41973 validation-auc:0.96658 validation-aucpr:0.97065
[22] validation-logloss:0.41310 validation-auc:0.96643 validation-aucpr:0.97058
[23] validation-logloss:0.40655 validation-auc:0.96627 validation-aucpr:0.97048
[24] validation-logloss:0.40042 validation-auc:0.96617 validation-aucpr:0.97036
[25] validation-logloss:0.39349 validation-auc:0.96633 validation-aucpr:0.97047
[26] validation-logloss:0.38726 validation-auc:0.96671 validation-aucpr:0.97108
[27] validation-logloss:0.38046 validation-auc:0.96677 validation-aucpr:0.97106
[28] validation-logloss:0.37488 validation-auc:0.96687 validation-aucpr:0.97117
[29] validation-logloss:0.36994 validation-auc:0.96664 validation-aucpr:0.97089
[30] validation-logloss:0.36392 validation-auc:0.96672 validation-aucpr:0.97101
[31] validation-logloss:0.35797 validation-auc:0.96688 validation-aucpr:0.97114
[32] validation-logloss:0.35342 validation-auc:0.96690 validation-aucpr:0.97114
[33] validation-logloss:0.34925 validation-auc:0.96669 validation-aucpr:0.97093
[34] validation-logloss:0.34468 validation-auc:0.96665 validation-aucpr:0.97090
[35] validation-logloss:0.34050 validation-auc:0.96666 validation-aucpr:0.97089
[36] validation-logloss:0.33651 validation-auc:0.96673 validation-aucpr:0.97089
[37] validation-logloss:0.33154 validation-auc:0.96679 validation-aucpr:0.97096
[38] validation-logloss:0.32811 validation-auc:0.96672 validation-aucpr:0.97086
[39] validation-logloss:0.32395 validation-auc:0.96670 validation-aucpr:0.97092
[40] validation-logloss:0.31958 validation-auc:0.96685 validation-aucpr:0.97103
[41] validation-logloss:0.31621 validation-auc:0.96696 validation-aucpr:0.97108
[42] validation-logloss:0.31314 validation-auc:0.96698 validation-aucpr:0.97108
[43] validation-logloss:0.30910 validation-auc:0.96706 validation-aucpr:0.97115
[44] validation-logloss:0.30536 validation-auc:0.96707 validation-aucpr:0.97117
[45] validation-logloss:0.30139 validation-auc:0.96720 validation-aucpr:0.97128
[46] validation-logloss:0.29812 validation-auc:0.96721 validation-aucpr:0.97128
[47] validation-logloss:0.29489 validation-auc:0.96721 validation-aucpr:0.97128
[48] validation-logloss:0.29177 validation-auc:0.96729 validation-aucpr:0.97133
[49] validation-logloss:0.28904 validation-auc:0.96746 validation-aucpr:0.97147
[50] validation-logloss:0.28652 validation-auc:0.96752 validation-aucpr:0.97150
[51] validation-logloss:0.28436 validation-auc:0.96736 validation-aucpr:0.97139
[52] validation-logloss:0.28127 validation-auc:0.96752 validation-aucpr:0.97153
[53] validation-logloss:0.27843 validation-auc:0.96766 validation-aucpr:0.97163
[54] validation-logloss:0.27564 validation-auc:0.96774 validation-aucpr:0.97170
[55] validation-logloss:0.27368 validation-auc:0.96764 validation-aucpr:0.97162
[56] validation-logloss:0.27099 validation-auc:0.96773 validation-aucpr:0.97171
[57] validation-logloss:0.26857 validation-auc:0.96771 validation-aucpr:0.97174
[58] validation-logloss:0.26610 validation-auc:0.96780 validation-aucpr:0.97181
[59] validation-logloss:0.26350 validation-auc:0.96799 validation-aucpr:0.97197
[60] validation-logloss:0.26175 validation-auc:0.96795 validation-aucpr:0.97193
[61] validation-logloss:0.26025 validation-auc:0.96792 validation-aucpr:0.97189
[62] validation-logloss:0.25847 validation-auc:0.96800 validation-aucpr:0.97194
[63] validation-logloss:0.25648 validation-auc:0.96797 validation-aucpr:0.97193
[64] validation-logloss:0.25473 validation-auc:0.96805 validation-aucpr:0.97198
[65] validation-logloss:0.25331 validation-auc:0.96806 validation-aucpr:0.97200
[66] validation-logloss:0.25124 validation-auc:0.96819 validation-aucpr:0.97212
[67] validation-logloss:0.24997 validation-auc:0.96817 validation-aucpr:0.97211
[68] validation-logloss:0.24784 validation-auc:0.96843 validation-aucpr:0.97230
[69] validation-logloss:0.24644 validation-auc:0.96838 validation-aucpr:0.97227
[70] validation-logloss:0.24508 validation-auc:0.96835 validation-aucpr:0.97225
[71] validation-logloss:0.24339 validation-auc:0.96845 validation-aucpr:0.97249
[72] validation-logloss:0.24184 validation-auc:0.96846 validation-aucpr:0.97248
[73] validation-logloss:0.24035 validation-auc:0.96846 validation-aucpr:0.97247
[74] validation-logloss:0.23920 validation-auc:0.96847 validation-aucpr:0.97246
[75] validation-logloss:0.23802 validation-auc:0.96852 validation-aucpr:0.97250
[76] validation-logloss:0.23682 validation-auc:0.96858 validation-aucpr:0.97254
[77] validation-logloss:0.23557 validation-auc:0.96859 validation-aucpr:0.97255
[78] validation-logloss:0.23422 validation-auc:0.96871 validation-aucpr:0.97264
[79] validation-logloss:0.23296 validation-auc:0.96874 validation-aucpr:0.97266
[80] validation-logloss:0.23200 validation-auc:0.96881 validation-aucpr:0.97277
[81] validation-logloss:0.23081 validation-auc:0.96886 validation-aucpr:0.97282
[82] validation-logloss:0.23009 validation-auc:0.96886 validation-aucpr:0.97291
[83] validation-logloss:0.22931 validation-auc:0.96886 validation-aucpr:0.97290
[84] validation-logloss:0.22837 validation-auc:0.96882 validation-aucpr:0.97292
[85] validation-logloss:0.22710 validation-auc:0.96894 validation-aucpr:0.97297
[86] validation-logloss:0.22593 validation-auc:0.96903 validation-aucpr:0.97306
[87] validation-logloss:0.22486 validation-auc:0.96911 validation-aucpr:0.97314
[88] validation-logloss:0.22399 validation-auc:0.96910 validation-aucpr:0.97308
[89] validation-logloss:0.22313 validation-auc:0.96915 validation-aucpr:0.97312
[90] validation-logloss:0.22215 validation-auc:0.96922 validation-aucpr:0.97317
[91] validation-logloss:0.22134 validation-auc:0.96919 validation-aucpr:0.97314
[92] validation-logloss:0.22078 validation-auc:0.96918 validation-aucpr:0.97314
[93] validation-logloss:0.22009 validation-auc:0.96926 validation-aucpr:0.97321
[94] validation-logloss:0.21960 validation-auc:0.96922 validation-aucpr:0.97317
[95] validation-logloss:0.21907 validation-auc:0.96923 validation-aucpr:0.97318
[96] validation-logloss:0.21822 validation-auc:0.96934 validation-aucpr:0.97325
[97] validation-logloss:0.21768 validation-auc:0.96934 validation-aucpr:0.97323
{'best_iteration': '96', 'best_score': '0.9732505139835228'}
Trial 70, Fold 5: Log loss = 0.21767668827980782, Average precision = 0.973240621004372, ROC-AUC = 0.969341879865485, Elapsed Time = 19.734101900001406 seconds
Optimization Progress: 71%|#######1 | 71/100 [3:25:57<2:27:49, 305.83s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 71, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68602 validation-auc:0.87396 validation-aucpr:0.80837
[1] validation-logloss:0.67872 validation-auc:0.93204 validation-aucpr:0.89510
[2] validation-logloss:0.67175 validation-auc:0.94868 validation-aucpr:0.93511
[3] validation-logloss:0.66563 validation-auc:0.95252 validation-aucpr:0.94511
[4] validation-logloss:0.65966 validation-auc:0.95486 validation-aucpr:0.95009
[5] validation-logloss:0.65359 validation-auc:0.95698 validation-aucpr:0.95965
[6] validation-logloss:0.64780 validation-auc:0.95800 validation-aucpr:0.96071
[7] validation-logloss:0.64125 validation-auc:0.95999 validation-aucpr:0.96304
[8] validation-logloss:0.63498 validation-auc:0.96099 validation-aucpr:0.96406
[9] validation-logloss:0.62864 validation-auc:0.96217 validation-aucpr:0.96529
[10] validation-logloss:0.62239 validation-auc:0.96315 validation-aucpr:0.96884
[11] validation-logloss:0.61650 validation-auc:0.96332 validation-aucpr:0.96932
[12] validation-logloss:0.61059 validation-auc:0.96381 validation-aucpr:0.96912
[13] validation-logloss:0.60489 validation-auc:0.96403 validation-aucpr:0.96870
[14] validation-logloss:0.59938 validation-auc:0.96394 validation-aucpr:0.96854
[15] validation-logloss:0.59392 validation-auc:0.96440 validation-aucpr:0.96897
[16] validation-logloss:0.58853 validation-auc:0.96458 validation-aucpr:0.96912
[17] validation-logloss:0.58328 validation-auc:0.96480 validation-aucpr:0.96849
[18] validation-logloss:0.57804 validation-auc:0.96520 validation-aucpr:0.96881
[19] validation-logloss:0.57279 validation-auc:0.96553 validation-aucpr:0.96899
[20] validation-logloss:0.56767 validation-auc:0.96562 validation-aucpr:0.96912
[21] validation-logloss:0.56269 validation-auc:0.96559 validation-aucpr:0.96926
[22] validation-logloss:0.55781 validation-auc:0.96574 validation-aucpr:0.97087
[23] validation-logloss:0.55294 validation-auc:0.96561 validation-aucpr:0.97106
[24] validation-logloss:0.54820 validation-auc:0.96566 validation-aucpr:0.97112
[25] validation-logloss:0.54330 validation-auc:0.96587 validation-aucpr:0.97127
[26] validation-logloss:0.53854 validation-auc:0.96616 validation-aucpr:0.97154
[27] validation-logloss:0.53400 validation-auc:0.96591 validation-aucpr:0.97140
[28] validation-logloss:0.52965 validation-auc:0.96586 validation-aucpr:0.97144
[29] validation-logloss:0.52531 validation-auc:0.96603 validation-aucpr:0.97154
[30] validation-logloss:0.52106 validation-auc:0.96606 validation-aucpr:0.97159
[31] validation-logloss:0.51684 validation-auc:0.96641 validation-aucpr:0.97183
[32] validation-logloss:0.51279 validation-auc:0.96678 validation-aucpr:0.97208
[33] validation-logloss:0.50859 validation-auc:0.96695 validation-aucpr:0.97221
[34] validation-logloss:0.50454 validation-auc:0.96694 validation-aucpr:0.97220
[35] validation-logloss:0.50037 validation-auc:0.96717 validation-aucpr:0.97240
[36] validation-logloss:0.49617 validation-auc:0.96746 validation-aucpr:0.97262
[37] validation-logloss:0.49234 validation-auc:0.96733 validation-aucpr:0.97255
[38] validation-logloss:0.48849 validation-auc:0.96737 validation-aucpr:0.97241
[39] validation-logloss:0.48464 validation-auc:0.96733 validation-aucpr:0.97239
[40] validation-logloss:0.48100 validation-auc:0.96722 validation-aucpr:0.97236
[41] validation-logloss:0.47741 validation-auc:0.96705 validation-aucpr:0.97226
[42] validation-logloss:0.47387 validation-auc:0.96708 validation-aucpr:0.97231
[43] validation-logloss:0.47055 validation-auc:0.96689 validation-aucpr:0.97213
[44] validation-logloss:0.46704 validation-auc:0.96693 validation-aucpr:0.97218
[45] validation-logloss:0.46391 validation-auc:0.96702 validation-aucpr:0.97225
[46] validation-logloss:0.46039 validation-auc:0.96711 validation-aucpr:0.97231
[47] validation-logloss:0.45697 validation-auc:0.96716 validation-aucpr:0.97244
[48] validation-logloss:0.45400 validation-auc:0.96729 validation-aucpr:0.97251
[49] validation-logloss:0.45071 validation-auc:0.96734 validation-aucpr:0.97257
[50] validation-logloss:0.44752 validation-auc:0.96737 validation-aucpr:0.97259
[51] validation-logloss:0.44438 validation-auc:0.96735 validation-aucpr:0.97257
[52] validation-logloss:0.44137 validation-auc:0.96729 validation-aucpr:0.97254
[53] validation-logloss:0.43869 validation-auc:0.96723 validation-aucpr:0.97247
[54] validation-logloss:0.43568 validation-auc:0.96720 validation-aucpr:0.97248
[55] validation-logloss:0.43266 validation-auc:0.96723 validation-aucpr:0.97251
[56] validation-logloss:0.42969 validation-auc:0.96721 validation-aucpr:0.97250
[57] validation-logloss:0.42729 validation-auc:0.96705 validation-aucpr:0.97236
[58] validation-logloss:0.42448 validation-auc:0.96703 validation-aucpr:0.97235
[59] validation-logloss:0.42169 validation-auc:0.96713 validation-aucpr:0.97259
[60] validation-logloss:0.41882 validation-auc:0.96722 validation-aucpr:0.97262
[61] validation-logloss:0.41597 validation-auc:0.96726 validation-aucpr:0.97262
[62] validation-logloss:0.41333 validation-auc:0.96729 validation-aucpr:0.97261
[63] validation-logloss:0.41071 validation-auc:0.96731 validation-aucpr:0.97260
[64] validation-logloss:0.40799 validation-auc:0.96734 validation-aucpr:0.97262
[65] validation-logloss:0.40542 validation-auc:0.96737 validation-aucpr:0.97261
[66] validation-logloss:0.40295 validation-auc:0.96742 validation-aucpr:0.97263
[67] validation-logloss:0.40063 validation-auc:0.96738 validation-aucpr:0.97259
[68] validation-logloss:0.39805 validation-auc:0.96744 validation-aucpr:0.97267
[69] validation-logloss:0.39577 validation-auc:0.96745 validation-aucpr:0.97267
[70] validation-logloss:0.39324 validation-auc:0.96751 validation-aucpr:0.97271
[71] validation-logloss:0.39082 validation-auc:0.96745 validation-aucpr:0.97259
[72] validation-logloss:0.38853 validation-auc:0.96749 validation-aucpr:0.97259
[73] validation-logloss:0.38614 validation-auc:0.96752 validation-aucpr:0.97262
[74] validation-logloss:0.38373 validation-auc:0.96760 validation-aucpr:0.97268
[75] validation-logloss:0.38167 validation-auc:0.96765 validation-aucpr:0.97274
[76] validation-logloss:0.37930 validation-auc:0.96774 validation-aucpr:0.97270
[77] validation-logloss:0.37709 validation-auc:0.96770 validation-aucpr:0.97266
[78] validation-logloss:0.37505 validation-auc:0.96765 validation-aucpr:0.97257
[79] validation-logloss:0.37296 validation-auc:0.96770 validation-aucpr:0.97262
{'best_iteration': '75', 'best_score': '0.9727365897316989'}
Trial 71, Fold 1: Log loss = 0.37296171100424547, Average precision = 0.9726960282798622, ROC-AUC = 0.9677037180424714, Elapsed Time = 6.413239400000748 seconds
Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 71, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68524 validation-auc:0.89374 validation-aucpr:0.83216
[1] validation-logloss:0.67781 validation-auc:0.93814 validation-aucpr:0.89778
[2] validation-logloss:0.67085 validation-auc:0.95140 validation-aucpr:0.93410
[3] validation-logloss:0.66426 validation-auc:0.95769 validation-aucpr:0.95362
[4] validation-logloss:0.65750 validation-auc:0.96125 validation-aucpr:0.96416
[5] validation-logloss:0.65073 validation-auc:0.96299 validation-aucpr:0.96662
[6] validation-logloss:0.64418 validation-auc:0.96381 validation-aucpr:0.96771
[7] validation-logloss:0.63771 validation-auc:0.96488 validation-aucpr:0.96881
[8] validation-logloss:0.63155 validation-auc:0.96526 validation-aucpr:0.96944
[9] validation-logloss:0.62520 validation-auc:0.96615 validation-aucpr:0.96970
[10] validation-logloss:0.61926 validation-auc:0.96578 validation-aucpr:0.96916
[11] validation-logloss:0.61337 validation-auc:0.96600 validation-aucpr:0.96962
[12] validation-logloss:0.60764 validation-auc:0.96606 validation-aucpr:0.96938
[13] validation-logloss:0.60193 validation-auc:0.96609 validation-aucpr:0.96906
[14] validation-logloss:0.59623 validation-auc:0.96629 validation-aucpr:0.96885
[15] validation-logloss:0.59071 validation-auc:0.96637 validation-aucpr:0.96901
[16] validation-logloss:0.58579 validation-auc:0.96666 validation-aucpr:0.96912
[17] validation-logloss:0.58017 validation-auc:0.96699 validation-aucpr:0.96967
[18] validation-logloss:0.57491 validation-auc:0.96703 validation-aucpr:0.96995
[19] validation-logloss:0.56969 validation-auc:0.96758 validation-aucpr:0.97070
[20] validation-logloss:0.56450 validation-auc:0.96810 validation-aucpr:0.97098
[21] validation-logloss:0.55942 validation-auc:0.96812 validation-aucpr:0.97115
[22] validation-logloss:0.55459 validation-auc:0.96846 validation-aucpr:0.97217
[23] validation-logloss:0.54968 validation-auc:0.96880 validation-aucpr:0.97186
[24] validation-logloss:0.54469 validation-auc:0.96938 validation-aucpr:0.97237
[25] validation-logloss:0.53972 validation-auc:0.96965 validation-aucpr:0.97256
[26] validation-logloss:0.53541 validation-auc:0.97000 validation-aucpr:0.97281
[27] validation-logloss:0.53085 validation-auc:0.97013 validation-aucpr:0.97298
[28] validation-logloss:0.52634 validation-auc:0.97020 validation-aucpr:0.97305
[29] validation-logloss:0.52194 validation-auc:0.97024 validation-aucpr:0.97312
[30] validation-logloss:0.51748 validation-auc:0.97049 validation-aucpr:0.97326
[31] validation-logloss:0.51362 validation-auc:0.97056 validation-aucpr:0.97330
[32] validation-logloss:0.50940 validation-auc:0.97085 validation-aucpr:0.97350
[33] validation-logloss:0.50526 validation-auc:0.97097 validation-aucpr:0.97360
[34] validation-logloss:0.50118 validation-auc:0.97099 validation-aucpr:0.97338
[35] validation-logloss:0.49720 validation-auc:0.97086 validation-aucpr:0.97327
[36] validation-logloss:0.49333 validation-auc:0.97088 validation-aucpr:0.97329
[37] validation-logloss:0.48942 validation-auc:0.97071 validation-aucpr:0.97317
[38] validation-logloss:0.48557 validation-auc:0.97096 validation-aucpr:0.97342
[39] validation-logloss:0.48171 validation-auc:0.97118 validation-aucpr:0.97370
[40] validation-logloss:0.47851 validation-auc:0.97101 validation-aucpr:0.97356
[41] validation-logloss:0.47498 validation-auc:0.97083 validation-aucpr:0.97374
[42] validation-logloss:0.47139 validation-auc:0.97092 validation-aucpr:0.97378
[43] validation-logloss:0.46777 validation-auc:0.97086 validation-aucpr:0.97373
[44] validation-logloss:0.46420 validation-auc:0.97079 validation-aucpr:0.97369
[45] validation-logloss:0.46073 validation-auc:0.97091 validation-aucpr:0.97377
[46] validation-logloss:0.45716 validation-auc:0.97102 validation-aucpr:0.97392
[47] validation-logloss:0.45391 validation-auc:0.97104 validation-aucpr:0.97391
[48] validation-logloss:0.45052 validation-auc:0.97119 validation-aucpr:0.97402
[49] validation-logloss:0.44763 validation-auc:0.97128 validation-aucpr:0.97398
[50] validation-logloss:0.44445 validation-auc:0.97122 validation-aucpr:0.97381
[51] validation-logloss:0.44117 validation-auc:0.97144 validation-aucpr:0.97401
[52] validation-logloss:0.43813 validation-auc:0.97151 validation-aucpr:0.97408
[53] validation-logloss:0.43489 validation-auc:0.97148 validation-aucpr:0.97407
[54] validation-logloss:0.43216 validation-auc:0.97156 validation-aucpr:0.97413
[55] validation-logloss:0.42889 validation-auc:0.97178 validation-aucpr:0.97434
[56] validation-logloss:0.42605 validation-auc:0.97175 validation-aucpr:0.97431
[57] validation-logloss:0.42350 validation-auc:0.97169 validation-aucpr:0.97427
[58] validation-logloss:0.42070 validation-auc:0.97173 validation-aucpr:0.97434
[59] validation-logloss:0.41780 validation-auc:0.97174 validation-aucpr:0.97438
[60] validation-logloss:0.41497 validation-auc:0.97171 validation-aucpr:0.97436
[61] validation-logloss:0.41222 validation-auc:0.97174 validation-aucpr:0.97438
[62] validation-logloss:0.40983 validation-auc:0.97163 validation-aucpr:0.97433
[63] validation-logloss:0.40716 validation-auc:0.97151 validation-aucpr:0.97424
[64] validation-logloss:0.40447 validation-auc:0.97153 validation-aucpr:0.97424
[65] validation-logloss:0.40193 validation-auc:0.97144 validation-aucpr:0.97419
[66] validation-logloss:0.39939 validation-auc:0.97144 validation-aucpr:0.97420
[67] validation-logloss:0.39690 validation-auc:0.97153 validation-aucpr:0.97472
[68] validation-logloss:0.39433 validation-auc:0.97161 validation-aucpr:0.97478
[69] validation-logloss:0.39178 validation-auc:0.97154 validation-aucpr:0.97473
[70] validation-logloss:0.38939 validation-auc:0.97146 validation-aucpr:0.97468
[71] validation-logloss:0.38686 validation-auc:0.97149 validation-aucpr:0.97470
[72] validation-logloss:0.38458 validation-auc:0.97132 validation-aucpr:0.97457
[73] validation-logloss:0.38228 validation-auc:0.97137 validation-aucpr:0.97460
[74] validation-logloss:0.37997 validation-auc:0.97147 validation-aucpr:0.97469
[75] validation-logloss:0.37784 validation-auc:0.97140 validation-aucpr:0.97463
[76] validation-logloss:0.37548 validation-auc:0.97145 validation-aucpr:0.97468
[77] validation-logloss:0.37320 validation-auc:0.97148 validation-aucpr:0.97471
[78] validation-logloss:0.37102 validation-auc:0.97144 validation-aucpr:0.97464
[79] validation-logloss:0.36909 validation-auc:0.97148 validation-aucpr:0.97464
{'best_iteration': '68', 'best_score': '0.9747844255814891'}
Trial 71, Fold 2: Log loss = 0.36908960303185984, Average precision = 0.9747278218123956, ROC-AUC = 0.9714808246945849, Elapsed Time = 5.026995500000339 seconds
Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 71, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68556 validation-auc:0.87778 validation-aucpr:0.79278
[1] validation-logloss:0.67836 validation-auc:0.93716 validation-aucpr:0.90135
[2] validation-logloss:0.67119 validation-auc:0.95347 validation-aucpr:0.94140
[3] validation-logloss:0.66417 validation-auc:0.95902 validation-aucpr:0.95109
[4] validation-logloss:0.65731 validation-auc:0.96171 validation-aucpr:0.95796
[5] validation-logloss:0.65079 validation-auc:0.96148 validation-aucpr:0.95872
[6] validation-logloss:0.64443 validation-auc:0.96200 validation-aucpr:0.96199
[7] validation-logloss:0.63853 validation-auc:0.96237 validation-aucpr:0.96683
[8] validation-logloss:0.63295 validation-auc:0.96219 validation-aucpr:0.96576
[9] validation-logloss:0.62704 validation-auc:0.96273 validation-aucpr:0.96657
[10] validation-logloss:0.62084 validation-auc:0.96302 validation-aucpr:0.96595
[11] validation-logloss:0.61471 validation-auc:0.96386 validation-aucpr:0.96732
[12] validation-logloss:0.60910 validation-auc:0.96416 validation-aucpr:0.96836
[13] validation-logloss:0.60332 validation-auc:0.96452 validation-aucpr:0.96791
[14] validation-logloss:0.59766 validation-auc:0.96489 validation-aucpr:0.96828
[15] validation-logloss:0.59218 validation-auc:0.96502 validation-aucpr:0.96819
[16] validation-logloss:0.58659 validation-auc:0.96549 validation-aucpr:0.96885
[17] validation-logloss:0.58180 validation-auc:0.96549 validation-aucpr:0.96906
[18] validation-logloss:0.57644 validation-auc:0.96597 validation-aucpr:0.96915
[19] validation-logloss:0.57128 validation-auc:0.96607 validation-aucpr:0.96922
[20] validation-logloss:0.56639 validation-auc:0.96612 validation-aucpr:0.96912
[21] validation-logloss:0.56133 validation-auc:0.96649 validation-aucpr:0.96935
[22] validation-logloss:0.55645 validation-auc:0.96643 validation-aucpr:0.96937
[23] validation-logloss:0.55196 validation-auc:0.96644 validation-aucpr:0.96907
[24] validation-logloss:0.54704 validation-auc:0.96652 validation-aucpr:0.96910
[25] validation-logloss:0.54231 validation-auc:0.96705 validation-aucpr:0.97070
[26] validation-logloss:0.53769 validation-auc:0.96693 validation-aucpr:0.97069
[27] validation-logloss:0.53300 validation-auc:0.96724 validation-aucpr:0.97101
[28] validation-logloss:0.52846 validation-auc:0.96722 validation-aucpr:0.97048
[29] validation-logloss:0.52399 validation-auc:0.96729 validation-aucpr:0.97122
[30] validation-logloss:0.51947 validation-auc:0.96768 validation-aucpr:0.97158
[31] validation-logloss:0.51538 validation-auc:0.96752 validation-aucpr:0.97150
[32] validation-logloss:0.51113 validation-auc:0.96764 validation-aucpr:0.97151
[33] validation-logloss:0.50726 validation-auc:0.96785 validation-aucpr:0.97163
[34] validation-logloss:0.50302 validation-auc:0.96792 validation-aucpr:0.97177
[35] validation-logloss:0.49890 validation-auc:0.96812 validation-aucpr:0.97192
[36] validation-logloss:0.49492 validation-auc:0.96796 validation-aucpr:0.97183
[37] validation-logloss:0.49106 validation-auc:0.96825 validation-aucpr:0.97214
[38] validation-logloss:0.48704 validation-auc:0.96827 validation-aucpr:0.97237
[39] validation-logloss:0.48317 validation-auc:0.96850 validation-aucpr:0.97258
[40] validation-logloss:0.47938 validation-auc:0.96862 validation-aucpr:0.97276
[41] validation-logloss:0.47565 validation-auc:0.96868 validation-aucpr:0.97274
[42] validation-logloss:0.47212 validation-auc:0.96883 validation-aucpr:0.97283
[43] validation-logloss:0.46842 validation-auc:0.96893 validation-aucpr:0.97282
[44] validation-logloss:0.46486 validation-auc:0.96888 validation-aucpr:0.97286
[45] validation-logloss:0.46143 validation-auc:0.96878 validation-aucpr:0.97280
[46] validation-logloss:0.45815 validation-auc:0.96865 validation-aucpr:0.97257
[47] validation-logloss:0.45476 validation-auc:0.96868 validation-aucpr:0.97275
[48] validation-logloss:0.45137 validation-auc:0.96864 validation-aucpr:0.97225
[49] validation-logloss:0.44808 validation-auc:0.96872 validation-aucpr:0.97225
[50] validation-logloss:0.44465 validation-auc:0.96889 validation-aucpr:0.97253
[51] validation-logloss:0.44148 validation-auc:0.96884 validation-aucpr:0.97250
[52] validation-logloss:0.43837 validation-auc:0.96879 validation-aucpr:0.97256
[53] validation-logloss:0.43519 validation-auc:0.96893 validation-aucpr:0.97270
[54] validation-logloss:0.43206 validation-auc:0.96893 validation-aucpr:0.97264
[55] validation-logloss:0.42896 validation-auc:0.96896 validation-aucpr:0.97266
[56] validation-logloss:0.42600 validation-auc:0.96889 validation-aucpr:0.97236
[57] validation-logloss:0.42335 validation-auc:0.96913 validation-aucpr:0.97298
[58] validation-logloss:0.42036 validation-auc:0.96920 validation-aucpr:0.97305
[59] validation-logloss:0.41758 validation-auc:0.96921 validation-aucpr:0.97283
[60] validation-logloss:0.41475 validation-auc:0.96920 validation-aucpr:0.97290
[61] validation-logloss:0.41200 validation-auc:0.96913 validation-aucpr:0.97286
[62] validation-logloss:0.40918 validation-auc:0.96926 validation-aucpr:0.97289
[63] validation-logloss:0.40658 validation-auc:0.96918 validation-aucpr:0.97282
[64] validation-logloss:0.40410 validation-auc:0.96914 validation-aucpr:0.97277
[65] validation-logloss:0.40171 validation-auc:0.96910 validation-aucpr:0.97242
[66] validation-logloss:0.39914 validation-auc:0.96920 validation-aucpr:0.97242
[67] validation-logloss:0.39644 validation-auc:0.96922 validation-aucpr:0.97225
[68] validation-logloss:0.39410 validation-auc:0.96917 validation-aucpr:0.97213
[69] validation-logloss:0.39156 validation-auc:0.96922 validation-aucpr:0.97223
[70] validation-logloss:0.38908 validation-auc:0.96925 validation-aucpr:0.97215
[71] validation-logloss:0.38659 validation-auc:0.96932 validation-aucpr:0.97221
[72] validation-logloss:0.38424 validation-auc:0.96940 validation-aucpr:0.97273
[73] validation-logloss:0.38187 validation-auc:0.96940 validation-aucpr:0.97274
[74] validation-logloss:0.37965 validation-auc:0.96926 validation-aucpr:0.97261
[75] validation-logloss:0.37728 validation-auc:0.96928 validation-aucpr:0.97262
[76] validation-logloss:0.37522 validation-auc:0.96933 validation-aucpr:0.97264
[77] validation-logloss:0.37310 validation-auc:0.96931 validation-aucpr:0.97264
[78] validation-logloss:0.37081 validation-auc:0.96934 validation-aucpr:0.97266
[79] validation-logloss:0.36866 validation-auc:0.96927 validation-aucpr:0.97259
{'best_iteration': '58', 'best_score': '0.9730494269389566'}
Trial 71, Fold 3: Log loss = 0.3686551244484038, Average precision = 0.9727055199215334, ROC-AUC = 0.9692703768624011, Elapsed Time = 5.743944699999702 seconds
Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 71, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68543 validation-auc:0.91799 validation-aucpr:0.87581
[1] validation-logloss:0.67808 validation-auc:0.93524 validation-aucpr:0.89612
[2] validation-logloss:0.67092 validation-auc:0.95223 validation-aucpr:0.93929
[3] validation-logloss:0.66413 validation-auc:0.95879 validation-aucpr:0.95791
[4] validation-logloss:0.65794 validation-auc:0.96139 validation-aucpr:0.96407
[5] validation-logloss:0.65120 validation-auc:0.96168 validation-aucpr:0.96285
[6] validation-logloss:0.64454 validation-auc:0.96318 validation-aucpr:0.96619
[7] validation-logloss:0.63851 validation-auc:0.96315 validation-aucpr:0.96764
[8] validation-logloss:0.63226 validation-auc:0.96392 validation-aucpr:0.96885
[9] validation-logloss:0.62593 validation-auc:0.96525 validation-aucpr:0.97016
[10] validation-logloss:0.61999 validation-auc:0.96574 validation-aucpr:0.97148
[11] validation-logloss:0.61458 validation-auc:0.96537 validation-aucpr:0.97125
[12] validation-logloss:0.60875 validation-auc:0.96565 validation-aucpr:0.97144
[13] validation-logloss:0.60311 validation-auc:0.96562 validation-aucpr:0.97148
[14] validation-logloss:0.59751 validation-auc:0.96552 validation-aucpr:0.97158
[15] validation-logloss:0.59188 validation-auc:0.96606 validation-aucpr:0.97203
[16] validation-logloss:0.58629 validation-auc:0.96639 validation-aucpr:0.97224
[17] validation-logloss:0.58111 validation-auc:0.96633 validation-aucpr:0.97219
[18] validation-logloss:0.57583 validation-auc:0.96648 validation-aucpr:0.97227
[19] validation-logloss:0.57060 validation-auc:0.96639 validation-aucpr:0.97221
[20] validation-logloss:0.56571 validation-auc:0.96662 validation-aucpr:0.97242
[21] validation-logloss:0.56062 validation-auc:0.96679 validation-aucpr:0.97252
[22] validation-logloss:0.55575 validation-auc:0.96684 validation-aucpr:0.97256
[23] validation-logloss:0.55079 validation-auc:0.96715 validation-aucpr:0.97275
[24] validation-logloss:0.54590 validation-auc:0.96753 validation-aucpr:0.97304
[25] validation-logloss:0.54109 validation-auc:0.96747 validation-aucpr:0.97300
[26] validation-logloss:0.53653 validation-auc:0.96720 validation-aucpr:0.97281
[27] validation-logloss:0.53205 validation-auc:0.96697 validation-aucpr:0.97265
[28] validation-logloss:0.52772 validation-auc:0.96684 validation-aucpr:0.97256
[29] validation-logloss:0.52349 validation-auc:0.96651 validation-aucpr:0.97233
[30] validation-logloss:0.51911 validation-auc:0.96680 validation-aucpr:0.97256
[31] validation-logloss:0.51497 validation-auc:0.96670 validation-aucpr:0.97250
[32] validation-logloss:0.51080 validation-auc:0.96672 validation-aucpr:0.97254
[33] validation-logloss:0.50662 validation-auc:0.96686 validation-aucpr:0.97266
[34] validation-logloss:0.50284 validation-auc:0.96692 validation-aucpr:0.97268
[35] validation-logloss:0.49870 validation-auc:0.96718 validation-aucpr:0.97286
[36] validation-logloss:0.49466 validation-auc:0.96745 validation-aucpr:0.97308
[37] validation-logloss:0.49128 validation-auc:0.96745 validation-aucpr:0.97307
[38] validation-logloss:0.48737 validation-auc:0.96753 validation-aucpr:0.97312
[39] validation-logloss:0.48347 validation-auc:0.96777 validation-aucpr:0.97326
[40] validation-logloss:0.47977 validation-auc:0.96784 validation-aucpr:0.97328
[41] validation-logloss:0.47619 validation-auc:0.96769 validation-aucpr:0.97318
[42] validation-logloss:0.47253 validation-auc:0.96771 validation-aucpr:0.97322
[43] validation-logloss:0.46882 validation-auc:0.96793 validation-aucpr:0.97339
[44] validation-logloss:0.46516 validation-auc:0.96806 validation-aucpr:0.97351
[45] validation-logloss:0.46180 validation-auc:0.96796 validation-aucpr:0.97339
[46] validation-logloss:0.45818 validation-auc:0.96805 validation-aucpr:0.97346
[47] validation-logloss:0.45524 validation-auc:0.96792 validation-aucpr:0.97336
[48] validation-logloss:0.45232 validation-auc:0.96787 validation-aucpr:0.97330
[49] validation-logloss:0.44918 validation-auc:0.96812 validation-aucpr:0.97345
[50] validation-logloss:0.44606 validation-auc:0.96824 validation-aucpr:0.97356
[51] validation-logloss:0.44317 validation-auc:0.96827 validation-aucpr:0.97356
[52] validation-logloss:0.44002 validation-auc:0.96829 validation-aucpr:0.97356
[53] validation-logloss:0.43684 validation-auc:0.96845 validation-aucpr:0.97371
[54] validation-logloss:0.43370 validation-auc:0.96840 validation-aucpr:0.97368
[55] validation-logloss:0.43063 validation-auc:0.96837 validation-aucpr:0.97370
[56] validation-logloss:0.42770 validation-auc:0.96828 validation-aucpr:0.97363
[57] validation-logloss:0.42485 validation-auc:0.96841 validation-aucpr:0.97371
[58] validation-logloss:0.42194 validation-auc:0.96843 validation-aucpr:0.97374
[59] validation-logloss:0.41906 validation-auc:0.96839 validation-aucpr:0.97372
[60] validation-logloss:0.41634 validation-auc:0.96830 validation-aucpr:0.97366
[61] validation-logloss:0.41357 validation-auc:0.96832 validation-aucpr:0.97367
[62] validation-logloss:0.41079 validation-auc:0.96842 validation-aucpr:0.97374
[63] validation-logloss:0.40819 validation-auc:0.96840 validation-aucpr:0.97372
[64] validation-logloss:0.40548 validation-auc:0.96848 validation-aucpr:0.97376
[65] validation-logloss:0.40274 validation-auc:0.96849 validation-aucpr:0.97377
[66] validation-logloss:0.40012 validation-auc:0.96861 validation-aucpr:0.97385
[67] validation-logloss:0.39751 validation-auc:0.96871 validation-aucpr:0.97392
[68] validation-logloss:0.39495 validation-auc:0.96871 validation-aucpr:0.97394
[69] validation-logloss:0.39245 validation-auc:0.96874 validation-aucpr:0.97395
[70] validation-logloss:0.39013 validation-auc:0.96868 validation-aucpr:0.97389
[71] validation-logloss:0.38768 validation-auc:0.96875 validation-aucpr:0.97395
[72] validation-logloss:0.38565 validation-auc:0.96875 validation-aucpr:0.97393
[73] validation-logloss:0.38331 validation-auc:0.96873 validation-aucpr:0.97391
[74] validation-logloss:0.38094 validation-auc:0.96880 validation-aucpr:0.97393
[75] validation-logloss:0.37869 validation-auc:0.96886 validation-aucpr:0.97397
[76] validation-logloss:0.37642 validation-auc:0.96885 validation-aucpr:0.97398
[77] validation-logloss:0.37424 validation-auc:0.96889 validation-aucpr:0.97399
[78] validation-logloss:0.37207 validation-auc:0.96889 validation-aucpr:0.97399
[79] validation-logloss:0.37004 validation-auc:0.96888 validation-aucpr:0.97398
{'best_iteration': '78', 'best_score': '0.9739904032743923'}
Trial 71, Fold 4: Log loss = 0.3700416251857441, Average precision = 0.9739796125881007, ROC-AUC = 0.9688816641272703, Elapsed Time = 5.088956399999006 seconds
Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 71, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68568 validation-auc:0.90673 validation-aucpr:0.84899
[1] validation-logloss:0.67851 validation-auc:0.93090 validation-aucpr:0.90040
[2] validation-logloss:0.67134 validation-auc:0.94797 validation-aucpr:0.93347
[3] validation-logloss:0.66457 validation-auc:0.95152 validation-aucpr:0.94272
[4] validation-logloss:0.65825 validation-auc:0.95428 validation-aucpr:0.95744
[5] validation-logloss:0.65153 validation-auc:0.95624 validation-aucpr:0.96116
[6] validation-logloss:0.64499 validation-auc:0.95835 validation-aucpr:0.96292
[7] validation-logloss:0.63851 validation-auc:0.95894 validation-aucpr:0.96571
[8] validation-logloss:0.63227 validation-auc:0.95984 validation-aucpr:0.96626
[9] validation-logloss:0.62610 validation-auc:0.96109 validation-aucpr:0.96722
[10] validation-logloss:0.62014 validation-auc:0.96131 validation-aucpr:0.96745
[11] validation-logloss:0.61419 validation-auc:0.96147 validation-aucpr:0.96708
[12] validation-logloss:0.60834 validation-auc:0.96163 validation-aucpr:0.96739
[13] validation-logloss:0.60259 validation-auc:0.96209 validation-aucpr:0.96780
[14] validation-logloss:0.59701 validation-auc:0.96237 validation-aucpr:0.96765
[15] validation-logloss:0.59147 validation-auc:0.96290 validation-aucpr:0.96790
[16] validation-logloss:0.58621 validation-auc:0.96301 validation-aucpr:0.96763
[17] validation-logloss:0.58067 validation-auc:0.96357 validation-aucpr:0.96808
[18] validation-logloss:0.57531 validation-auc:0.96403 validation-aucpr:0.96842
[19] validation-logloss:0.57016 validation-auc:0.96433 validation-aucpr:0.96866
[20] validation-logloss:0.56512 validation-auc:0.96407 validation-aucpr:0.96861
[21] validation-logloss:0.56015 validation-auc:0.96430 validation-aucpr:0.96879
[22] validation-logloss:0.55527 validation-auc:0.96412 validation-aucpr:0.96867
[23] validation-logloss:0.55058 validation-auc:0.96430 validation-aucpr:0.96885
[24] validation-logloss:0.54642 validation-auc:0.96425 validation-aucpr:0.96883
[25] validation-logloss:0.54170 validation-auc:0.96463 validation-aucpr:0.96901
[26] validation-logloss:0.53706 validation-auc:0.96485 validation-aucpr:0.96928
[27] validation-logloss:0.53247 validation-auc:0.96480 validation-aucpr:0.96923
[28] validation-logloss:0.52807 validation-auc:0.96513 validation-aucpr:0.97001
[29] validation-logloss:0.52385 validation-auc:0.96515 validation-aucpr:0.96998
[30] validation-logloss:0.51998 validation-auc:0.96512 validation-aucpr:0.96975
[31] validation-logloss:0.51552 validation-auc:0.96547 validation-aucpr:0.96986
[32] validation-logloss:0.51130 validation-auc:0.96551 validation-aucpr:0.96990
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[43] validation-logloss:0.46977 validation-auc:0.96643 validation-aucpr:0.97053
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[49] validation-logloss:0.44976 validation-auc:0.96692 validation-aucpr:0.97082
[50] validation-logloss:0.44649 validation-auc:0.96708 validation-aucpr:0.97092
[51] validation-logloss:0.44328 validation-auc:0.96704 validation-aucpr:0.97099
[52] validation-logloss:0.44019 validation-auc:0.96706 validation-aucpr:0.97104
[53] validation-logloss:0.43749 validation-auc:0.96706 validation-aucpr:0.97101
[54] validation-logloss:0.43445 validation-auc:0.96709 validation-aucpr:0.97076
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[56] validation-logloss:0.42873 validation-auc:0.96710 validation-aucpr:0.97073
[57] validation-logloss:0.42576 validation-auc:0.96723 validation-aucpr:0.97083
[58] validation-logloss:0.42287 validation-auc:0.96729 validation-aucpr:0.97082
[59] validation-logloss:0.42014 validation-auc:0.96737 validation-aucpr:0.97087
[60] validation-logloss:0.41733 validation-auc:0.96746 validation-aucpr:0.97095
[61] validation-logloss:0.41460 validation-auc:0.96755 validation-aucpr:0.97099
[62] validation-logloss:0.41189 validation-auc:0.96754 validation-aucpr:0.97099
[63] validation-logloss:0.40923 validation-auc:0.96754 validation-aucpr:0.97098
[64] validation-logloss:0.40654 validation-auc:0.96758 validation-aucpr:0.97100
[65] validation-logloss:0.40382 validation-auc:0.96776 validation-aucpr:0.97108
[66] validation-logloss:0.40127 validation-auc:0.96771 validation-aucpr:0.97110
[67] validation-logloss:0.39909 validation-auc:0.96751 validation-aucpr:0.97096
[68] validation-logloss:0.39665 validation-auc:0.96750 validation-aucpr:0.97094
[69] validation-logloss:0.39411 validation-auc:0.96754 validation-aucpr:0.97084
[70] validation-logloss:0.39181 validation-auc:0.96768 validation-aucpr:0.97102
[71] validation-logloss:0.38938 validation-auc:0.96780 validation-aucpr:0.97112
[72] validation-logloss:0.38701 validation-auc:0.96779 validation-aucpr:0.97111
[73] validation-logloss:0.38481 validation-auc:0.96771 validation-aucpr:0.97105
[74] validation-logloss:0.38263 validation-auc:0.96781 validation-aucpr:0.97095
[75] validation-logloss:0.38030 validation-auc:0.96784 validation-aucpr:0.97097
[76] validation-logloss:0.37836 validation-auc:0.96788 validation-aucpr:0.97091
[77] validation-logloss:0.37626 validation-auc:0.96793 validation-aucpr:0.97094
[78] validation-logloss:0.37414 validation-auc:0.96793 validation-aucpr:0.97103
[79] validation-logloss:0.37183 validation-auc:0.96796 validation-aucpr:0.97104
{'best_iteration': '46', 'best_score': '0.9713535849713747'}
Trial 71, Fold 5: Log loss = 0.3718332745016931, Average precision = 0.9711940729228865, ROC-AUC = 0.9679583077866339, Elapsed Time = 5.344965599997522 seconds
Optimization Progress: 72%|#######2 | 72/100 [3:26:35<1:45:09, 225.35s/it]
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 72, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63479 validation-auc:0.95862 validation-aucpr:0.96396
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58509 validation-auc:0.96387 validation-aucpr:0.96927
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54597 validation-auc:0.96471 validation-aucpr:0.96937
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.51280 validation-auc:0.96481 validation-aucpr:0.96913
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.48051 validation-auc:0.96534 validation-aucpr:0.96970
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.45177 validation-auc:0.96644 validation-aucpr:0.97122
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.42585 validation-auc:0.96732 validation-aucpr:0.97216
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.40293 validation-auc:0.96790 validation-aucpr:0.97295
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.38464 validation-auc:0.96848 validation-aucpr:0.97343
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.36603 validation-auc:0.96873 validation-aucpr:0.97162
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.34968 validation-auc:0.96902 validation-aucpr:0.97160
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.33498 validation-auc:0.96952 validation-aucpr:0.97203
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.32329 validation-auc:0.96981 validation-aucpr:0.97218
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.31113 validation-auc:0.96993 validation-aucpr:0.97227
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.30081 validation-auc:0.97003 validation-aucpr:0.97234
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.29289 validation-auc:0.96968 validation-aucpr:0.97212
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.28387 validation-auc:0.96992 validation-aucpr:0.97225
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.27559 validation-auc:0.97030 validation-aucpr:0.97337
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.26793 validation-auc:0.97054 validation-aucpr:0.97357
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.26252 validation-auc:0.97042 validation-aucpr:0.97332
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.25663 validation-auc:0.97087 validation-aucpr:0.97361
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.25189 validation-auc:0.97096 validation-aucpr:0.97534
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.24657 validation-auc:0.97100 validation-aucpr:0.97546
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.24305 validation-auc:0.97094 validation-aucpr:0.97538
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.23976 validation-auc:0.97078 validation-aucpr:0.97523
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.23582 validation-auc:0.97094 validation-aucpr:0.97531
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23299 validation-auc:0.97091 validation-aucpr:0.97540
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22990 validation-auc:0.97096 validation-aucpr:0.97539
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.22666 validation-auc:0.97112 validation-aucpr:0.97550
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22455 validation-auc:0.97102 validation-aucpr:0.97539
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22152 validation-auc:0.97107 validation-aucpr:0.97542
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21898 validation-auc:0.97098 validation-aucpr:0.97540
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21673 validation-auc:0.97116 validation-aucpr:0.97561
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21486 validation-auc:0.97120 validation-aucpr:0.97564
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21233 validation-auc:0.97142 validation-aucpr:0.97582
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21038 validation-auc:0.97157 validation-aucpr:0.97596
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20846 validation-auc:0.97172 validation-aucpr:0.97608
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20671 validation-auc:0.97190 validation-aucpr:0.97618
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20543 validation-auc:0.97184 validation-aucpr:0.97612
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20442 validation-auc:0.97188 validation-aucpr:0.97614
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20296 validation-auc:0.97203 validation-aucpr:0.97638
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20167 validation-auc:0.97220 validation-aucpr:0.97650
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20079 validation-auc:0.97223 validation-aucpr:0.97654
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19997 validation-auc:0.97220 validation-aucpr:0.97652
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19926 validation-auc:0.97224 validation-aucpr:0.97655
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19850 validation-auc:0.97228 validation-aucpr:0.97652
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19752 validation-auc:0.97240 validation-aucpr:0.97663
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19686 validation-auc:0.97242 validation-aucpr:0.97664
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19625 validation-auc:0.97250 validation-aucpr:0.97673
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19582 validation-auc:0.97253 validation-aucpr:0.97676
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19535 validation-auc:0.97259 validation-aucpr:0.97677
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19509 validation-auc:0.97254 validation-aucpr:0.97673
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19491 validation-auc:0.97253 validation-aucpr:0.97668
[21:25:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19474 validation-auc:0.97249 validation-aucpr:0.97666
{'best_iteration': '50', 'best_score': '0.9767685335164963'}
Trial 72, Fold 1: Log loss = 0.19473958140355582, Average precision = 0.9766591920176859, ROC-AUC = 0.9724918209279094, Elapsed Time = 6.8159644000006665 seconds
Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 72, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63593 validation-auc:0.95733 validation-aucpr:0.96176
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58589 validation-auc:0.96568 validation-aucpr:0.96911
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54672 validation-auc:0.96585 validation-aucpr:0.96950
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.51010 validation-auc:0.96673 validation-aucpr:0.97029
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.47851 validation-auc:0.96782 validation-aucpr:0.97105
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.44841 validation-auc:0.96928 validation-aucpr:0.97230
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.42594 validation-auc:0.96903 validation-aucpr:0.97225
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.40177 validation-auc:0.96956 validation-aucpr:0.97282
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.38107 validation-auc:0.96962 validation-aucpr:0.97294
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.36276 validation-auc:0.96967 validation-aucpr:0.97292
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.34636 validation-auc:0.97010 validation-aucpr:0.97326
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.33115 validation-auc:0.97042 validation-aucpr:0.97348
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.31946 validation-auc:0.97029 validation-aucpr:0.97335
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.30711 validation-auc:0.97063 validation-aucpr:0.97359
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.29606 validation-auc:0.97065 validation-aucpr:0.97363
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.28644 validation-auc:0.97086 validation-aucpr:0.97384
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.27874 validation-auc:0.97075 validation-aucpr:0.97379
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.27079 validation-auc:0.97097 validation-aucpr:0.97392
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.26468 validation-auc:0.97095 validation-aucpr:0.97386
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.25857 validation-auc:0.97102 validation-aucpr:0.97387
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.25311 validation-auc:0.97115 validation-aucpr:0.97400
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.24857 validation-auc:0.97111 validation-aucpr:0.97394
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.24284 validation-auc:0.97130 validation-aucpr:0.97401
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.23756 validation-auc:0.97153 validation-aucpr:0.97423
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.23293 validation-auc:0.97175 validation-aucpr:0.97438
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.22852 validation-auc:0.97176 validation-aucpr:0.97367
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.22477 validation-auc:0.97195 validation-aucpr:0.97488
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22184 validation-auc:0.97204 validation-aucpr:0.97486
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21841 validation-auc:0.97235 validation-aucpr:0.97510
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.21566 validation-auc:0.97224 validation-aucpr:0.97506
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21296 validation-auc:0.97209 validation-aucpr:0.97493
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21077 validation-auc:0.97212 validation-aucpr:0.97496
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20933 validation-auc:0.97190 validation-aucpr:0.97476
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20709 validation-auc:0.97187 validation-aucpr:0.97478
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20542 validation-auc:0.97196 validation-aucpr:0.97481
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20374 validation-auc:0.97217 validation-aucpr:0.97503
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20176 validation-auc:0.97225 validation-aucpr:0.97467
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20049 validation-auc:0.97217 validation-aucpr:0.97460
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19912 validation-auc:0.97228 validation-aucpr:0.97468
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19782 validation-auc:0.97235 validation-aucpr:0.97472
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19660 validation-auc:0.97227 validation-aucpr:0.97460
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19510 validation-auc:0.97249 validation-aucpr:0.97478
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19476 validation-auc:0.97236 validation-aucpr:0.97465
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19377 validation-auc:0.97232 validation-aucpr:0.97459
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19245 validation-auc:0.97248 validation-aucpr:0.97473
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19133 validation-auc:0.97263 validation-aucpr:0.97485
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19048 validation-auc:0.97281 validation-aucpr:0.97575
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.18984 validation-auc:0.97290 validation-aucpr:0.97578
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.18904 validation-auc:0.97293 validation-aucpr:0.97580
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.18872 validation-auc:0.97293 validation-aucpr:0.97582
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.18799 validation-auc:0.97307 validation-aucpr:0.97587
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.18766 validation-auc:0.97295 validation-aucpr:0.97571
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.18715 validation-auc:0.97306 validation-aucpr:0.97591
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.18652 validation-auc:0.97301 validation-aucpr:0.97578
{'best_iteration': '52', 'best_score': '0.9759079413380883'}
Trial 72, Fold 2: Log loss = 0.18651565331945985, Average precision = 0.9757842427832537, ROC-AUC = 0.9730090766420401, Elapsed Time = 7.354374000002281 seconds
Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 72, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63908 validation-auc:0.94558 validation-aucpr:0.95267
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58812 validation-auc:0.96595 validation-aucpr:0.96939
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54491 validation-auc:0.96801 validation-aucpr:0.97151
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.51018 validation-auc:0.96880 validation-aucpr:0.97192
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.47795 validation-auc:0.96847 validation-aucpr:0.97190
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.44799 validation-auc:0.96963 validation-aucpr:0.97300
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.42190 validation-auc:0.96994 validation-aucpr:0.97329
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.39899 validation-auc:0.97041 validation-aucpr:0.97357
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.38088 validation-auc:0.97020 validation-aucpr:0.97320
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.36289 validation-auc:0.97018 validation-aucpr:0.97293
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.34803 validation-auc:0.97027 validation-aucpr:0.97300
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.33386 validation-auc:0.96978 validation-aucpr:0.97263
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.31990 validation-auc:0.97017 validation-aucpr:0.97294
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.30734 validation-auc:0.97054 validation-aucpr:0.97360
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.29624 validation-auc:0.97062 validation-aucpr:0.97389
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.28787 validation-auc:0.97062 validation-aucpr:0.97384
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.27853 validation-auc:0.97114 validation-aucpr:0.97420
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.26982 validation-auc:0.97168 validation-aucpr:0.97560
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.26188 validation-auc:0.97177 validation-aucpr:0.97568
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.25544 validation-auc:0.97156 validation-aucpr:0.97546
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.24884 validation-auc:0.97182 validation-aucpr:0.97572
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.24330 validation-auc:0.97174 validation-aucpr:0.97568
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.23817 validation-auc:0.97181 validation-aucpr:0.97574
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.23423 validation-auc:0.97183 validation-aucpr:0.97576
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22991 validation-auc:0.97193 validation-aucpr:0.97588
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.22601 validation-auc:0.97189 validation-aucpr:0.97583
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.22288 validation-auc:0.97187 validation-aucpr:0.97485
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22021 validation-auc:0.97179 validation-aucpr:0.97472
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21686 validation-auc:0.97195 validation-aucpr:0.97486
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.21445 validation-auc:0.97192 validation-aucpr:0.97495
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21232 validation-auc:0.97211 validation-aucpr:0.97629
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21030 validation-auc:0.97195 validation-aucpr:0.97614
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20858 validation-auc:0.97183 validation-aucpr:0.97604
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20687 validation-auc:0.97183 validation-aucpr:0.97600
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20543 validation-auc:0.97173 validation-aucpr:0.97594
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20346 validation-auc:0.97191 validation-aucpr:0.97606
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20212 validation-auc:0.97192 validation-aucpr:0.97606
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20056 validation-auc:0.97203 validation-aucpr:0.97622
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19926 validation-auc:0.97200 validation-aucpr:0.97617
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19855 validation-auc:0.97191 validation-aucpr:0.97608
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19808 validation-auc:0.97186 validation-aucpr:0.97596
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19748 validation-auc:0.97173 validation-aucpr:0.97586
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19673 validation-auc:0.97167 validation-aucpr:0.97579
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19598 validation-auc:0.97167 validation-aucpr:0.97583
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19544 validation-auc:0.97171 validation-aucpr:0.97593
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19466 validation-auc:0.97180 validation-aucpr:0.97606
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19373 validation-auc:0.97197 validation-aucpr:0.97620
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19315 validation-auc:0.97201 validation-aucpr:0.97620
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19244 validation-auc:0.97202 validation-aucpr:0.97619
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19208 validation-auc:0.97200 validation-aucpr:0.97618
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19152 validation-auc:0.97208 validation-aucpr:0.97621
[21:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19106 validation-auc:0.97219 validation-aucpr:0.97640
[21:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19105 validation-auc:0.97216 validation-aucpr:0.97635
[21:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19085 validation-auc:0.97213 validation-aucpr:0.97630
{'best_iteration': '51', 'best_score': '0.9764002137695778'}
Trial 72, Fold 3: Log loss = 0.19085098155620686, Average precision = 0.9763054316523052, ROC-AUC = 0.97213192290838, Elapsed Time = 6.96847870000056 seconds
Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 72, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63561 validation-auc:0.95069 validation-aucpr:0.95005
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58593 validation-auc:0.96165 validation-aucpr:0.96418
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54323 validation-auc:0.96460 validation-aucpr:0.97045
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.50675 validation-auc:0.96527 validation-aucpr:0.97088
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.47843 validation-auc:0.96388 validation-aucpr:0.96988
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.44957 validation-auc:0.96529 validation-aucpr:0.97101
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.42455 validation-auc:0.96597 validation-aucpr:0.97152
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.40281 validation-auc:0.96649 validation-aucpr:0.97180
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.38329 validation-auc:0.96671 validation-aucpr:0.97206
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.36544 validation-auc:0.96715 validation-aucpr:0.97231
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.34904 validation-auc:0.96763 validation-aucpr:0.97277
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.33525 validation-auc:0.96778 validation-aucpr:0.97278
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.32436 validation-auc:0.96765 validation-aucpr:0.97286
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.31452 validation-auc:0.96707 validation-aucpr:0.97249
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.30327 validation-auc:0.96725 validation-aucpr:0.97266
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.29394 validation-auc:0.96786 validation-aucpr:0.97306
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.28626 validation-auc:0.96785 validation-aucpr:0.97308
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.27920 validation-auc:0.96788 validation-aucpr:0.97313
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.27295 validation-auc:0.96782 validation-aucpr:0.97313
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.26733 validation-auc:0.96778 validation-aucpr:0.97311
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.26043 validation-auc:0.96818 validation-aucpr:0.97343
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.25523 validation-auc:0.96840 validation-aucpr:0.97360
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.25057 validation-auc:0.96855 validation-aucpr:0.97368
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.24550 validation-auc:0.96880 validation-aucpr:0.97388
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24164 validation-auc:0.96888 validation-aucpr:0.97396
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.23699 validation-auc:0.96919 validation-aucpr:0.97420
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23358 validation-auc:0.96911 validation-aucpr:0.97413
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22971 validation-auc:0.96938 validation-aucpr:0.97434
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.22671 validation-auc:0.96965 validation-aucpr:0.97454
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22409 validation-auc:0.96963 validation-aucpr:0.97458
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22053 validation-auc:0.97000 validation-aucpr:0.97485
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21709 validation-auc:0.97037 validation-aucpr:0.97511
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21527 validation-auc:0.97041 validation-aucpr:0.97514
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21306 validation-auc:0.97060 validation-aucpr:0.97527
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21116 validation-auc:0.97075 validation-aucpr:0.97539
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20924 validation-auc:0.97087 validation-aucpr:0.97548
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20719 validation-auc:0.97112 validation-aucpr:0.97569
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20612 validation-auc:0.97105 validation-aucpr:0.97563
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20411 validation-auc:0.97136 validation-aucpr:0.97587
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20268 validation-auc:0.97139 validation-aucpr:0.97588
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20090 validation-auc:0.97168 validation-aucpr:0.97609
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19927 validation-auc:0.97192 validation-aucpr:0.97628
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19854 validation-auc:0.97184 validation-aucpr:0.97620
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19782 validation-auc:0.97172 validation-aucpr:0.97612
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19699 validation-auc:0.97185 validation-aucpr:0.97622
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19609 validation-auc:0.97191 validation-aucpr:0.97625
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19544 validation-auc:0.97200 validation-aucpr:0.97629
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19455 validation-auc:0.97209 validation-aucpr:0.97635
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19404 validation-auc:0.97216 validation-aucpr:0.97641
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19402 validation-auc:0.97199 validation-aucpr:0.97630
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19337 validation-auc:0.97204 validation-aucpr:0.97634
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19285 validation-auc:0.97203 validation-aucpr:0.97634
[21:25:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19231 validation-auc:0.97218 validation-aucpr:0.97643
[21:25:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19179 validation-auc:0.97232 validation-aucpr:0.97653
{'best_iteration': '53', 'best_score': '0.9765328333816946'}
Trial 72, Fold 4: Log loss = 0.1917944273199209, Average precision = 0.9765367007556147, ROC-AUC = 0.9723240067144979, Elapsed Time = 7.33326169999782 seconds
Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 72, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63486 validation-auc:0.95834 validation-aucpr:0.96338
[21:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.58509 validation-auc:0.96294 validation-aucpr:0.96551
[21:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.54780 validation-auc:0.96268 validation-aucpr:0.96743
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.51017 validation-auc:0.96475 validation-aucpr:0.96938
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.48221 validation-auc:0.96398 validation-aucpr:0.96878
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.45338 validation-auc:0.96498 validation-aucpr:0.96938
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.42716 validation-auc:0.96556 validation-aucpr:0.96986
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.40416 validation-auc:0.96620 validation-aucpr:0.97036
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.38420 validation-auc:0.96707 validation-aucpr:0.97107
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.36584 validation-auc:0.96771 validation-aucpr:0.97156
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.34969 validation-auc:0.96790 validation-aucpr:0.97172
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.33478 validation-auc:0.96846 validation-aucpr:0.97211
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.32356 validation-auc:0.96850 validation-aucpr:0.97202
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.31169 validation-auc:0.96855 validation-aucpr:0.97208
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.30058 validation-auc:0.96894 validation-aucpr:0.97226
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.29067 validation-auc:0.96948 validation-aucpr:0.97377
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.28270 validation-auc:0.96956 validation-aucpr:0.97385
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.27603 validation-auc:0.96973 validation-aucpr:0.97381
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.26845 validation-auc:0.96985 validation-aucpr:0.97391
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.26232 validation-auc:0.96991 validation-aucpr:0.97387
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.25577 validation-auc:0.97001 validation-aucpr:0.97398
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.25121 validation-auc:0.96978 validation-aucpr:0.97236
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.24593 validation-auc:0.97007 validation-aucpr:0.97257
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.24169 validation-auc:0.97005 validation-aucpr:0.97258
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.23741 validation-auc:0.97020 validation-aucpr:0.97269
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.23375 validation-auc:0.97025 validation-aucpr:0.97275
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23013 validation-auc:0.97046 validation-aucpr:0.97287
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22742 validation-auc:0.97048 validation-aucpr:0.97303
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.22396 validation-auc:0.97086 validation-aucpr:0.97326
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22091 validation-auc:0.97115 validation-aucpr:0.97339
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21820 validation-auc:0.97140 validation-aucpr:0.97357
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21586 validation-auc:0.97135 validation-aucpr:0.97351
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21414 validation-auc:0.97127 validation-aucpr:0.97348
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21251 validation-auc:0.97135 validation-aucpr:0.97377
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21091 validation-auc:0.97125 validation-aucpr:0.97342
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20952 validation-auc:0.97126 validation-aucpr:0.97338
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20803 validation-auc:0.97137 validation-aucpr:0.97350
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20695 validation-auc:0.97140 validation-aucpr:0.97347
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20588 validation-auc:0.97137 validation-aucpr:0.97344
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20455 validation-auc:0.97153 validation-aucpr:0.97355
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20322 validation-auc:0.97188 validation-aucpr:0.97498
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20194 validation-auc:0.97207 validation-aucpr:0.97512
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20116 validation-auc:0.97209 validation-aucpr:0.97501
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20019 validation-auc:0.97212 validation-aucpr:0.97495
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19953 validation-auc:0.97216 validation-aucpr:0.97554
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19895 validation-auc:0.97216 validation-aucpr:0.97544
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19823 validation-auc:0.97221 validation-aucpr:0.97545
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19771 validation-auc:0.97212 validation-aucpr:0.97538
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19706 validation-auc:0.97212 validation-aucpr:0.97536
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19638 validation-auc:0.97222 validation-aucpr:0.97542
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19578 validation-auc:0.97223 validation-aucpr:0.97537
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19542 validation-auc:0.97223 validation-aucpr:0.97537
[21:26:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19499 validation-auc:0.97235 validation-aucpr:0.97552
[21:26:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19465 validation-auc:0.97235 validation-aucpr:0.97560
{'best_iteration': '53', 'best_score': '0.9756027609368605'}
Trial 72, Fold 5: Log loss = 0.19464730760572593, Average precision = 0.9756072587949387, ROC-AUC = 0.9723509280419153, Elapsed Time = 7.446603000000323 seconds
Optimization Progress: 73%|#######3 | 73/100 [3:27:19<1:16:53, 170.88s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 73, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66221 validation-auc:0.95893 validation-aucpr:0.96232
[1] validation-logloss:0.63373 validation-auc:0.96436 validation-aucpr:0.96920
[2] validation-logloss:0.60796 validation-auc:0.96393 validation-aucpr:0.96892
[3] validation-logloss:0.58642 validation-auc:0.96412 validation-aucpr:0.96906
[4] validation-logloss:0.56384 validation-auc:0.96473 validation-aucpr:0.96955
[5] validation-logloss:0.54293 validation-auc:0.96522 validation-aucpr:0.97028
[6] validation-logloss:0.52508 validation-auc:0.96544 validation-aucpr:0.97039
[7] validation-logloss:0.50736 validation-auc:0.96576 validation-aucpr:0.97049
[8] validation-logloss:0.49035 validation-auc:0.96622 validation-aucpr:0.97123
[9] validation-logloss:0.47459 validation-auc:0.96620 validation-aucpr:0.97130
[10] validation-logloss:0.45961 validation-auc:0.96631 validation-aucpr:0.97137
[11] validation-logloss:0.44543 validation-auc:0.96639 validation-aucpr:0.97145
[12] validation-logloss:0.43265 validation-auc:0.96645 validation-aucpr:0.97152
[13] validation-logloss:0.42071 validation-auc:0.96654 validation-aucpr:0.97190
[14] validation-logloss:0.40943 validation-auc:0.96661 validation-aucpr:0.97197
[15] validation-logloss:0.39851 validation-auc:0.96699 validation-aucpr:0.97223
[16] validation-logloss:0.38834 validation-auc:0.96691 validation-aucpr:0.97217
[17] validation-logloss:0.37839 validation-auc:0.96715 validation-aucpr:0.97232
[18] validation-logloss:0.36913 validation-auc:0.96727 validation-aucpr:0.97239
[19] validation-logloss:0.36175 validation-auc:0.96725 validation-aucpr:0.97234
[20] validation-logloss:0.35374 validation-auc:0.96734 validation-aucpr:0.97237
[21] validation-logloss:0.34657 validation-auc:0.96720 validation-aucpr:0.97230
[22] validation-logloss:0.33906 validation-auc:0.96747 validation-aucpr:0.97248
[23] validation-logloss:0.33236 validation-auc:0.96742 validation-aucpr:0.97247
[24] validation-logloss:0.32558 validation-auc:0.96758 validation-aucpr:0.97255
[25] validation-logloss:0.31926 validation-auc:0.96771 validation-aucpr:0.97264
[26] validation-logloss:0.31390 validation-auc:0.96783 validation-aucpr:0.97274
[27] validation-logloss:0.30809 validation-auc:0.96792 validation-aucpr:0.97281
[28] validation-logloss:0.30267 validation-auc:0.96801 validation-aucpr:0.97285
[29] validation-logloss:0.29765 validation-auc:0.96828 validation-aucpr:0.97363
[30] validation-logloss:0.29344 validation-auc:0.96830 validation-aucpr:0.97362
[31] validation-logloss:0.28881 validation-auc:0.96856 validation-aucpr:0.97380
[32] validation-logloss:0.28421 validation-auc:0.96874 validation-aucpr:0.97393
[33] validation-logloss:0.28055 validation-auc:0.96886 validation-aucpr:0.97401
[34] validation-logloss:0.27648 validation-auc:0.96907 validation-aucpr:0.97411
[35] validation-logloss:0.27261 validation-auc:0.96921 validation-aucpr:0.97422
[36] validation-logloss:0.26899 validation-auc:0.96927 validation-aucpr:0.97425
[37] validation-logloss:0.26567 validation-auc:0.96930 validation-aucpr:0.97426
[38] validation-logloss:0.26241 validation-auc:0.96932 validation-aucpr:0.97428
[39] validation-logloss:0.25972 validation-auc:0.96938 validation-aucpr:0.97430
[40] validation-logloss:0.25659 validation-auc:0.96949 validation-aucpr:0.97436
[41] validation-logloss:0.25374 validation-auc:0.96959 validation-aucpr:0.97445
[42] validation-logloss:0.25129 validation-auc:0.96970 validation-aucpr:0.97457
{'best_iteration': '42', 'best_score': '0.9745716988150757'}
Trial 73, Fold 1: Log loss = 0.25128757216862274, Average precision = 0.9745746531559298, ROC-AUC = 0.9696964333510179, Elapsed Time = 1.0446025999990525 seconds
Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 73, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66232 validation-auc:0.96079 validation-aucpr:0.96036
[1] validation-logloss:0.63411 validation-auc:0.96436 validation-aucpr:0.96732
[2] validation-logloss:0.60766 validation-auc:0.96736 validation-aucpr:0.97094
[3] validation-logloss:0.58347 validation-auc:0.96748 validation-aucpr:0.97103
[4] validation-logloss:0.56134 validation-auc:0.96845 validation-aucpr:0.97189
[5] validation-logloss:0.54033 validation-auc:0.96884 validation-aucpr:0.97221
[6] validation-logloss:0.52085 validation-auc:0.96888 validation-aucpr:0.97220
[7] validation-logloss:0.50500 validation-auc:0.96859 validation-aucpr:0.97199
[8] validation-logloss:0.48768 validation-auc:0.96905 validation-aucpr:0.97233
[9] validation-logloss:0.47156 validation-auc:0.96946 validation-aucpr:0.97264
[10] validation-logloss:0.45651 validation-auc:0.96976 validation-aucpr:0.97284
[11] validation-logloss:0.44261 validation-auc:0.96967 validation-aucpr:0.97278
[12] validation-logloss:0.43086 validation-auc:0.96969 validation-aucpr:0.97283
[13] validation-logloss:0.41852 validation-auc:0.97000 validation-aucpr:0.97301
[14] validation-logloss:0.40708 validation-auc:0.97007 validation-aucpr:0.97302
[15] validation-logloss:0.39585 validation-auc:0.97020 validation-aucpr:0.97312
[16] validation-logloss:0.38553 validation-auc:0.97022 validation-aucpr:0.97314
[17] validation-logloss:0.37584 validation-auc:0.97032 validation-aucpr:0.97322
[18] validation-logloss:0.36656 validation-auc:0.97034 validation-aucpr:0.97323
[19] validation-logloss:0.35874 validation-auc:0.97055 validation-aucpr:0.97338
[20] validation-logloss:0.35063 validation-auc:0.97049 validation-aucpr:0.97336
[21] validation-logloss:0.34274 validation-auc:0.97056 validation-aucpr:0.97342
[22] validation-logloss:0.33619 validation-auc:0.97062 validation-aucpr:0.97348
[23] validation-logloss:0.32934 validation-auc:0.97070 validation-aucpr:0.97356
[24] validation-logloss:0.32258 validation-auc:0.97070 validation-aucpr:0.97358
[25] validation-logloss:0.31621 validation-auc:0.97081 validation-aucpr:0.97366
[26] validation-logloss:0.31095 validation-auc:0.97078 validation-aucpr:0.97364
[27] validation-logloss:0.30513 validation-auc:0.97087 validation-aucpr:0.97372
[28] validation-logloss:0.30036 validation-auc:0.97077 validation-aucpr:0.97365
[29] validation-logloss:0.29538 validation-auc:0.97076 validation-aucpr:0.97365
[30] validation-logloss:0.29032 validation-auc:0.97088 validation-aucpr:0.97374
[31] validation-logloss:0.28535 validation-auc:0.97102 validation-aucpr:0.97386
[32] validation-logloss:0.28084 validation-auc:0.97104 validation-aucpr:0.97388
[33] validation-logloss:0.27670 validation-auc:0.97105 validation-aucpr:0.97389
[34] validation-logloss:0.27271 validation-auc:0.97118 validation-aucpr:0.97396
[35] validation-logloss:0.26930 validation-auc:0.97129 validation-aucpr:0.97405
[36] validation-logloss:0.26552 validation-auc:0.97131 validation-aucpr:0.97408
[37] validation-logloss:0.26201 validation-auc:0.97139 validation-aucpr:0.97407
[38] validation-logloss:0.25857 validation-auc:0.97142 validation-aucpr:0.97409
[39] validation-logloss:0.25520 validation-auc:0.97151 validation-aucpr:0.97417
[40] validation-logloss:0.25217 validation-auc:0.97151 validation-aucpr:0.97418
[41] validation-logloss:0.24933 validation-auc:0.97154 validation-aucpr:0.97418
[42] validation-logloss:0.24689 validation-auc:0.97166 validation-aucpr:0.97428
{'best_iteration': '42', 'best_score': '0.9742818391162781'}
Trial 73, Fold 2: Log loss = 0.24689143229259836, Average precision = 0.9742754776508732, ROC-AUC = 0.9716611769376143, Elapsed Time = 1.2765495000021474 seconds
Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 73, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66213 validation-auc:0.96246 validation-aucpr:0.96394
[1] validation-logloss:0.63405 validation-auc:0.96415 validation-aucpr:0.96683
[2] validation-logloss:0.60788 validation-auc:0.96467 validation-aucpr:0.96728
[3] validation-logloss:0.58403 validation-auc:0.96499 validation-aucpr:0.96704
[4] validation-logloss:0.56355 validation-auc:0.96602 validation-aucpr:0.96843
[5] validation-logloss:0.54273 validation-auc:0.96700 validation-aucpr:0.97014
[6] validation-logloss:0.52286 validation-auc:0.96751 validation-aucpr:0.97058
[7] validation-logloss:0.50495 validation-auc:0.96759 validation-aucpr:0.97057
[8] validation-logloss:0.48795 validation-auc:0.96782 validation-aucpr:0.97069
[9] validation-logloss:0.47198 validation-auc:0.96846 validation-aucpr:0.97249
[10] validation-logloss:0.45684 validation-auc:0.96869 validation-aucpr:0.97273
[11] validation-logloss:0.44263 validation-auc:0.96889 validation-aucpr:0.97289
[12] validation-logloss:0.42975 validation-auc:0.96923 validation-aucpr:0.97347
[13] validation-logloss:0.41851 validation-auc:0.96942 validation-aucpr:0.97364
[14] validation-logloss:0.40790 validation-auc:0.96956 validation-aucpr:0.97377
[15] validation-logloss:0.39684 validation-auc:0.96962 validation-aucpr:0.97380
[16] validation-logloss:0.38670 validation-auc:0.96960 validation-aucpr:0.97383
[17] validation-logloss:0.37721 validation-auc:0.96959 validation-aucpr:0.97380
[18] validation-logloss:0.36798 validation-auc:0.96964 validation-aucpr:0.97385
[19] validation-logloss:0.35934 validation-auc:0.96989 validation-aucpr:0.97404
[20] validation-logloss:0.35106 validation-auc:0.96995 validation-aucpr:0.97412
[21] validation-logloss:0.34318 validation-auc:0.97001 validation-aucpr:0.97415
[22] validation-logloss:0.33563 validation-auc:0.97003 validation-aucpr:0.97415
[23] validation-logloss:0.32875 validation-auc:0.96995 validation-aucpr:0.97407
[24] validation-logloss:0.32259 validation-auc:0.97005 validation-aucpr:0.97415
[25] validation-logloss:0.31612 validation-auc:0.97013 validation-aucpr:0.97421
[26] validation-logloss:0.31093 validation-auc:0.97012 validation-aucpr:0.97419
[27] validation-logloss:0.30530 validation-auc:0.97012 validation-aucpr:0.97419
[28] validation-logloss:0.29981 validation-auc:0.97017 validation-aucpr:0.97432
[29] validation-logloss:0.29480 validation-auc:0.97022 validation-aucpr:0.97435
[30] validation-logloss:0.28996 validation-auc:0.97030 validation-aucpr:0.97440
[31] validation-logloss:0.28523 validation-auc:0.97043 validation-aucpr:0.97449
[32] validation-logloss:0.28082 validation-auc:0.97047 validation-aucpr:0.97452
[33] validation-logloss:0.27672 validation-auc:0.97045 validation-aucpr:0.97450
[34] validation-logloss:0.27259 validation-auc:0.97059 validation-aucpr:0.97462
[35] validation-logloss:0.26864 validation-auc:0.97063 validation-aucpr:0.97466
[36] validation-logloss:0.26501 validation-auc:0.97065 validation-aucpr:0.97467
[37] validation-logloss:0.26154 validation-auc:0.97071 validation-aucpr:0.97473
[38] validation-logloss:0.25815 validation-auc:0.97079 validation-aucpr:0.97479
[39] validation-logloss:0.25506 validation-auc:0.97086 validation-aucpr:0.97481
[40] validation-logloss:0.25242 validation-auc:0.97096 validation-aucpr:0.97489
[41] validation-logloss:0.24960 validation-auc:0.97098 validation-aucpr:0.97490
[42] validation-logloss:0.24717 validation-auc:0.97102 validation-aucpr:0.97493
{'best_iteration': '42', 'best_score': '0.974931153155075'}
Trial 73, Fold 3: Log loss = 0.24717121871577077, Average precision = 0.9749117773427436, ROC-AUC = 0.9710236766957845, Elapsed Time = 1.2841706999970484 seconds
Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 73, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66257 validation-auc:0.95890 validation-aucpr:0.96040
[1] validation-logloss:0.63457 validation-auc:0.96546 validation-aucpr:0.97052
[2] validation-logloss:0.61137 validation-auc:0.96493 validation-aucpr:0.96950
[3] validation-logloss:0.58698 validation-auc:0.96604 validation-aucpr:0.97025
[4] validation-logloss:0.56421 validation-auc:0.96624 validation-aucpr:0.97053
[5] validation-logloss:0.54369 validation-auc:0.96600 validation-aucpr:0.97028
[6] validation-logloss:0.52434 validation-auc:0.96650 validation-aucpr:0.97091
[7] validation-logloss:0.50810 validation-auc:0.96650 validation-aucpr:0.97129
[8] validation-logloss:0.49099 validation-auc:0.96656 validation-aucpr:0.97141
[9] validation-logloss:0.47485 validation-auc:0.96692 validation-aucpr:0.97174
[10] validation-logloss:0.45963 validation-auc:0.96734 validation-aucpr:0.97209
[11] validation-logloss:0.44534 validation-auc:0.96782 validation-aucpr:0.97259
[12] validation-logloss:0.43252 validation-auc:0.96829 validation-aucpr:0.97304
[13] validation-logloss:0.41980 validation-auc:0.96853 validation-aucpr:0.97322
[14] validation-logloss:0.40819 validation-auc:0.96883 validation-aucpr:0.97345
[15] validation-logloss:0.39732 validation-auc:0.96898 validation-aucpr:0.97356
[16] validation-logloss:0.38820 validation-auc:0.96881 validation-aucpr:0.97349
[17] validation-logloss:0.37830 validation-auc:0.96895 validation-aucpr:0.97365
[18] validation-logloss:0.36889 validation-auc:0.96922 validation-aucpr:0.97384
[19] validation-logloss:0.35996 validation-auc:0.96937 validation-aucpr:0.97395
[20] validation-logloss:0.35176 validation-auc:0.96947 validation-aucpr:0.97400
[21] validation-logloss:0.34387 validation-auc:0.96957 validation-aucpr:0.97410
[22] validation-logloss:0.33636 validation-auc:0.96968 validation-aucpr:0.97423
[23] validation-logloss:0.33032 validation-auc:0.96953 validation-aucpr:0.97410
[24] validation-logloss:0.32371 validation-auc:0.96965 validation-aucpr:0.97419
[25] validation-logloss:0.31749 validation-auc:0.96959 validation-aucpr:0.97416
[26] validation-logloss:0.31213 validation-auc:0.96955 validation-aucpr:0.97414
[27] validation-logloss:0.30647 validation-auc:0.96970 validation-aucpr:0.97425
[28] validation-logloss:0.30094 validation-auc:0.96982 validation-aucpr:0.97434
[29] validation-logloss:0.29647 validation-auc:0.96972 validation-aucpr:0.97428
[30] validation-logloss:0.29150 validation-auc:0.96974 validation-aucpr:0.97431
[31] validation-logloss:0.28749 validation-auc:0.96973 validation-aucpr:0.97428
[32] validation-logloss:0.28299 validation-auc:0.96979 validation-aucpr:0.97435
[33] validation-logloss:0.27873 validation-auc:0.96998 validation-aucpr:0.97449
[34] validation-logloss:0.27479 validation-auc:0.96994 validation-aucpr:0.97446
[35] validation-logloss:0.27084 validation-auc:0.97003 validation-aucpr:0.97453
[36] validation-logloss:0.26714 validation-auc:0.97005 validation-aucpr:0.97456
[37] validation-logloss:0.26348 validation-auc:0.97020 validation-aucpr:0.97466
[38] validation-logloss:0.26006 validation-auc:0.97026 validation-aucpr:0.97472
[39] validation-logloss:0.25698 validation-auc:0.97032 validation-aucpr:0.97476
[40] validation-logloss:0.25433 validation-auc:0.97038 validation-aucpr:0.97481
[41] validation-logloss:0.25143 validation-auc:0.97040 validation-aucpr:0.97483
[42] validation-logloss:0.24939 validation-auc:0.97027 validation-aucpr:0.97472
{'best_iteration': '41', 'best_score': '0.9748262195518107'}
Trial 73, Fold 4: Log loss = 0.2493894087452096, Average precision = 0.9747037275156256, ROC-AUC = 0.9702705828212813, Elapsed Time = 1.254519900001469 seconds
Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 73, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66242 validation-auc:0.95556 validation-aucpr:0.95546
[1] validation-logloss:0.63502 validation-auc:0.96010 validation-aucpr:0.96559
[2] validation-logloss:0.60888 validation-auc:0.96173 validation-aucpr:0.96679
[3] validation-logloss:0.58724 validation-auc:0.96131 validation-aucpr:0.96589
[4] validation-logloss:0.56487 validation-auc:0.96170 validation-aucpr:0.96641
[5] validation-logloss:0.54386 validation-auc:0.96290 validation-aucpr:0.96790
[6] validation-logloss:0.52449 validation-auc:0.96336 validation-aucpr:0.96819
[7] validation-logloss:0.50653 validation-auc:0.96335 validation-aucpr:0.96823
[8] validation-logloss:0.48948 validation-auc:0.96382 validation-aucpr:0.96888
[9] validation-logloss:0.47386 validation-auc:0.96408 validation-aucpr:0.96901
[10] validation-logloss:0.45934 validation-auc:0.96484 validation-aucpr:0.96940
[11] validation-logloss:0.44557 validation-auc:0.96517 validation-aucpr:0.96966
[12] validation-logloss:0.43241 validation-auc:0.96547 validation-aucpr:0.96988
[13] validation-logloss:0.42017 validation-auc:0.96572 validation-aucpr:0.97002
[14] validation-logloss:0.40878 validation-auc:0.96587 validation-aucpr:0.97011
[15] validation-logloss:0.39920 validation-auc:0.96635 validation-aucpr:0.97125
[16] validation-logloss:0.38891 validation-auc:0.96658 validation-aucpr:0.97139
[17] validation-logloss:0.37948 validation-auc:0.96659 validation-aucpr:0.97141
[18] validation-logloss:0.37016 validation-auc:0.96668 validation-aucpr:0.97148
[19] validation-logloss:0.36176 validation-auc:0.96682 validation-aucpr:0.97153
[20] validation-logloss:0.35374 validation-auc:0.96694 validation-aucpr:0.97161
[21] validation-logloss:0.34619 validation-auc:0.96718 validation-aucpr:0.97177
[22] validation-logloss:0.33880 validation-auc:0.96730 validation-aucpr:0.97181
[23] validation-logloss:0.33212 validation-auc:0.96728 validation-aucpr:0.97182
[24] validation-logloss:0.32574 validation-auc:0.96739 validation-aucpr:0.97187
[25] validation-logloss:0.31957 validation-auc:0.96749 validation-aucpr:0.97197
[26] validation-logloss:0.31394 validation-auc:0.96761 validation-aucpr:0.97200
[27] validation-logloss:0.30834 validation-auc:0.96768 validation-aucpr:0.97202
[28] validation-logloss:0.30353 validation-auc:0.96781 validation-aucpr:0.97214
[29] validation-logloss:0.29902 validation-auc:0.96801 validation-aucpr:0.97225
[30] validation-logloss:0.29430 validation-auc:0.96807 validation-aucpr:0.97231
[31] validation-logloss:0.28972 validation-auc:0.96814 validation-aucpr:0.97233
[32] validation-logloss:0.28566 validation-auc:0.96823 validation-aucpr:0.97244
[33] validation-logloss:0.28187 validation-auc:0.96811 validation-aucpr:0.97235
[34] validation-logloss:0.27788 validation-auc:0.96821 validation-aucpr:0.97243
[35] validation-logloss:0.27406 validation-auc:0.96836 validation-aucpr:0.97255
[36] validation-logloss:0.27055 validation-auc:0.96846 validation-aucpr:0.97262
[37] validation-logloss:0.26719 validation-auc:0.96855 validation-aucpr:0.97267
[38] validation-logloss:0.26443 validation-auc:0.96872 validation-aucpr:0.97284
[39] validation-logloss:0.26149 validation-auc:0.96881 validation-aucpr:0.97291
[40] validation-logloss:0.25859 validation-auc:0.96882 validation-aucpr:0.97292
[41] validation-logloss:0.25619 validation-auc:0.96884 validation-aucpr:0.97291
[42] validation-logloss:0.25354 validation-auc:0.96887 validation-aucpr:0.97293
{'best_iteration': '42', 'best_score': '0.9729319944965127'}
Trial 73, Fold 5: Log loss = 0.25353626477607055, Average precision = 0.9729489695128064, ROC-AUC = 0.968868922207978, Elapsed Time = 1.2993155000003753 seconds
Optimization Progress: 74%|#######4 | 74/100 [3:27:33<53:39, 123.83s/it]
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 74, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68198 validation-auc:0.94945 validation-aucpr:0.95422
[1] validation-logloss:0.67126 validation-auc:0.95865 validation-aucpr:0.96317
[2] validation-logloss:0.66095 validation-auc:0.96173 validation-aucpr:0.96655
[3] validation-logloss:0.65066 validation-auc:0.96379 validation-aucpr:0.96784
[4] validation-logloss:0.64073 validation-auc:0.96415 validation-aucpr:0.96847
[5] validation-logloss:0.63119 validation-auc:0.96441 validation-aucpr:0.96861
{'best_iteration': '5', 'best_score': '0.968609721886661'}
Trial 74, Fold 1: Log loss = 0.6311894079791622, Average precision = 0.9684504452958994, ROC-AUC = 0.9644080418978793, Elapsed Time = 0.4654045999996015 seconds
Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 74, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68211 validation-auc:0.95194 validation-aucpr:0.95339
[1] validation-logloss:0.67099 validation-auc:0.96093 validation-aucpr:0.96313
[2] validation-logloss:0.66030 validation-auc:0.96405 validation-aucpr:0.96679
[3] validation-logloss:0.65158 validation-auc:0.96409 validation-aucpr:0.96527
[4] validation-logloss:0.64153 validation-auc:0.96526 validation-aucpr:0.96644
[5] validation-logloss:0.63184 validation-auc:0.96703 validation-aucpr:0.96916
{'best_iteration': '5', 'best_score': '0.9691621140187124'}
Trial 74, Fold 2: Log loss = 0.6318357320456525, Average precision = 0.9696004383607427, ROC-AUC = 0.9670322624896773, Elapsed Time = 0.4971901999997499 seconds
Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 74, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68193 validation-auc:0.95612 validation-aucpr:0.95651
[1] validation-logloss:0.67224 validation-auc:0.95906 validation-aucpr:0.96173
[2] validation-logloss:0.66207 validation-auc:0.96266 validation-aucpr:0.96536
[3] validation-logloss:0.65210 validation-auc:0.96273 validation-aucpr:0.96726
[4] validation-logloss:0.64232 validation-auc:0.96351 validation-aucpr:0.96798
[5] validation-logloss:0.63264 validation-auc:0.96433 validation-aucpr:0.96881
{'best_iteration': '5', 'best_score': '0.9688053773871279'}
Trial 74, Fold 3: Log loss = 0.6326356588194274, Average precision = 0.9686266263110179, ROC-AUC = 0.9643291291858885, Elapsed Time = 0.5874459000006027 seconds
Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 74, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68194 validation-auc:0.95429 validation-aucpr:0.96112
[1] validation-logloss:0.67105 validation-auc:0.95921 validation-aucpr:0.96506
[2] validation-logloss:0.66069 validation-auc:0.96126 validation-aucpr:0.96711
[3] validation-logloss:0.65036 validation-auc:0.96230 validation-aucpr:0.96624
[4] validation-logloss:0.64061 validation-auc:0.96461 validation-aucpr:0.96963
[5] validation-logloss:0.63092 validation-auc:0.96428 validation-aucpr:0.96778
{'best_iteration': '4', 'best_score': '0.969634442922803'}
Trial 74, Fold 4: Log loss = 0.6309225193685589, Average precision = 0.9680199805645648, ROC-AUC = 0.9642778904411397, Elapsed Time = 0.5735706000014034 seconds
Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 74, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68212 validation-auc:0.94916 validation-aucpr:0.94737
[1] validation-logloss:0.67140 validation-auc:0.95587 validation-aucpr:0.95626
[2] validation-logloss:0.66091 validation-auc:0.95810 validation-aucpr:0.95942
[3] validation-logloss:0.65063 validation-auc:0.95948 validation-aucpr:0.96048
[4] validation-logloss:0.64079 validation-auc:0.96033 validation-aucpr:0.96113
[5] validation-logloss:0.63112 validation-auc:0.96114 validation-aucpr:0.96241
{'best_iteration': '5', 'best_score': '0.9624078909143001'}
Trial 74, Fold 5: Log loss = 0.6311159088796015, Average precision = 0.9619067479372349, ROC-AUC = 0.9611428571428572, Elapsed Time = 0.578624599998875 seconds
Optimization Progress: 75%|#######5 | 75/100 [3:27:43<37:24, 89.78s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 75, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68520 validation-auc:0.96108 validation-aucpr:0.96645
[1] validation-logloss:0.67745 validation-auc:0.96484 validation-aucpr:0.96898
[2] validation-logloss:0.67011 validation-auc:0.96581 validation-aucpr:0.97084
[3] validation-logloss:0.66370 validation-auc:0.96473 validation-aucpr:0.97008
[4] validation-logloss:0.65640 validation-auc:0.96545 validation-aucpr:0.97065
[5] validation-logloss:0.64993 validation-auc:0.96599 validation-aucpr:0.97107
[6] validation-logloss:0.64306 validation-auc:0.96662 validation-aucpr:0.97164
[7] validation-logloss:0.63706 validation-auc:0.96605 validation-aucpr:0.97109
[8] validation-logloss:0.63108 validation-auc:0.96553 validation-aucpr:0.97105
[9] validation-logloss:0.62520 validation-auc:0.96538 validation-aucpr:0.97085
[10] validation-logloss:0.61936 validation-auc:0.96544 validation-aucpr:0.97082
[11] validation-logloss:0.61288 validation-auc:0.96615 validation-aucpr:0.97143
[12] validation-logloss:0.60647 validation-auc:0.96655 validation-aucpr:0.97180
[13] validation-logloss:0.60124 validation-auc:0.96619 validation-aucpr:0.97155
[14] validation-logloss:0.59540 validation-auc:0.96616 validation-aucpr:0.97171
[15] validation-logloss:0.58946 validation-auc:0.96650 validation-aucpr:0.97201
[16] validation-logloss:0.58392 validation-auc:0.96635 validation-aucpr:0.97186
[17] validation-logloss:0.57882 validation-auc:0.96627 validation-aucpr:0.97173
[18] validation-logloss:0.57378 validation-auc:0.96627 validation-aucpr:0.97164
[19] validation-logloss:0.56900 validation-auc:0.96604 validation-aucpr:0.97146
[20] validation-logloss:0.56450 validation-auc:0.96591 validation-aucpr:0.97131
[21] validation-logloss:0.55906 validation-auc:0.96615 validation-aucpr:0.97154
[22] validation-logloss:0.55365 validation-auc:0.96640 validation-aucpr:0.97173
[23] validation-logloss:0.54929 validation-auc:0.96628 validation-aucpr:0.97165
[24] validation-logloss:0.54412 validation-auc:0.96654 validation-aucpr:0.97186
[25] validation-logloss:0.53992 validation-auc:0.96637 validation-aucpr:0.97168
[26] validation-logloss:0.53571 validation-auc:0.96629 validation-aucpr:0.97159
[27] validation-logloss:0.53092 validation-auc:0.96637 validation-aucpr:0.97166
[28] validation-logloss:0.52619 validation-auc:0.96648 validation-aucpr:0.97176
[29] validation-logloss:0.52200 validation-auc:0.96650 validation-aucpr:0.97175
[30] validation-logloss:0.51787 validation-auc:0.96643 validation-aucpr:0.97169
[31] validation-logloss:0.51321 validation-auc:0.96664 validation-aucpr:0.97184
[32] validation-logloss:0.50940 validation-auc:0.96661 validation-aucpr:0.97178
[33] validation-logloss:0.50574 validation-auc:0.96653 validation-aucpr:0.97172
[34] validation-logloss:0.50147 validation-auc:0.96674 validation-aucpr:0.97189
[35] validation-logloss:0.49728 validation-auc:0.96680 validation-aucpr:0.97196
[36] validation-logloss:0.49297 validation-auc:0.96695 validation-aucpr:0.97210
[37] validation-logloss:0.48882 validation-auc:0.96709 validation-aucpr:0.97226
[38] validation-logloss:0.48489 validation-auc:0.96720 validation-aucpr:0.97230
{'best_iteration': '38', 'best_score': '0.9723014364966841'}
Trial 75, Fold 1: Log loss = 0.48489445876785703, Average precision = 0.9722991842862466, ROC-AUC = 0.9671955132800888, Elapsed Time = 0.7628678000000946 seconds
Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 75, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68538 validation-auc:0.96151 validation-aucpr:0.96532
[1] validation-logloss:0.67845 validation-auc:0.96332 validation-aucpr:0.96709
[2] validation-logloss:0.67073 validation-auc:0.96615 validation-aucpr:0.97000
[3] validation-logloss:0.66326 validation-auc:0.96708 validation-aucpr:0.97098
[4] validation-logloss:0.65601 validation-auc:0.96793 validation-aucpr:0.97179
[5] validation-logloss:0.64977 validation-auc:0.96821 validation-aucpr:0.97174
[6] validation-logloss:0.64270 validation-auc:0.96862 validation-aucpr:0.97212
[7] validation-logloss:0.63596 validation-auc:0.96870 validation-aucpr:0.97229
[8] validation-logloss:0.62931 validation-auc:0.96865 validation-aucpr:0.97229
[9] validation-logloss:0.62352 validation-auc:0.96906 validation-aucpr:0.97239
[10] validation-logloss:0.61717 validation-auc:0.96915 validation-aucpr:0.97245
[11] validation-logloss:0.61139 validation-auc:0.96916 validation-aucpr:0.97241
[12] validation-logloss:0.60508 validation-auc:0.96939 validation-aucpr:0.97263
[13] validation-logloss:0.59969 validation-auc:0.96937 validation-aucpr:0.97257
[14] validation-logloss:0.59419 validation-auc:0.96928 validation-aucpr:0.97249
[15] validation-logloss:0.58887 validation-auc:0.96934 validation-aucpr:0.97253
[16] validation-logloss:0.58375 validation-auc:0.96933 validation-aucpr:0.97252
[17] validation-logloss:0.57853 validation-auc:0.96920 validation-aucpr:0.97237
[18] validation-logloss:0.57300 validation-auc:0.96915 validation-aucpr:0.97231
[19] validation-logloss:0.56746 validation-auc:0.96921 validation-aucpr:0.97235
[20] validation-logloss:0.56253 validation-auc:0.96917 validation-aucpr:0.97231
[21] validation-logloss:0.55782 validation-auc:0.96909 validation-aucpr:0.97222
[22] validation-logloss:0.55249 validation-auc:0.96914 validation-aucpr:0.97229
[23] validation-logloss:0.54732 validation-auc:0.96930 validation-aucpr:0.97243
[24] validation-logloss:0.54210 validation-auc:0.96950 validation-aucpr:0.97260
[25] validation-logloss:0.53759 validation-auc:0.96943 validation-aucpr:0.97254
[26] validation-logloss:0.53268 validation-auc:0.96970 validation-aucpr:0.97278
[27] validation-logloss:0.52785 validation-auc:0.96976 validation-aucpr:0.97285
[28] validation-logloss:0.52319 validation-auc:0.96973 validation-aucpr:0.97284
[29] validation-logloss:0.51905 validation-auc:0.96957 validation-aucpr:0.97267
[30] validation-logloss:0.51499 validation-auc:0.96955 validation-aucpr:0.97265
[31] validation-logloss:0.51099 validation-auc:0.96950 validation-aucpr:0.97259
[32] validation-logloss:0.50692 validation-auc:0.96960 validation-aucpr:0.97267
[33] validation-logloss:0.50297 validation-auc:0.96962 validation-aucpr:0.97266
[34] validation-logloss:0.49860 validation-auc:0.96977 validation-aucpr:0.97280
[35] validation-logloss:0.49434 validation-auc:0.96983 validation-aucpr:0.97286
[36] validation-logloss:0.49055 validation-auc:0.96971 validation-aucpr:0.97275
[37] validation-logloss:0.48643 validation-auc:0.96980 validation-aucpr:0.97283
[38] validation-logloss:0.48235 validation-auc:0.96987 validation-aucpr:0.97289
{'best_iteration': '38', 'best_score': '0.9728927317821991'}
Trial 75, Fold 2: Log loss = 0.4823548176019976, Average precision = 0.9728926908705433, ROC-AUC = 0.969873663898686, Elapsed Time = 0.9614445000006526 seconds
Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 75, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68520 validation-auc:0.96196 validation-aucpr:0.96490
[1] validation-logloss:0.67738 validation-auc:0.96760 validation-aucpr:0.97189
[2] validation-logloss:0.67076 validation-auc:0.96759 validation-aucpr:0.97146
[3] validation-logloss:0.66369 validation-auc:0.96795 validation-aucpr:0.97194
[4] validation-logloss:0.65705 validation-auc:0.96823 validation-aucpr:0.97223
[5] validation-logloss:0.64979 validation-auc:0.96885 validation-aucpr:0.97271
[6] validation-logloss:0.64351 validation-auc:0.96870 validation-aucpr:0.97261
[7] validation-logloss:0.63662 validation-auc:0.96945 validation-aucpr:0.97353
[8] validation-logloss:0.62978 validation-auc:0.96968 validation-aucpr:0.97380
[9] validation-logloss:0.62306 validation-auc:0.96995 validation-aucpr:0.97404
[10] validation-logloss:0.61646 validation-auc:0.97016 validation-aucpr:0.97423
[11] validation-logloss:0.61004 validation-auc:0.97055 validation-aucpr:0.97490
[12] validation-logloss:0.60383 validation-auc:0.97055 validation-aucpr:0.97492
[13] validation-logloss:0.59832 validation-auc:0.97024 validation-aucpr:0.97463
[14] validation-logloss:0.59295 validation-auc:0.97010 validation-aucpr:0.97450
[15] validation-logloss:0.58770 validation-auc:0.96991 validation-aucpr:0.97429
[16] validation-logloss:0.58254 validation-auc:0.96987 validation-aucpr:0.97420
[17] validation-logloss:0.57739 validation-auc:0.96978 validation-aucpr:0.97417
[18] validation-logloss:0.57163 validation-auc:0.97000 validation-aucpr:0.97437
[19] validation-logloss:0.56656 validation-auc:0.96997 validation-aucpr:0.97429
[20] validation-logloss:0.56101 validation-auc:0.97011 validation-aucpr:0.97444
[21] validation-logloss:0.55568 validation-auc:0.97028 validation-aucpr:0.97452
[22] validation-logloss:0.55091 validation-auc:0.97017 validation-aucpr:0.97439
[23] validation-logloss:0.54635 validation-auc:0.97017 validation-aucpr:0.97437
[24] validation-logloss:0.54135 validation-auc:0.97024 validation-aucpr:0.97447
[25] validation-logloss:0.53701 validation-auc:0.97022 validation-aucpr:0.97446
[26] validation-logloss:0.53260 validation-auc:0.97022 validation-aucpr:0.97444
[27] validation-logloss:0.52838 validation-auc:0.97018 validation-aucpr:0.97437
[28] validation-logloss:0.52434 validation-auc:0.97013 validation-aucpr:0.97433
[29] validation-logloss:0.51953 validation-auc:0.97017 validation-aucpr:0.97438
[30] validation-logloss:0.51557 validation-auc:0.97010 validation-aucpr:0.97433
[31] validation-logloss:0.51100 validation-auc:0.97017 validation-aucpr:0.97440
[32] validation-logloss:0.50649 validation-auc:0.97016 validation-aucpr:0.97442
[33] validation-logloss:0.50268 validation-auc:0.97012 validation-aucpr:0.97436
[34] validation-logloss:0.49827 validation-auc:0.97025 validation-aucpr:0.97447
[35] validation-logloss:0.49390 validation-auc:0.97036 validation-aucpr:0.97458
[36] validation-logloss:0.49026 validation-auc:0.97030 validation-aucpr:0.97451
[37] validation-logloss:0.48610 validation-auc:0.97043 validation-aucpr:0.97462
[38] validation-logloss:0.48194 validation-auc:0.97053 validation-aucpr:0.97472
{'best_iteration': '12', 'best_score': '0.9749222820063228'}
Trial 75, Fold 3: Log loss = 0.4819393169675427, Average precision = 0.9747282369942932, ROC-AUC = 0.970529709937302, Elapsed Time = 1.0155684999990626 seconds
Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 75, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68524 validation-auc:0.96118 validation-aucpr:0.96774
[1] validation-logloss:0.67771 validation-auc:0.96477 validation-aucpr:0.97058
[2] validation-logloss:0.67012 validation-auc:0.96584 validation-aucpr:0.97144
[3] validation-logloss:0.66333 validation-auc:0.96563 validation-aucpr:0.97149
[4] validation-logloss:0.65606 validation-auc:0.96598 validation-aucpr:0.97182
[5] validation-logloss:0.64892 validation-auc:0.96707 validation-aucpr:0.97260
[6] validation-logloss:0.64224 validation-auc:0.96750 validation-aucpr:0.97303
[7] validation-logloss:0.63609 validation-auc:0.96701 validation-aucpr:0.97258
[8] validation-logloss:0.63014 validation-auc:0.96695 validation-aucpr:0.97246
[9] validation-logloss:0.62353 validation-auc:0.96755 validation-aucpr:0.97287
[10] validation-logloss:0.61771 validation-auc:0.96768 validation-aucpr:0.97289
[11] validation-logloss:0.61214 validation-auc:0.96745 validation-aucpr:0.97265
[12] validation-logloss:0.60590 validation-auc:0.96768 validation-aucpr:0.97291
[13] validation-logloss:0.59968 validation-auc:0.96809 validation-aucpr:0.97325
[14] validation-logloss:0.59357 validation-auc:0.96821 validation-aucpr:0.97337
[15] validation-logloss:0.58771 validation-auc:0.96852 validation-aucpr:0.97367
[16] validation-logloss:0.58210 validation-auc:0.96875 validation-aucpr:0.97383
[17] validation-logloss:0.57638 validation-auc:0.96893 validation-aucpr:0.97396
[18] validation-logloss:0.57147 validation-auc:0.96859 validation-aucpr:0.97367
[19] validation-logloss:0.56593 validation-auc:0.96873 validation-aucpr:0.97380
[20] validation-logloss:0.56130 validation-auc:0.96857 validation-aucpr:0.97365
[21] validation-logloss:0.55594 validation-auc:0.96875 validation-aucpr:0.97382
[22] validation-logloss:0.55143 validation-auc:0.96863 validation-aucpr:0.97371
[23] validation-logloss:0.54674 validation-auc:0.96870 validation-aucpr:0.97374
[24] validation-logloss:0.54222 validation-auc:0.96875 validation-aucpr:0.97377
[25] validation-logloss:0.53794 validation-auc:0.96867 validation-aucpr:0.97372
[26] validation-logloss:0.53304 validation-auc:0.96872 validation-aucpr:0.97378
[27] validation-logloss:0.52814 validation-auc:0.96885 validation-aucpr:0.97390
[28] validation-logloss:0.52396 validation-auc:0.96884 validation-aucpr:0.97388
[29] validation-logloss:0.51916 validation-auc:0.96895 validation-aucpr:0.97396
[30] validation-logloss:0.51457 validation-auc:0.96906 validation-aucpr:0.97407
[31] validation-logloss:0.51000 validation-auc:0.96926 validation-aucpr:0.97423
[32] validation-logloss:0.50563 validation-auc:0.96934 validation-aucpr:0.97432
[33] validation-logloss:0.50120 validation-auc:0.96942 validation-aucpr:0.97438
[34] validation-logloss:0.49739 validation-auc:0.96937 validation-aucpr:0.97433
[35] validation-logloss:0.49321 validation-auc:0.96932 validation-aucpr:0.97430
[36] validation-logloss:0.48949 validation-auc:0.96923 validation-aucpr:0.97421
[37] validation-logloss:0.48535 validation-auc:0.96925 validation-aucpr:0.97423
[38] validation-logloss:0.48120 validation-auc:0.96934 validation-aucpr:0.97431
{'best_iteration': '33', 'best_score': '0.974379595658719'}
Trial 75, Fold 4: Log loss = 0.48119895275105207, Average precision = 0.9743045863136259, ROC-AUC = 0.9693426748547299, Elapsed Time = 0.9456118000016431 seconds
Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 75, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68529 validation-auc:0.95807 validation-aucpr:0.96371
[1] validation-logloss:0.67757 validation-auc:0.96367 validation-aucpr:0.96895
[2] validation-logloss:0.67090 validation-auc:0.96391 validation-aucpr:0.96884
[3] validation-logloss:0.66434 validation-auc:0.96452 validation-aucpr:0.96890
[4] validation-logloss:0.65789 validation-auc:0.96409 validation-aucpr:0.96841
[5] validation-logloss:0.65181 validation-auc:0.96425 validation-aucpr:0.96843
[6] validation-logloss:0.64589 validation-auc:0.96385 validation-aucpr:0.96789
[7] validation-logloss:0.63896 validation-auc:0.96482 validation-aucpr:0.96886
[8] validation-logloss:0.63318 validation-auc:0.96461 validation-aucpr:0.96856
[9] validation-logloss:0.62740 validation-auc:0.96456 validation-aucpr:0.96842
[10] validation-logloss:0.62077 validation-auc:0.96512 validation-aucpr:0.96908
[11] validation-logloss:0.61483 validation-auc:0.96502 validation-aucpr:0.96897
[12] validation-logloss:0.60916 validation-auc:0.96548 validation-aucpr:0.96932
[13] validation-logloss:0.60311 validation-auc:0.96566 validation-aucpr:0.96953
[14] validation-logloss:0.59711 validation-auc:0.96600 validation-aucpr:0.96975
[15] validation-logloss:0.59199 validation-auc:0.96596 validation-aucpr:0.96970
[16] validation-logloss:0.58622 validation-auc:0.96617 validation-aucpr:0.96995
[17] validation-logloss:0.58052 validation-auc:0.96626 validation-aucpr:0.97007
[18] validation-logloss:0.57547 validation-auc:0.96631 validation-aucpr:0.97015
[19] validation-logloss:0.57066 validation-auc:0.96624 validation-aucpr:0.97005
[20] validation-logloss:0.56587 validation-auc:0.96624 validation-aucpr:0.97042
[21] validation-logloss:0.56041 validation-auc:0.96650 validation-aucpr:0.97064
[22] validation-logloss:0.55513 validation-auc:0.96657 validation-aucpr:0.97071
[23] validation-logloss:0.55006 validation-auc:0.96663 validation-aucpr:0.97075
[24] validation-logloss:0.54507 validation-auc:0.96667 validation-aucpr:0.97081
[25] validation-logloss:0.54009 validation-auc:0.96688 validation-aucpr:0.97100
[26] validation-logloss:0.53589 validation-auc:0.96673 validation-aucpr:0.97082
[27] validation-logloss:0.53166 validation-auc:0.96667 validation-aucpr:0.97077
[28] validation-logloss:0.52735 validation-auc:0.96674 validation-aucpr:0.97081
[29] validation-logloss:0.52271 validation-auc:0.96686 validation-aucpr:0.97092
[30] validation-logloss:0.51806 validation-auc:0.96694 validation-aucpr:0.97099
[31] validation-logloss:0.51408 validation-auc:0.96690 validation-aucpr:0.97094
[32] validation-logloss:0.50964 validation-auc:0.96694 validation-aucpr:0.97099
[33] validation-logloss:0.50519 validation-auc:0.96710 validation-aucpr:0.97112
[34] validation-logloss:0.50157 validation-auc:0.96701 validation-aucpr:0.97104
[35] validation-logloss:0.49784 validation-auc:0.96700 validation-aucpr:0.97101
[36] validation-logloss:0.49433 validation-auc:0.96699 validation-aucpr:0.97096
[37] validation-logloss:0.49012 validation-auc:0.96718 validation-aucpr:0.97113
[38] validation-logloss:0.48670 validation-auc:0.96704 validation-aucpr:0.97102
{'best_iteration': '37', 'best_score': '0.9711287945679274'}
Trial 75, Fold 5: Log loss = 0.4866973349365086, Average precision = 0.9710253972086283, ROC-AUC = 0.9670447949761255, Elapsed Time = 0.9918016000010539 seconds
Optimization Progress: 76%|#######6 | 76/100 [3:27:56<26:38, 66.61s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 76, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.64608 validation-auc:0.94500 validation-aucpr:0.93574
[1] validation-logloss:0.60419 validation-auc:0.96165 validation-aucpr:0.95555
[2] validation-logloss:0.57083 validation-auc:0.96431 validation-aucpr:0.96414
[3] validation-logloss:0.53722 validation-auc:0.96636 validation-aucpr:0.96455
[4] validation-logloss:0.50807 validation-auc:0.96670 validation-aucpr:0.96465
[5] validation-logloss:0.48209 validation-auc:0.96723 validation-aucpr:0.96472
[6] validation-logloss:0.45801 validation-auc:0.96734 validation-aucpr:0.96508
[7] validation-logloss:0.43931 validation-auc:0.96743 validation-aucpr:0.96713
[8] validation-logloss:0.41907 validation-auc:0.96811 validation-aucpr:0.96767
[9] validation-logloss:0.40061 validation-auc:0.96850 validation-aucpr:0.96786
[10] validation-logloss:0.38401 validation-auc:0.96878 validation-aucpr:0.96794
[11] validation-logloss:0.37031 validation-auc:0.96934 validation-aucpr:0.97037
[12] validation-logloss:0.35621 validation-auc:0.96947 validation-aucpr:0.97043
[13] validation-logloss:0.34498 validation-auc:0.96927 validation-aucpr:0.96968
[14] validation-logloss:0.33586 validation-auc:0.96946 validation-aucpr:0.97358
[15] validation-logloss:0.32629 validation-auc:0.96934 validation-aucpr:0.97342
[16] validation-logloss:0.31768 validation-auc:0.96928 validation-aucpr:0.97342
[17] validation-logloss:0.30808 validation-auc:0.96968 validation-aucpr:0.97400
[18] validation-logloss:0.30050 validation-auc:0.96970 validation-aucpr:0.97400
[19] validation-logloss:0.29237 validation-auc:0.96995 validation-aucpr:0.97420
[20] validation-logloss:0.28472 validation-auc:0.97006 validation-aucpr:0.97427
[21] validation-logloss:0.27861 validation-auc:0.97014 validation-aucpr:0.97434
[22] validation-logloss:0.27216 validation-auc:0.97022 validation-aucpr:0.97441
[23] validation-logloss:0.26724 validation-auc:0.97028 validation-aucpr:0.97440
[24] validation-logloss:0.26157 validation-auc:0.97037 validation-aucpr:0.97456
[25] validation-logloss:0.25602 validation-auc:0.97080 validation-aucpr:0.97492
[26] validation-logloss:0.25123 validation-auc:0.97089 validation-aucpr:0.97498
[27] validation-logloss:0.24680 validation-auc:0.97091 validation-aucpr:0.97432
[28] validation-logloss:0.24280 validation-auc:0.97109 validation-aucpr:0.97516
[29] validation-logloss:0.23996 validation-auc:0.97102 validation-aucpr:0.97549
[30] validation-logloss:0.23712 validation-auc:0.97100 validation-aucpr:0.97544
[31] validation-logloss:0.23370 validation-auc:0.97107 validation-aucpr:0.97549
[32] validation-logloss:0.23060 validation-auc:0.97115 validation-aucpr:0.97555
[33] validation-logloss:0.22813 validation-auc:0.97123 validation-aucpr:0.97557
[34] validation-logloss:0.22565 validation-auc:0.97129 validation-aucpr:0.97564
[35] validation-logloss:0.22329 validation-auc:0.97126 validation-aucpr:0.97562
[36] validation-logloss:0.22090 validation-auc:0.97135 validation-aucpr:0.97574
[37] validation-logloss:0.21873 validation-auc:0.97132 validation-aucpr:0.97572
[38] validation-logloss:0.21661 validation-auc:0.97138 validation-aucpr:0.97574
[39] validation-logloss:0.21496 validation-auc:0.97133 validation-aucpr:0.97571
[40] validation-logloss:0.21345 validation-auc:0.97143 validation-aucpr:0.97580
[41] validation-logloss:0.21238 validation-auc:0.97142 validation-aucpr:0.97577
[42] validation-logloss:0.21103 validation-auc:0.97146 validation-aucpr:0.97579
[43] validation-logloss:0.20994 validation-auc:0.97140 validation-aucpr:0.97574
[44] validation-logloss:0.20875 validation-auc:0.97145 validation-aucpr:0.97579
[45] validation-logloss:0.20809 validation-auc:0.97132 validation-aucpr:0.97567
[46] validation-logloss:0.20702 validation-auc:0.97147 validation-aucpr:0.97575
[47] validation-logloss:0.20582 validation-auc:0.97161 validation-aucpr:0.97588
[48] validation-logloss:0.20496 validation-auc:0.97156 validation-aucpr:0.97579
[49] validation-logloss:0.20457 validation-auc:0.97150 validation-aucpr:0.97572
[50] validation-logloss:0.20404 validation-auc:0.97145 validation-aucpr:0.97567
[51] validation-logloss:0.20346 validation-auc:0.97140 validation-aucpr:0.97561
[52] validation-logloss:0.20295 validation-auc:0.97141 validation-aucpr:0.97563
[53] validation-logloss:0.20212 validation-auc:0.97149 validation-aucpr:0.97569
[54] validation-logloss:0.20126 validation-auc:0.97159 validation-aucpr:0.97578
[55] validation-logloss:0.20067 validation-auc:0.97161 validation-aucpr:0.97580
[56] validation-logloss:0.20049 validation-auc:0.97162 validation-aucpr:0.97596
[57] validation-logloss:0.19994 validation-auc:0.97166 validation-aucpr:0.97600
[58] validation-logloss:0.19936 validation-auc:0.97167 validation-aucpr:0.97601
[59] validation-logloss:0.19872 validation-auc:0.97173 validation-aucpr:0.97606
[60] validation-logloss:0.19836 validation-auc:0.97175 validation-aucpr:0.97612
[61] validation-logloss:0.19770 validation-auc:0.97190 validation-aucpr:0.97628
[62] validation-logloss:0.19746 validation-auc:0.97187 validation-aucpr:0.97627
[63] validation-logloss:0.19706 validation-auc:0.97193 validation-aucpr:0.97635
[64] validation-logloss:0.19684 validation-auc:0.97189 validation-aucpr:0.97634
[65] validation-logloss:0.19653 validation-auc:0.97196 validation-aucpr:0.97643
[66] validation-logloss:0.19623 validation-auc:0.97199 validation-aucpr:0.97647
[67] validation-logloss:0.19586 validation-auc:0.97205 validation-aucpr:0.97651
[68] validation-logloss:0.19568 validation-auc:0.97205 validation-aucpr:0.97651
[69] validation-logloss:0.19548 validation-auc:0.97208 validation-aucpr:0.97653
[70] validation-logloss:0.19539 validation-auc:0.97206 validation-aucpr:0.97650
[71] validation-logloss:0.19514 validation-auc:0.97210 validation-aucpr:0.97653
[72] validation-logloss:0.19503 validation-auc:0.97209 validation-aucpr:0.97651
{'best_iteration': '69', 'best_score': '0.9765318621486272'}
Trial 76, Fold 1: Log loss = 0.19503386461973604, Average precision = 0.9765164255619482, ROC-AUC = 0.9720876469126243, Elapsed Time = 3.084799500000372 seconds
Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 76, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.64557 validation-auc:0.95077 validation-aucpr:0.92546
[1] validation-logloss:0.60372 validation-auc:0.96275 validation-aucpr:0.95631
[2] validation-logloss:0.57040 validation-auc:0.96634 validation-aucpr:0.97026
[3] validation-logloss:0.54066 validation-auc:0.96691 validation-aucpr:0.96904
[4] validation-logloss:0.51390 validation-auc:0.96783 validation-aucpr:0.97153
[5] validation-logloss:0.48639 validation-auc:0.96948 validation-aucpr:0.97304
[6] validation-logloss:0.46163 validation-auc:0.97035 validation-aucpr:0.97373
[7] validation-logloss:0.43963 validation-auc:0.97058 validation-aucpr:0.97403
[8] validation-logloss:0.41950 validation-auc:0.97099 validation-aucpr:0.97475
[9] validation-logloss:0.40118 validation-auc:0.97112 validation-aucpr:0.97479
[10] validation-logloss:0.38650 validation-auc:0.97084 validation-aucpr:0.97436
[11] validation-logloss:0.37254 validation-auc:0.97097 validation-aucpr:0.97435
[12] validation-logloss:0.35792 validation-auc:0.97144 validation-aucpr:0.97482
[13] validation-logloss:0.34459 validation-auc:0.97170 validation-aucpr:0.97503
[14] validation-logloss:0.33389 validation-auc:0.97156 validation-aucpr:0.97489
[15] validation-logloss:0.32223 validation-auc:0.97189 validation-aucpr:0.97520
[16] validation-logloss:0.31181 validation-auc:0.97223 validation-aucpr:0.97517
[17] validation-logloss:0.30233 validation-auc:0.97223 validation-aucpr:0.97517
[18] validation-logloss:0.29361 validation-auc:0.97226 validation-aucpr:0.97521
[19] validation-logloss:0.28562 validation-auc:0.97217 validation-aucpr:0.97516
[20] validation-logloss:0.27810 validation-auc:0.97242 validation-aucpr:0.97536
[21] validation-logloss:0.27126 validation-auc:0.97241 validation-aucpr:0.97532
[22] validation-logloss:0.26546 validation-auc:0.97249 validation-aucpr:0.97535
[23] validation-logloss:0.26019 validation-auc:0.97277 validation-aucpr:0.97551
[24] validation-logloss:0.25444 validation-auc:0.97282 validation-aucpr:0.97552
[25] validation-logloss:0.25027 validation-auc:0.97260 validation-aucpr:0.97533
[26] validation-logloss:0.24577 validation-auc:0.97270 validation-aucpr:0.97539
[27] validation-logloss:0.24190 validation-auc:0.97264 validation-aucpr:0.97534
[28] validation-logloss:0.23761 validation-auc:0.97258 validation-aucpr:0.97543
[29] validation-logloss:0.23434 validation-auc:0.97240 validation-aucpr:0.97531
[30] validation-logloss:0.23048 validation-auc:0.97250 validation-aucpr:0.97561
[31] validation-logloss:0.22696 validation-auc:0.97247 validation-aucpr:0.97559
[32] validation-logloss:0.22367 validation-auc:0.97251 validation-aucpr:0.97562
[33] validation-logloss:0.22111 validation-auc:0.97261 validation-aucpr:0.97567
[34] validation-logloss:0.21842 validation-auc:0.97280 validation-aucpr:0.97580
[35] validation-logloss:0.21641 validation-auc:0.97269 validation-aucpr:0.97571
[36] validation-logloss:0.21438 validation-auc:0.97265 validation-aucpr:0.97570
[37] validation-logloss:0.21240 validation-auc:0.97270 validation-aucpr:0.97575
[38] validation-logloss:0.21037 validation-auc:0.97278 validation-aucpr:0.97583
[39] validation-logloss:0.20847 validation-auc:0.97294 validation-aucpr:0.97595
[40] validation-logloss:0.20676 validation-auc:0.97300 validation-aucpr:0.97601
[41] validation-logloss:0.20475 validation-auc:0.97308 validation-aucpr:0.97608
[42] validation-logloss:0.20273 validation-auc:0.97312 validation-aucpr:0.97613
[43] validation-logloss:0.20166 validation-auc:0.97301 validation-aucpr:0.97605
[44] validation-logloss:0.19983 validation-auc:0.97318 validation-aucpr:0.97619
[45] validation-logloss:0.19863 validation-auc:0.97321 validation-aucpr:0.97622
[46] validation-logloss:0.19721 validation-auc:0.97331 validation-aucpr:0.97628
[47] validation-logloss:0.19570 validation-auc:0.97342 validation-aucpr:0.97635
[48] validation-logloss:0.19442 validation-auc:0.97350 validation-aucpr:0.97645
[49] validation-logloss:0.19342 validation-auc:0.97356 validation-aucpr:0.97650
[50] validation-logloss:0.19208 validation-auc:0.97368 validation-aucpr:0.97669
[51] validation-logloss:0.19119 validation-auc:0.97372 validation-aucpr:0.97671
[52] validation-logloss:0.19047 validation-auc:0.97377 validation-aucpr:0.97674
[53] validation-logloss:0.18961 validation-auc:0.97380 validation-aucpr:0.97678
[54] validation-logloss:0.18924 validation-auc:0.97378 validation-aucpr:0.97677
[55] validation-logloss:0.18846 validation-auc:0.97384 validation-aucpr:0.97683
[56] validation-logloss:0.18736 validation-auc:0.97403 validation-aucpr:0.97698
[57] validation-logloss:0.18648 validation-auc:0.97412 validation-aucpr:0.97703
[58] validation-logloss:0.18609 validation-auc:0.97403 validation-aucpr:0.97690
[59] validation-logloss:0.18595 validation-auc:0.97391 validation-aucpr:0.97683
[60] validation-logloss:0.18555 validation-auc:0.97395 validation-aucpr:0.97693
[61] validation-logloss:0.18504 validation-auc:0.97390 validation-aucpr:0.97629
[62] validation-logloss:0.18472 validation-auc:0.97388 validation-aucpr:0.97627
[63] validation-logloss:0.18416 validation-auc:0.97390 validation-aucpr:0.97619
[64] validation-logloss:0.18404 validation-auc:0.97383 validation-aucpr:0.97600
[65] validation-logloss:0.18367 validation-auc:0.97382 validation-aucpr:0.97597
[66] validation-logloss:0.18338 validation-auc:0.97383 validation-aucpr:0.97602
[67] validation-logloss:0.18301 validation-auc:0.97382 validation-aucpr:0.97591
[68] validation-logloss:0.18276 validation-auc:0.97383 validation-aucpr:0.97589
[69] validation-logloss:0.18241 validation-auc:0.97393 validation-aucpr:0.97594
[70] validation-logloss:0.18215 validation-auc:0.97391 validation-aucpr:0.97590
[71] validation-logloss:0.18196 validation-auc:0.97397 validation-aucpr:0.97656
[72] validation-logloss:0.18172 validation-auc:0.97394 validation-aucpr:0.97627
{'best_iteration': '57', 'best_score': '0.9770323608248587'}
Trial 76, Fold 2: Log loss = 0.18172125853299292, Average precision = 0.9762585221188115, ROC-AUC = 0.9739378489630013, Elapsed Time = 3.2547775999992155 seconds
Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 76, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.64618 validation-auc:0.94209 validation-aucpr:0.91996
[1] validation-logloss:0.60481 validation-auc:0.95842 validation-aucpr:0.95056
[2] validation-logloss:0.57157 validation-auc:0.96142 validation-aucpr:0.96026
[3] validation-logloss:0.54184 validation-auc:0.96483 validation-aucpr:0.96469
[4] validation-logloss:0.51496 validation-auc:0.96569 validation-aucpr:0.96904
[5] validation-logloss:0.48797 validation-auc:0.96747 validation-aucpr:0.97111
[6] validation-logloss:0.46349 validation-auc:0.96825 validation-aucpr:0.96973
[7] validation-logloss:0.44460 validation-auc:0.96793 validation-aucpr:0.97005
[8] validation-logloss:0.42620 validation-auc:0.96820 validation-aucpr:0.97088
[9] validation-logloss:0.40728 validation-auc:0.96878 validation-aucpr:0.97074
[10] validation-logloss:0.39298 validation-auc:0.96857 validation-aucpr:0.96968
[11] validation-logloss:0.37665 validation-auc:0.96890 validation-aucpr:0.97021
[12] validation-logloss:0.36352 validation-auc:0.96897 validation-aucpr:0.97048
[13] validation-logloss:0.35209 validation-auc:0.96927 validation-aucpr:0.97079
[14] validation-logloss:0.33936 validation-auc:0.96943 validation-aucpr:0.97088
[15] validation-logloss:0.32925 validation-auc:0.96986 validation-aucpr:0.97121
[16] validation-logloss:0.31842 validation-auc:0.97015 validation-aucpr:0.97058
[17] validation-logloss:0.30837 validation-auc:0.97037 validation-aucpr:0.97086
[18] validation-logloss:0.30054 validation-auc:0.97033 validation-aucpr:0.97073
[19] validation-logloss:0.29165 validation-auc:0.97055 validation-aucpr:0.97123
[20] validation-logloss:0.28526 validation-auc:0.97080 validation-aucpr:0.97218
[21] validation-logloss:0.27761 validation-auc:0.97107 validation-aucpr:0.97264
[22] validation-logloss:0.27075 validation-auc:0.97118 validation-aucpr:0.97272
[23] validation-logloss:0.26455 validation-auc:0.97124 validation-aucpr:0.97274
[24] validation-logloss:0.25865 validation-auc:0.97135 validation-aucpr:0.97273
[25] validation-logloss:0.25437 validation-auc:0.97123 validation-aucpr:0.97269
[26] validation-logloss:0.25003 validation-auc:0.97130 validation-aucpr:0.97267
[27] validation-logloss:0.24507 validation-auc:0.97150 validation-aucpr:0.97372
[28] validation-logloss:0.24065 validation-auc:0.97138 validation-aucpr:0.97272
[29] validation-logloss:0.23704 validation-auc:0.97174 validation-aucpr:0.97447
[30] validation-logloss:0.23280 validation-auc:0.97198 validation-aucpr:0.97471
[31] validation-logloss:0.23012 validation-auc:0.97187 validation-aucpr:0.97438
[32] validation-logloss:0.22757 validation-auc:0.97177 validation-aucpr:0.97445
[33] validation-logloss:0.22501 validation-auc:0.97195 validation-aucpr:0.97587
[34] validation-logloss:0.22200 validation-auc:0.97211 validation-aucpr:0.97599
[35] validation-logloss:0.21891 validation-auc:0.97231 validation-aucpr:0.97615
[36] validation-logloss:0.21710 validation-auc:0.97230 validation-aucpr:0.97614
[37] validation-logloss:0.21514 validation-auc:0.97238 validation-aucpr:0.97618
[38] validation-logloss:0.21320 validation-auc:0.97251 validation-aucpr:0.97628
[39] validation-logloss:0.21105 validation-auc:0.97252 validation-aucpr:0.97627
[40] validation-logloss:0.20893 validation-auc:0.97261 validation-aucpr:0.97636
[41] validation-logloss:0.20670 validation-auc:0.97279 validation-aucpr:0.97648
[42] validation-logloss:0.20553 validation-auc:0.97276 validation-aucpr:0.97656
[43] validation-logloss:0.20443 validation-auc:0.97272 validation-aucpr:0.97654
[44] validation-logloss:0.20301 validation-auc:0.97284 validation-aucpr:0.97661
[45] validation-logloss:0.20141 validation-auc:0.97298 validation-aucpr:0.97674
[46] validation-logloss:0.20023 validation-auc:0.97290 validation-aucpr:0.97667
[47] validation-logloss:0.19882 validation-auc:0.97301 validation-aucpr:0.97677
[48] validation-logloss:0.19801 validation-auc:0.97303 validation-aucpr:0.97676
[49] validation-logloss:0.19686 validation-auc:0.97310 validation-aucpr:0.97682
[50] validation-logloss:0.19611 validation-auc:0.97319 validation-aucpr:0.97688
[51] validation-logloss:0.19541 validation-auc:0.97315 validation-aucpr:0.97688
[52] validation-logloss:0.19477 validation-auc:0.97318 validation-aucpr:0.97688
[53] validation-logloss:0.19429 validation-auc:0.97324 validation-aucpr:0.97692
[54] validation-logloss:0.19368 validation-auc:0.97328 validation-aucpr:0.97694
[55] validation-logloss:0.19292 validation-auc:0.97329 validation-aucpr:0.97692
[56] validation-logloss:0.19235 validation-auc:0.97324 validation-aucpr:0.97688
[57] validation-logloss:0.19209 validation-auc:0.97321 validation-aucpr:0.97685
[58] validation-logloss:0.19150 validation-auc:0.97318 validation-aucpr:0.97676
[59] validation-logloss:0.19086 validation-auc:0.97325 validation-aucpr:0.97683
[60] validation-logloss:0.19028 validation-auc:0.97327 validation-aucpr:0.97685
[61] validation-logloss:0.18947 validation-auc:0.97347 validation-aucpr:0.97700
[62] validation-logloss:0.18888 validation-auc:0.97358 validation-aucpr:0.97709
[63] validation-logloss:0.18880 validation-auc:0.97356 validation-aucpr:0.97707
[64] validation-logloss:0.18843 validation-auc:0.97360 validation-aucpr:0.97708
[65] validation-logloss:0.18815 validation-auc:0.97360 validation-aucpr:0.97706
[66] validation-logloss:0.18784 validation-auc:0.97362 validation-aucpr:0.97702
[67] validation-logloss:0.18781 validation-auc:0.97360 validation-aucpr:0.97701
[68] validation-logloss:0.18744 validation-auc:0.97360 validation-aucpr:0.97694
[69] validation-logloss:0.18704 validation-auc:0.97363 validation-aucpr:0.97684
[70] validation-logloss:0.18681 validation-auc:0.97365 validation-aucpr:0.97685
[71] validation-logloss:0.18665 validation-auc:0.97371 validation-aucpr:0.97689
[72] validation-logloss:0.18657 validation-auc:0.97372 validation-aucpr:0.97690
{'best_iteration': '62', 'best_score': '0.977090692573389'}
Trial 76, Fold 3: Log loss = 0.18657246436692548, Average precision = 0.9769053106660622, ROC-AUC = 0.9737197645693483, Elapsed Time = 3.1280955000001995 seconds
Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 76, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.64659 validation-auc:0.93764 validation-aucpr:0.92147
[1] validation-logloss:0.60502 validation-auc:0.95930 validation-aucpr:0.95793
[2] validation-logloss:0.57194 validation-auc:0.96263 validation-aucpr:0.96390
[3] validation-logloss:0.53894 validation-auc:0.96500 validation-aucpr:0.96913
[4] validation-logloss:0.50959 validation-auc:0.96607 validation-aucpr:0.96995
[5] validation-logloss:0.48599 validation-auc:0.96667 validation-aucpr:0.97177
[6] validation-logloss:0.46111 validation-auc:0.96792 validation-aucpr:0.97280
[7] validation-logloss:0.44152 validation-auc:0.96790 validation-aucpr:0.97294
[8] validation-logloss:0.42174 validation-auc:0.96815 validation-aucpr:0.97308
[9] validation-logloss:0.40373 validation-auc:0.96839 validation-aucpr:0.97337
[10] validation-logloss:0.38711 validation-auc:0.96869 validation-aucpr:0.97357
[11] validation-logloss:0.37177 validation-auc:0.96899 validation-aucpr:0.97384
[12] validation-logloss:0.35759 validation-auc:0.96949 validation-aucpr:0.97417
[13] validation-logloss:0.34603 validation-auc:0.96966 validation-aucpr:0.97439
[14] validation-logloss:0.33556 validation-auc:0.96959 validation-aucpr:0.97433
[15] validation-logloss:0.32472 validation-auc:0.96965 validation-aucpr:0.97439
[16] validation-logloss:0.31410 validation-auc:0.97007 validation-aucpr:0.97476
[17] validation-logloss:0.30594 validation-auc:0.97003 validation-aucpr:0.97471
[18] validation-logloss:0.29787 validation-auc:0.97028 validation-aucpr:0.97485
[19] validation-logloss:0.29105 validation-auc:0.97005 validation-aucpr:0.97471
[20] validation-logloss:0.28309 validation-auc:0.97055 validation-aucpr:0.97511
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[23] validation-logloss:0.26511 validation-auc:0.97075 validation-aucpr:0.97528
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[25] validation-logloss:0.25613 validation-auc:0.97061 validation-aucpr:0.97514
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[27] validation-logloss:0.24679 validation-auc:0.97109 validation-aucpr:0.97550
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[29] validation-logloss:0.23924 validation-auc:0.97112 validation-aucpr:0.97556
[30] validation-logloss:0.23540 validation-auc:0.97110 validation-aucpr:0.97556
[31] validation-logloss:0.23229 validation-auc:0.97122 validation-aucpr:0.97562
[32] validation-logloss:0.22890 validation-auc:0.97123 validation-aucpr:0.97562
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[34] validation-logloss:0.22369 validation-auc:0.97105 validation-aucpr:0.97553
[35] validation-logloss:0.22082 validation-auc:0.97119 validation-aucpr:0.97565
[36] validation-logloss:0.21816 validation-auc:0.97136 validation-aucpr:0.97577
[37] validation-logloss:0.21624 validation-auc:0.97142 validation-aucpr:0.97582
[38] validation-logloss:0.21421 validation-auc:0.97138 validation-aucpr:0.97580
[39] validation-logloss:0.21197 validation-auc:0.97148 validation-aucpr:0.97590
[40] validation-logloss:0.21039 validation-auc:0.97161 validation-aucpr:0.97597
[41] validation-logloss:0.20841 validation-auc:0.97182 validation-aucpr:0.97613
[42] validation-logloss:0.20650 validation-auc:0.97197 validation-aucpr:0.97628
[43] validation-logloss:0.20530 validation-auc:0.97199 validation-aucpr:0.97631
[44] validation-logloss:0.20385 validation-auc:0.97198 validation-aucpr:0.97632
[45] validation-logloss:0.20265 validation-auc:0.97202 validation-aucpr:0.97634
[46] validation-logloss:0.20146 validation-auc:0.97200 validation-aucpr:0.97630
[47] validation-logloss:0.20000 validation-auc:0.97225 validation-aucpr:0.97650
[48] validation-logloss:0.19882 validation-auc:0.97229 validation-aucpr:0.97654
[49] validation-logloss:0.19821 validation-auc:0.97220 validation-aucpr:0.97646
[50] validation-logloss:0.19717 validation-auc:0.97227 validation-aucpr:0.97651
[51] validation-logloss:0.19675 validation-auc:0.97223 validation-aucpr:0.97648
[52] validation-logloss:0.19603 validation-auc:0.97231 validation-aucpr:0.97653
[53] validation-logloss:0.19545 validation-auc:0.97238 validation-aucpr:0.97656
[54] validation-logloss:0.19468 validation-auc:0.97244 validation-aucpr:0.97660
[55] validation-logloss:0.19380 validation-auc:0.97252 validation-aucpr:0.97666
[56] validation-logloss:0.19348 validation-auc:0.97249 validation-aucpr:0.97662
[57] validation-logloss:0.19285 validation-auc:0.97254 validation-aucpr:0.97664
[58] validation-logloss:0.19217 validation-auc:0.97260 validation-aucpr:0.97668
[59] validation-logloss:0.19195 validation-auc:0.97258 validation-aucpr:0.97667
[60] validation-logloss:0.19129 validation-auc:0.97267 validation-aucpr:0.97673
[61] validation-logloss:0.19107 validation-auc:0.97262 validation-aucpr:0.97670
[62] validation-logloss:0.19046 validation-auc:0.97270 validation-aucpr:0.97676
[63] validation-logloss:0.19013 validation-auc:0.97268 validation-aucpr:0.97675
[64] validation-logloss:0.18967 validation-auc:0.97274 validation-aucpr:0.97679
[65] validation-logloss:0.18939 validation-auc:0.97272 validation-aucpr:0.97679
[66] validation-logloss:0.18891 validation-auc:0.97281 validation-aucpr:0.97687
[67] validation-logloss:0.18854 validation-auc:0.97287 validation-aucpr:0.97690
[68] validation-logloss:0.18828 validation-auc:0.97288 validation-aucpr:0.97690
[69] validation-logloss:0.18805 validation-auc:0.97292 validation-aucpr:0.97692
[70] validation-logloss:0.18774 validation-auc:0.97300 validation-aucpr:0.97697
[71] validation-logloss:0.18749 validation-auc:0.97302 validation-aucpr:0.97698
[72] validation-logloss:0.18729 validation-auc:0.97305 validation-aucpr:0.97700
{'best_iteration': '72', 'best_score': '0.9770020497729272'}
Trial 76, Fold 4: Log loss = 0.18728633940029324, Average precision = 0.9770056518527942, ROC-AUC = 0.9730516242599385, Elapsed Time = 2.883299500001158 seconds
Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 76, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.64594 validation-auc:0.94709 validation-aucpr:0.92992
[1] validation-logloss:0.60545 validation-auc:0.96148 validation-aucpr:0.96125
[2] validation-logloss:0.57336 validation-auc:0.96311 validation-aucpr:0.96487
[3] validation-logloss:0.54018 validation-auc:0.96505 validation-aucpr:0.96831
[4] validation-logloss:0.51340 validation-auc:0.96578 validation-aucpr:0.96861
[5] validation-logloss:0.48612 validation-auc:0.96710 validation-aucpr:0.96967
[6] validation-logloss:0.46197 validation-auc:0.96746 validation-aucpr:0.96999
[7] validation-logloss:0.44051 validation-auc:0.96773 validation-aucpr:0.97035
[8] validation-logloss:0.41998 validation-auc:0.96834 validation-aucpr:0.97097
[9] validation-logloss:0.40408 validation-auc:0.96789 validation-aucpr:0.97060
[10] validation-logloss:0.38930 validation-auc:0.96816 validation-aucpr:0.97255
[11] validation-logloss:0.37574 validation-auc:0.96845 validation-aucpr:0.97268
[12] validation-logloss:0.36118 validation-auc:0.96933 validation-aucpr:0.97334
[13] validation-logloss:0.34984 validation-auc:0.96969 validation-aucpr:0.97361
[14] validation-logloss:0.33777 validation-auc:0.97013 validation-aucpr:0.97401
[15] validation-logloss:0.32650 validation-auc:0.97030 validation-aucpr:0.97411
[16] validation-logloss:0.31739 validation-auc:0.97023 validation-aucpr:0.97403
[17] validation-logloss:0.30940 validation-auc:0.96984 validation-aucpr:0.97373
[18] validation-logloss:0.30048 validation-auc:0.96986 validation-aucpr:0.97384
[19] validation-logloss:0.29383 validation-auc:0.96981 validation-aucpr:0.97382
[20] validation-logloss:0.28712 validation-auc:0.96993 validation-aucpr:0.97393
[21] validation-logloss:0.28001 validation-auc:0.97012 validation-aucpr:0.97411
[22] validation-logloss:0.27424 validation-auc:0.97037 validation-aucpr:0.97430
[23] validation-logloss:0.26793 validation-auc:0.97078 validation-aucpr:0.97452
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[35] validation-logloss:0.22538 validation-auc:0.97154 validation-aucpr:0.97517
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[52] validation-logloss:0.20129 validation-auc:0.97221 validation-aucpr:0.97559
[53] validation-logloss:0.20044 validation-auc:0.97220 validation-aucpr:0.97563
[54] validation-logloss:0.19983 validation-auc:0.97225 validation-aucpr:0.97567
[55] validation-logloss:0.19925 validation-auc:0.97225 validation-aucpr:0.97556
[56] validation-logloss:0.19840 validation-auc:0.97231 validation-aucpr:0.97561
[57] validation-logloss:0.19826 validation-auc:0.97225 validation-aucpr:0.97554
[58] validation-logloss:0.19822 validation-auc:0.97210 validation-aucpr:0.97541
[59] validation-logloss:0.19797 validation-auc:0.97204 validation-aucpr:0.97527
[60] validation-logloss:0.19733 validation-auc:0.97206 validation-aucpr:0.97526
[61] validation-logloss:0.19700 validation-auc:0.97207 validation-aucpr:0.97518
[62] validation-logloss:0.19668 validation-auc:0.97205 validation-aucpr:0.97530
[63] validation-logloss:0.19610 validation-auc:0.97211 validation-aucpr:0.97533
[64] validation-logloss:0.19541 validation-auc:0.97221 validation-aucpr:0.97537
[65] validation-logloss:0.19489 validation-auc:0.97238 validation-aucpr:0.97573
[66] validation-logloss:0.19482 validation-auc:0.97234 validation-aucpr:0.97567
[67] validation-logloss:0.19435 validation-auc:0.97239 validation-aucpr:0.97570
[68] validation-logloss:0.19395 validation-auc:0.97242 validation-aucpr:0.97569
[69] validation-logloss:0.19384 validation-auc:0.97243 validation-aucpr:0.97567
[70] validation-logloss:0.19325 validation-auc:0.97259 validation-aucpr:0.97575
[71] validation-logloss:0.19291 validation-auc:0.97266 validation-aucpr:0.97588
[72] validation-logloss:0.19265 validation-auc:0.97267 validation-aucpr:0.97589
{'best_iteration': '72', 'best_score': '0.9758881601556328'}
Trial 76, Fold 5: Log loss = 0.19264611417775424, Average precision = 0.9758934275824167, ROC-AUC = 0.9726742099102614, Elapsed Time = 2.890385700000479 seconds
Optimization Progress: 77%|#######7 | 77/100 [3:28:19<20:32, 53.57s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 77, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64124 validation-auc:0.94910 validation-aucpr:0.95290
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59675 validation-auc:0.95926 validation-aucpr:0.95782
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.55690 validation-auc:0.96340 validation-aucpr:0.96624
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.52630 validation-auc:0.96339 validation-aucpr:0.96582
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49429 validation-auc:0.96532 validation-aucpr:0.97072
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.46657 validation-auc:0.96587 validation-aucpr:0.97128
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44524 validation-auc:0.96575 validation-aucpr:0.97127
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42281 validation-auc:0.96566 validation-aucpr:0.97133
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40169 validation-auc:0.96646 validation-aucpr:0.97201
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38353 validation-auc:0.96657 validation-aucpr:0.97228
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.36888 validation-auc:0.96677 validation-aucpr:0.97232
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35346 validation-auc:0.96710 validation-aucpr:0.97270
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.33982 validation-auc:0.96740 validation-aucpr:0.97293
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.32805 validation-auc:0.96748 validation-aucpr:0.97297
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.31919 validation-auc:0.96714 validation-aucpr:0.97255
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.31018 validation-auc:0.96737 validation-aucpr:0.97268
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.30094 validation-auc:0.96755 validation-aucpr:0.97280
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.29209 validation-auc:0.96757 validation-aucpr:0.97283
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28461 validation-auc:0.96795 validation-aucpr:0.97308
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27727 validation-auc:0.96816 validation-aucpr:0.97319
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.27078 validation-auc:0.96829 validation-aucpr:0.97325
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[21] validation-logloss:0.26427 validation-auc:0.96878 validation-aucpr:0.97368
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[22] validation-logloss:0.25896 validation-auc:0.96872 validation-aucpr:0.97359
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25461 validation-auc:0.96873 validation-aucpr:0.97364
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.25072 validation-auc:0.96865 validation-aucpr:0.97356
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.24637 validation-auc:0.96885 validation-aucpr:0.97376
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.24208 validation-auc:0.96912 validation-aucpr:0.97392
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23809 validation-auc:0.96920 validation-aucpr:0.97398
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23496 validation-auc:0.96919 validation-aucpr:0.97395
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.23216 validation-auc:0.96917 validation-aucpr:0.97398
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22992 validation-auc:0.96904 validation-aucpr:0.97387
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22818 validation-auc:0.96874 validation-aucpr:0.97278
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.22556 validation-auc:0.96893 validation-aucpr:0.97293
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22341 validation-auc:0.96901 validation-aucpr:0.97297
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22187 validation-auc:0.96892 validation-aucpr:0.97290
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22019 validation-auc:0.96889 validation-aucpr:0.97289
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.21836 validation-auc:0.96900 validation-aucpr:0.97297
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21702 validation-auc:0.96911 validation-aucpr:0.97398
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21616 validation-auc:0.96908 validation-aucpr:0.97396
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21498 validation-auc:0.96915 validation-aucpr:0.97400
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21394 validation-auc:0.96921 validation-aucpr:0.97400
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21258 validation-auc:0.96940 validation-aucpr:0.97419
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21167 validation-auc:0.96935 validation-aucpr:0.97416
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.21047 validation-auc:0.96956 validation-aucpr:0.97432
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20967 validation-auc:0.96958 validation-aucpr:0.97431
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20897 validation-auc:0.96965 validation-aucpr:0.97435
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20833 validation-auc:0.96972 validation-aucpr:0.97439
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20764 validation-auc:0.96980 validation-aucpr:0.97450
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20710 validation-auc:0.96982 validation-aucpr:0.97459
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20713 validation-auc:0.96972 validation-aucpr:0.97450
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20682 validation-auc:0.96979 validation-aucpr:0.97454
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20640 validation-auc:0.96980 validation-aucpr:0.97460
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20576 validation-auc:0.96987 validation-aucpr:0.97469
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20552 validation-auc:0.96992 validation-aucpr:0.97471
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20507 validation-auc:0.97011 validation-aucpr:0.97483
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20495 validation-auc:0.97007 validation-aucpr:0.97480
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20468 validation-auc:0.97001 validation-aucpr:0.97478
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20445 validation-auc:0.97007 validation-aucpr:0.97480
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20439 validation-auc:0.97007 validation-aucpr:0.97479
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20412 validation-auc:0.97010 validation-aucpr:0.97479
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20385 validation-auc:0.97023 validation-aucpr:0.97489
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20420 validation-auc:0.97013 validation-aucpr:0.97483
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20448 validation-auc:0.97007 validation-aucpr:0.97476
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20443 validation-auc:0.97011 validation-aucpr:0.97480
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20463 validation-auc:0.97012 validation-aucpr:0.97480
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20464 validation-auc:0.97011 validation-aucpr:0.97481
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20489 validation-auc:0.97001 validation-aucpr:0.97472
[21:27:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20531 validation-auc:0.96993 validation-aucpr:0.97466
[21:27:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20533 validation-auc:0.97004 validation-aucpr:0.97474
[21:27:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20556 validation-auc:0.97008 validation-aucpr:0.97478
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20598 validation-auc:0.96997 validation-aucpr:0.97471
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20631 validation-auc:0.96991 validation-aucpr:0.97469
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20650 validation-auc:0.96992 validation-aucpr:0.97470
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20667 validation-auc:0.96998 validation-aucpr:0.97474
{'best_iteration': '60', 'best_score': '0.9748918052690786'}
Trial 77, Fold 1: Log loss = 0.20666904008919706, Average precision = 0.9747405903084637, ROC-AUC = 0.9699798480568579, Elapsed Time = 17.862047299997357 seconds
Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 77, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.63982 validation-auc:0.95206 validation-aucpr:0.94168
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59510 validation-auc:0.96174 validation-aucpr:0.96048
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.55920 validation-auc:0.96427 validation-aucpr:0.96810
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.52369 validation-auc:0.96565 validation-aucpr:0.96799
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49574 validation-auc:0.96634 validation-aucpr:0.97002
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.46684 validation-auc:0.96787 validation-aucpr:0.97141
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44162 validation-auc:0.96820 validation-aucpr:0.97189
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42232 validation-auc:0.96779 validation-aucpr:0.97148
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40197 validation-auc:0.96833 validation-aucpr:0.97181
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38408 validation-auc:0.96835 validation-aucpr:0.97189
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.36711 validation-auc:0.96926 validation-aucpr:0.97262
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35153 validation-auc:0.96962 validation-aucpr:0.97289
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.33790 validation-auc:0.96951 validation-aucpr:0.97287
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.32695 validation-auc:0.96956 validation-aucpr:0.97287
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.31540 validation-auc:0.96969 validation-aucpr:0.97313
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.30624 validation-auc:0.96981 validation-aucpr:0.97323
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.29676 validation-auc:0.96988 validation-aucpr:0.97329
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.28729 validation-auc:0.97045 validation-aucpr:0.97365
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.27911 validation-auc:0.97067 validation-aucpr:0.97382
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27139 validation-auc:0.97089 validation-aucpr:0.97400
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.26394 validation-auc:0.97127 validation-aucpr:0.97431
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.25736 validation-auc:0.97158 validation-aucpr:0.97449
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.25109 validation-auc:0.97186 validation-aucpr:0.97469
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.24653 validation-auc:0.97176 validation-aucpr:0.97456
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24181 validation-auc:0.97197 validation-aucpr:0.97475
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.23683 validation-auc:0.97206 validation-aucpr:0.97486
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23278 validation-auc:0.97201 validation-aucpr:0.97505
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22876 validation-auc:0.97204 validation-aucpr:0.97507
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.22594 validation-auc:0.97197 validation-aucpr:0.97499
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22266 validation-auc:0.97187 validation-aucpr:0.97491
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21937 validation-auc:0.97199 validation-aucpr:0.97504
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21704 validation-auc:0.97178 validation-aucpr:0.97450
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21484 validation-auc:0.97169 validation-aucpr:0.97442
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21261 validation-auc:0.97189 validation-aucpr:0.97460
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21034 validation-auc:0.97192 validation-aucpr:0.97461
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20812 validation-auc:0.97202 validation-aucpr:0.97465
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20627 validation-auc:0.97203 validation-aucpr:0.97475
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20410 validation-auc:0.97220 validation-aucpr:0.97485
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20256 validation-auc:0.97226 validation-aucpr:0.97485
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20162 validation-auc:0.97220 validation-aucpr:0.97476
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20080 validation-auc:0.97212 validation-aucpr:0.97473
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19963 validation-auc:0.97205 validation-aucpr:0.97470
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19851 validation-auc:0.97219 validation-aucpr:0.97470
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19726 validation-auc:0.97221 validation-aucpr:0.97464
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19623 validation-auc:0.97226 validation-aucpr:0.97467
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19520 validation-auc:0.97231 validation-aucpr:0.97449
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19427 validation-auc:0.97242 validation-aucpr:0.97498
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19366 validation-auc:0.97235 validation-aucpr:0.97490
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19305 validation-auc:0.97229 validation-aucpr:0.97507
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19242 validation-auc:0.97234 validation-aucpr:0.97512
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19197 validation-auc:0.97234 validation-aucpr:0.97497
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19120 validation-auc:0.97256 validation-aucpr:0.97510
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19063 validation-auc:0.97257 validation-aucpr:0.97511
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19044 validation-auc:0.97257 validation-aucpr:0.97508
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.18966 validation-auc:0.97262 validation-aucpr:0.97514
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.18946 validation-auc:0.97255 validation-aucpr:0.97510
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.18899 validation-auc:0.97261 validation-aucpr:0.97515
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.18876 validation-auc:0.97262 validation-aucpr:0.97453
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.18818 validation-auc:0.97279 validation-aucpr:0.97452
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.18800 validation-auc:0.97268 validation-aucpr:0.97436
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.18768 validation-auc:0.97278 validation-aucpr:0.97457
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.18733 validation-auc:0.97280 validation-aucpr:0.97455
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.18702 validation-auc:0.97300 validation-aucpr:0.97566
[21:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.18684 validation-auc:0.97299 validation-aucpr:0.97563
[21:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.18671 validation-auc:0.97296 validation-aucpr:0.97573
[21:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.18660 validation-auc:0.97299 validation-aucpr:0.97587
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.18659 validation-auc:0.97301 validation-aucpr:0.97581
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.18641 validation-auc:0.97300 validation-aucpr:0.97577
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.18650 validation-auc:0.97295 validation-aucpr:0.97565
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.18637 validation-auc:0.97299 validation-aucpr:0.97565
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.18649 validation-auc:0.97301 validation-aucpr:0.97569
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.18668 validation-auc:0.97299 validation-aucpr:0.97563
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.18699 validation-auc:0.97290 validation-aucpr:0.97542
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.18704 validation-auc:0.97290 validation-aucpr:0.97538
{'best_iteration': '65', 'best_score': '0.9758713375134983'}
Trial 77, Fold 2: Log loss = 0.18703814435806546, Average precision = 0.9753815790402707, ROC-AUC = 0.9728992112453606, Elapsed Time = 18.450700099998357 seconds
Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 77, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64064 validation-auc:0.95030 validation-aucpr:0.94347
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59775 validation-auc:0.95936 validation-aucpr:0.95585
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56142 validation-auc:0.96249 validation-aucpr:0.96331
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.52527 validation-auc:0.96578 validation-aucpr:0.96963
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49362 validation-auc:0.96684 validation-aucpr:0.96861
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.46545 validation-auc:0.96709 validation-aucpr:0.96766
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44088 validation-auc:0.96728 validation-aucpr:0.96980
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42082 validation-auc:0.96754 validation-aucpr:0.96976
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40211 validation-auc:0.96831 validation-aucpr:0.97035
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38525 validation-auc:0.96887 validation-aucpr:0.97070
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37034 validation-auc:0.96902 validation-aucpr:0.97083
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35419 validation-auc:0.96927 validation-aucpr:0.97099
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.33951 validation-auc:0.96979 validation-aucpr:0.97125
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.32854 validation-auc:0.96988 validation-aucpr:0.97128
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.31829 validation-auc:0.97010 validation-aucpr:0.97147
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.30711 validation-auc:0.97035 validation-aucpr:0.97172
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.29755 validation-auc:0.97019 validation-aucpr:0.97164
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.28795 validation-auc:0.97057 validation-aucpr:0.97186
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.27982 validation-auc:0.97077 validation-aucpr:0.97199
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27195 validation-auc:0.97071 validation-aucpr:0.97213
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.26461 validation-auc:0.97088 validation-aucpr:0.97224
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.25850 validation-auc:0.97078 validation-aucpr:0.97215
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.25362 validation-auc:0.97092 validation-aucpr:0.97221
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.24877 validation-auc:0.97084 validation-aucpr:0.97199
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24395 validation-auc:0.97121 validation-aucpr:0.97519
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.23963 validation-auc:0.97115 validation-aucpr:0.97515
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23578 validation-auc:0.97095 validation-aucpr:0.97498
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23217 validation-auc:0.97090 validation-aucpr:0.97494
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.22843 validation-auc:0.97122 validation-aucpr:0.97520
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22485 validation-auc:0.97142 validation-aucpr:0.97532
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22164 validation-auc:0.97161 validation-aucpr:0.97558
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21937 validation-auc:0.97172 validation-aucpr:0.97564
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21728 validation-auc:0.97175 validation-aucpr:0.97564
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21538 validation-auc:0.97160 validation-aucpr:0.97554
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21333 validation-auc:0.97164 validation-aucpr:0.97559
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21184 validation-auc:0.97162 validation-aucpr:0.97556
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20987 validation-auc:0.97176 validation-aucpr:0.97568
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20825 validation-auc:0.97177 validation-aucpr:0.97571
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20673 validation-auc:0.97190 validation-aucpr:0.97577
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20513 validation-auc:0.97195 validation-aucpr:0.97581
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20368 validation-auc:0.97196 validation-aucpr:0.97578
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20266 validation-auc:0.97185 validation-aucpr:0.97571
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20183 validation-auc:0.97185 validation-aucpr:0.97571
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20085 validation-auc:0.97184 validation-aucpr:0.97572
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19977 validation-auc:0.97193 validation-aucpr:0.97588
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19894 validation-auc:0.97192 validation-aucpr:0.97587
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19785 validation-auc:0.97199 validation-aucpr:0.97594
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19699 validation-auc:0.97212 validation-aucpr:0.97592
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19584 validation-auc:0.97226 validation-aucpr:0.97605
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19494 validation-auc:0.97240 validation-aucpr:0.97611
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19429 validation-auc:0.97243 validation-aucpr:0.97613
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19376 validation-auc:0.97251 validation-aucpr:0.97620
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19347 validation-auc:0.97253 validation-aucpr:0.97619
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19315 validation-auc:0.97257 validation-aucpr:0.97619
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19251 validation-auc:0.97267 validation-aucpr:0.97620
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19262 validation-auc:0.97260 validation-aucpr:0.97612
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19228 validation-auc:0.97267 validation-aucpr:0.97609
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19174 validation-auc:0.97279 validation-aucpr:0.97618
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19175 validation-auc:0.97272 validation-aucpr:0.97604
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19162 validation-auc:0.97274 validation-aucpr:0.97603
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19156 validation-auc:0.97272 validation-aucpr:0.97597
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.19153 validation-auc:0.97271 validation-aucpr:0.97596
[21:28:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.19164 validation-auc:0.97263 validation-aucpr:0.97584
[21:28:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.19160 validation-auc:0.97265 validation-aucpr:0.97576
[21:28:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.19170 validation-auc:0.97265 validation-aucpr:0.97567
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.19170 validation-auc:0.97267 validation-aucpr:0.97556
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.19160 validation-auc:0.97266 validation-aucpr:0.97547
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.19156 validation-auc:0.97271 validation-aucpr:0.97554
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.19179 validation-auc:0.97268 validation-aucpr:0.97552
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.19168 validation-auc:0.97279 validation-aucpr:0.97556
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.19168 validation-auc:0.97282 validation-aucpr:0.97575
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.19206 validation-auc:0.97274 validation-aucpr:0.97561
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.19217 validation-auc:0.97279 validation-aucpr:0.97581
[21:28:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.19239 validation-auc:0.97276 validation-aucpr:0.97567
{'best_iteration': '51', 'best_score': '0.9761959525993174'}
Trial 77, Fold 3: Log loss = 0.1923887270627478, Average precision = 0.975678562191017, ROC-AUC = 0.9727598212479895, Elapsed Time = 18.685897399998794 seconds
Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 77, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:28:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64013 validation-auc:0.95125 validation-aucpr:0.95040
[21:28:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59626 validation-auc:0.95914 validation-aucpr:0.96319
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.55707 validation-auc:0.96243 validation-aucpr:0.96865
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.52566 validation-auc:0.96284 validation-aucpr:0.96903
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49407 validation-auc:0.96478 validation-aucpr:0.97051
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.46892 validation-auc:0.96527 validation-aucpr:0.97100
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44417 validation-auc:0.96540 validation-aucpr:0.97121
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42383 validation-auc:0.96579 validation-aucpr:0.97156
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40444 validation-auc:0.96615 validation-aucpr:0.97194
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38775 validation-auc:0.96647 validation-aucpr:0.97221
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.37094 validation-auc:0.96684 validation-aucpr:0.97257
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35772 validation-auc:0.96693 validation-aucpr:0.97252
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.34462 validation-auc:0.96700 validation-aucpr:0.97261
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.33165 validation-auc:0.96732 validation-aucpr:0.97289
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.31952 validation-auc:0.96784 validation-aucpr:0.97330
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.30864 validation-auc:0.96796 validation-aucpr:0.97340
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.29956 validation-auc:0.96796 validation-aucpr:0.97342
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.29048 validation-auc:0.96808 validation-aucpr:0.97352
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28335 validation-auc:0.96802 validation-aucpr:0.97349
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27546 validation-auc:0.96847 validation-aucpr:0.97385
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.26838 validation-auc:0.96876 validation-aucpr:0.97406
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.26266 validation-auc:0.96886 validation-aucpr:0.97414
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.25681 validation-auc:0.96910 validation-aucpr:0.97433
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25150 validation-auc:0.96906 validation-aucpr:0.97430
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24609 validation-auc:0.96944 validation-aucpr:0.97453
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.24177 validation-auc:0.96931 validation-aucpr:0.97447
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23742 validation-auc:0.96954 validation-aucpr:0.97459
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23340 validation-auc:0.96970 validation-aucpr:0.97472
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23042 validation-auc:0.96939 validation-aucpr:0.97454
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22666 validation-auc:0.96981 validation-aucpr:0.97482
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22340 validation-auc:0.97017 validation-aucpr:0.97505
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22031 validation-auc:0.97045 validation-aucpr:0.97527
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21774 validation-auc:0.97064 validation-aucpr:0.97539
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.21522 validation-auc:0.97099 validation-aucpr:0.97562
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21304 validation-auc:0.97106 validation-aucpr:0.97566
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21174 validation-auc:0.97094 validation-aucpr:0.97559
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20963 validation-auc:0.97109 validation-aucpr:0.97572
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20797 validation-auc:0.97115 validation-aucpr:0.97578
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20643 validation-auc:0.97114 validation-aucpr:0.97575
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20468 validation-auc:0.97137 validation-aucpr:0.97591
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20319 validation-auc:0.97156 validation-aucpr:0.97604
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20203 validation-auc:0.97158 validation-aucpr:0.97605
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20098 validation-auc:0.97157 validation-aucpr:0.97605
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19991 validation-auc:0.97167 validation-aucpr:0.97612
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19921 validation-auc:0.97167 validation-aucpr:0.97612
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19885 validation-auc:0.97148 validation-aucpr:0.97597
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19807 validation-auc:0.97158 validation-aucpr:0.97604
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19750 validation-auc:0.97163 validation-aucpr:0.97609
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19693 validation-auc:0.97164 validation-aucpr:0.97608
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19615 validation-auc:0.97174 validation-aucpr:0.97616
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19548 validation-auc:0.97183 validation-aucpr:0.97622
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19544 validation-auc:0.97169 validation-aucpr:0.97611
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19485 validation-auc:0.97177 validation-aucpr:0.97617
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19432 validation-auc:0.97186 validation-aucpr:0.97624
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19432 validation-auc:0.97180 validation-aucpr:0.97621
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19378 validation-auc:0.97185 validation-aucpr:0.97622
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19398 validation-auc:0.97174 validation-aucpr:0.97613
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19402 validation-auc:0.97167 validation-aucpr:0.97608
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19372 validation-auc:0.97175 validation-aucpr:0.97612
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19356 validation-auc:0.97171 validation-aucpr:0.97609
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19359 validation-auc:0.97169 validation-aucpr:0.97608
[21:28:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.19308 validation-auc:0.97187 validation-aucpr:0.97622
[21:28:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.19326 validation-auc:0.97186 validation-aucpr:0.97621
[21:28:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.19318 validation-auc:0.97187 validation-aucpr:0.97624
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.19310 validation-auc:0.97192 validation-aucpr:0.97627
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.19315 validation-auc:0.97195 validation-aucpr:0.97627
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.19281 validation-auc:0.97206 validation-aucpr:0.97638
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.19275 validation-auc:0.97203 validation-aucpr:0.97639
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.19298 validation-auc:0.97202 validation-aucpr:0.97637
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.19341 validation-auc:0.97193 validation-aucpr:0.97630
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.19336 validation-auc:0.97199 validation-aucpr:0.97635
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.19369 validation-auc:0.97194 validation-aucpr:0.97631
[21:28:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.19353 validation-auc:0.97201 validation-aucpr:0.97637
[21:28:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.19371 validation-auc:0.97196 validation-aucpr:0.97631
{'best_iteration': '67', 'best_score': '0.9763874139328066'}
Trial 77, Fold 4: Log loss = 0.19370908844916987, Average precision = 0.976315444049942, ROC-AUC = 0.9719556154042409, Elapsed Time = 18.724122399999032 seconds
Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 77, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64019 validation-auc:0.94752 validation-aucpr:0.94616
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.59981 validation-auc:0.95419 validation-aucpr:0.95434
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.56013 validation-auc:0.95930 validation-aucpr:0.96385
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.52420 validation-auc:0.96318 validation-aucpr:0.96572
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.49357 validation-auc:0.96375 validation-aucpr:0.96887
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.46594 validation-auc:0.96571 validation-aucpr:0.97069
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.44097 validation-auc:0.96600 validation-aucpr:0.97123
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.42117 validation-auc:0.96620 validation-aucpr:0.97124
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.40138 validation-auc:0.96645 validation-aucpr:0.97147
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.38282 validation-auc:0.96729 validation-aucpr:0.97222
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.36594 validation-auc:0.96781 validation-aucpr:0.97261
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.35293 validation-auc:0.96784 validation-aucpr:0.97255
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.33939 validation-auc:0.96806 validation-aucpr:0.97272
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.32797 validation-auc:0.96836 validation-aucpr:0.97275
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.31683 validation-auc:0.96829 validation-aucpr:0.97277
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.30635 validation-auc:0.96878 validation-aucpr:0.97312
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.29713 validation-auc:0.96886 validation-aucpr:0.97315
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.28814 validation-auc:0.96929 validation-aucpr:0.97350
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.28009 validation-auc:0.96975 validation-aucpr:0.97384
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.27320 validation-auc:0.96969 validation-aucpr:0.97380
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.26630 validation-auc:0.97002 validation-aucpr:0.97410
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.26135 validation-auc:0.96985 validation-aucpr:0.97394
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.25568 validation-auc:0.97008 validation-aucpr:0.97411
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.25113 validation-auc:0.96997 validation-aucpr:0.97407
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.24645 validation-auc:0.96997 validation-aucpr:0.97406
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.24306 validation-auc:0.96998 validation-aucpr:0.97401
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.23883 validation-auc:0.97024 validation-aucpr:0.97414
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.23611 validation-auc:0.97018 validation-aucpr:0.97407
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.23283 validation-auc:0.97023 validation-aucpr:0.97415
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.22952 validation-auc:0.97044 validation-aucpr:0.97429
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.22636 validation-auc:0.97066 validation-aucpr:0.97446
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.22401 validation-auc:0.97074 validation-aucpr:0.97471
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.22167 validation-auc:0.97065 validation-aucpr:0.97465
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22001 validation-auc:0.97057 validation-aucpr:0.97455
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.21787 validation-auc:0.97074 validation-aucpr:0.97468
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.21646 validation-auc:0.97068 validation-aucpr:0.97442
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.21499 validation-auc:0.97073 validation-aucpr:0.97444
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21325 validation-auc:0.97077 validation-aucpr:0.97449
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21214 validation-auc:0.97066 validation-aucpr:0.97440
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21022 validation-auc:0.97095 validation-aucpr:0.97468
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20870 validation-auc:0.97112 validation-aucpr:0.97480
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20742 validation-auc:0.97112 validation-aucpr:0.97481
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20656 validation-auc:0.97106 validation-aucpr:0.97477
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20555 validation-auc:0.97117 validation-aucpr:0.97480
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20484 validation-auc:0.97114 validation-aucpr:0.97480
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20435 validation-auc:0.97110 validation-aucpr:0.97478
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20374 validation-auc:0.97108 validation-aucpr:0.97467
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20353 validation-auc:0.97100 validation-aucpr:0.97458
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20274 validation-auc:0.97108 validation-aucpr:0.97461
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20208 validation-auc:0.97122 validation-aucpr:0.97468
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20176 validation-auc:0.97120 validation-aucpr:0.97460
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20113 validation-auc:0.97131 validation-aucpr:0.97498
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20034 validation-auc:0.97142 validation-aucpr:0.97497
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19986 validation-auc:0.97154 validation-aucpr:0.97512
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19947 validation-auc:0.97166 validation-aucpr:0.97526
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19964 validation-auc:0.97153 validation-aucpr:0.97522
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20003 validation-auc:0.97132 validation-aucpr:0.97505
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20002 validation-auc:0.97126 validation-aucpr:0.97494
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19993 validation-auc:0.97132 validation-aucpr:0.97493
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19983 validation-auc:0.97133 validation-aucpr:0.97490
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19956 validation-auc:0.97140 validation-aucpr:0.97494
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.19935 validation-auc:0.97148 validation-aucpr:0.97501
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.19941 validation-auc:0.97144 validation-aucpr:0.97493
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.19950 validation-auc:0.97144 validation-aucpr:0.97482
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.19973 validation-auc:0.97137 validation-aucpr:0.97477
[21:28:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.19986 validation-auc:0.97132 validation-aucpr:0.97469
[21:28:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.19992 validation-auc:0.97139 validation-aucpr:0.97468
[21:28:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20011 validation-auc:0.97133 validation-aucpr:0.97456
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20035 validation-auc:0.97128 validation-aucpr:0.97449
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20002 validation-auc:0.97145 validation-aucpr:0.97502
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.19996 validation-auc:0.97146 validation-aucpr:0.97501
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.19986 validation-auc:0.97154 validation-aucpr:0.97513
[21:28:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20026 validation-auc:0.97149 validation-aucpr:0.97507
[21:28:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20060 validation-auc:0.97139 validation-aucpr:0.97493
{'best_iteration': '54', 'best_score': '0.9752626205930159'}
Trial 77, Fold 5: Log loss = 0.20059891678402936, Average precision = 0.9749373043194629, ROC-AUC = 0.9713875480742434, Elapsed Time = 18.439465700001165 seconds
Optimization Progress: 78%|#######8 | 78/100 [3:29:59<24:45, 67.54s/it]
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 78, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66663 validation-auc:0.94171 validation-aucpr:0.94109
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64201 validation-auc:0.96091 validation-aucpr:0.95450
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61922 validation-auc:0.96447 validation-aucpr:0.96116
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59807 validation-auc:0.96590 validation-aucpr:0.97163
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57823 validation-auc:0.96652 validation-aucpr:0.97227
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.56166 validation-auc:0.96675 validation-aucpr:0.97258
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.54621 validation-auc:0.96617 validation-aucpr:0.97196
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52950 validation-auc:0.96628 validation-aucpr:0.97213
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.51374 validation-auc:0.96620 validation-aucpr:0.97204
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.50042 validation-auc:0.96596 validation-aucpr:0.97177
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.48820 validation-auc:0.96597 validation-aucpr:0.97164
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.47439 validation-auc:0.96634 validation-aucpr:0.97190
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.46125 validation-auc:0.96692 validation-aucpr:0.97242
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.44984 validation-auc:0.96731 validation-aucpr:0.97269
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.43864 validation-auc:0.96730 validation-aucpr:0.97268
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.42793 validation-auc:0.96719 validation-aucpr:0.97260
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.41757 validation-auc:0.96747 validation-aucpr:0.97287
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.40755 validation-auc:0.96773 validation-aucpr:0.97304
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.39867 validation-auc:0.96805 validation-aucpr:0.97329
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.39055 validation-auc:0.96818 validation-aucpr:0.97334
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.38315 validation-auc:0.96796 validation-aucpr:0.97317
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.37470 validation-auc:0.96817 validation-aucpr:0.97334
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.36665 validation-auc:0.96836 validation-aucpr:0.97346
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.36061 validation-auc:0.96817 validation-aucpr:0.97328
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.35471 validation-auc:0.96813 validation-aucpr:0.97319
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.34775 validation-auc:0.96832 validation-aucpr:0.97332
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.34221 validation-auc:0.96836 validation-aucpr:0.97333
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.33556 validation-auc:0.96869 validation-aucpr:0.97356
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.32957 validation-auc:0.96863 validation-aucpr:0.97353
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.32399 validation-auc:0.96854 validation-aucpr:0.97344
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.31850 validation-auc:0.96857 validation-aucpr:0.97348
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.31311 validation-auc:0.96863 validation-aucpr:0.97355
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.30810 validation-auc:0.96874 validation-aucpr:0.97366
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.30305 validation-auc:0.96903 validation-aucpr:0.97386
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.29832 validation-auc:0.96916 validation-aucpr:0.97396
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.29376 validation-auc:0.96944 validation-aucpr:0.97421
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.28972 validation-auc:0.96944 validation-aucpr:0.97422
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.28561 validation-auc:0.96955 validation-aucpr:0.97429
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.28227 validation-auc:0.96952 validation-aucpr:0.97426
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.27906 validation-auc:0.96955 validation-aucpr:0.97424
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.27555 validation-auc:0.96953 validation-aucpr:0.97422
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.27196 validation-auc:0.96967 validation-aucpr:0.97432
[21:29:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.26921 validation-auc:0.96966 validation-aucpr:0.97430
[21:29:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.26660 validation-auc:0.96971 validation-aucpr:0.97429
[21:29:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.26347 validation-auc:0.96975 validation-aucpr:0.97434
{'best_iteration': '44', 'best_score': '0.9743414384395959'}
Trial 78, Fold 1: Log loss = 0.2634666136903201, Average precision = 0.9743449721498787, ROC-AUC = 0.9697506518164828, Elapsed Time = 7.150237099998776 seconds
Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 78, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66660 validation-auc:0.94258 validation-aucpr:0.91905
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64419 validation-auc:0.96093 validation-aucpr:0.95317
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.62103 validation-auc:0.96378 validation-aucpr:0.96142
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59913 validation-auc:0.96715 validation-aucpr:0.96839
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57891 validation-auc:0.96845 validation-aucpr:0.97224
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55967 validation-auc:0.96904 validation-aucpr:0.97274
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.54164 validation-auc:0.96914 validation-aucpr:0.97281
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52494 validation-auc:0.96906 validation-aucpr:0.97278
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50911 validation-auc:0.96911 validation-aucpr:0.97282
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.49435 validation-auc:0.96934 validation-aucpr:0.97302
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.48176 validation-auc:0.96954 validation-aucpr:0.97311
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.46845 validation-auc:0.96965 validation-aucpr:0.97315
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.45737 validation-auc:0.96975 validation-aucpr:0.97320
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.44521 validation-auc:0.96982 validation-aucpr:0.97322
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.43365 validation-auc:0.96982 validation-aucpr:0.97322
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.42311 validation-auc:0.96970 validation-aucpr:0.97314
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.41271 validation-auc:0.96989 validation-aucpr:0.97325
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.40279 validation-auc:0.97014 validation-aucpr:0.97347
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.39337 validation-auc:0.97046 validation-aucpr:0.97363
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.38431 validation-auc:0.97055 validation-aucpr:0.97366
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.37660 validation-auc:0.97064 validation-aucpr:0.97372
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.36910 validation-auc:0.97082 validation-aucpr:0.97395
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.36109 validation-auc:0.97101 validation-aucpr:0.97413
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.35379 validation-auc:0.97089 validation-aucpr:0.97404
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.34745 validation-auc:0.97110 validation-aucpr:0.97416
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.34142 validation-auc:0.97123 validation-aucpr:0.97425
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.33480 validation-auc:0.97137 validation-aucpr:0.97467
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.32924 validation-auc:0.97135 validation-aucpr:0.97470
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.32314 validation-auc:0.97155 validation-aucpr:0.97484
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.31739 validation-auc:0.97164 validation-aucpr:0.97491
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.31247 validation-auc:0.97165 validation-aucpr:0.97489
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.30778 validation-auc:0.97182 validation-aucpr:0.97503
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.30239 validation-auc:0.97213 validation-aucpr:0.97525
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.29785 validation-auc:0.97231 validation-aucpr:0.97538
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.29402 validation-auc:0.97216 validation-aucpr:0.97525
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.28944 validation-auc:0.97215 validation-aucpr:0.97526
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.28513 validation-auc:0.97215 validation-aucpr:0.97515
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.28078 validation-auc:0.97236 validation-aucpr:0.97533
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.27663 validation-auc:0.97248 validation-aucpr:0.97542
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.27339 validation-auc:0.97245 validation-aucpr:0.97537
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.26972 validation-auc:0.97247 validation-aucpr:0.97538
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.26687 validation-auc:0.97240 validation-aucpr:0.97532
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.26392 validation-auc:0.97255 validation-aucpr:0.97540
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.26067 validation-auc:0.97263 validation-aucpr:0.97548
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25753 validation-auc:0.97261 validation-aucpr:0.97546
{'best_iteration': '43', 'best_score': '0.9754849993403878'}
Trial 78, Fold 2: Log loss = 0.25753349892157557, Average precision = 0.9754106024749805, ROC-AUC = 0.9726118768135756, Elapsed Time = 5.847930500000075 seconds
Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 78, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:29:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66640 validation-auc:0.93718 validation-aucpr:0.89307
[21:29:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64415 validation-auc:0.95825 validation-aucpr:0.94779
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.62339 validation-auc:0.96143 validation-aucpr:0.95842
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.60217 validation-auc:0.96362 validation-aucpr:0.96591
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.58403 validation-auc:0.96465 validation-aucpr:0.96847
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.56457 validation-auc:0.96590 validation-aucpr:0.96969
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.54855 validation-auc:0.96645 validation-aucpr:0.97121
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.53085 validation-auc:0.96767 validation-aucpr:0.97206
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.51647 validation-auc:0.96795 validation-aucpr:0.97229
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.50280 validation-auc:0.96803 validation-aucpr:0.97265
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.48803 validation-auc:0.96832 validation-aucpr:0.97293
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.47399 validation-auc:0.96876 validation-aucpr:0.97335
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.46217 validation-auc:0.96925 validation-aucpr:0.97364
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.45143 validation-auc:0.96925 validation-aucpr:0.97369
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.43936 validation-auc:0.96967 validation-aucpr:0.97404
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.42941 validation-auc:0.96959 validation-aucpr:0.97400
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.41834 validation-auc:0.96986 validation-aucpr:0.97426
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.40824 validation-auc:0.97011 validation-aucpr:0.97446
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.39822 validation-auc:0.97030 validation-aucpr:0.97462
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.38872 validation-auc:0.97065 validation-aucpr:0.97496
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.37978 validation-auc:0.97085 validation-aucpr:0.97514
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.37121 validation-auc:0.97102 validation-aucpr:0.97527
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.36463 validation-auc:0.97073 validation-aucpr:0.97500
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.35712 validation-auc:0.97072 validation-aucpr:0.97499
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.35068 validation-auc:0.97078 validation-aucpr:0.97499
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.34402 validation-auc:0.97070 validation-aucpr:0.97495
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.33741 validation-auc:0.97082 validation-aucpr:0.97504
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.33091 validation-auc:0.97100 validation-aucpr:0.97518
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.32460 validation-auc:0.97108 validation-aucpr:0.97528
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.31868 validation-auc:0.97121 validation-aucpr:0.97539
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.31284 validation-auc:0.97138 validation-aucpr:0.97552
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.30823 validation-auc:0.97150 validation-aucpr:0.97560
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.30289 validation-auc:0.97156 validation-aucpr:0.97568
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.29858 validation-auc:0.97164 validation-aucpr:0.97568
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.29388 validation-auc:0.97170 validation-aucpr:0.97574
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.28937 validation-auc:0.97180 validation-aucpr:0.97580
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.28585 validation-auc:0.97180 validation-aucpr:0.97579
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.28155 validation-auc:0.97198 validation-aucpr:0.97590
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.27798 validation-auc:0.97193 validation-aucpr:0.97585
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.27399 validation-auc:0.97193 validation-aucpr:0.97586
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.27051 validation-auc:0.97180 validation-aucpr:0.97578
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.26770 validation-auc:0.97173 validation-aucpr:0.97570
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.26416 validation-auc:0.97176 validation-aucpr:0.97572
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.26119 validation-auc:0.97185 validation-aucpr:0.97577
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.25780 validation-auc:0.97200 validation-aucpr:0.97589
{'best_iteration': '37', 'best_score': '0.9759014238086381'}
Trial 78, Fold 3: Log loss = 0.25779883428118283, Average precision = 0.9758932484338817, ROC-AUC = 0.9719994209340177, Elapsed Time = 5.3621460999966075 seconds
Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 78, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66641 validation-auc:0.93185 validation-aucpr:0.90151
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64424 validation-auc:0.95972 validation-aucpr:0.95595
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.62119 validation-auc:0.96387 validation-aucpr:0.96402
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.60025 validation-auc:0.96519 validation-aucpr:0.96611
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.58232 validation-auc:0.96579 validation-aucpr:0.97122
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.56548 validation-auc:0.96609 validation-aucpr:0.97147
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.54932 validation-auc:0.96608 validation-aucpr:0.97150
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.53427 validation-auc:0.96647 validation-aucpr:0.97159
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.51783 validation-auc:0.96669 validation-aucpr:0.97192
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.50426 validation-auc:0.96679 validation-aucpr:0.97197
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.49001 validation-auc:0.96746 validation-aucpr:0.97252
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.47603 validation-auc:0.96777 validation-aucpr:0.97283
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.46507 validation-auc:0.96763 validation-aucpr:0.97266
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.45254 validation-auc:0.96763 validation-aucpr:0.97269
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.44031 validation-auc:0.96814 validation-aucpr:0.97312
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.43050 validation-auc:0.96822 validation-aucpr:0.97316
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.41979 validation-auc:0.96841 validation-aucpr:0.97337
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.40966 validation-auc:0.96841 validation-aucpr:0.97337
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.40134 validation-auc:0.96841 validation-aucpr:0.97334
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.39195 validation-auc:0.96851 validation-aucpr:0.97344
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.38308 validation-auc:0.96868 validation-aucpr:0.97362
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.37462 validation-auc:0.96881 validation-aucpr:0.97373
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.36652 validation-auc:0.96900 validation-aucpr:0.97390
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.36000 validation-auc:0.96900 validation-aucpr:0.97387
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.35396 validation-auc:0.96908 validation-aucpr:0.97392
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.34677 validation-auc:0.96928 validation-aucpr:0.97407
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.33972 validation-auc:0.96943 validation-aucpr:0.97419
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.33434 validation-auc:0.96947 validation-aucpr:0.97422
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.32940 validation-auc:0.96946 validation-aucpr:0.97422
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.32462 validation-auc:0.96946 validation-aucpr:0.97421
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.31964 validation-auc:0.96946 validation-aucpr:0.97420
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.31510 validation-auc:0.96937 validation-aucpr:0.97412
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.30987 validation-auc:0.96943 validation-aucpr:0.97420
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.30572 validation-auc:0.96942 validation-aucpr:0.97420
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.30157 validation-auc:0.96953 validation-aucpr:0.97426
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.29670 validation-auc:0.96957 validation-aucpr:0.97430
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.29218 validation-auc:0.96958 validation-aucpr:0.97433
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.28767 validation-auc:0.96979 validation-aucpr:0.97448
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.28379 validation-auc:0.96984 validation-aucpr:0.97451
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.28068 validation-auc:0.96976 validation-aucpr:0.97444
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.27762 validation-auc:0.96973 validation-aucpr:0.97440
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.27476 validation-auc:0.96966 validation-aucpr:0.97436
[21:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.27107 validation-auc:0.96970 validation-aucpr:0.97441
[21:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.26832 validation-auc:0.96968 validation-aucpr:0.97441
[21:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.26568 validation-auc:0.96968 validation-aucpr:0.97444
{'best_iteration': '38', 'best_score': '0.9745071265456206'}
Trial 78, Fold 4: Log loss = 0.26568473621462146, Average precision = 0.9744429625257657, ROC-AUC = 0.9696823787348798, Elapsed Time = 5.064143999999942 seconds
Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 78, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66649 validation-auc:0.94023 validation-aucpr:0.90318
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64191 validation-auc:0.96175 validation-aucpr:0.95572
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.62133 validation-auc:0.96121 validation-aucpr:0.95928
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.60181 validation-auc:0.96250 validation-aucpr:0.96298
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.58141 validation-auc:0.96401 validation-aucpr:0.96614
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.56207 validation-auc:0.96564 validation-aucpr:0.96820
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.54427 validation-auc:0.96648 validation-aucpr:0.97033
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52953 validation-auc:0.96651 validation-aucpr:0.97007
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.51536 validation-auc:0.96716 validation-aucpr:0.97036
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.50229 validation-auc:0.96688 validation-aucpr:0.97000
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.49014 validation-auc:0.96678 validation-aucpr:0.97114
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.47858 validation-auc:0.96683 validation-aucpr:0.97107
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.46550 validation-auc:0.96703 validation-aucpr:0.97125
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.45298 validation-auc:0.96751 validation-aucpr:0.97163
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.44273 validation-auc:0.96774 validation-aucpr:0.97177
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.43314 validation-auc:0.96785 validation-aucpr:0.97181
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.42218 validation-auc:0.96810 validation-aucpr:0.97204
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.41345 validation-auc:0.96791 validation-aucpr:0.97184
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.40395 validation-auc:0.96798 validation-aucpr:0.97193
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.39503 validation-auc:0.96821 validation-aucpr:0.97217
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.38682 validation-auc:0.96847 validation-aucpr:0.97277
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.37973 validation-auc:0.96833 validation-aucpr:0.97262
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.37172 validation-auc:0.96864 validation-aucpr:0.97282
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.36388 validation-auc:0.96876 validation-aucpr:0.97294
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.35756 validation-auc:0.96862 validation-aucpr:0.97283
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.35167 validation-auc:0.96850 validation-aucpr:0.97271
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.34505 validation-auc:0.96853 validation-aucpr:0.97274
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.33843 validation-auc:0.96870 validation-aucpr:0.97293
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.33330 validation-auc:0.96864 validation-aucpr:0.97285
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.32694 validation-auc:0.96902 validation-aucpr:0.97314
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.32232 validation-auc:0.96896 validation-aucpr:0.97308
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.31683 validation-auc:0.96905 validation-aucpr:0.97314
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.31144 validation-auc:0.96921 validation-aucpr:0.97326
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.30640 validation-auc:0.96927 validation-aucpr:0.97333
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.30173 validation-auc:0.96941 validation-aucpr:0.97347
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.29740 validation-auc:0.96948 validation-aucpr:0.97352
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.29391 validation-auc:0.96941 validation-aucpr:0.97344
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.29059 validation-auc:0.96926 validation-aucpr:0.97330
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.28721 validation-auc:0.96928 validation-aucpr:0.97329
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.28318 validation-auc:0.96936 validation-aucpr:0.97336
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.27995 validation-auc:0.96946 validation-aucpr:0.97343
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.27615 validation-auc:0.96967 validation-aucpr:0.97362
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.27337 validation-auc:0.96978 validation-aucpr:0.97369
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.27063 validation-auc:0.96978 validation-aucpr:0.97367
[21:29:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.26746 validation-auc:0.96984 validation-aucpr:0.97371
{'best_iteration': '44', 'best_score': '0.9737137481701315'}
Trial 78, Fold 5: Log loss = 0.26746121894649966, Average precision = 0.9737135246742918, ROC-AUC = 0.9698394366720547, Elapsed Time = 5.0369890999972995 seconds
Optimization Progress: 79%|#######9 | 79/100 [3:30:36<20:23, 58.25s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 79, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:29:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68590 validation-auc:0.95495 validation-aucpr:0.95753
[21:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67954 validation-auc:0.95942 validation-aucpr:0.96511
[21:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67347 validation-auc:0.95824 validation-aucpr:0.96384
[21:29:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66658 validation-auc:0.96148 validation-aucpr:0.96716
[21:29:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65966 validation-auc:0.96332 validation-aucpr:0.96875
[21:29:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65328 validation-auc:0.96403 validation-aucpr:0.96940
[21:29:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64700 validation-auc:0.96436 validation-aucpr:0.97035
[21:29:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64135 validation-auc:0.96499 validation-aucpr:0.97074
[21:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63588 validation-auc:0.96486 validation-aucpr:0.97050
[21:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.63057 validation-auc:0.96474 validation-aucpr:0.97038
[21:29:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62444 validation-auc:0.96540 validation-aucpr:0.97100
[21:29:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61837 validation-auc:0.96565 validation-aucpr:0.97126
[21:29:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61323 validation-auc:0.96516 validation-aucpr:0.97079
[21:29:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60753 validation-auc:0.96558 validation-aucpr:0.97115
[21:29:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.60192 validation-auc:0.96600 validation-aucpr:0.97151
[21:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59630 validation-auc:0.96621 validation-aucpr:0.97173
[21:29:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.59077 validation-auc:0.96616 validation-aucpr:0.97174
[21:29:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58610 validation-auc:0.96589 validation-aucpr:0.97150
[21:29:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.58079 validation-auc:0.96603 validation-aucpr:0.97169
[21:29:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57555 validation-auc:0.96616 validation-aucpr:0.97180
[21:29:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.57027 validation-auc:0.96643 validation-aucpr:0.97204
[21:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56511 validation-auc:0.96675 validation-aucpr:0.97233
[21:30:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.56010 validation-auc:0.96681 validation-aucpr:0.97240
[21:30:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55540 validation-auc:0.96665 validation-aucpr:0.97234
[21:30:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.55067 validation-auc:0.96663 validation-aucpr:0.97232
[21:30:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54595 validation-auc:0.96677 validation-aucpr:0.97242
[21:30:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54119 validation-auc:0.96690 validation-aucpr:0.97252
[21:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53729 validation-auc:0.96685 validation-aucpr:0.97246
[21:30:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53295 validation-auc:0.96689 validation-aucpr:0.97248
[21:30:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52850 validation-auc:0.96706 validation-aucpr:0.97259
[21:30:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52413 validation-auc:0.96740 validation-aucpr:0.97333
[21:30:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51988 validation-auc:0.96731 validation-aucpr:0.97326
[21:30:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51553 validation-auc:0.96726 validation-aucpr:0.97325
[21:30:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51140 validation-auc:0.96712 validation-aucpr:0.97316
[21:30:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50720 validation-auc:0.96729 validation-aucpr:0.97326
[21:30:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50314 validation-auc:0.96731 validation-aucpr:0.97330
[21:30:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49919 validation-auc:0.96744 validation-aucpr:0.97338
[21:30:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49563 validation-auc:0.96752 validation-aucpr:0.97341
[21:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49172 validation-auc:0.96757 validation-aucpr:0.97344
[21:30:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48777 validation-auc:0.96767 validation-aucpr:0.97354
[21:30:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48397 validation-auc:0.96771 validation-aucpr:0.97354
[21:30:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48072 validation-auc:0.96778 validation-aucpr:0.97357
[21:30:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47714 validation-auc:0.96778 validation-aucpr:0.97359
[21:30:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47366 validation-auc:0.96782 validation-aucpr:0.97361
[21:30:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47059 validation-auc:0.96796 validation-aucpr:0.97367
[21:30:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46750 validation-auc:0.96797 validation-aucpr:0.97369
[21:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46398 validation-auc:0.96806 validation-aucpr:0.97375
[21:30:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.46073 validation-auc:0.96805 validation-aucpr:0.97376
[21:30:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45743 validation-auc:0.96809 validation-aucpr:0.97378
[21:30:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45467 validation-auc:0.96810 validation-aucpr:0.97373
[21:30:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45145 validation-auc:0.96808 validation-aucpr:0.97374
[21:30:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44830 validation-auc:0.96809 validation-aucpr:0.97373
[21:30:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44516 validation-auc:0.96812 validation-aucpr:0.97375
[21:30:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44251 validation-auc:0.96815 validation-aucpr:0.97374
[21:30:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43985 validation-auc:0.96815 validation-aucpr:0.97372
[21:30:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43695 validation-auc:0.96811 validation-aucpr:0.97371
[21:30:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43394 validation-auc:0.96817 validation-aucpr:0.97375
[21:30:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.43096 validation-auc:0.96824 validation-aucpr:0.97382
[21:30:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42801 validation-auc:0.96829 validation-aucpr:0.97388
[21:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42564 validation-auc:0.96821 validation-aucpr:0.97380
[21:30:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42322 validation-auc:0.96819 validation-aucpr:0.97376
[21:30:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.42055 validation-auc:0.96821 validation-aucpr:0.97378
[21:31:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41823 validation-auc:0.96823 validation-aucpr:0.97378
[21:31:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41586 validation-auc:0.96820 validation-aucpr:0.97373
[21:31:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41319 validation-auc:0.96822 validation-aucpr:0.97374
[21:31:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.41059 validation-auc:0.96825 validation-aucpr:0.97378
[21:31:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40791 validation-auc:0.96829 validation-aucpr:0.97381
[21:31:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40536 validation-auc:0.96827 validation-aucpr:0.97379
[21:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40284 validation-auc:0.96830 validation-aucpr:0.97382
[21:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.40034 validation-auc:0.96824 validation-aucpr:0.97377
[21:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39779 validation-auc:0.96831 validation-aucpr:0.97381
[21:31:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39525 validation-auc:0.96843 validation-aucpr:0.97390
[21:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39323 validation-auc:0.96841 validation-aucpr:0.97385
[21:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.39084 validation-auc:0.96838 validation-aucpr:0.97384
[21:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38847 validation-auc:0.96844 validation-aucpr:0.97389
[21:31:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38612 validation-auc:0.96859 validation-aucpr:0.97400
[21:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38377 validation-auc:0.96862 validation-aucpr:0.97402
[21:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.38149 validation-auc:0.96867 validation-aucpr:0.97406
[21:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37957 validation-auc:0.96865 validation-aucpr:0.97403
[21:31:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37742 validation-auc:0.96866 validation-aucpr:0.97404
[21:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37518 validation-auc:0.96869 validation-aucpr:0.97407
[21:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37317 validation-auc:0.96863 validation-aucpr:0.97404
[21:31:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.37146 validation-auc:0.96857 validation-aucpr:0.97397
[21:31:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36946 validation-auc:0.96858 validation-aucpr:0.97399
[21:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36747 validation-auc:0.96860 validation-aucpr:0.97403
[21:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36542 validation-auc:0.96862 validation-aucpr:0.97405
[21:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36345 validation-auc:0.96864 validation-aucpr:0.97405
[21:31:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.36151 validation-auc:0.96863 validation-aucpr:0.97406
[21:31:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.35980 validation-auc:0.96863 validation-aucpr:0.97405
{'best_iteration': '80', 'best_score': '0.9740666100234081'}
Trial 79, Fold 1: Log loss = 0.35979713209064984, Average precision = 0.9740550756475213, ROC-AUC = 0.9686318545284218, Elapsed Time = 132.87924360000034 seconds
Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 79, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:31:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68605 validation-auc:0.95094 validation-aucpr:0.95519
[21:31:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67981 validation-auc:0.95808 validation-aucpr:0.96221
[21:31:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67373 validation-auc:0.95829 validation-aucpr:0.96214
[21:31:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66684 validation-auc:0.96122 validation-aucpr:0.96543
[21:31:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.66015 validation-auc:0.96275 validation-aucpr:0.96702
[21:31:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65359 validation-auc:0.96371 validation-aucpr:0.96797
[21:31:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64721 validation-auc:0.96480 validation-aucpr:0.96897
[21:31:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64075 validation-auc:0.96549 validation-aucpr:0.96960
[21:31:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63449 validation-auc:0.96575 validation-aucpr:0.96980
[21:31:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62856 validation-auc:0.96641 validation-aucpr:0.97035
[21:31:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62306 validation-auc:0.96669 validation-aucpr:0.97046
[21:31:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61715 validation-auc:0.96680 validation-aucpr:0.97051
[21:32:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61143 validation-auc:0.96684 validation-aucpr:0.97057
[21:32:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60555 validation-auc:0.96732 validation-aucpr:0.97099
[21:32:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59989 validation-auc:0.96783 validation-aucpr:0.97128
[21:32:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59432 validation-auc:0.96801 validation-aucpr:0.97139
[21:32:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58878 validation-auc:0.96843 validation-aucpr:0.97176
[21:32:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58347 validation-auc:0.96885 validation-aucpr:0.97210
[21:32:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57819 validation-auc:0.96900 validation-aucpr:0.97223
[21:32:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57367 validation-auc:0.96897 validation-aucpr:0.97214
[21:32:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56867 validation-auc:0.96889 validation-aucpr:0.97207
[21:32:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56373 validation-auc:0.96890 validation-aucpr:0.97209
[21:32:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55891 validation-auc:0.96895 validation-aucpr:0.97210
[21:32:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55409 validation-auc:0.96911 validation-aucpr:0.97221
[21:32:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54930 validation-auc:0.96925 validation-aucpr:0.97232
[21:32:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54467 validation-auc:0.96936 validation-aucpr:0.97242
[21:32:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53998 validation-auc:0.96943 validation-aucpr:0.97249
[21:32:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53586 validation-auc:0.96916 validation-aucpr:0.97231
[21:32:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53139 validation-auc:0.96925 validation-aucpr:0.97237
[21:32:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52688 validation-auc:0.96944 validation-aucpr:0.97257
[21:32:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52314 validation-auc:0.96932 validation-aucpr:0.97237
[21:32:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51885 validation-auc:0.96949 validation-aucpr:0.97248
[21:32:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51459 validation-auc:0.96947 validation-aucpr:0.97248
[21:32:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51038 validation-auc:0.96966 validation-aucpr:0.97263
[21:32:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50622 validation-auc:0.96974 validation-aucpr:0.97268
[21:32:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50213 validation-auc:0.96981 validation-aucpr:0.97274
[21:32:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49809 validation-auc:0.96989 validation-aucpr:0.97279
[21:32:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49419 validation-auc:0.96992 validation-aucpr:0.97282
[21:32:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49075 validation-auc:0.96993 validation-aucpr:0.97285
[21:32:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48700 validation-auc:0.96985 validation-aucpr:0.97277
[21:32:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48313 validation-auc:0.96992 validation-aucpr:0.97283
[21:32:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47951 validation-auc:0.96986 validation-aucpr:0.97279
[21:32:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47598 validation-auc:0.96985 validation-aucpr:0.97279
[21:32:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47239 validation-auc:0.96982 validation-aucpr:0.97276
[21:32:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.46930 validation-auc:0.96977 validation-aucpr:0.97271
[21:32:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46590 validation-auc:0.96988 validation-aucpr:0.97279
[21:32:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46250 validation-auc:0.97005 validation-aucpr:0.97292
[21:32:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.45914 validation-auc:0.97001 validation-aucpr:0.97289
[21:32:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45581 validation-auc:0.97005 validation-aucpr:0.97291
[21:32:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45263 validation-auc:0.97008 validation-aucpr:0.97325
[21:32:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.44935 validation-auc:0.97009 validation-aucpr:0.97327
[21:32:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44658 validation-auc:0.97007 validation-aucpr:0.97327
[21:32:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44343 validation-auc:0.97004 validation-aucpr:0.97324
[21:32:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44029 validation-auc:0.97003 validation-aucpr:0.97324
[21:32:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43721 validation-auc:0.97008 validation-aucpr:0.97327
[21:33:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43459 validation-auc:0.97000 validation-aucpr:0.97320
[21:33:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43163 validation-auc:0.97009 validation-aucpr:0.97327
[21:33:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42905 validation-auc:0.97005 validation-aucpr:0.97323
[21:33:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42610 validation-auc:0.97009 validation-aucpr:0.97328
[21:33:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42363 validation-auc:0.97009 validation-aucpr:0.97328
[21:33:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42082 validation-auc:0.97017 validation-aucpr:0.97332
[21:33:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41809 validation-auc:0.97015 validation-aucpr:0.97333
[21:33:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41570 validation-auc:0.97016 validation-aucpr:0.97335
[21:33:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41301 validation-auc:0.97013 validation-aucpr:0.97333
[21:33:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41038 validation-auc:0.97013 validation-aucpr:0.97333
[21:33:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40773 validation-auc:0.97025 validation-aucpr:0.97343
[21:33:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40517 validation-auc:0.97031 validation-aucpr:0.97349
[21:33:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40265 validation-auc:0.97031 validation-aucpr:0.97352
[21:33:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40014 validation-auc:0.97025 validation-aucpr:0.97347
[21:33:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39762 validation-auc:0.97024 validation-aucpr:0.97348
[21:33:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39507 validation-auc:0.97028 validation-aucpr:0.97351
[21:33:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39260 validation-auc:0.97035 validation-aucpr:0.97356
[21:33:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39022 validation-auc:0.97032 validation-aucpr:0.97355
[21:33:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38794 validation-auc:0.97038 validation-aucpr:0.97359
[21:33:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38556 validation-auc:0.97040 validation-aucpr:0.97359
[21:33:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38324 validation-auc:0.97039 validation-aucpr:0.97359
[21:33:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38091 validation-auc:0.97045 validation-aucpr:0.97362
[21:33:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.37877 validation-auc:0.97039 validation-aucpr:0.97358
[21:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37686 validation-auc:0.97037 validation-aucpr:0.97357
[21:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37476 validation-auc:0.97032 validation-aucpr:0.97353
[21:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37274 validation-auc:0.97029 validation-aucpr:0.97349
[21:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37052 validation-auc:0.97035 validation-aucpr:0.97361
[21:33:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.36845 validation-auc:0.97037 validation-aucpr:0.97362
[21:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36666 validation-auc:0.97035 validation-aucpr:0.97346
[21:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36451 validation-auc:0.97045 validation-aucpr:0.97355
[21:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36252 validation-auc:0.97046 validation-aucpr:0.97357
[21:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36047 validation-auc:0.97053 validation-aucpr:0.97363
[21:33:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.35844 validation-auc:0.97050 validation-aucpr:0.97360
[21:33:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.35648 validation-auc:0.97045 validation-aucpr:0.97357
{'best_iteration': '86', 'best_score': '0.9736287736436597'}
Trial 79, Fold 2: Log loss = 0.35647722594563985, Average precision = 0.9735348578673697, ROC-AUC = 0.9704456013021172, Elapsed Time = 126.08551309999893 seconds
Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 79, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68583 validation-auc:0.95657 validation-aucpr:0.95778
[21:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67871 validation-auc:0.96236 validation-aucpr:0.96444
[21:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67191 validation-auc:0.96376 validation-aucpr:0.96792
[21:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66503 validation-auc:0.96431 validation-aucpr:0.96843
[21:33:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65824 validation-auc:0.96523 validation-aucpr:0.96927
[21:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65242 validation-auc:0.96612 validation-aucpr:0.97044
[21:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64601 validation-auc:0.96652 validation-aucpr:0.97130
[21:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64026 validation-auc:0.96709 validation-aucpr:0.97163
[21:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63468 validation-auc:0.96723 validation-aucpr:0.97166
[21:34:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62852 validation-auc:0.96721 validation-aucpr:0.97173
[21:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62249 validation-auc:0.96728 validation-aucpr:0.97179
[21:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61643 validation-auc:0.96765 validation-aucpr:0.97207
[21:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61119 validation-auc:0.96762 validation-aucpr:0.97194
[21:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60593 validation-auc:0.96765 validation-aucpr:0.97194
[21:34:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.60109 validation-auc:0.96752 validation-aucpr:0.97182
[21:34:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59564 validation-auc:0.96766 validation-aucpr:0.97191
[21:34:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.59012 validation-auc:0.96772 validation-aucpr:0.97194
[21:34:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58486 validation-auc:0.96761 validation-aucpr:0.97175
[21:34:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57959 validation-auc:0.96774 validation-aucpr:0.97185
[21:34:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57496 validation-auc:0.96794 validation-aucpr:0.97201
[21:34:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56983 validation-auc:0.96814 validation-aucpr:0.97218
[21:34:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56496 validation-auc:0.96798 validation-aucpr:0.97206
[21:34:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55993 validation-auc:0.96808 validation-aucpr:0.97214
[21:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55505 validation-auc:0.96824 validation-aucpr:0.97227
[21:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.55016 validation-auc:0.96836 validation-aucpr:0.97235
[21:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54544 validation-auc:0.96851 validation-aucpr:0.97246
[21:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54075 validation-auc:0.96850 validation-aucpr:0.97250
[21:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53626 validation-auc:0.96871 validation-aucpr:0.97296
[21:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53166 validation-auc:0.96884 validation-aucpr:0.97306
[21:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52730 validation-auc:0.96882 validation-aucpr:0.97302
[21:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52292 validation-auc:0.96878 validation-aucpr:0.97298
[21:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51867 validation-auc:0.96884 validation-aucpr:0.97301
[21:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51500 validation-auc:0.96880 validation-aucpr:0.97294
[21:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51143 validation-auc:0.96865 validation-aucpr:0.97283
[21:34:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50766 validation-auc:0.96869 validation-aucpr:0.97287
[21:34:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50364 validation-auc:0.96861 validation-aucpr:0.97281
[21:34:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49952 validation-auc:0.96876 validation-aucpr:0.97291
[21:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49552 validation-auc:0.96892 validation-aucpr:0.97303
[21:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49215 validation-auc:0.96895 validation-aucpr:0.97307
[21:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48833 validation-auc:0.96897 validation-aucpr:0.97310
[21:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48504 validation-auc:0.96901 validation-aucpr:0.97316
[21:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48146 validation-auc:0.96903 validation-aucpr:0.97317
[21:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47835 validation-auc:0.96893 validation-aucpr:0.97310
[21:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47475 validation-auc:0.96896 validation-aucpr:0.97312
[21:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47116 validation-auc:0.96895 validation-aucpr:0.97312
[21:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46805 validation-auc:0.96893 validation-aucpr:0.97311
[21:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46506 validation-auc:0.96896 validation-aucpr:0.97313
[21:34:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.46207 validation-auc:0.96887 validation-aucpr:0.97303
[21:35:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45904 validation-auc:0.96891 validation-aucpr:0.97308
[21:35:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45585 validation-auc:0.96899 validation-aucpr:0.97314
[21:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45252 validation-auc:0.96915 validation-aucpr:0.97328
[21:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44978 validation-auc:0.96913 validation-aucpr:0.97324
[21:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44652 validation-auc:0.96923 validation-aucpr:0.97333
[21:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44379 validation-auc:0.96917 validation-aucpr:0.97326
[21:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.44069 validation-auc:0.96928 validation-aucpr:0.97335
[21:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43803 validation-auc:0.96928 validation-aucpr:0.97341
[21:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43544 validation-auc:0.96919 validation-aucpr:0.97335
[21:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.43241 validation-auc:0.96932 validation-aucpr:0.97345
[21:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42999 validation-auc:0.96926 validation-aucpr:0.97340
[21:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42703 validation-auc:0.96935 validation-aucpr:0.97358
[21:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42422 validation-auc:0.96936 validation-aucpr:0.97359
[21:35:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.42131 validation-auc:0.96942 validation-aucpr:0.97366
[21:35:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41858 validation-auc:0.96942 validation-aucpr:0.97366
[21:35:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41625 validation-auc:0.96943 validation-aucpr:0.97365
[21:35:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41353 validation-auc:0.96947 validation-aucpr:0.97368
[21:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.41079 validation-auc:0.96950 validation-aucpr:0.97370
[21:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40811 validation-auc:0.96951 validation-aucpr:0.97371
[21:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40588 validation-auc:0.96948 validation-aucpr:0.97367
[21:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40333 validation-auc:0.96954 validation-aucpr:0.97370
[21:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.40120 validation-auc:0.96955 validation-aucpr:0.97370
[21:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39900 validation-auc:0.96954 validation-aucpr:0.97367
[21:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39696 validation-auc:0.96950 validation-aucpr:0.97365
[21:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39447 validation-auc:0.96954 validation-aucpr:0.97369
[21:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.39209 validation-auc:0.96956 validation-aucpr:0.97371
[21:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38963 validation-auc:0.96956 validation-aucpr:0.97372
[21:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38724 validation-auc:0.96965 validation-aucpr:0.97378
[21:35:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38526 validation-auc:0.96961 validation-aucpr:0.97375
[21:35:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.38296 validation-auc:0.96967 validation-aucpr:0.97380
[21:35:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.38074 validation-auc:0.96964 validation-aucpr:0.97377
[21:35:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37877 validation-auc:0.96966 validation-aucpr:0.97378
[21:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37693 validation-auc:0.96961 validation-aucpr:0.97374
[21:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37473 validation-auc:0.96964 validation-aucpr:0.97376
[21:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.37257 validation-auc:0.96966 validation-aucpr:0.97378
[21:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.37076 validation-auc:0.96958 validation-aucpr:0.97371
[21:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36860 validation-auc:0.96965 validation-aucpr:0.97377
[21:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36651 validation-auc:0.96965 validation-aucpr:0.97377
[21:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36443 validation-auc:0.96969 validation-aucpr:0.97380
[21:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.36237 validation-auc:0.96978 validation-aucpr:0.97385
[21:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.36042 validation-auc:0.96981 validation-aucpr:0.97388
{'best_iteration': '88', 'best_score': '0.9738848959265011'}
Trial 79, Fold 3: Log loss = 0.3604212559366444, Average precision = 0.9738371377642552, ROC-AUC = 0.9698099405524363, Elapsed Time = 131.3719807000016 seconds
Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 79, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68578 validation-auc:0.95664 validation-aucpr:0.96286
[21:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67868 validation-auc:0.96245 validation-aucpr:0.96876
[21:36:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67179 validation-auc:0.96277 validation-aucpr:0.96940
[21:36:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66498 validation-auc:0.96323 validation-aucpr:0.96985
[21:36:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65822 validation-auc:0.96500 validation-aucpr:0.97089
[21:36:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65229 validation-auc:0.96512 validation-aucpr:0.97133
[21:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64572 validation-auc:0.96562 validation-aucpr:0.97165
[21:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63929 validation-auc:0.96661 validation-aucpr:0.97224
[21:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63388 validation-auc:0.96613 validation-aucpr:0.97183
[21:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62763 validation-auc:0.96682 validation-aucpr:0.97235
[21:36:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62161 validation-auc:0.96696 validation-aucpr:0.97247
[21:36:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61637 validation-auc:0.96676 validation-aucpr:0.97230
[21:36:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61072 validation-auc:0.96705 validation-aucpr:0.97248
[21:36:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60495 validation-auc:0.96696 validation-aucpr:0.97242
[21:36:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59995 validation-auc:0.96676 validation-aucpr:0.97223
[21:36:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59497 validation-auc:0.96685 validation-aucpr:0.97223
[21:36:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58957 validation-auc:0.96695 validation-aucpr:0.97230
[21:36:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58428 validation-auc:0.96711 validation-aucpr:0.97244
[21:36:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57904 validation-auc:0.96710 validation-aucpr:0.97245
[21:36:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57441 validation-auc:0.96706 validation-aucpr:0.97237
[21:36:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56929 validation-auc:0.96707 validation-aucpr:0.97240
[21:36:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56489 validation-auc:0.96687 validation-aucpr:0.97221
[21:36:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55997 validation-auc:0.96716 validation-aucpr:0.97242
[21:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55511 validation-auc:0.96734 validation-aucpr:0.97254
[21:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.55020 validation-auc:0.96755 validation-aucpr:0.97269
[21:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54542 validation-auc:0.96763 validation-aucpr:0.97274
[21:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54071 validation-auc:0.96775 validation-aucpr:0.97283
[21:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53625 validation-auc:0.96798 validation-aucpr:0.97305
[21:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53193 validation-auc:0.96805 validation-aucpr:0.97309
[21:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52795 validation-auc:0.96809 validation-aucpr:0.97307
[21:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52363 validation-auc:0.96827 validation-aucpr:0.97320
[21:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51932 validation-auc:0.96834 validation-aucpr:0.97325
[21:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51512 validation-auc:0.96837 validation-aucpr:0.97328
[21:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.51145 validation-auc:0.96831 validation-aucpr:0.97324
[21:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50748 validation-auc:0.96841 validation-aucpr:0.97332
[21:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50348 validation-auc:0.96838 validation-aucpr:0.97330
[21:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49946 validation-auc:0.96849 validation-aucpr:0.97340
[21:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49601 validation-auc:0.96840 validation-aucpr:0.97334
[21:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49213 validation-auc:0.96845 validation-aucpr:0.97338
[21:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48880 validation-auc:0.96820 validation-aucpr:0.97320
[21:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48505 validation-auc:0.96843 validation-aucpr:0.97337
[21:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48124 validation-auc:0.96851 validation-aucpr:0.97343
[21:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47749 validation-auc:0.96877 validation-aucpr:0.97362
[21:37:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47407 validation-auc:0.96887 validation-aucpr:0.97371
[21:37:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47047 validation-auc:0.96892 validation-aucpr:0.97374
[21:37:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46695 validation-auc:0.96902 validation-aucpr:0.97382
[21:37:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46362 validation-auc:0.96900 validation-aucpr:0.97380
[21:37:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.46038 validation-auc:0.96900 validation-aucpr:0.97379
[21:37:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45711 validation-auc:0.96907 validation-aucpr:0.97385
[21:37:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45429 validation-auc:0.96889 validation-aucpr:0.97372
[21:37:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45099 validation-auc:0.96894 validation-aucpr:0.97375
[21:37:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.44790 validation-auc:0.96892 validation-aucpr:0.97374
[21:37:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44479 validation-auc:0.96886 validation-aucpr:0.97370
[21:37:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44180 validation-auc:0.96880 validation-aucpr:0.97365
[21:37:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.43888 validation-auc:0.96885 validation-aucpr:0.97371
[21:37:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43583 validation-auc:0.96887 validation-aucpr:0.97373
[21:37:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43292 validation-auc:0.96877 validation-aucpr:0.97365
[21:37:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.42997 validation-auc:0.96874 validation-aucpr:0.97364
[21:37:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.42704 validation-auc:0.96876 validation-aucpr:0.97365
[21:37:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42412 validation-auc:0.96886 validation-aucpr:0.97375
[21:37:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42132 validation-auc:0.96891 validation-aucpr:0.97377
[21:37:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.41853 validation-auc:0.96886 validation-aucpr:0.97374
[21:37:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41579 validation-auc:0.96898 validation-aucpr:0.97382
[21:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41310 validation-auc:0.96891 validation-aucpr:0.97376
[21:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41074 validation-auc:0.96888 validation-aucpr:0.97374
[21:37:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.40811 validation-auc:0.96889 validation-aucpr:0.97374
[21:37:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40561 validation-auc:0.96891 validation-aucpr:0.97375
[21:37:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40350 validation-auc:0.96882 validation-aucpr:0.97370
[21:37:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40123 validation-auc:0.96880 validation-aucpr:0.97369
[21:37:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.39906 validation-auc:0.96889 validation-aucpr:0.97373
[21:37:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39657 validation-auc:0.96888 validation-aucpr:0.97373
[21:37:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39411 validation-auc:0.96892 validation-aucpr:0.97376
[21:37:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39167 validation-auc:0.96898 validation-aucpr:0.97380
[21:37:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.38967 validation-auc:0.96896 validation-aucpr:0.97377
[21:37:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38737 validation-auc:0.96896 validation-aucpr:0.97377
[21:37:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38515 validation-auc:0.96897 validation-aucpr:0.97377
[21:37:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38326 validation-auc:0.96888 validation-aucpr:0.97370
[21:37:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.38101 validation-auc:0.96886 validation-aucpr:0.97369
[21:37:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.37884 validation-auc:0.96897 validation-aucpr:0.97377
[21:37:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37696 validation-auc:0.96898 validation-aucpr:0.97377
[21:37:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37519 validation-auc:0.96894 validation-aucpr:0.97373
[21:37:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37334 validation-auc:0.96894 validation-aucpr:0.97374
[21:38:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.37123 validation-auc:0.96901 validation-aucpr:0.97379
[21:38:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.36922 validation-auc:0.96899 validation-aucpr:0.97378
[21:38:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36718 validation-auc:0.96908 validation-aucpr:0.97384
[21:38:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36514 validation-auc:0.96917 validation-aucpr:0.97391
[21:38:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36313 validation-auc:0.96915 validation-aucpr:0.97389
[21:38:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.36116 validation-auc:0.96913 validation-aucpr:0.97389
[21:38:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.35930 validation-auc:0.96914 validation-aucpr:0.97389
{'best_iteration': '85', 'best_score': '0.9739107500294601'}
Trial 79, Fold 4: Log loss = 0.35929864099093195, Average precision = 0.9738837765660591, ROC-AUC = 0.9691444707921726, Elapsed Time = 127.51060520000101 seconds
Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 79, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:38:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68595 validation-auc:0.95081 validation-aucpr:0.95268
[21:38:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67976 validation-auc:0.95345 validation-aucpr:0.95767
[21:38:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67292 validation-auc:0.95778 validation-aucpr:0.96435
[21:38:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66600 validation-auc:0.96044 validation-aucpr:0.96643
[21:38:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65921 validation-auc:0.96249 validation-aucpr:0.96808
[21:38:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65286 validation-auc:0.96266 validation-aucpr:0.96809
[21:38:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64709 validation-auc:0.96269 validation-aucpr:0.96786
[21:38:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.64074 validation-auc:0.96344 validation-aucpr:0.96843
[21:38:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63440 validation-auc:0.96396 validation-aucpr:0.96873
[21:38:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62835 validation-auc:0.96428 validation-aucpr:0.96904
[21:38:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62242 validation-auc:0.96491 validation-aucpr:0.96944
[21:38:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61653 validation-auc:0.96516 validation-aucpr:0.96974
[21:38:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.61076 validation-auc:0.96506 validation-aucpr:0.96978
[21:38:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60514 validation-auc:0.96464 validation-aucpr:0.96948
[21:38:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.60015 validation-auc:0.96454 validation-aucpr:0.96945
[21:38:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59520 validation-auc:0.96473 validation-aucpr:0.96952
[21:38:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58992 validation-auc:0.96483 validation-aucpr:0.96962
[21:38:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58459 validation-auc:0.96490 validation-aucpr:0.96966
[21:38:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57966 validation-auc:0.96483 validation-aucpr:0.96961
[21:38:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57448 validation-auc:0.96497 validation-aucpr:0.96968
[21:38:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56950 validation-auc:0.96518 validation-aucpr:0.96981
[21:38:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56452 validation-auc:0.96525 validation-aucpr:0.96986
[21:38:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55970 validation-auc:0.96557 validation-aucpr:0.97007
[21:38:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55503 validation-auc:0.96577 validation-aucpr:0.97021
[21:38:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.55085 validation-auc:0.96552 validation-aucpr:0.97001
[21:38:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54687 validation-auc:0.96549 validation-aucpr:0.97034
[21:38:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.54272 validation-auc:0.96554 validation-aucpr:0.97034
[21:38:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53815 validation-auc:0.96571 validation-aucpr:0.97050
[21:38:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.53376 validation-auc:0.96567 validation-aucpr:0.97048
[21:38:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52943 validation-auc:0.96574 validation-aucpr:0.97054
[21:38:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.52514 validation-auc:0.96578 validation-aucpr:0.97055
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[31] validation-logloss:0.52142 validation-auc:0.96559 validation-aucpr:0.97041
[21:38:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51732 validation-auc:0.96568 validation-aucpr:0.97047
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[33] validation-logloss:0.51320 validation-auc:0.96576 validation-aucpr:0.97054
[21:38:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50975 validation-auc:0.96573 validation-aucpr:0.97085
[21:38:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.50574 validation-auc:0.96583 validation-aucpr:0.97092
[21:39:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.50179 validation-auc:0.96601 validation-aucpr:0.97105
[21:39:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49833 validation-auc:0.96598 validation-aucpr:0.97100
[21:39:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.49449 validation-auc:0.96600 validation-aucpr:0.97101
[21:39:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.49065 validation-auc:0.96605 validation-aucpr:0.97103
[21:39:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.48692 validation-auc:0.96611 validation-aucpr:0.97107
[21:39:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.48321 validation-auc:0.96624 validation-aucpr:0.97119
[21:39:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47965 validation-auc:0.96636 validation-aucpr:0.97126
[21:39:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.47622 validation-auc:0.96634 validation-aucpr:0.97123
[21:39:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.47315 validation-auc:0.96622 validation-aucpr:0.97114
[21:39:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.46964 validation-auc:0.96627 validation-aucpr:0.97117
[21:39:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.46622 validation-auc:0.96642 validation-aucpr:0.97127
[21:39:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.46299 validation-auc:0.96640 validation-aucpr:0.97123
[21:39:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.45981 validation-auc:0.96645 validation-aucpr:0.97128
[21:39:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.45651 validation-auc:0.96643 validation-aucpr:0.97128
[21:39:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.45332 validation-auc:0.96649 validation-aucpr:0.97135
[21:39:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.45007 validation-auc:0.96645 validation-aucpr:0.97133
[21:39:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.44695 validation-auc:0.96652 validation-aucpr:0.97141
[21:39:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.44431 validation-auc:0.96646 validation-aucpr:0.97136
[21:39:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.44171 validation-auc:0.96638 validation-aucpr:0.97128
[21:39:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.43875 validation-auc:0.96644 validation-aucpr:0.97134
[21:39:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.43614 validation-auc:0.96636 validation-aucpr:0.97128
[21:39:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.43330 validation-auc:0.96636 validation-aucpr:0.97130
[21:39:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.43039 validation-auc:0.96644 validation-aucpr:0.97136
[21:39:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.42757 validation-auc:0.96653 validation-aucpr:0.97142
[21:39:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.42480 validation-auc:0.96660 validation-aucpr:0.97149
[21:39:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.42201 validation-auc:0.96667 validation-aucpr:0.97154
[21:39:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.41932 validation-auc:0.96665 validation-aucpr:0.97151
[21:39:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.41651 validation-auc:0.96676 validation-aucpr:0.97161
[21:39:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.41391 validation-auc:0.96678 validation-aucpr:0.97162
[21:39:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.41126 validation-auc:0.96680 validation-aucpr:0.97162
[21:39:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.40862 validation-auc:0.96699 validation-aucpr:0.97176
[21:39:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.40622 validation-auc:0.96703 validation-aucpr:0.97178
[21:39:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.40371 validation-auc:0.96707 validation-aucpr:0.97180
[21:39:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.40130 validation-auc:0.96708 validation-aucpr:0.97180
[21:39:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.39885 validation-auc:0.96710 validation-aucpr:0.97183
[21:39:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.39646 validation-auc:0.96714 validation-aucpr:0.97186
[21:39:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.39413 validation-auc:0.96717 validation-aucpr:0.97188
[21:39:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.39208 validation-auc:0.96726 validation-aucpr:0.97194
[21:40:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.38973 validation-auc:0.96734 validation-aucpr:0.97199
[21:40:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.38750 validation-auc:0.96730 validation-aucpr:0.97195
[21:40:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.38525 validation-auc:0.96736 validation-aucpr:0.97195
[21:40:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.38305 validation-auc:0.96735 validation-aucpr:0.97195
[21:40:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.38083 validation-auc:0.96743 validation-aucpr:0.97201
[21:40:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.37870 validation-auc:0.96746 validation-aucpr:0.97216
[21:40:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80] validation-logloss:0.37644 validation-auc:0.96762 validation-aucpr:0.97228
[21:40:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81] validation-logloss:0.37436 validation-auc:0.96763 validation-aucpr:0.97231
[21:40:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82] validation-logloss:0.37229 validation-auc:0.96769 validation-aucpr:0.97234
[21:40:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83] validation-logloss:0.37056 validation-auc:0.96767 validation-aucpr:0.97231
[21:40:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84] validation-logloss:0.36853 validation-auc:0.96770 validation-aucpr:0.97234
[21:40:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85] validation-logloss:0.36651 validation-auc:0.96769 validation-aucpr:0.97233
[21:40:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86] validation-logloss:0.36448 validation-auc:0.96781 validation-aucpr:0.97241
[21:40:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87] validation-logloss:0.36248 validation-auc:0.96784 validation-aucpr:0.97243
[21:40:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88] validation-logloss:0.36055 validation-auc:0.96792 validation-aucpr:0.97248
{'best_iteration': '88', 'best_score': '0.9724756751127037'}
Trial 79, Fold 5: Log loss = 0.36055336322219295, Average precision = 0.9724776594775508, ROC-AUC = 0.9679193653270907, Elapsed Time = 133.4455952999997 seconds
Optimization Progress: 80%|######## | 80/100 [3:41:35<1:19:33, 238.65s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 80, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65533 validation-auc:0.93478 validation-aucpr:0.92750
[1] validation-logloss:0.61742 validation-auc:0.95600 validation-aucpr:0.96118
[2] validation-logloss:0.58746 validation-auc:0.95738 validation-aucpr:0.96255
[3] validation-logloss:0.56010 validation-auc:0.95831 validation-aucpr:0.96297
[4] validation-logloss:0.53597 validation-auc:0.95916 validation-aucpr:0.96383
[5] validation-logloss:0.51466 validation-auc:0.95938 validation-aucpr:0.96429
[6] validation-logloss:0.49433 validation-auc:0.95993 validation-aucpr:0.96474
[7] validation-logloss:0.47623 validation-auc:0.96009 validation-aucpr:0.96453
[8] validation-logloss:0.45946 validation-auc:0.96034 validation-aucpr:0.96501
[9] validation-logloss:0.44537 validation-auc:0.96008 validation-aucpr:0.96468
[10] validation-logloss:0.43100 validation-auc:0.96034 validation-aucpr:0.96487
[11] validation-logloss:0.41344 validation-auc:0.96248 validation-aucpr:0.96735
[12] validation-logloss:0.40175 validation-auc:0.96239 validation-aucpr:0.96726
[13] validation-logloss:0.38653 validation-auc:0.96354 validation-aucpr:0.96861
[14] validation-logloss:0.37610 validation-auc:0.96364 validation-aucpr:0.96870
[15] validation-logloss:0.36606 validation-auc:0.96361 validation-aucpr:0.96874
[16] validation-logloss:0.35691 validation-auc:0.96360 validation-aucpr:0.96869
[17] validation-logloss:0.34883 validation-auc:0.96371 validation-aucpr:0.96864
[18] validation-logloss:0.33813 validation-auc:0.96445 validation-aucpr:0.96954
[19] validation-logloss:0.33035 validation-auc:0.96463 validation-aucpr:0.96965
[20] validation-logloss:0.32439 validation-auc:0.96455 validation-aucpr:0.96955
[21] validation-logloss:0.31525 validation-auc:0.96498 validation-aucpr:0.97005
[22] validation-logloss:0.30812 validation-auc:0.96506 validation-aucpr:0.97014
[23] validation-logloss:0.30077 validation-auc:0.96533 validation-aucpr:0.97059
[24] validation-logloss:0.29346 validation-auc:0.96570 validation-aucpr:0.97091
[25] validation-logloss:0.28878 validation-auc:0.96569 validation-aucpr:0.97089
[26] validation-logloss:0.28417 validation-auc:0.96580 validation-aucpr:0.97092
[27] validation-logloss:0.27844 validation-auc:0.96609 validation-aucpr:0.97137
[28] validation-logloss:0.27446 validation-auc:0.96616 validation-aucpr:0.97139
[29] validation-logloss:0.27105 validation-auc:0.96626 validation-aucpr:0.97139
[30] validation-logloss:0.26756 validation-auc:0.96622 validation-aucpr:0.97138
[31] validation-logloss:0.26467 validation-auc:0.96624 validation-aucpr:0.97133
[32] validation-logloss:0.26140 validation-auc:0.96629 validation-aucpr:0.97136
[33] validation-logloss:0.25781 validation-auc:0.96646 validation-aucpr:0.97151
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[36] validation-logloss:0.24984 validation-auc:0.96691 validation-aucpr:0.97188
[37] validation-logloss:0.24759 validation-auc:0.96695 validation-aucpr:0.97191
[38] validation-logloss:0.24360 validation-auc:0.96727 validation-aucpr:0.97219
[39] validation-logloss:0.24166 validation-auc:0.96745 validation-aucpr:0.97228
[40] validation-logloss:0.23828 validation-auc:0.96773 validation-aucpr:0.97256
[41] validation-logloss:0.23632 validation-auc:0.96784 validation-aucpr:0.97261
[42] validation-logloss:0.23461 validation-auc:0.96785 validation-aucpr:0.97262
[43] validation-logloss:0.23161 validation-auc:0.96809 validation-aucpr:0.97283
[44] validation-logloss:0.22868 validation-auc:0.96840 validation-aucpr:0.97313
[45] validation-logloss:0.22706 validation-auc:0.96857 validation-aucpr:0.97324
[46] validation-logloss:0.22572 validation-auc:0.96860 validation-aucpr:0.97328
[47] validation-logloss:0.22461 validation-auc:0.96864 validation-aucpr:0.97326
[48] validation-logloss:0.22363 validation-auc:0.96869 validation-aucpr:0.97329
[49] validation-logloss:0.22238 validation-auc:0.96880 validation-aucpr:0.97333
{'best_iteration': '49', 'best_score': '0.9733320179106767'}
Trial 80, Fold 1: Log loss = 0.22238400995920363, Average precision = 0.9733366848619621, ROC-AUC = 0.9687998869627144, Elapsed Time = 1.4775561999995261 seconds
Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 80, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65402 validation-auc:0.94272 validation-aucpr:0.94373
[1] validation-logloss:0.61759 validation-auc:0.95731 validation-aucpr:0.95915
[2] validation-logloss:0.58746 validation-auc:0.95880 validation-aucpr:0.95995
[3] validation-logloss:0.56106 validation-auc:0.95912 validation-aucpr:0.96075
[4] validation-logloss:0.53748 validation-auc:0.95895 validation-aucpr:0.96050
[5] validation-logloss:0.51407 validation-auc:0.96039 validation-aucpr:0.96260
[6] validation-logloss:0.49406 validation-auc:0.96073 validation-aucpr:0.96255
[7] validation-logloss:0.47616 validation-auc:0.96078 validation-aucpr:0.96316
[8] validation-logloss:0.45925 validation-auc:0.96111 validation-aucpr:0.96338
[9] validation-logloss:0.43884 validation-auc:0.96421 validation-aucpr:0.96687
[10] validation-logloss:0.42451 validation-auc:0.96405 validation-aucpr:0.96658
[11] validation-logloss:0.40673 validation-auc:0.96547 validation-aucpr:0.96827
[12] validation-logloss:0.39538 validation-auc:0.96546 validation-aucpr:0.96849
[13] validation-logloss:0.38054 validation-auc:0.96611 validation-aucpr:0.96924
[14] validation-logloss:0.37019 validation-auc:0.96609 validation-aucpr:0.96912
[15] validation-logloss:0.36089 validation-auc:0.96604 validation-aucpr:0.96992
[16] validation-logloss:0.34938 validation-auc:0.96651 validation-aucpr:0.97037
[17] validation-logloss:0.33788 validation-auc:0.96696 validation-aucpr:0.97084
[18] validation-logloss:0.33014 validation-auc:0.96715 validation-aucpr:0.97102
[19] validation-logloss:0.32366 validation-auc:0.96731 validation-aucpr:0.97107
[20] validation-logloss:0.31612 validation-auc:0.96756 validation-aucpr:0.97126
[21] validation-logloss:0.30994 validation-auc:0.96763 validation-aucpr:0.97124
[22] validation-logloss:0.30357 validation-auc:0.96766 validation-aucpr:0.97128
[23] validation-logloss:0.29569 validation-auc:0.96783 validation-aucpr:0.97148
[24] validation-logloss:0.28836 validation-auc:0.96809 validation-aucpr:0.97157
[25] validation-logloss:0.28353 validation-auc:0.96814 validation-aucpr:0.97158
[26] validation-logloss:0.27651 validation-auc:0.96861 validation-aucpr:0.97196
[27] validation-logloss:0.27213 validation-auc:0.96866 validation-aucpr:0.97194
[28] validation-logloss:0.26618 validation-auc:0.96891 validation-aucpr:0.97217
[29] validation-logloss:0.26230 validation-auc:0.96909 validation-aucpr:0.97225
[30] validation-logloss:0.25904 validation-auc:0.96921 validation-aucpr:0.97236
[31] validation-logloss:0.25592 validation-auc:0.96927 validation-aucpr:0.97239
[32] validation-logloss:0.25297 validation-auc:0.96934 validation-aucpr:0.97255
[33] validation-logloss:0.25040 validation-auc:0.96926 validation-aucpr:0.97248
[34] validation-logloss:0.24813 validation-auc:0.96914 validation-aucpr:0.97232
[35] validation-logloss:0.24381 validation-auc:0.96941 validation-aucpr:0.97254
[36] validation-logloss:0.24031 validation-auc:0.96954 validation-aucpr:0.97276
[37] validation-logloss:0.23785 validation-auc:0.96967 validation-aucpr:0.97287
[38] validation-logloss:0.23413 validation-auc:0.96995 validation-aucpr:0.97302
[39] validation-logloss:0.23209 validation-auc:0.97006 validation-aucpr:0.97312
[40] validation-logloss:0.23031 validation-auc:0.97007 validation-aucpr:0.97315
[41] validation-logloss:0.22808 validation-auc:0.97027 validation-aucpr:0.97335
[42] validation-logloss:0.22613 validation-auc:0.97032 validation-aucpr:0.97337
[43] validation-logloss:0.22338 validation-auc:0.97046 validation-aucpr:0.97359
[44] validation-logloss:0.22142 validation-auc:0.97062 validation-aucpr:0.97367
[45] validation-logloss:0.21851 validation-auc:0.97089 validation-aucpr:0.97399
[46] validation-logloss:0.21730 validation-auc:0.97093 validation-aucpr:0.97430
[47] validation-logloss:0.21561 validation-auc:0.97103 validation-aucpr:0.97439
[48] validation-logloss:0.21432 validation-auc:0.97114 validation-aucpr:0.97456
[49] validation-logloss:0.21187 validation-auc:0.97133 validation-aucpr:0.97472
{'best_iteration': '49', 'best_score': '0.9747160669731868'}
Trial 80, Fold 2: Log loss = 0.21186570833554003, Average precision = 0.9747205743499491, ROC-AUC = 0.9713305185130781, Elapsed Time = 1.8612036000013177 seconds
Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 80, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65376 validation-auc:0.94462 validation-aucpr:0.94750
[1] validation-logloss:0.62036 validation-auc:0.95173 validation-aucpr:0.95404
[2] validation-logloss:0.58383 validation-auc:0.96344 validation-aucpr:0.96719
[3] validation-logloss:0.55726 validation-auc:0.96357 validation-aucpr:0.96767
[4] validation-logloss:0.53355 validation-auc:0.96278 validation-aucpr:0.96694
[5] validation-logloss:0.51090 validation-auc:0.96318 validation-aucpr:0.96770
[6] validation-logloss:0.48865 validation-auc:0.96368 validation-aucpr:0.96833
[7] validation-logloss:0.46807 validation-auc:0.96439 validation-aucpr:0.96901
[8] validation-logloss:0.45127 validation-auc:0.96430 validation-aucpr:0.96885
[9] validation-logloss:0.43555 validation-auc:0.96475 validation-aucpr:0.96903
[10] validation-logloss:0.42111 validation-auc:0.96505 validation-aucpr:0.96934
[11] validation-logloss:0.40809 validation-auc:0.96506 validation-aucpr:0.96952
[12] validation-logloss:0.39647 validation-auc:0.96516 validation-aucpr:0.96950
[13] validation-logloss:0.38509 validation-auc:0.96521 validation-aucpr:0.96959
[14] validation-logloss:0.37432 validation-auc:0.96518 validation-aucpr:0.96949
[15] validation-logloss:0.36371 validation-auc:0.96543 validation-aucpr:0.96976
[16] validation-logloss:0.35475 validation-auc:0.96563 validation-aucpr:0.96986
[17] validation-logloss:0.34299 validation-auc:0.96641 validation-aucpr:0.97067
[18] validation-logloss:0.33576 validation-auc:0.96627 validation-aucpr:0.97060
[19] validation-logloss:0.32862 validation-auc:0.96637 validation-aucpr:0.97076
[20] validation-logloss:0.32162 validation-auc:0.96654 validation-aucpr:0.97094
[21] validation-logloss:0.31567 validation-auc:0.96653 validation-aucpr:0.97092
[22] validation-logloss:0.31016 validation-auc:0.96632 validation-aucpr:0.97075
[23] validation-logloss:0.30461 validation-auc:0.96633 validation-aucpr:0.97073
[24] validation-logloss:0.29695 validation-auc:0.96672 validation-aucpr:0.97113
[25] validation-logloss:0.29207 validation-auc:0.96689 validation-aucpr:0.97127
[26] validation-logloss:0.28713 validation-auc:0.96716 validation-aucpr:0.97144
[27] validation-logloss:0.28267 validation-auc:0.96724 validation-aucpr:0.97147
[28] validation-logloss:0.27852 validation-auc:0.96740 validation-aucpr:0.97162
[29] validation-logloss:0.27214 validation-auc:0.96778 validation-aucpr:0.97201
[30] validation-logloss:0.26870 validation-auc:0.96784 validation-aucpr:0.97212
[31] validation-logloss:0.26521 validation-auc:0.96788 validation-aucpr:0.97217
[32] validation-logloss:0.26241 validation-auc:0.96786 validation-aucpr:0.97212
[33] validation-logloss:0.25897 validation-auc:0.96807 validation-aucpr:0.97230
[34] validation-logloss:0.25367 validation-auc:0.96856 validation-aucpr:0.97273
[35] validation-logloss:0.25119 validation-auc:0.96863 validation-aucpr:0.97276
[36] validation-logloss:0.24718 validation-auc:0.96889 validation-aucpr:0.97298
[37] validation-logloss:0.24480 validation-auc:0.96903 validation-aucpr:0.97303
[38] validation-logloss:0.24199 validation-auc:0.96918 validation-aucpr:0.97315
[39] validation-logloss:0.23784 validation-auc:0.96954 validation-aucpr:0.97347
[40] validation-logloss:0.23613 validation-auc:0.96955 validation-aucpr:0.97350
[41] validation-logloss:0.23262 validation-auc:0.96974 validation-aucpr:0.97371
[42] validation-logloss:0.22988 validation-auc:0.97011 validation-aucpr:0.97406
[43] validation-logloss:0.22840 validation-auc:0.97010 validation-aucpr:0.97406
[44] validation-logloss:0.22517 validation-auc:0.97040 validation-aucpr:0.97436
[45] validation-logloss:0.22359 validation-auc:0.97055 validation-aucpr:0.97446
[46] validation-logloss:0.22068 validation-auc:0.97082 validation-aucpr:0.97471
[47] validation-logloss:0.21966 validation-auc:0.97081 validation-aucpr:0.97475
[48] validation-logloss:0.21864 validation-auc:0.97076 validation-aucpr:0.97470
[49] validation-logloss:0.21779 validation-auc:0.97073 validation-aucpr:0.97465
{'best_iteration': '47', 'best_score': '0.9747548137468102'}
Trial 80, Fold 3: Log loss = 0.21778798181689787, Average precision = 0.9746578127203783, ROC-AUC = 0.9707257165490075, Elapsed Time = 1.7147274000017205 seconds
Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 80, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65456 validation-auc:0.94003 validation-aucpr:0.94400
[1] validation-logloss:0.61672 validation-auc:0.95860 validation-aucpr:0.96379
[2] validation-logloss:0.58654 validation-auc:0.95883 validation-aucpr:0.96450
[3] validation-logloss:0.55935 validation-auc:0.95933 validation-aucpr:0.96450
[4] validation-logloss:0.53390 validation-auc:0.96083 validation-aucpr:0.96645
[5] validation-logloss:0.50728 validation-auc:0.96183 validation-aucpr:0.96783
[6] validation-logloss:0.48299 validation-auc:0.96273 validation-aucpr:0.96863
[7] validation-logloss:0.46173 validation-auc:0.96385 validation-aucpr:0.96969
[8] validation-logloss:0.44543 validation-auc:0.96361 validation-aucpr:0.96963
[9] validation-logloss:0.42956 validation-auc:0.96405 validation-aucpr:0.96999
[10] validation-logloss:0.41507 validation-auc:0.96418 validation-aucpr:0.97011
[11] validation-logloss:0.40258 validation-auc:0.96418 validation-aucpr:0.97002
[12] validation-logloss:0.39091 validation-auc:0.96441 validation-aucpr:0.97020
[13] validation-logloss:0.38037 validation-auc:0.96443 validation-aucpr:0.97017
[14] validation-logloss:0.37022 validation-auc:0.96479 validation-aucpr:0.97037
[15] validation-logloss:0.35768 validation-auc:0.96526 validation-aucpr:0.97088
[16] validation-logloss:0.34916 validation-auc:0.96534 validation-aucpr:0.97091
[17] validation-logloss:0.34153 validation-auc:0.96543 validation-aucpr:0.97093
[18] validation-logloss:0.33189 validation-auc:0.96590 validation-aucpr:0.97132
[19] validation-logloss:0.32530 validation-auc:0.96568 validation-aucpr:0.97115
[20] validation-logloss:0.31846 validation-auc:0.96567 validation-aucpr:0.97115
[21] validation-logloss:0.31212 validation-auc:0.96596 validation-aucpr:0.97131
[22] validation-logloss:0.30621 validation-auc:0.96611 validation-aucpr:0.97144
[23] validation-logloss:0.30028 validation-auc:0.96613 validation-aucpr:0.97147
[24] validation-logloss:0.29545 validation-auc:0.96614 validation-aucpr:0.97147
[25] validation-logloss:0.29112 validation-auc:0.96611 validation-aucpr:0.97139
[26] validation-logloss:0.28413 validation-auc:0.96639 validation-aucpr:0.97167
[27] validation-logloss:0.27983 validation-auc:0.96639 validation-aucpr:0.97169
[28] validation-logloss:0.27586 validation-auc:0.96645 validation-aucpr:0.97170
[29] validation-logloss:0.27226 validation-auc:0.96658 validation-aucpr:0.97183
[30] validation-logloss:0.26897 validation-auc:0.96641 validation-aucpr:0.97173
[31] validation-logloss:0.26566 validation-auc:0.96655 validation-aucpr:0.97179
[32] validation-logloss:0.25989 validation-auc:0.96693 validation-aucpr:0.97214
[33] validation-logloss:0.25542 validation-auc:0.96684 validation-aucpr:0.97219
[34] validation-logloss:0.25286 validation-auc:0.96698 validation-aucpr:0.97228
[35] validation-logloss:0.25021 validation-auc:0.96712 validation-aucpr:0.97237
[36] validation-logloss:0.24779 validation-auc:0.96714 validation-aucpr:0.97239
[37] validation-logloss:0.24595 validation-auc:0.96707 validation-aucpr:0.97237
[38] validation-logloss:0.24282 validation-auc:0.96712 validation-aucpr:0.97243
[39] validation-logloss:0.24117 validation-auc:0.96703 validation-aucpr:0.97236
[40] validation-logloss:0.23910 validation-auc:0.96724 validation-aucpr:0.97252
[41] validation-logloss:0.23552 validation-auc:0.96750 validation-aucpr:0.97277
[42] validation-logloss:0.23372 validation-auc:0.96762 validation-aucpr:0.97287
[43] validation-logloss:0.23213 validation-auc:0.96768 validation-aucpr:0.97291
[44] validation-logloss:0.22871 validation-auc:0.96799 validation-aucpr:0.97317
[45] validation-logloss:0.22711 validation-auc:0.96810 validation-aucpr:0.97326
[46] validation-logloss:0.22598 validation-auc:0.96814 validation-aucpr:0.97327
[47] validation-logloss:0.22322 validation-auc:0.96826 validation-aucpr:0.97341
[48] validation-logloss:0.22074 validation-auc:0.96835 validation-aucpr:0.97349
[49] validation-logloss:0.21964 validation-auc:0.96840 validation-aucpr:0.97350
{'best_iteration': '49', 'best_score': '0.9734999163688453'}
Trial 80, Fold 4: Log loss = 0.21964332522966643, Average precision = 0.973504239992713, ROC-AUC = 0.9684028448393027, Elapsed Time = 1.6603410000025178 seconds
Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 80, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.64959 validation-auc:0.95433 validation-aucpr:0.95874
[1] validation-logloss:0.61442 validation-auc:0.95794 validation-aucpr:0.96264
[2] validation-logloss:0.58089 validation-auc:0.96110 validation-aucpr:0.96519
[3] validation-logloss:0.55395 validation-auc:0.96038 validation-aucpr:0.96570
[4] validation-logloss:0.53024 validation-auc:0.96010 validation-aucpr:0.96546
[5] validation-logloss:0.50856 validation-auc:0.96017 validation-aucpr:0.96536
[6] validation-logloss:0.48844 validation-auc:0.96061 validation-aucpr:0.96573
[7] validation-logloss:0.47027 validation-auc:0.96081 validation-aucpr:0.96596
[8] validation-logloss:0.45368 validation-auc:0.96094 validation-aucpr:0.96600
[9] validation-logloss:0.43961 validation-auc:0.96071 validation-aucpr:0.96577
[10] validation-logloss:0.42562 validation-auc:0.96077 validation-aucpr:0.96596
[11] validation-logloss:0.41295 validation-auc:0.96094 validation-aucpr:0.96608
[12] validation-logloss:0.40169 validation-auc:0.96074 validation-aucpr:0.96575
[13] validation-logloss:0.39029 validation-auc:0.96078 validation-aucpr:0.96587
[14] validation-logloss:0.37974 validation-auc:0.96091 validation-aucpr:0.96597
[15] validation-logloss:0.36909 validation-auc:0.96136 validation-aucpr:0.96638
[16] validation-logloss:0.35984 validation-auc:0.96172 validation-aucpr:0.96655
[17] validation-logloss:0.35116 validation-auc:0.96201 validation-aucpr:0.96675
[18] validation-logloss:0.34334 validation-auc:0.96227 validation-aucpr:0.96685
[19] validation-logloss:0.33647 validation-auc:0.96225 validation-aucpr:0.96678
[20] validation-logloss:0.32650 validation-auc:0.96319 validation-aucpr:0.96769
[21] validation-logloss:0.31995 validation-auc:0.96326 validation-aucpr:0.96774
[22] validation-logloss:0.31109 validation-auc:0.96390 validation-aucpr:0.96837
[23] validation-logloss:0.30295 validation-auc:0.96435 validation-aucpr:0.96889
[24] validation-logloss:0.29614 validation-auc:0.96462 validation-aucpr:0.96911
[25] validation-logloss:0.29111 validation-auc:0.96464 validation-aucpr:0.96909
[26] validation-logloss:0.28589 validation-auc:0.96482 validation-aucpr:0.96919
[27] validation-logloss:0.28150 validation-auc:0.96501 validation-aucpr:0.96941
[28] validation-logloss:0.27760 validation-auc:0.96518 validation-aucpr:0.96953
[29] validation-logloss:0.27148 validation-auc:0.96563 validation-aucpr:0.96996
[30] validation-logloss:0.26822 validation-auc:0.96572 validation-aucpr:0.97012
[31] validation-logloss:0.26320 validation-auc:0.96594 validation-aucpr:0.97031
[32] validation-logloss:0.26022 validation-auc:0.96598 validation-aucpr:0.97030
[33] validation-logloss:0.25566 validation-auc:0.96634 validation-aucpr:0.97063
[34] validation-logloss:0.25321 validation-auc:0.96639 validation-aucpr:0.97060
[35] validation-logloss:0.25107 validation-auc:0.96648 validation-aucpr:0.97062
[36] validation-logloss:0.24876 validation-auc:0.96649 validation-aucpr:0.97062
[37] validation-logloss:0.24628 validation-auc:0.96652 validation-aucpr:0.97066
[38] validation-logloss:0.24442 validation-auc:0.96651 validation-aucpr:0.97067
[39] validation-logloss:0.24284 validation-auc:0.96649 validation-aucpr:0.97052
[40] validation-logloss:0.23969 validation-auc:0.96669 validation-aucpr:0.97073
[41] validation-logloss:0.23814 validation-auc:0.96672 validation-aucpr:0.97080
[42] validation-logloss:0.23652 validation-auc:0.96671 validation-aucpr:0.97073
[43] validation-logloss:0.23515 validation-auc:0.96674 validation-aucpr:0.97072
[44] validation-logloss:0.23216 validation-auc:0.96702 validation-aucpr:0.97097
[45] validation-logloss:0.23103 validation-auc:0.96708 validation-aucpr:0.97133
[46] validation-logloss:0.22971 validation-auc:0.96722 validation-aucpr:0.97140
[47] validation-logloss:0.22879 validation-auc:0.96718 validation-aucpr:0.97132
[48] validation-logloss:0.22767 validation-auc:0.96728 validation-aucpr:0.97136
[49] validation-logloss:0.22644 validation-auc:0.96733 validation-aucpr:0.97139
{'best_iteration': '46', 'best_score': '0.9713952085868922'}
Trial 80, Fold 5: Log loss = 0.2264398003286916, Average precision = 0.971399548138062, ROC-AUC = 0.9673263846311058, Elapsed Time = 1.7558240999969712 seconds
Optimization Progress: 81%|########1 | 81/100 [3:41:52<54:27, 171.96s/it]
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 81, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66439 validation-auc:0.95973 validation-aucpr:0.96404
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63782 validation-auc:0.96425 validation-aucpr:0.96873
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61348 validation-auc:0.96444 validation-aucpr:0.96878
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59234 validation-auc:0.96531 validation-aucpr:0.96962
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57198 validation-auc:0.96639 validation-aucpr:0.97135
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55183 validation-auc:0.96726 validation-aucpr:0.97221
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53357 validation-auc:0.96764 validation-aucpr:0.97281
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51769 validation-auc:0.96735 validation-aucpr:0.97255
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50084 validation-auc:0.96778 validation-aucpr:0.97296
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.48514 validation-auc:0.96789 validation-aucpr:0.97304
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47038 validation-auc:0.96796 validation-aucpr:0.97312
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.45701 validation-auc:0.96781 validation-aucpr:0.97302
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44416 validation-auc:0.96798 validation-aucpr:0.97316
{'best_iteration': '12', 'best_score': '0.9731590378806133'}
Trial 81, Fold 1: Log loss = 0.44415862414143403, Average precision = 0.9731129177684463, ROC-AUC = 0.9679839214617895, Elapsed Time = 0.706329900000128 seconds
Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 81, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66391 validation-auc:0.96426 validation-aucpr:0.96762
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.64014 validation-auc:0.96421 validation-aucpr:0.96796
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61528 validation-auc:0.96705 validation-aucpr:0.97057
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59523 validation-auc:0.96707 validation-aucpr:0.97068
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57355 validation-auc:0.96800 validation-aucpr:0.97157
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55572 validation-auc:0.96775 validation-aucpr:0.97122
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53911 validation-auc:0.96777 validation-aucpr:0.97114
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.52110 validation-auc:0.96827 validation-aucpr:0.97153
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50399 validation-auc:0.96878 validation-aucpr:0.97193
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.49005 validation-auc:0.96905 validation-aucpr:0.97204
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47493 validation-auc:0.96971 validation-aucpr:0.97276
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.46275 validation-auc:0.96938 validation-aucpr:0.97230
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44916 validation-auc:0.96987 validation-aucpr:0.97278
{'best_iteration': '12', 'best_score': '0.972779721455394'}
Trial 81, Fold 2: Log loss = 0.44916450695000154, Average precision = 0.9726980587779851, ROC-AUC = 0.9698728292858658, Elapsed Time = 0.8429739999992307 seconds
Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 81, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66408 validation-auc:0.96278 validation-aucpr:0.96586
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63717 validation-auc:0.96630 validation-aucpr:0.97063
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61231 validation-auc:0.96720 validation-aucpr:0.97136
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.58920 validation-auc:0.96770 validation-aucpr:0.97171
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56785 validation-auc:0.96851 validation-aucpr:0.97262
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.54993 validation-auc:0.96975 validation-aucpr:0.97351
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53285 validation-auc:0.96942 validation-aucpr:0.97319
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51481 validation-auc:0.96965 validation-aucpr:0.97342
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.49791 validation-auc:0.97032 validation-aucpr:0.97366
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.48242 validation-auc:0.97037 validation-aucpr:0.97470
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.46758 validation-auc:0.97087 validation-aucpr:0.97510
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.45390 validation-auc:0.97110 validation-aucpr:0.97531
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44238 validation-auc:0.97106 validation-aucpr:0.97527
{'best_iteration': '11', 'best_score': '0.975307214390828'}
Trial 81, Fold 3: Log loss = 0.44238187145411595, Average precision = 0.9752327037809126, ROC-AUC = 0.9710605455018251, Elapsed Time = 0.8559263000024657 seconds
Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 81, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66407 validation-auc:0.96168 validation-aucpr:0.96773
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63820 validation-auc:0.96481 validation-aucpr:0.97022
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61422 validation-auc:0.96630 validation-aucpr:0.97147
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59109 validation-auc:0.96894 validation-aucpr:0.97339
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.56992 validation-auc:0.96826 validation-aucpr:0.97303
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55230 validation-auc:0.96743 validation-aucpr:0.97269
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53403 validation-auc:0.96768 validation-aucpr:0.97288
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51724 validation-auc:0.96822 validation-aucpr:0.97325
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50079 validation-auc:0.96870 validation-aucpr:0.97359
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.48502 validation-auc:0.96899 validation-aucpr:0.97391
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47198 validation-auc:0.96888 validation-aucpr:0.97389
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.45789 validation-auc:0.96910 validation-aucpr:0.97408
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44504 validation-auc:0.96901 validation-aucpr:0.97403
{'best_iteration': '11', 'best_score': '0.9740761196721902'}
Trial 81, Fold 4: Log loss = 0.44503664140822147, Average precision = 0.9739748843423582, ROC-AUC = 0.9690058769739933, Elapsed Time = 0.8716018000013719 seconds
Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 81, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66431 validation-auc:0.96151 validation-aucpr:0.96659
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63806 validation-auc:0.96450 validation-aucpr:0.96951
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.61372 validation-auc:0.96548 validation-aucpr:0.97003
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.59104 validation-auc:0.96700 validation-aucpr:0.97096
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.57164 validation-auc:0.96555 validation-aucpr:0.96999
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.55163 validation-auc:0.96634 validation-aucpr:0.97067
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.53362 validation-auc:0.96668 validation-aucpr:0.97090
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.51637 validation-auc:0.96681 validation-aucpr:0.97106
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.50208 validation-auc:0.96657 validation-aucpr:0.97070
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.48906 validation-auc:0.96645 validation-aucpr:0.97083
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.47680 validation-auc:0.96575 validation-aucpr:0.97021
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.46304 validation-auc:0.96648 validation-aucpr:0.97075
[21:40:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.44987 validation-auc:0.96701 validation-aucpr:0.97118
{'best_iteration': '12', 'best_score': '0.9711795928982662'}
Trial 81, Fold 5: Log loss = 0.44987265941620824, Average precision = 0.9710778666302752, ROC-AUC = 0.9670054066105569, Elapsed Time = 0.9390024000022095 seconds
Optimization Progress: 82%|########2 | 82/100 [3:42:04<37:11, 123.97s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 82, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:41:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.66022 validation-auc:0.95140 validation-aucpr:0.95862
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63158 validation-auc:0.95985 validation-aucpr:0.96379
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60299 validation-auc:0.96466 validation-aucpr:0.97005
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57893 validation-auc:0.96492 validation-aucpr:0.97004
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55352 validation-auc:0.96680 validation-aucpr:0.97186
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53363 validation-auc:0.96682 validation-aucpr:0.97172
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51515 validation-auc:0.96664 validation-aucpr:0.97154
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49533 validation-auc:0.96719 validation-aucpr:0.97214
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47949 validation-auc:0.96715 validation-aucpr:0.97228
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46524 validation-auc:0.96706 validation-aucpr:0.97213
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44909 validation-auc:0.96763 validation-aucpr:0.97263
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43392 validation-auc:0.96795 validation-aucpr:0.97296
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42032 validation-auc:0.96823 validation-aucpr:0.97318
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40691 validation-auc:0.96846 validation-aucpr:0.97343
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39433 validation-auc:0.96868 validation-aucpr:0.97365
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38313 validation-auc:0.96869 validation-aucpr:0.97370
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37264 validation-auc:0.96881 validation-aucpr:0.97378
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36408 validation-auc:0.96891 validation-aucpr:0.97381
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35645 validation-auc:0.96897 validation-aucpr:0.97384
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34878 validation-auc:0.96892 validation-aucpr:0.97379
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34190 validation-auc:0.96887 validation-aucpr:0.97367
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33506 validation-auc:0.96882 validation-aucpr:0.97370
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32710 validation-auc:0.96895 validation-aucpr:0.97384
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31955 validation-auc:0.96908 validation-aucpr:0.97398
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31255 validation-auc:0.96928 validation-aucpr:0.97413
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30734 validation-auc:0.96927 validation-aucpr:0.97414
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30157 validation-auc:0.96942 validation-aucpr:0.97431
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29611 validation-auc:0.96959 validation-aucpr:0.97460
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29173 validation-auc:0.96963 validation-aucpr:0.97462
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28730 validation-auc:0.96965 validation-aucpr:0.97463
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28223 validation-auc:0.96978 validation-aucpr:0.97480
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27879 validation-auc:0.96967 validation-aucpr:0.97471
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27538 validation-auc:0.96969 validation-aucpr:0.97473
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.27105 validation-auc:0.96977 validation-aucpr:0.97482
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26781 validation-auc:0.96977 validation-aucpr:0.97482
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26466 validation-auc:0.96982 validation-aucpr:0.97486
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.26094 validation-auc:0.96989 validation-aucpr:0.97496
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25720 validation-auc:0.97010 validation-aucpr:0.97511
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25449 validation-auc:0.97015 validation-aucpr:0.97513
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.25112 validation-auc:0.97021 validation-aucpr:0.97520
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24876 validation-auc:0.97028 validation-aucpr:0.97524
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24637 validation-auc:0.97034 validation-aucpr:0.97526
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.24430 validation-auc:0.97036 validation-aucpr:0.97525
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.24175 validation-auc:0.97041 validation-aucpr:0.97530
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23970 validation-auc:0.97047 validation-aucpr:0.97533
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23714 validation-auc:0.97054 validation-aucpr:0.97540
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23480 validation-auc:0.97059 validation-aucpr:0.97546
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.23270 validation-auc:0.97067 validation-aucpr:0.97552
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.23110 validation-auc:0.97067 validation-aucpr:0.97551
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22951 validation-auc:0.97071 validation-aucpr:0.97556
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22767 validation-auc:0.97073 validation-aucpr:0.97561
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22565 validation-auc:0.97085 validation-aucpr:0.97571
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22424 validation-auc:0.97080 validation-aucpr:0.97567
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.22297 validation-auc:0.97083 validation-aucpr:0.97569
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.22182 validation-auc:0.97084 validation-aucpr:0.97569
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.22034 validation-auc:0.97086 validation-aucpr:0.97569
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.21888 validation-auc:0.97087 validation-aucpr:0.97572
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.21785 validation-auc:0.97087 validation-aucpr:0.97573
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.21675 validation-auc:0.97095 validation-aucpr:0.97579
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.21552 validation-auc:0.97103 validation-aucpr:0.97587
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.21425 validation-auc:0.97114 validation-aucpr:0.97594
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.21330 validation-auc:0.97112 validation-aucpr:0.97593
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.21213 validation-auc:0.97117 validation-aucpr:0.97598
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.21138 validation-auc:0.97119 validation-aucpr:0.97597
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.21012 validation-auc:0.97127 validation-aucpr:0.97603
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20956 validation-auc:0.97127 validation-aucpr:0.97602
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20869 validation-auc:0.97126 validation-aucpr:0.97602
[21:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20776 validation-auc:0.97131 validation-aucpr:0.97605
[21:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20705 validation-auc:0.97137 validation-aucpr:0.97608
[21:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20626 validation-auc:0.97137 validation-aucpr:0.97608
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20541 validation-auc:0.97144 validation-aucpr:0.97613
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20478 validation-auc:0.97140 validation-aucpr:0.97614
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20407 validation-auc:0.97143 validation-aucpr:0.97616
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20360 validation-auc:0.97147 validation-aucpr:0.97617
[21:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.20287 validation-auc:0.97156 validation-aucpr:0.97623
[21:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.20243 validation-auc:0.97159 validation-aucpr:0.97625
[21:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.20191 validation-auc:0.97162 validation-aucpr:0.97627
[21:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.20146 validation-auc:0.97161 validation-aucpr:0.97628
[21:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.20109 validation-auc:0.97161 validation-aucpr:0.97626
[21:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.20056 validation-auc:0.97164 validation-aucpr:0.97629
{'best_iteration': '79', 'best_score': '0.9762865072365965'}
Trial 82, Fold 1: Log loss = 0.20056391807788446, Average precision = 0.9762904774080723, ROC-AUC = 0.9716443400104195, Elapsed Time = 21.972112199997355 seconds
Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 82, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:41:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65852 validation-auc:0.96324 validation-aucpr:0.96664
[21:41:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62994 validation-auc:0.96438 validation-aucpr:0.96797
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60106 validation-auc:0.96664 validation-aucpr:0.97067
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57880 validation-auc:0.96552 validation-aucpr:0.97007
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55430 validation-auc:0.96654 validation-aucpr:0.97091
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53413 validation-auc:0.96695 validation-aucpr:0.97102
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51642 validation-auc:0.96688 validation-aucpr:0.97069
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49698 validation-auc:0.96785 validation-aucpr:0.97155
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47898 validation-auc:0.96839 validation-aucpr:0.97205
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46398 validation-auc:0.96845 validation-aucpr:0.97208
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44807 validation-auc:0.96889 validation-aucpr:0.97240
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43286 validation-auc:0.96911 validation-aucpr:0.97265
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41842 validation-auc:0.96966 validation-aucpr:0.97312
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40715 validation-auc:0.96953 validation-aucpr:0.97297
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39453 validation-auc:0.96985 validation-aucpr:0.97326
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38331 validation-auc:0.97000 validation-aucpr:0.97333
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37254 validation-auc:0.96997 validation-aucpr:0.97326
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36256 validation-auc:0.97021 validation-aucpr:0.97335
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35322 validation-auc:0.97019 validation-aucpr:0.97336
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34460 validation-auc:0.97005 validation-aucpr:0.97329
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33771 validation-auc:0.96996 validation-aucpr:0.97319
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33074 validation-auc:0.97000 validation-aucpr:0.97321
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32393 validation-auc:0.97009 validation-aucpr:0.97327
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31633 validation-auc:0.97047 validation-aucpr:0.97356
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31062 validation-auc:0.97046 validation-aucpr:0.97354
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30435 validation-auc:0.97066 validation-aucpr:0.97370
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29952 validation-auc:0.97069 validation-aucpr:0.97370
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29377 validation-auc:0.97085 validation-aucpr:0.97441
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28952 validation-auc:0.97079 validation-aucpr:0.97434
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28420 validation-auc:0.97086 validation-aucpr:0.97438
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27905 validation-auc:0.97083 validation-aucpr:0.97438
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27502 validation-auc:0.97094 validation-aucpr:0.97448
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27154 validation-auc:0.97102 validation-aucpr:0.97450
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26720 validation-auc:0.97120 validation-aucpr:0.97465
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26309 validation-auc:0.97118 validation-aucpr:0.97466
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25912 validation-auc:0.97126 validation-aucpr:0.97478
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25509 validation-auc:0.97140 validation-aucpr:0.97489
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25160 validation-auc:0.97146 validation-aucpr:0.97493
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.24891 validation-auc:0.97146 validation-aucpr:0.97494
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24574 validation-auc:0.97156 validation-aucpr:0.97498
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24268 validation-auc:0.97161 validation-aucpr:0.97502
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.23995 validation-auc:0.97171 validation-aucpr:0.97508
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23790 validation-auc:0.97167 validation-aucpr:0.97504
[21:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23516 validation-auc:0.97175 validation-aucpr:0.97511
[21:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23270 validation-auc:0.97182 validation-aucpr:0.97522
[21:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23085 validation-auc:0.97193 validation-aucpr:0.97528
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.22865 validation-auc:0.97206 validation-aucpr:0.97538
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.22635 validation-auc:0.97215 validation-aucpr:0.97544
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.22464 validation-auc:0.97217 validation-aucpr:0.97544
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22286 validation-auc:0.97221 validation-aucpr:0.97541
[21:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22076 validation-auc:0.97232 validation-aucpr:0.97559
[21:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.21947 validation-auc:0.97230 validation-aucpr:0.97556
[21:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.21821 validation-auc:0.97229 validation-aucpr:0.97555
[21:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.21706 validation-auc:0.97231 validation-aucpr:0.97557
[21:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.21544 validation-auc:0.97232 validation-aucpr:0.97557
[21:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.21390 validation-auc:0.97242 validation-aucpr:0.97564
[21:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.21245 validation-auc:0.97244 validation-aucpr:0.97564
[21:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.21142 validation-auc:0.97247 validation-aucpr:0.97565
[21:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20965 validation-auc:0.97270 validation-aucpr:0.97582
[21:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20828 validation-auc:0.97275 validation-aucpr:0.97585
[21:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20703 validation-auc:0.97278 validation-aucpr:0.97587
[21:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20604 validation-auc:0.97283 validation-aucpr:0.97591
[21:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20491 validation-auc:0.97287 validation-aucpr:0.97595
[21:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20411 validation-auc:0.97286 validation-aucpr:0.97595
[21:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20313 validation-auc:0.97287 validation-aucpr:0.97594
[21:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20258 validation-auc:0.97285 validation-aucpr:0.97587
[21:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20152 validation-auc:0.97296 validation-aucpr:0.97594
[21:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20068 validation-auc:0.97300 validation-aucpr:0.97597
[21:41:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20021 validation-auc:0.97292 validation-aucpr:0.97591
[21:41:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.19932 validation-auc:0.97301 validation-aucpr:0.97593
[21:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.19841 validation-auc:0.97305 validation-aucpr:0.97596
[21:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.19785 validation-auc:0.97310 validation-aucpr:0.97611
[21:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.19691 validation-auc:0.97314 validation-aucpr:0.97616
[21:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.19613 validation-auc:0.97317 validation-aucpr:0.97617
[21:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.19551 validation-auc:0.97314 validation-aucpr:0.97614
[21:41:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.19509 validation-auc:0.97305 validation-aucpr:0.97601
[21:41:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.19467 validation-auc:0.97303 validation-aucpr:0.97594
[21:41:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.19436 validation-auc:0.97300 validation-aucpr:0.97599
[21:41:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.19418 validation-auc:0.97295 validation-aucpr:0.97599
[21:41:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.19370 validation-auc:0.97299 validation-aucpr:0.97603
{'best_iteration': '73', 'best_score': '0.9761700567932763'}
Trial 82, Fold 2: Log loss = 0.193696307689738, Average precision = 0.9760314877461944, ROC-AUC = 0.9729945088546351, Elapsed Time = 27.715898099999322 seconds
Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 82, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65969 validation-auc:0.95616 validation-aucpr:0.96138
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.62780 validation-auc:0.96550 validation-aucpr:0.97001
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60241 validation-auc:0.96617 validation-aucpr:0.97053
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57612 validation-auc:0.96671 validation-aucpr:0.97084
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55490 validation-auc:0.96624 validation-aucpr:0.97064
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53444 validation-auc:0.96705 validation-aucpr:0.97130
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51546 validation-auc:0.96762 validation-aucpr:0.97163
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49534 validation-auc:0.96825 validation-aucpr:0.97227
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47684 validation-auc:0.96860 validation-aucpr:0.97259
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46186 validation-auc:0.96845 validation-aucpr:0.97241
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44637 validation-auc:0.96845 validation-aucpr:0.97244
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43114 validation-auc:0.96883 validation-aucpr:0.97280
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41744 validation-auc:0.96901 validation-aucpr:0.97293
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40437 validation-auc:0.96915 validation-aucpr:0.97305
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39322 validation-auc:0.96930 validation-aucpr:0.97326
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38328 validation-auc:0.96921 validation-aucpr:0.97316
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37279 validation-auc:0.96926 validation-aucpr:0.97319
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36451 validation-auc:0.96926 validation-aucpr:0.97384
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35614 validation-auc:0.96937 validation-aucpr:0.97393
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34686 validation-auc:0.96958 validation-aucpr:0.97412
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33938 validation-auc:0.96965 validation-aucpr:0.97418
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33154 validation-auc:0.96965 validation-aucpr:0.97420
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32370 validation-auc:0.96985 validation-aucpr:0.97436
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31787 validation-auc:0.96971 validation-aucpr:0.97422
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31264 validation-auc:0.96972 validation-aucpr:0.97422
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30605 validation-auc:0.96987 validation-aucpr:0.97436
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29983 validation-auc:0.96996 validation-aucpr:0.97445
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29397 validation-auc:0.97007 validation-aucpr:0.97453
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28841 validation-auc:0.97021 validation-aucpr:0.97462
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28291 validation-auc:0.97035 validation-aucpr:0.97471
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27821 validation-auc:0.97038 validation-aucpr:0.97474
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27374 validation-auc:0.97038 validation-aucpr:0.97474
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27010 validation-auc:0.97033 validation-aucpr:0.97469
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26627 validation-auc:0.97025 validation-aucpr:0.97471
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26275 validation-auc:0.97035 validation-aucpr:0.97480
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.25987 validation-auc:0.97041 validation-aucpr:0.97485
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25676 validation-auc:0.97051 validation-aucpr:0.97491
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25403 validation-auc:0.97044 validation-aucpr:0.97485
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25157 validation-auc:0.97043 validation-aucpr:0.97482
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.24830 validation-auc:0.97055 validation-aucpr:0.97490
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24546 validation-auc:0.97047 validation-aucpr:0.97483
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24233 validation-auc:0.97059 validation-aucpr:0.97492
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.23956 validation-auc:0.97066 validation-aucpr:0.97495
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23690 validation-auc:0.97066 validation-aucpr:0.97496
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23419 validation-auc:0.97076 validation-aucpr:0.97505
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23217 validation-auc:0.97087 validation-aucpr:0.97513
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23050 validation-auc:0.97085 validation-aucpr:0.97510
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.22833 validation-auc:0.97092 validation-aucpr:0.97514
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.22673 validation-auc:0.97096 validation-aucpr:0.97522
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22486 validation-auc:0.97104 validation-aucpr:0.97532
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22282 validation-auc:0.97117 validation-aucpr:0.97541
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22138 validation-auc:0.97122 validation-aucpr:0.97542
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22030 validation-auc:0.97118 validation-aucpr:0.97537
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.21916 validation-auc:0.97119 validation-aucpr:0.97536
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.21785 validation-auc:0.97112 validation-aucpr:0.97530
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.21622 validation-auc:0.97122 validation-aucpr:0.97535
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.21481 validation-auc:0.97125 validation-aucpr:0.97537
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.21341 validation-auc:0.97131 validation-aucpr:0.97543
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.21206 validation-auc:0.97140 validation-aucpr:0.97553
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.21106 validation-auc:0.97145 validation-aucpr:0.97556
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20988 validation-auc:0.97147 validation-aucpr:0.97558
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.20865 validation-auc:0.97154 validation-aucpr:0.97562
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.20731 validation-auc:0.97170 validation-aucpr:0.97572
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.20645 validation-auc:0.97176 validation-aucpr:0.97574
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20568 validation-auc:0.97184 validation-aucpr:0.97578
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20460 validation-auc:0.97191 validation-aucpr:0.97584
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20367 validation-auc:0.97192 validation-aucpr:0.97583
[21:42:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20292 validation-auc:0.97199 validation-aucpr:0.97587
[21:42:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20232 validation-auc:0.97199 validation-aucpr:0.97586
[21:42:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20165 validation-auc:0.97197 validation-aucpr:0.97583
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20095 validation-auc:0.97197 validation-aucpr:0.97583
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20025 validation-auc:0.97200 validation-aucpr:0.97586
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.19964 validation-auc:0.97200 validation-aucpr:0.97582
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.19917 validation-auc:0.97201 validation-aucpr:0.97587
[21:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.19870 validation-auc:0.97204 validation-aucpr:0.97589
[21:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.19816 validation-auc:0.97208 validation-aucpr:0.97592
[21:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.19755 validation-auc:0.97215 validation-aucpr:0.97595
[21:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.19710 validation-auc:0.97212 validation-aucpr:0.97592
[21:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.19660 validation-auc:0.97219 validation-aucpr:0.97597
[21:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.19618 validation-auc:0.97219 validation-aucpr:0.97595
{'best_iteration': '78', 'best_score': '0.975968739866936'}
Trial 82, Fold 3: Log loss = 0.1961760106049815, Average precision = 0.9759582368256433, ROC-AUC = 0.9721851193285241, Elapsed Time = 22.21717020000142 seconds
Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 82, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:42:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65844 validation-auc:0.95920 validation-aucpr:0.96307
[21:42:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63012 validation-auc:0.96310 validation-aucpr:0.96845
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60162 validation-auc:0.96409 validation-aucpr:0.97045
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57455 validation-auc:0.96526 validation-aucpr:0.97157
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55353 validation-auc:0.96459 validation-aucpr:0.97083
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.53391 validation-auc:0.96454 validation-aucpr:0.97067
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.51284 validation-auc:0.96493 validation-aucpr:0.97097
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49576 validation-auc:0.96486 validation-aucpr:0.97070
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47775 validation-auc:0.96532 validation-aucpr:0.97118
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.46365 validation-auc:0.96514 validation-aucpr:0.97099
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44946 validation-auc:0.96532 validation-aucpr:0.97108
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.43454 validation-auc:0.96570 validation-aucpr:0.97135
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.42270 validation-auc:0.96575 validation-aucpr:0.97133
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40997 validation-auc:0.96610 validation-aucpr:0.97167
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39947 validation-auc:0.96607 validation-aucpr:0.97159
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38966 validation-auc:0.96626 validation-aucpr:0.97165
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.38065 validation-auc:0.96626 validation-aucpr:0.97164
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.37008 validation-auc:0.96656 validation-aucpr:0.97190
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35992 validation-auc:0.96693 validation-aucpr:0.97221
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.35059 validation-auc:0.96711 validation-aucpr:0.97239
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.34337 validation-auc:0.96713 validation-aucpr:0.97236
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33478 validation-auc:0.96747 validation-aucpr:0.97266
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32689 validation-auc:0.96788 validation-aucpr:0.97297
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31944 validation-auc:0.96838 validation-aucpr:0.97337
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31388 validation-auc:0.96822 validation-aucpr:0.97324
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30846 validation-auc:0.96827 validation-aucpr:0.97328
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.30366 validation-auc:0.96811 validation-aucpr:0.97313
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29902 validation-auc:0.96803 validation-aucpr:0.97309
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.29328 validation-auc:0.96811 validation-aucpr:0.97317
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28777 validation-auc:0.96836 validation-aucpr:0.97341
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.28265 validation-auc:0.96856 validation-aucpr:0.97359
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27772 validation-auc:0.96878 validation-aucpr:0.97377
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27329 validation-auc:0.96877 validation-aucpr:0.97377
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26900 validation-auc:0.96895 validation-aucpr:0.97392
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26580 validation-auc:0.96890 validation-aucpr:0.97389
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26284 validation-auc:0.96900 validation-aucpr:0.97396
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25933 validation-auc:0.96913 validation-aucpr:0.97407
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25564 validation-auc:0.96937 validation-aucpr:0.97426
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25316 validation-auc:0.96944 validation-aucpr:0.97432
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.25002 validation-auc:0.96945 validation-aucpr:0.97434
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24767 validation-auc:0.96954 validation-aucpr:0.97439
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24454 validation-auc:0.96974 validation-aucpr:0.97455
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.24178 validation-auc:0.96984 validation-aucpr:0.97463
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.23897 validation-auc:0.97000 validation-aucpr:0.97475
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23651 validation-auc:0.97014 validation-aucpr:0.97486
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23400 validation-auc:0.97025 validation-aucpr:0.97494
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23242 validation-auc:0.97021 validation-aucpr:0.97490
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.23029 validation-auc:0.97018 validation-aucpr:0.97490
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.22819 validation-auc:0.97028 validation-aucpr:0.97498
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22653 validation-auc:0.97039 validation-aucpr:0.97504
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22516 validation-auc:0.97042 validation-aucpr:0.97505
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22384 validation-auc:0.97047 validation-aucpr:0.97509
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22190 validation-auc:0.97060 validation-aucpr:0.97518
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.22055 validation-auc:0.97071 validation-aucpr:0.97524
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.21937 validation-auc:0.97071 validation-aucpr:0.97522
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.21775 validation-auc:0.97078 validation-aucpr:0.97527
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.21642 validation-auc:0.97081 validation-aucpr:0.97529
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.21533 validation-auc:0.97090 validation-aucpr:0.97535
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.21435 validation-auc:0.97091 validation-aucpr:0.97537
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.21337 validation-auc:0.97090 validation-aucpr:0.97535
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.21248 validation-auc:0.97091 validation-aucpr:0.97536
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.21157 validation-auc:0.97094 validation-aucpr:0.97538
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.21077 validation-auc:0.97094 validation-aucpr:0.97537
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.21026 validation-auc:0.97088 validation-aucpr:0.97533
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.20956 validation-auc:0.97087 validation-aucpr:0.97532
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.20875 validation-auc:0.97088 validation-aucpr:0.97533
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.20800 validation-auc:0.97089 validation-aucpr:0.97535
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.20728 validation-auc:0.97096 validation-aucpr:0.97540
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20667 validation-auc:0.97099 validation-aucpr:0.97540
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20610 validation-auc:0.97095 validation-aucpr:0.97539
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20522 validation-auc:0.97102 validation-aucpr:0.97544
[21:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20453 validation-auc:0.97111 validation-aucpr:0.97550
[21:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20354 validation-auc:0.97124 validation-aucpr:0.97560
[21:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20273 validation-auc:0.97131 validation-aucpr:0.97565
[21:42:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.20190 validation-auc:0.97144 validation-aucpr:0.97576
[21:42:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.20140 validation-auc:0.97149 validation-aucpr:0.97578
[21:42:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.20091 validation-auc:0.97150 validation-aucpr:0.97579
[21:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.19996 validation-auc:0.97162 validation-aucpr:0.97589
[21:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.19941 validation-auc:0.97164 validation-aucpr:0.97591
[21:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.19866 validation-auc:0.97174 validation-aucpr:0.97598
{'best_iteration': '79', 'best_score': '0.9759841043421987'}
Trial 82, Fold 4: Log loss = 0.1986568267174289, Average precision = 0.9759880043423315, ROC-AUC = 0.9717427323190055, Elapsed Time = 22.36301789999925 seconds
Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 82, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.65841 validation-auc:0.96003 validation-aucpr:0.96572
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.63094 validation-auc:0.96140 validation-aucpr:0.96647
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.60218 validation-auc:0.96399 validation-aucpr:0.96934
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.57621 validation-auc:0.96503 validation-aucpr:0.97020
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.55177 validation-auc:0.96614 validation-aucpr:0.97085
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.52964 validation-auc:0.96619 validation-aucpr:0.97119
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.50891 validation-auc:0.96641 validation-aucpr:0.97138
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.49048 validation-auc:0.96635 validation-aucpr:0.97131
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.47244 validation-auc:0.96709 validation-aucpr:0.97176
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.45609 validation-auc:0.96711 validation-aucpr:0.97180
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.44099 validation-auc:0.96714 validation-aucpr:0.97172
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.42691 validation-auc:0.96722 validation-aucpr:0.97176
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.41372 validation-auc:0.96777 validation-aucpr:0.97213
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.40274 validation-auc:0.96760 validation-aucpr:0.97210
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.39146 validation-auc:0.96752 validation-aucpr:0.97199
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.38195 validation-auc:0.96743 validation-aucpr:0.97194
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.37155 validation-auc:0.96770 validation-aucpr:0.97217
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.36297 validation-auc:0.96764 validation-aucpr:0.97215
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.35341 validation-auc:0.96786 validation-aucpr:0.97234
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.34495 validation-auc:0.96781 validation-aucpr:0.97232
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.33735 validation-auc:0.96792 validation-aucpr:0.97248
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.33008 validation-auc:0.96799 validation-aucpr:0.97252
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.32267 validation-auc:0.96813 validation-aucpr:0.97261
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.31655 validation-auc:0.96823 validation-aucpr:0.97272
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.31009 validation-auc:0.96820 validation-aucpr:0.97272
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.30439 validation-auc:0.96826 validation-aucpr:0.97287
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.29898 validation-auc:0.96817 validation-aucpr:0.97278
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.29418 validation-auc:0.96830 validation-aucpr:0.97284
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.28891 validation-auc:0.96837 validation-aucpr:0.97291
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.28382 validation-auc:0.96852 validation-aucpr:0.97302
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.27985 validation-auc:0.96865 validation-aucpr:0.97314
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.27534 validation-auc:0.96871 validation-aucpr:0.97320
[21:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.27115 validation-auc:0.96881 validation-aucpr:0.97331
[21:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.26803 validation-auc:0.96885 validation-aucpr:0.97347
[21:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.26511 validation-auc:0.96876 validation-aucpr:0.97338
[21:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.26264 validation-auc:0.96855 validation-aucpr:0.97319
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.25974 validation-auc:0.96859 validation-aucpr:0.97318
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.25657 validation-auc:0.96864 validation-aucpr:0.97322
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.25417 validation-auc:0.96863 validation-aucpr:0.97321
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.25178 validation-auc:0.96871 validation-aucpr:0.97327
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.24940 validation-auc:0.96871 validation-aucpr:0.97326
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.24668 validation-auc:0.96874 validation-aucpr:0.97327
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.24406 validation-auc:0.96879 validation-aucpr:0.97333
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.24173 validation-auc:0.96886 validation-aucpr:0.97342
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.23987 validation-auc:0.96887 validation-aucpr:0.97344
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.23770 validation-auc:0.96896 validation-aucpr:0.97355
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.23536 validation-auc:0.96912 validation-aucpr:0.97368
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.23324 validation-auc:0.96932 validation-aucpr:0.97387
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.23179 validation-auc:0.96935 validation-aucpr:0.97389
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.22986 validation-auc:0.96940 validation-aucpr:0.97392
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.22806 validation-auc:0.96955 validation-aucpr:0.97404
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.22632 validation-auc:0.96962 validation-aucpr:0.97408
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.22525 validation-auc:0.96957 validation-aucpr:0.97401
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.22381 validation-auc:0.96957 validation-aucpr:0.97398
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.22227 validation-auc:0.96972 validation-aucpr:0.97408
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.22096 validation-auc:0.96979 validation-aucpr:0.97412
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.21974 validation-auc:0.96982 validation-aucpr:0.97415
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.21837 validation-auc:0.96995 validation-aucpr:0.97426
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.21744 validation-auc:0.97002 validation-aucpr:0.97430
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.21631 validation-auc:0.97002 validation-aucpr:0.97429
[21:42:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.21554 validation-auc:0.96997 validation-aucpr:0.97427
[21:42:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61] validation-logloss:0.21465 validation-auc:0.96998 validation-aucpr:0.97426
[21:42:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62] validation-logloss:0.21394 validation-auc:0.96993 validation-aucpr:0.97419
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63] validation-logloss:0.21308 validation-auc:0.96992 validation-aucpr:0.97416
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64] validation-logloss:0.21235 validation-auc:0.96997 validation-aucpr:0.97418
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65] validation-logloss:0.21180 validation-auc:0.96998 validation-aucpr:0.97418
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66] validation-logloss:0.21117 validation-auc:0.96998 validation-aucpr:0.97419
[21:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67] validation-logloss:0.21033 validation-auc:0.97004 validation-aucpr:0.97420
[21:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68] validation-logloss:0.20987 validation-auc:0.97001 validation-aucpr:0.97416
[21:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69] validation-logloss:0.20912 validation-auc:0.97003 validation-aucpr:0.97416
[21:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70] validation-logloss:0.20855 validation-auc:0.96999 validation-aucpr:0.97411
[21:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71] validation-logloss:0.20782 validation-auc:0.97004 validation-aucpr:0.97414
[21:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72] validation-logloss:0.20707 validation-auc:0.97015 validation-aucpr:0.97418
[21:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73] validation-logloss:0.20640 validation-auc:0.97022 validation-aucpr:0.97420
[21:42:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74] validation-logloss:0.20593 validation-auc:0.97024 validation-aucpr:0.97419
[21:42:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75] validation-logloss:0.20544 validation-auc:0.97029 validation-aucpr:0.97426
[21:42:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76] validation-logloss:0.20484 validation-auc:0.97036 validation-aucpr:0.97429
[21:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77] validation-logloss:0.20445 validation-auc:0.97037 validation-aucpr:0.97430
[21:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78] validation-logloss:0.20403 validation-auc:0.97038 validation-aucpr:0.97427
[21:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79] validation-logloss:0.20365 validation-auc:0.97033 validation-aucpr:0.97423
{'best_iteration': '77', 'best_score': '0.9743001257171137'}
Trial 82, Fold 5: Log loss = 0.2036515239633219, Average precision = 0.9742372043288416, ROC-AUC = 0.9703341694071308, Elapsed Time = 22.53138409999883 seconds
Optimization Progress: 83%|########2 | 83/100 [3:44:08<35:11, 124.22s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 83, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67223 validation-auc:0.91925 validation-aucpr:0.92320
[1] validation-logloss:0.65105 validation-auc:0.94335 validation-aucpr:0.94678
[2] validation-logloss:0.63256 validation-auc:0.94560 validation-aucpr:0.94919
[3] validation-logloss:0.61417 validation-auc:0.94817 validation-aucpr:0.95285
[4] validation-logloss:0.59744 validation-auc:0.95010 validation-aucpr:0.95527
[5] validation-logloss:0.58148 validation-auc:0.95143 validation-aucpr:0.95652
[6] validation-logloss:0.56842 validation-auc:0.95139 validation-aucpr:0.95635
[7] validation-logloss:0.55433 validation-auc:0.95200 validation-aucpr:0.95683
[8] validation-logloss:0.53832 validation-auc:0.95750 validation-aucpr:0.96241
[9] validation-logloss:0.52726 validation-auc:0.95686 validation-aucpr:0.96173
[10] validation-logloss:0.51634 validation-auc:0.95645 validation-aucpr:0.96134
[11] validation-logloss:0.50580 validation-auc:0.95617 validation-aucpr:0.96100
[12] validation-logloss:0.49557 validation-auc:0.95592 validation-aucpr:0.96066
[13] validation-logloss:0.48549 validation-auc:0.95596 validation-aucpr:0.96079
[14] validation-logloss:0.47593 validation-auc:0.95602 validation-aucpr:0.96065
[15] validation-logloss:0.46453 validation-auc:0.95798 validation-aucpr:0.96345
[16] validation-logloss:0.45637 validation-auc:0.95780 validation-aucpr:0.96330
[17] validation-logloss:0.44784 validation-auc:0.95833 validation-aucpr:0.96377
[18] validation-logloss:0.43932 validation-auc:0.95862 validation-aucpr:0.96401
[19] validation-logloss:0.43174 validation-auc:0.95864 validation-aucpr:0.96395
[20] validation-logloss:0.42202 validation-auc:0.95981 validation-aucpr:0.96522
[21] validation-logloss:0.41564 validation-auc:0.95973 validation-aucpr:0.96522
[22] validation-logloss:0.40837 validation-auc:0.96005 validation-aucpr:0.96560
[23] validation-logloss:0.39975 validation-auc:0.96060 validation-aucpr:0.96617
[24] validation-logloss:0.39368 validation-auc:0.96074 validation-aucpr:0.96632
[25] validation-logloss:0.38775 validation-auc:0.96083 validation-aucpr:0.96635
[26] validation-logloss:0.38179 validation-auc:0.96121 validation-aucpr:0.96679
[27] validation-logloss:0.37692 validation-auc:0.96119 validation-aucpr:0.96678
[28] validation-logloss:0.36912 validation-auc:0.96181 validation-aucpr:0.96739
[29] validation-logloss:0.36392 validation-auc:0.96209 validation-aucpr:0.96758
[30] validation-logloss:0.35898 validation-auc:0.96224 validation-aucpr:0.96773
[31] validation-logloss:0.35456 validation-auc:0.96233 validation-aucpr:0.96775
[32] validation-logloss:0.35042 validation-auc:0.96224 validation-aucpr:0.96765
[33] validation-logloss:0.34433 validation-auc:0.96256 validation-aucpr:0.96801
[34] validation-logloss:0.34098 validation-auc:0.96255 validation-aucpr:0.96798
[35] validation-logloss:0.33695 validation-auc:0.96260 validation-aucpr:0.96802
[36] validation-logloss:0.33302 validation-auc:0.96273 validation-aucpr:0.96814
[37] validation-logloss:0.32967 validation-auc:0.96280 validation-aucpr:0.96822
[38] validation-logloss:0.32629 validation-auc:0.96294 validation-aucpr:0.96830
[39] validation-logloss:0.32336 validation-auc:0.96295 validation-aucpr:0.96830
[40] validation-logloss:0.32027 validation-auc:0.96295 validation-aucpr:0.96827
[41] validation-logloss:0.31732 validation-auc:0.96304 validation-aucpr:0.96844
[42] validation-logloss:0.31478 validation-auc:0.96304 validation-aucpr:0.96841
[43] validation-logloss:0.31163 validation-auc:0.96308 validation-aucpr:0.96843
[44] validation-logloss:0.30930 validation-auc:0.96301 validation-aucpr:0.96833
{'best_iteration': '41', 'best_score': '0.9684363819628538'}
Trial 83, Fold 1: Log loss = 0.30929638720353547, Average precision = 0.9683288201497099, ROC-AUC = 0.963007398206698, Elapsed Time = 0.9747122000007948 seconds
Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 83, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67241 validation-auc:0.92366 validation-aucpr:0.92099
[1] validation-logloss:0.65275 validation-auc:0.93620 validation-aucpr:0.93624
[2] validation-logloss:0.63482 validation-auc:0.93809 validation-aucpr:0.93925
[3] validation-logloss:0.61323 validation-auc:0.95316 validation-aucpr:0.95566
[4] validation-logloss:0.59301 validation-auc:0.95891 validation-aucpr:0.96264
[5] validation-logloss:0.57816 validation-auc:0.95847 validation-aucpr:0.96152
[6] validation-logloss:0.56341 validation-auc:0.95844 validation-aucpr:0.96152
[7] validation-logloss:0.55052 validation-auc:0.95825 validation-aucpr:0.96060
[8] validation-logloss:0.53769 validation-auc:0.95796 validation-aucpr:0.96014
[9] validation-logloss:0.52170 validation-auc:0.96019 validation-aucpr:0.96257
[10] validation-logloss:0.50951 validation-auc:0.96053 validation-aucpr:0.96288
[11] validation-logloss:0.49885 validation-auc:0.96036 validation-aucpr:0.96254
[12] validation-logloss:0.48795 validation-auc:0.96128 validation-aucpr:0.96337
[13] validation-logloss:0.47844 validation-auc:0.96114 validation-aucpr:0.96333
[14] validation-logloss:0.46896 validation-auc:0.96126 validation-aucpr:0.96344
[15] validation-logloss:0.46017 validation-auc:0.96127 validation-aucpr:0.96335
[16] validation-logloss:0.45220 validation-auc:0.96120 validation-aucpr:0.96330
[17] validation-logloss:0.44383 validation-auc:0.96158 validation-aucpr:0.96355
[18] validation-logloss:0.43627 validation-auc:0.96135 validation-aucpr:0.96332
[19] validation-logloss:0.42876 validation-auc:0.96150 validation-aucpr:0.96338
[20] validation-logloss:0.42017 validation-auc:0.96173 validation-aucpr:0.96365
[21] validation-logloss:0.41059 validation-auc:0.96252 validation-aucpr:0.96452
[22] validation-logloss:0.40446 validation-auc:0.96263 validation-aucpr:0.96535
[23] validation-logloss:0.39797 validation-auc:0.96276 validation-aucpr:0.96544
[24] validation-logloss:0.38918 validation-auc:0.96335 validation-aucpr:0.96613
[25] validation-logloss:0.38329 validation-auc:0.96336 validation-aucpr:0.96612
[26] validation-logloss:0.37564 validation-auc:0.96368 validation-aucpr:0.96647
[27] validation-logloss:0.36814 validation-auc:0.96397 validation-aucpr:0.96683
[28] validation-logloss:0.36340 validation-auc:0.96384 validation-aucpr:0.96673
[29] validation-logloss:0.35857 validation-auc:0.96379 validation-aucpr:0.96637
[30] validation-logloss:0.35391 validation-auc:0.96399 validation-aucpr:0.96655
[31] validation-logloss:0.34897 validation-auc:0.96416 validation-aucpr:0.96670
[32] validation-logloss:0.34464 validation-auc:0.96416 validation-aucpr:0.96705
[33] validation-logloss:0.34070 validation-auc:0.96423 validation-aucpr:0.96712
[34] validation-logloss:0.33642 validation-auc:0.96429 validation-aucpr:0.96718
[35] validation-logloss:0.33299 validation-auc:0.96426 validation-aucpr:0.96711
[36] validation-logloss:0.32957 validation-auc:0.96430 validation-aucpr:0.96727
[37] validation-logloss:0.32367 validation-auc:0.96464 validation-aucpr:0.96761
[38] validation-logloss:0.32007 validation-auc:0.96480 validation-aucpr:0.96762
[39] validation-logloss:0.31689 validation-auc:0.96487 validation-aucpr:0.96724
[40] validation-logloss:0.31195 validation-auc:0.96518 validation-aucpr:0.96794
[41] validation-logloss:0.30835 validation-auc:0.96528 validation-aucpr:0.96827
[42] validation-logloss:0.30388 validation-auc:0.96560 validation-aucpr:0.96900
[43] validation-logloss:0.30113 validation-auc:0.96565 validation-aucpr:0.96900
[44] validation-logloss:0.29833 validation-auc:0.96569 validation-aucpr:0.96901
{'best_iteration': '44', 'best_score': '0.9690120397681583'}
Trial 83, Fold 2: Log loss = 0.2983271238709228, Average precision = 0.9690123800135595, ROC-AUC = 0.965694833382449, Elapsed Time = 1.1981218000000808 seconds
Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 83, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67127 validation-auc:0.93340 validation-aucpr:0.93520
[1] validation-logloss:0.64749 validation-auc:0.95962 validation-aucpr:0.96329
[2] validation-logloss:0.62856 validation-auc:0.96076 validation-aucpr:0.96361
[3] validation-logloss:0.61204 validation-auc:0.95898 validation-aucpr:0.96137
[4] validation-logloss:0.59547 validation-auc:0.95885 validation-aucpr:0.96133
[5] validation-logloss:0.58060 validation-auc:0.95870 validation-aucpr:0.96233
[6] validation-logloss:0.56688 validation-auc:0.95913 validation-aucpr:0.96348
[7] validation-logloss:0.55315 validation-auc:0.95845 validation-aucpr:0.96280
[8] validation-logloss:0.53699 validation-auc:0.95979 validation-aucpr:0.96426
[9] validation-logloss:0.52384 validation-auc:0.96084 validation-aucpr:0.96381
[10] validation-logloss:0.51142 validation-auc:0.96083 validation-aucpr:0.96376
[11] validation-logloss:0.49979 validation-auc:0.96119 validation-aucpr:0.96410
[12] validation-logloss:0.48579 validation-auc:0.96221 validation-aucpr:0.96519
[13] validation-logloss:0.47580 validation-auc:0.96215 validation-aucpr:0.96552
[14] validation-logloss:0.46577 validation-auc:0.96230 validation-aucpr:0.96549
[15] validation-logloss:0.45417 validation-auc:0.96305 validation-aucpr:0.96737
[16] validation-logloss:0.44563 validation-auc:0.96319 validation-aucpr:0.96741
[17] validation-logloss:0.43799 validation-auc:0.96358 validation-aucpr:0.96852
[18] validation-logloss:0.42974 validation-auc:0.96362 validation-aucpr:0.96849
[19] validation-logloss:0.41938 validation-auc:0.96396 validation-aucpr:0.96881
[20] validation-logloss:0.41213 validation-auc:0.96394 validation-aucpr:0.96881
[21] validation-logloss:0.40511 validation-auc:0.96385 validation-aucpr:0.96873
[22] validation-logloss:0.39575 validation-auc:0.96436 validation-aucpr:0.96914
[23] validation-logloss:0.38936 validation-auc:0.96447 validation-aucpr:0.96929
[24] validation-logloss:0.38385 validation-auc:0.96435 validation-aucpr:0.96912
[25] validation-logloss:0.37855 validation-auc:0.96429 validation-aucpr:0.96921
[26] validation-logloss:0.37263 validation-auc:0.96441 validation-aucpr:0.96933
[27] validation-logloss:0.36594 validation-auc:0.96444 validation-aucpr:0.96938
[28] validation-logloss:0.36107 validation-auc:0.96457 validation-aucpr:0.96945
[29] validation-logloss:0.35668 validation-auc:0.96458 validation-aucpr:0.96942
[30] validation-logloss:0.35219 validation-auc:0.96450 validation-aucpr:0.96934
[31] validation-logloss:0.34820 validation-auc:0.96446 validation-aucpr:0.96930
[32] validation-logloss:0.34228 validation-auc:0.96457 validation-aucpr:0.96942
[33] validation-logloss:0.33874 validation-auc:0.96448 validation-aucpr:0.96932
[34] validation-logloss:0.33459 validation-auc:0.96446 validation-aucpr:0.96931
[35] validation-logloss:0.33122 validation-auc:0.96449 validation-aucpr:0.96935
[36] validation-logloss:0.32579 validation-auc:0.96462 validation-aucpr:0.96947
[37] validation-logloss:0.32164 validation-auc:0.96497 validation-aucpr:0.96974
[38] validation-logloss:0.31657 validation-auc:0.96508 validation-aucpr:0.96985
[39] validation-logloss:0.31356 validation-auc:0.96512 validation-aucpr:0.96987
[40] validation-logloss:0.31023 validation-auc:0.96521 validation-aucpr:0.96993
[41] validation-logloss:0.30704 validation-auc:0.96532 validation-aucpr:0.96999
[42] validation-logloss:0.30420 validation-auc:0.96543 validation-aucpr:0.97000
[43] validation-logloss:0.29993 validation-auc:0.96550 validation-aucpr:0.97014
[44] validation-logloss:0.29763 validation-auc:0.96553 validation-aucpr:0.97015
{'best_iteration': '44', 'best_score': '0.9701487873135217'}
Trial 83, Fold 3: Log loss = 0.2976307423822404, Average precision = 0.9701537445481727, ROC-AUC = 0.9655256348055974, Elapsed Time = 1.1555424999969546 seconds
Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 83, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67158 validation-auc:0.92830 validation-aucpr:0.92897
[1] validation-logloss:0.65023 validation-auc:0.94723 validation-aucpr:0.95055
[2] validation-logloss:0.63138 validation-auc:0.94807 validation-aucpr:0.95194
[3] validation-logloss:0.61474 validation-auc:0.94994 validation-aucpr:0.95445
[4] validation-logloss:0.59778 validation-auc:0.95102 validation-aucpr:0.95606
[5] validation-logloss:0.58273 validation-auc:0.95161 validation-aucpr:0.95671
[6] validation-logloss:0.56685 validation-auc:0.95257 validation-aucpr:0.95782
[7] validation-logloss:0.55357 validation-auc:0.95316 validation-aucpr:0.95841
[8] validation-logloss:0.53982 validation-auc:0.95453 validation-aucpr:0.95997
[9] validation-logloss:0.52812 validation-auc:0.95437 validation-aucpr:0.95985
[10] validation-logloss:0.51658 validation-auc:0.95449 validation-aucpr:0.95991
[11] validation-logloss:0.50351 validation-auc:0.95782 validation-aucpr:0.96340
[12] validation-logloss:0.49348 validation-auc:0.95750 validation-aucpr:0.96305
[13] validation-logloss:0.48375 validation-auc:0.95738 validation-aucpr:0.96297
[14] validation-logloss:0.47095 validation-auc:0.95983 validation-aucpr:0.96582
[15] validation-logloss:0.46174 validation-auc:0.95975 validation-aucpr:0.96576
[16] validation-logloss:0.45340 validation-auc:0.95984 validation-aucpr:0.96578
[17] validation-logloss:0.44503 validation-auc:0.96011 validation-aucpr:0.96599
[18] validation-logloss:0.43446 validation-auc:0.96112 validation-aucpr:0.96723
[19] validation-logloss:0.42644 validation-auc:0.96129 validation-aucpr:0.96736
[20] validation-logloss:0.41965 validation-auc:0.96123 validation-aucpr:0.96734
[21] validation-logloss:0.41093 validation-auc:0.96153 validation-aucpr:0.96767
[22] validation-logloss:0.40173 validation-auc:0.96200 validation-aucpr:0.96817
[23] validation-logloss:0.39619 validation-auc:0.96179 validation-aucpr:0.96797
[24] validation-logloss:0.38943 validation-auc:0.96236 validation-aucpr:0.96839
[25] validation-logloss:0.38387 validation-auc:0.96251 validation-aucpr:0.96852
[26] validation-logloss:0.37843 validation-auc:0.96255 validation-aucpr:0.96848
[27] validation-logloss:0.37380 validation-auc:0.96254 validation-aucpr:0.96855
[28] validation-logloss:0.36657 validation-auc:0.96289 validation-aucpr:0.96895
[29] validation-logloss:0.36132 validation-auc:0.96301 validation-aucpr:0.96904
[30] validation-logloss:0.35709 validation-auc:0.96303 validation-aucpr:0.96909
[31] validation-logloss:0.35061 validation-auc:0.96338 validation-aucpr:0.96945
[32] validation-logloss:0.34666 validation-auc:0.96331 validation-aucpr:0.96932
[33] validation-logloss:0.34091 validation-auc:0.96346 validation-aucpr:0.96948
[34] validation-logloss:0.33717 validation-auc:0.96342 validation-aucpr:0.96940
[35] validation-logloss:0.33388 validation-auc:0.96338 validation-aucpr:0.96933
[36] validation-logloss:0.33004 validation-auc:0.96338 validation-aucpr:0.96935
[37] validation-logloss:0.32666 validation-auc:0.96344 validation-aucpr:0.96939
[38] validation-logloss:0.32288 validation-auc:0.96356 validation-aucpr:0.96948
[39] validation-logloss:0.31996 validation-auc:0.96343 validation-aucpr:0.96941
[40] validation-logloss:0.31480 validation-auc:0.96366 validation-aucpr:0.96963
[41] validation-logloss:0.31004 validation-auc:0.96377 validation-aucpr:0.96974
[42] validation-logloss:0.30703 validation-auc:0.96382 validation-aucpr:0.96979
[43] validation-logloss:0.30224 validation-auc:0.96403 validation-aucpr:0.96997
[44] validation-logloss:0.29991 validation-auc:0.96413 validation-aucpr:0.97002
{'best_iteration': '44', 'best_score': '0.9700243644905334'}
Trial 83, Fold 4: Log loss = 0.29991398528608265, Average precision = 0.9700287573244741, ROC-AUC = 0.9641328140088843, Elapsed Time = 1.205062100001669 seconds
Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 83, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67158 validation-auc:0.92778 validation-aucpr:0.92848
[1] validation-logloss:0.64804 validation-auc:0.95263 validation-aucpr:0.95765
[2] validation-logloss:0.62899 validation-auc:0.95517 validation-aucpr:0.95894
[3] validation-logloss:0.61059 validation-auc:0.95643 validation-aucpr:0.95747
[4] validation-logloss:0.59455 validation-auc:0.95621 validation-aucpr:0.96105
[5] validation-logloss:0.57741 validation-auc:0.95642 validation-aucpr:0.96115
[6] validation-logloss:0.56226 validation-auc:0.95703 validation-aucpr:0.96162
[7] validation-logloss:0.54856 validation-auc:0.95710 validation-aucpr:0.96223
[8] validation-logloss:0.53547 validation-auc:0.95692 validation-aucpr:0.96204
[9] validation-logloss:0.52349 validation-auc:0.95670 validation-aucpr:0.96159
[10] validation-logloss:0.50925 validation-auc:0.95856 validation-aucpr:0.96377
[11] validation-logloss:0.49892 validation-auc:0.95835 validation-aucpr:0.96356
[12] validation-logloss:0.48538 validation-auc:0.95901 validation-aucpr:0.96436
[13] validation-logloss:0.47371 validation-auc:0.95916 validation-aucpr:0.96478
[14] validation-logloss:0.46406 validation-auc:0.95931 validation-aucpr:0.96487
[15] validation-logloss:0.45263 validation-auc:0.95969 validation-aucpr:0.96523
[16] validation-logloss:0.44433 validation-auc:0.95963 validation-aucpr:0.96514
[17] validation-logloss:0.43349 validation-auc:0.96033 validation-aucpr:0.96585
[18] validation-logloss:0.42564 validation-auc:0.96045 validation-aucpr:0.96593
[19] validation-logloss:0.41793 validation-auc:0.96055 validation-aucpr:0.96591
[20] validation-logloss:0.41107 validation-auc:0.96065 validation-aucpr:0.96599
[21] validation-logloss:0.40408 validation-auc:0.96091 validation-aucpr:0.96614
[22] validation-logloss:0.39781 validation-auc:0.96080 validation-aucpr:0.96601
[23] validation-logloss:0.38994 validation-auc:0.96092 validation-aucpr:0.96616
[24] validation-logloss:0.38462 validation-auc:0.96094 validation-aucpr:0.96596
[25] validation-logloss:0.37944 validation-auc:0.96089 validation-aucpr:0.96583
[26] validation-logloss:0.37406 validation-auc:0.96087 validation-aucpr:0.96576
[27] validation-logloss:0.36873 validation-auc:0.96085 validation-aucpr:0.96587
[28] validation-logloss:0.36350 validation-auc:0.96113 validation-aucpr:0.96595
[29] validation-logloss:0.35890 validation-auc:0.96134 validation-aucpr:0.96614
[30] validation-logloss:0.35414 validation-auc:0.96144 validation-aucpr:0.96616
[31] validation-logloss:0.35049 validation-auc:0.96135 validation-aucpr:0.96663
[32] validation-logloss:0.34640 validation-auc:0.96129 validation-aucpr:0.96656
[33] validation-logloss:0.34249 validation-auc:0.96140 validation-aucpr:0.96671
[34] validation-logloss:0.33917 validation-auc:0.96133 validation-aucpr:0.96662
[35] validation-logloss:0.33609 validation-auc:0.96123 validation-aucpr:0.96662
[36] validation-logloss:0.33254 validation-auc:0.96121 validation-aucpr:0.96660
[37] validation-logloss:0.32934 validation-auc:0.96115 validation-aucpr:0.96657
[38] validation-logloss:0.32597 validation-auc:0.96119 validation-aucpr:0.96659
[39] validation-logloss:0.32079 validation-auc:0.96136 validation-aucpr:0.96680
[40] validation-logloss:0.31752 validation-auc:0.96146 validation-aucpr:0.96684
[41] validation-logloss:0.31419 validation-auc:0.96158 validation-aucpr:0.96690
[42] validation-logloss:0.31127 validation-auc:0.96168 validation-aucpr:0.96694
[43] validation-logloss:0.30856 validation-auc:0.96176 validation-aucpr:0.96702
[44] validation-logloss:0.30585 validation-auc:0.96184 validation-aucpr:0.96707
{'best_iteration': '44', 'best_score': '0.9670711622018966'}
Trial 83, Fold 5: Log loss = 0.30585460800323083, Average precision = 0.9670759297537443, ROC-AUC = 0.9618388421306877, Elapsed Time = 1.164583699999639 seconds
Optimization Progress: 84%|########4 | 84/100 [3:44:22<24:18, 91.18s/it]
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 84, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68515 validation-auc:0.94861 validation-aucpr:0.92425
[1] validation-logloss:0.67829 validation-auc:0.96219 validation-aucpr:0.96205
[2] validation-logloss:0.67094 validation-auc:0.96421 validation-aucpr:0.96619
[3] validation-logloss:0.66356 validation-auc:0.96568 validation-aucpr:0.96876
[4] validation-logloss:0.65724 validation-auc:0.96543 validation-aucpr:0.96880
[5] validation-logloss:0.65032 validation-auc:0.96593 validation-aucpr:0.96942
[6] validation-logloss:0.64396 validation-auc:0.96623 validation-aucpr:0.96967
[7] validation-logloss:0.63782 validation-auc:0.96662 validation-aucpr:0.97154
[8] validation-logloss:0.63165 validation-auc:0.96649 validation-aucpr:0.97135
[9] validation-logloss:0.62510 validation-auc:0.96694 validation-aucpr:0.97169
[10] validation-logloss:0.61862 validation-auc:0.96716 validation-aucpr:0.97186
[11] validation-logloss:0.61284 validation-auc:0.96724 validation-aucpr:0.97182
[12] validation-logloss:0.60680 validation-auc:0.96734 validation-aucpr:0.97047
[13] validation-logloss:0.60066 validation-auc:0.96758 validation-aucpr:0.97075
[14] validation-logloss:0.59459 validation-auc:0.96805 validation-aucpr:0.97137
[15] validation-logloss:0.58865 validation-auc:0.96830 validation-aucpr:0.97154
[16] validation-logloss:0.58280 validation-auc:0.96849 validation-aucpr:0.97172
[17] validation-logloss:0.57709 validation-auc:0.96878 validation-aucpr:0.97159
[18] validation-logloss:0.57219 validation-auc:0.96850 validation-aucpr:0.97134
[19] validation-logloss:0.56734 validation-auc:0.96834 validation-aucpr:0.97099
[20] validation-logloss:0.56273 validation-auc:0.96814 validation-aucpr:0.97078
[21] validation-logloss:0.55741 validation-auc:0.96842 validation-aucpr:0.97137
[22] validation-logloss:0.55220 validation-auc:0.96862 validation-aucpr:0.97153
[23] validation-logloss:0.54771 validation-auc:0.96849 validation-aucpr:0.97336
[24] validation-logloss:0.54255 validation-auc:0.96866 validation-aucpr:0.97352
[25] validation-logloss:0.53767 validation-auc:0.96862 validation-aucpr:0.97344
[26] validation-logloss:0.53288 validation-auc:0.96879 validation-aucpr:0.97217
[27] validation-logloss:0.52815 validation-auc:0.96887 validation-aucpr:0.97226
[28] validation-logloss:0.52349 validation-auc:0.96887 validation-aucpr:0.97230
[29] validation-logloss:0.51878 validation-auc:0.96902 validation-aucpr:0.97246
[30] validation-logloss:0.51484 validation-auc:0.96886 validation-aucpr:0.97230
[31] validation-logloss:0.51031 validation-auc:0.96902 validation-aucpr:0.97244
[32] validation-logloss:0.50645 validation-auc:0.96882 validation-aucpr:0.97227
[33] validation-logloss:0.50212 validation-auc:0.96891 validation-aucpr:0.97236
[34] validation-logloss:0.49778 validation-auc:0.96896 validation-aucpr:0.97244
[35] validation-logloss:0.49420 validation-auc:0.96886 validation-aucpr:0.97361
[36] validation-logloss:0.49054 validation-auc:0.96884 validation-aucpr:0.97356
[37] validation-logloss:0.48654 validation-auc:0.96892 validation-aucpr:0.97362
[38] validation-logloss:0.48250 validation-auc:0.96896 validation-aucpr:0.97363
[39] validation-logloss:0.47853 validation-auc:0.96897 validation-aucpr:0.97210
[40] validation-logloss:0.47460 validation-auc:0.96903 validation-aucpr:0.97216
[41] validation-logloss:0.47073 validation-auc:0.96911 validation-aucpr:0.97222
{'best_iteration': '38', 'best_score': '0.9736331182222254'}
Trial 84, Fold 1: Log loss = 0.47073261328856786, Average precision = 0.9729380187269572, ROC-AUC = 0.9691089172796489, Elapsed Time = 1.6764398999985133 seconds
Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 84, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68546 validation-auc:0.94384 validation-aucpr:0.91530
[1] validation-logloss:0.67803 validation-auc:0.96147 validation-aucpr:0.95453
[2] validation-logloss:0.67052 validation-auc:0.96678 validation-aucpr:0.96787
[3] validation-logloss:0.66394 validation-auc:0.96759 validation-aucpr:0.97095
[4] validation-logloss:0.65766 validation-auc:0.96731 validation-aucpr:0.97088
[5] validation-logloss:0.65123 validation-auc:0.96732 validation-aucpr:0.97067
[6] validation-logloss:0.64434 validation-auc:0.96779 validation-aucpr:0.97125
[7] validation-logloss:0.63766 validation-auc:0.96773 validation-aucpr:0.97147
[8] validation-logloss:0.63100 validation-auc:0.96825 validation-aucpr:0.97193
[9] validation-logloss:0.62516 validation-auc:0.96835 validation-aucpr:0.97198
[10] validation-logloss:0.61868 validation-auc:0.96875 validation-aucpr:0.97233
[11] validation-logloss:0.61231 validation-auc:0.96948 validation-aucpr:0.97297
[12] validation-logloss:0.60673 validation-auc:0.96930 validation-aucpr:0.97277
[13] validation-logloss:0.60094 validation-auc:0.96897 validation-aucpr:0.97259
[14] validation-logloss:0.59554 validation-auc:0.96885 validation-aucpr:0.97250
[15] validation-logloss:0.58966 validation-auc:0.96907 validation-aucpr:0.97272
[16] validation-logloss:0.58385 validation-auc:0.96925 validation-aucpr:0.97290
[17] validation-logloss:0.57866 validation-auc:0.96938 validation-aucpr:0.97293
[18] validation-logloss:0.57387 validation-auc:0.96928 validation-aucpr:0.97281
[19] validation-logloss:0.56844 validation-auc:0.96920 validation-aucpr:0.97279
[20] validation-logloss:0.56299 validation-auc:0.96931 validation-aucpr:0.97283
[21] validation-logloss:0.55838 validation-auc:0.96936 validation-aucpr:0.97282
[22] validation-logloss:0.55374 validation-auc:0.96923 validation-aucpr:0.97265
[23] validation-logloss:0.54920 validation-auc:0.96903 validation-aucpr:0.97246
[24] validation-logloss:0.54483 validation-auc:0.96896 validation-aucpr:0.97238
[25] validation-logloss:0.54034 validation-auc:0.96906 validation-aucpr:0.97246
[26] validation-logloss:0.53534 validation-auc:0.96926 validation-aucpr:0.97264
[27] validation-logloss:0.53116 validation-auc:0.96919 validation-aucpr:0.97257
[28] validation-logloss:0.52645 validation-auc:0.96942 validation-aucpr:0.97277
[29] validation-logloss:0.52166 validation-auc:0.96961 validation-aucpr:0.97294
[30] validation-logloss:0.51705 validation-auc:0.96963 validation-aucpr:0.97296
[31] validation-logloss:0.51255 validation-auc:0.96963 validation-aucpr:0.97289
[32] validation-logloss:0.50798 validation-auc:0.96980 validation-aucpr:0.97305
[33] validation-logloss:0.50415 validation-auc:0.96976 validation-aucpr:0.97304
[34] validation-logloss:0.49980 validation-auc:0.96981 validation-aucpr:0.97309
[35] validation-logloss:0.49545 validation-auc:0.96994 validation-aucpr:0.97317
[36] validation-logloss:0.49119 validation-auc:0.97001 validation-aucpr:0.97324
[37] validation-logloss:0.48705 validation-auc:0.97016 validation-aucpr:0.97338
[38] validation-logloss:0.48294 validation-auc:0.97023 validation-aucpr:0.97345
[39] validation-logloss:0.47889 validation-auc:0.97037 validation-aucpr:0.97357
[40] validation-logloss:0.47495 validation-auc:0.97046 validation-aucpr:0.97365
[41] validation-logloss:0.47155 validation-auc:0.97039 validation-aucpr:0.97358
{'best_iteration': '40', 'best_score': '0.9736509735508068'}
Trial 84, Fold 2: Log loss = 0.47154738969745186, Average precision = 0.9735147670790396, ROC-AUC = 0.9703878612697465, Elapsed Time = 1.938225699999748 seconds
Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 84, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68526 validation-auc:0.94306 validation-aucpr:0.92744
[1] validation-logloss:0.67746 validation-auc:0.96507 validation-aucpr:0.96408
[2] validation-logloss:0.67032 validation-auc:0.96592 validation-aucpr:0.96783
[3] validation-logloss:0.66304 validation-auc:0.96704 validation-aucpr:0.96927
[4] validation-logloss:0.65669 validation-auc:0.96760 validation-aucpr:0.97000
[5] validation-logloss:0.65030 validation-auc:0.96725 validation-aucpr:0.96975
[6] validation-logloss:0.64333 validation-auc:0.96799 validation-aucpr:0.97172
[7] validation-logloss:0.63640 validation-auc:0.96903 validation-aucpr:0.97357
[8] validation-logloss:0.62983 validation-auc:0.96935 validation-aucpr:0.97393
[9] validation-logloss:0.62392 validation-auc:0.96938 validation-aucpr:0.97383
[10] validation-logloss:0.61816 validation-auc:0.96933 validation-aucpr:0.97369
[11] validation-logloss:0.61186 validation-auc:0.96968 validation-aucpr:0.97395
[12] validation-logloss:0.60628 validation-auc:0.96951 validation-aucpr:0.97384
[13] validation-logloss:0.60003 validation-auc:0.96992 validation-aucpr:0.97417
[14] validation-logloss:0.59396 validation-auc:0.97017 validation-aucpr:0.97439
[15] validation-logloss:0.58876 validation-auc:0.96993 validation-aucpr:0.97415
[16] validation-logloss:0.58306 validation-auc:0.97006 validation-aucpr:0.97426
[17] validation-logloss:0.57805 validation-auc:0.96994 validation-aucpr:0.97405
[18] validation-logloss:0.57233 validation-auc:0.97016 validation-aucpr:0.97418
[19] validation-logloss:0.56740 validation-auc:0.97000 validation-aucpr:0.97412
[20] validation-logloss:0.56258 validation-auc:0.97004 validation-aucpr:0.97413
[21] validation-logloss:0.55780 validation-auc:0.97016 validation-aucpr:0.97422
[22] validation-logloss:0.55253 validation-auc:0.97045 validation-aucpr:0.97459
[23] validation-logloss:0.54728 validation-auc:0.97068 validation-aucpr:0.97475
[24] validation-logloss:0.54275 validation-auc:0.97060 validation-aucpr:0.97467
[25] validation-logloss:0.53820 validation-auc:0.97059 validation-aucpr:0.97464
[26] validation-logloss:0.53327 validation-auc:0.97064 validation-aucpr:0.97469
[27] validation-logloss:0.52841 validation-auc:0.97072 validation-aucpr:0.97474
[28] validation-logloss:0.52358 validation-auc:0.97089 validation-aucpr:0.97486
[29] validation-logloss:0.51909 validation-auc:0.97097 validation-aucpr:0.97490
[30] validation-logloss:0.51449 validation-auc:0.97097 validation-aucpr:0.97491
[31] validation-logloss:0.51042 validation-auc:0.97104 validation-aucpr:0.97496
[32] validation-logloss:0.50648 validation-auc:0.97102 validation-aucpr:0.97495
[33] validation-logloss:0.50207 validation-auc:0.97108 validation-aucpr:0.97500
[34] validation-logloss:0.49833 validation-auc:0.97099 validation-aucpr:0.97494
[35] validation-logloss:0.49445 validation-auc:0.97096 validation-aucpr:0.97490
[36] validation-logloss:0.49077 validation-auc:0.97092 validation-aucpr:0.97483
[37] validation-logloss:0.48657 validation-auc:0.97105 validation-aucpr:0.97496
[38] validation-logloss:0.48247 validation-auc:0.97109 validation-aucpr:0.97500
[39] validation-logloss:0.47843 validation-auc:0.97110 validation-aucpr:0.97501
[40] validation-logloss:0.47497 validation-auc:0.97103 validation-aucpr:0.97495
[41] validation-logloss:0.47155 validation-auc:0.97103 validation-aucpr:0.97493
{'best_iteration': '39', 'best_score': '0.9750053475125442'}
Trial 84, Fold 3: Log loss = 0.4715512103153635, Average precision = 0.9749272533373887, ROC-AUC = 0.9710345116102128, Elapsed Time = 1.825174599998718 seconds
Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 84, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68530 validation-auc:0.93955 validation-aucpr:0.92656
[1] validation-logloss:0.67866 validation-auc:0.95745 validation-aucpr:0.96017
[2] validation-logloss:0.67118 validation-auc:0.96223 validation-aucpr:0.96612
[3] validation-logloss:0.66378 validation-auc:0.96525 validation-aucpr:0.97073
[4] validation-logloss:0.65664 validation-auc:0.96665 validation-aucpr:0.97220
[5] validation-logloss:0.64954 validation-auc:0.96688 validation-aucpr:0.97241
[6] validation-logloss:0.64324 validation-auc:0.96713 validation-aucpr:0.97254
[7] validation-logloss:0.63645 validation-auc:0.96759 validation-aucpr:0.97297
[8] validation-logloss:0.63032 validation-auc:0.96801 validation-aucpr:0.97327
[9] validation-logloss:0.62367 validation-auc:0.96840 validation-aucpr:0.97362
[10] validation-logloss:0.61727 validation-auc:0.96850 validation-aucpr:0.97374
[11] validation-logloss:0.61138 validation-auc:0.96822 validation-aucpr:0.97348
[12] validation-logloss:0.60527 validation-auc:0.96835 validation-aucpr:0.97363
[13] validation-logloss:0.59980 validation-auc:0.96854 validation-aucpr:0.97374
[14] validation-logloss:0.59379 validation-auc:0.96895 validation-aucpr:0.97404
[15] validation-logloss:0.58863 validation-auc:0.96887 validation-aucpr:0.97398
[16] validation-logloss:0.58282 validation-auc:0.96893 validation-aucpr:0.97402
[17] validation-logloss:0.57703 validation-auc:0.96922 validation-aucpr:0.97426
[18] validation-logloss:0.57220 validation-auc:0.96905 validation-aucpr:0.97411
[19] validation-logloss:0.56671 validation-auc:0.96913 validation-aucpr:0.97418
[20] validation-logloss:0.56127 validation-auc:0.96921 validation-aucpr:0.97426
[21] validation-logloss:0.55595 validation-auc:0.96942 validation-aucpr:0.97440
[22] validation-logloss:0.55073 validation-auc:0.96948 validation-aucpr:0.97446
[23] validation-logloss:0.54605 validation-auc:0.96952 validation-aucpr:0.97449
[24] validation-logloss:0.54106 validation-auc:0.96973 validation-aucpr:0.97465
[25] validation-logloss:0.53634 validation-auc:0.96967 validation-aucpr:0.97461
[26] validation-logloss:0.53149 validation-auc:0.96973 validation-aucpr:0.97467
[27] validation-logloss:0.52669 validation-auc:0.96985 validation-aucpr:0.97473
[28] validation-logloss:0.52191 validation-auc:0.97000 validation-aucpr:0.97486
[29] validation-logloss:0.51727 validation-auc:0.97014 validation-aucpr:0.97496
[30] validation-logloss:0.51270 validation-auc:0.97025 validation-aucpr:0.97507
[31] validation-logloss:0.50834 validation-auc:0.97021 validation-aucpr:0.97505
[32] validation-logloss:0.50438 validation-auc:0.97006 validation-aucpr:0.97494
[33] validation-logloss:0.50002 validation-auc:0.97013 validation-aucpr:0.97500
[34] validation-logloss:0.49580 validation-auc:0.97025 validation-aucpr:0.97509
[35] validation-logloss:0.49203 validation-auc:0.97005 validation-aucpr:0.97493
[36] validation-logloss:0.48834 validation-auc:0.96996 validation-aucpr:0.97484
[37] validation-logloss:0.48443 validation-auc:0.97001 validation-aucpr:0.97488
[38] validation-logloss:0.48080 validation-auc:0.96985 validation-aucpr:0.97474
[39] validation-logloss:0.47737 validation-auc:0.96974 validation-aucpr:0.97467
[40] validation-logloss:0.47389 validation-auc:0.96970 validation-aucpr:0.97462
[41] validation-logloss:0.47001 validation-auc:0.96977 validation-aucpr:0.97469
{'best_iteration': '34', 'best_score': '0.9750907393814487'}
Trial 84, Fold 4: Log loss = 0.4700130718515467, Average precision = 0.9746817828452765, ROC-AUC = 0.969767695897146, Elapsed Time = 1.8695074999995995 seconds
Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 84, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68533 validation-auc:0.93718 validation-aucpr:0.91104
[1] validation-logloss:0.67771 validation-auc:0.95918 validation-aucpr:0.95235
[2] validation-logloss:0.67031 validation-auc:0.96389 validation-aucpr:0.96061
[3] validation-logloss:0.66381 validation-auc:0.96514 validation-aucpr:0.96797
[4] validation-logloss:0.65673 validation-auc:0.96609 validation-aucpr:0.96704
[5] validation-logloss:0.65056 validation-auc:0.96544 validation-aucpr:0.96856
[6] validation-logloss:0.64433 validation-auc:0.96527 validation-aucpr:0.96825
[7] validation-logloss:0.63829 validation-auc:0.96553 validation-aucpr:0.96835
[8] validation-logloss:0.63258 validation-auc:0.96513 validation-aucpr:0.96766
[9] validation-logloss:0.62671 validation-auc:0.96496 validation-aucpr:0.96745
[10] validation-logloss:0.62023 validation-auc:0.96532 validation-aucpr:0.96796
[11] validation-logloss:0.61392 validation-auc:0.96582 validation-aucpr:0.96833
[12] validation-logloss:0.60777 validation-auc:0.96653 validation-aucpr:0.96896
[13] validation-logloss:0.60165 validation-auc:0.96680 validation-aucpr:0.96928
[14] validation-logloss:0.59644 validation-auc:0.96675 validation-aucpr:0.97101
[15] validation-logloss:0.59052 validation-auc:0.96711 validation-aucpr:0.97139
[16] validation-logloss:0.58478 validation-auc:0.96741 validation-aucpr:0.97167
[17] validation-logloss:0.57918 validation-auc:0.96747 validation-aucpr:0.97171
[18] validation-logloss:0.57353 validation-auc:0.96767 validation-aucpr:0.97192
[19] validation-logloss:0.56865 validation-auc:0.96762 validation-aucpr:0.97183
[20] validation-logloss:0.56391 validation-auc:0.96757 validation-aucpr:0.97179
[21] validation-logloss:0.55857 validation-auc:0.96791 validation-aucpr:0.97205
[22] validation-logloss:0.55349 validation-auc:0.96801 validation-aucpr:0.97217
[23] validation-logloss:0.54892 validation-auc:0.96797 validation-aucpr:0.97211
[24] validation-logloss:0.54392 validation-auc:0.96804 validation-aucpr:0.97222
[25] validation-logloss:0.53970 validation-auc:0.96780 validation-aucpr:0.97200
[26] validation-logloss:0.53490 validation-auc:0.96785 validation-aucpr:0.97206
[27] validation-logloss:0.53051 validation-auc:0.96774 validation-aucpr:0.97196
[28] validation-logloss:0.52645 validation-auc:0.96759 validation-aucpr:0.97180
[29] validation-logloss:0.52184 validation-auc:0.96774 validation-aucpr:0.97194
[30] validation-logloss:0.51782 validation-auc:0.96777 validation-aucpr:0.97197
[31] validation-logloss:0.51328 validation-auc:0.96788 validation-aucpr:0.97208
[32] validation-logloss:0.50889 validation-auc:0.96805 validation-aucpr:0.97226
[33] validation-logloss:0.50515 validation-auc:0.96777 validation-aucpr:0.97203
[34] validation-logloss:0.50093 validation-auc:0.96786 validation-aucpr:0.97214
[35] validation-logloss:0.49667 validation-auc:0.96793 validation-aucpr:0.97224
[36] validation-logloss:0.49252 validation-auc:0.96797 validation-aucpr:0.97230
[37] validation-logloss:0.48844 validation-auc:0.96799 validation-aucpr:0.97233
[38] validation-logloss:0.48495 validation-auc:0.96787 validation-aucpr:0.97219
[39] validation-logloss:0.48104 validation-auc:0.96789 validation-aucpr:0.97225
[40] validation-logloss:0.47710 validation-auc:0.96797 validation-aucpr:0.97232
[41] validation-logloss:0.47323 validation-auc:0.96813 validation-aucpr:0.97246
{'best_iteration': '41', 'best_score': '0.9724589335569357'}
Trial 84, Fold 5: Log loss = 0.4732269235506302, Average precision = 0.9724522509729793, ROC-AUC = 0.9681272318525538, Elapsed Time = 1.8348221000014746 seconds
Optimization Progress: 85%|########5 | 85/100 [3:44:40<17:15, 69.03s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 85, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66495 validation-auc:0.95626 validation-aucpr:0.96087
[1] validation-logloss:0.63938 validation-auc:0.96148 validation-aucpr:0.96693
[2] validation-logloss:0.61518 validation-auc:0.96324 validation-aucpr:0.96821
[3] validation-logloss:0.59263 validation-auc:0.96340 validation-aucpr:0.96846
[4] validation-logloss:0.57147 validation-auc:0.96446 validation-aucpr:0.96972
[5] validation-logloss:0.55163 validation-auc:0.96551 validation-aucpr:0.97048
[6] validation-logloss:0.53320 validation-auc:0.96628 validation-aucpr:0.97106
[7] validation-logloss:0.51596 validation-auc:0.96735 validation-aucpr:0.97224
[8] validation-logloss:0.50137 validation-auc:0.96757 validation-aucpr:0.97228
[9] validation-logloss:0.48649 validation-auc:0.96820 validation-aucpr:0.97197
[10] validation-logloss:0.47192 validation-auc:0.96832 validation-aucpr:0.97204
[11] validation-logloss:0.45854 validation-auc:0.96853 validation-aucpr:0.97214
[12] validation-logloss:0.44623 validation-auc:0.96822 validation-aucpr:0.97198
[13] validation-logloss:0.43410 validation-auc:0.96828 validation-aucpr:0.97198
[14] validation-logloss:0.42250 validation-auc:0.96852 validation-aucpr:0.97217
[15] validation-logloss:0.41193 validation-auc:0.96863 validation-aucpr:0.97219
[16] validation-logloss:0.40194 validation-auc:0.96865 validation-aucpr:0.97225
[17] validation-logloss:0.39339 validation-auc:0.96874 validation-aucpr:0.97228
[18] validation-logloss:0.38428 validation-auc:0.96885 validation-aucpr:0.97236
[19] validation-logloss:0.37558 validation-auc:0.96886 validation-aucpr:0.97234
[20] validation-logloss:0.36790 validation-auc:0.96908 validation-aucpr:0.97248
[21] validation-logloss:0.36033 validation-auc:0.96892 validation-aucpr:0.97240
[22] validation-logloss:0.35253 validation-auc:0.96924 validation-aucpr:0.97256
[23] validation-logloss:0.34501 validation-auc:0.96957 validation-aucpr:0.97281
[24] validation-logloss:0.33803 validation-auc:0.96964 validation-aucpr:0.97283
[25] validation-logloss:0.33149 validation-auc:0.96974 validation-aucpr:0.97287
[26] validation-logloss:0.32532 validation-auc:0.96975 validation-aucpr:0.97288
[27] validation-logloss:0.32003 validation-auc:0.96979 validation-aucpr:0.97288
[28] validation-logloss:0.31434 validation-auc:0.96989 validation-aucpr:0.97295
[29] validation-logloss:0.30885 validation-auc:0.97003 validation-aucpr:0.97306
[30] validation-logloss:0.30372 validation-auc:0.97005 validation-aucpr:0.97320
[31] validation-logloss:0.29903 validation-auc:0.96991 validation-aucpr:0.97313
[32] validation-logloss:0.29433 validation-auc:0.97001 validation-aucpr:0.97342
[33] validation-logloss:0.29005 validation-auc:0.97001 validation-aucpr:0.97336
[34] validation-logloss:0.28634 validation-auc:0.96995 validation-aucpr:0.97329
[35] validation-logloss:0.28248 validation-auc:0.96988 validation-aucpr:0.97322
[36] validation-logloss:0.27825 validation-auc:0.97022 validation-aucpr:0.97348
[37] validation-logloss:0.27458 validation-auc:0.97027 validation-aucpr:0.97352
[38] validation-logloss:0.27171 validation-auc:0.97026 validation-aucpr:0.97476
[39] validation-logloss:0.26832 validation-auc:0.97017 validation-aucpr:0.97470
[40] validation-logloss:0.26503 validation-auc:0.97017 validation-aucpr:0.97474
[41] validation-logloss:0.26212 validation-auc:0.97023 validation-aucpr:0.97471
[42] validation-logloss:0.25969 validation-auc:0.97020 validation-aucpr:0.97467
[43] validation-logloss:0.25666 validation-auc:0.97036 validation-aucpr:0.97478
[44] validation-logloss:0.25389 validation-auc:0.97034 validation-aucpr:0.97474
[45] validation-logloss:0.25149 validation-auc:0.97041 validation-aucpr:0.97479
[46] validation-logloss:0.24909 validation-auc:0.97049 validation-aucpr:0.97483
[47] validation-logloss:0.24652 validation-auc:0.97061 validation-aucpr:0.97491
[48] validation-logloss:0.24396 validation-auc:0.97074 validation-aucpr:0.97498
[49] validation-logloss:0.24208 validation-auc:0.97060 validation-aucpr:0.97488
[50] validation-logloss:0.24015 validation-auc:0.97063 validation-aucpr:0.97488
[51] validation-logloss:0.23834 validation-auc:0.97059 validation-aucpr:0.97488
[52] validation-logloss:0.23641 validation-auc:0.97065 validation-aucpr:0.97491
[53] validation-logloss:0.23446 validation-auc:0.97073 validation-aucpr:0.97497
[54] validation-logloss:0.23260 validation-auc:0.97075 validation-aucpr:0.97499
[55] validation-logloss:0.23086 validation-auc:0.97088 validation-aucpr:0.97508
[56] validation-logloss:0.22904 validation-auc:0.97092 validation-aucpr:0.97511
[57] validation-logloss:0.22746 validation-auc:0.97104 validation-aucpr:0.97517
[58] validation-logloss:0.22607 validation-auc:0.97098 validation-aucpr:0.97512
[59] validation-logloss:0.22500 validation-auc:0.97094 validation-aucpr:0.97511
[60] validation-logloss:0.22365 validation-auc:0.97096 validation-aucpr:0.97511
[61] validation-logloss:0.22242 validation-auc:0.97096 validation-aucpr:0.97509
[62] validation-logloss:0.22136 validation-auc:0.97096 validation-aucpr:0.97507
[63] validation-logloss:0.22047 validation-auc:0.97099 validation-aucpr:0.97522
[64] validation-logloss:0.21908 validation-auc:0.97115 validation-aucpr:0.97533
[65] validation-logloss:0.21801 validation-auc:0.97118 validation-aucpr:0.97534
[66] validation-logloss:0.21733 validation-auc:0.97112 validation-aucpr:0.97529
[67] validation-logloss:0.21649 validation-auc:0.97106 validation-aucpr:0.97522
[68] validation-logloss:0.21561 validation-auc:0.97107 validation-aucpr:0.97523
[69] validation-logloss:0.21471 validation-auc:0.97105 validation-aucpr:0.97522
[70] validation-logloss:0.21387 validation-auc:0.97107 validation-aucpr:0.97521
[71] validation-logloss:0.21285 validation-auc:0.97112 validation-aucpr:0.97527
[72] validation-logloss:0.21230 validation-auc:0.97103 validation-aucpr:0.97517
[73] validation-logloss:0.21139 validation-auc:0.97113 validation-aucpr:0.97522
[74] validation-logloss:0.21082 validation-auc:0.97108 validation-aucpr:0.97519
[75] validation-logloss:0.21027 validation-auc:0.97112 validation-aucpr:0.97520
[76] validation-logloss:0.20969 validation-auc:0.97118 validation-aucpr:0.97543
[77] validation-logloss:0.20897 validation-auc:0.97120 validation-aucpr:0.97542
[78] validation-logloss:0.20833 validation-auc:0.97119 validation-aucpr:0.97540
[79] validation-logloss:0.20775 validation-auc:0.97120 validation-aucpr:0.97540
[80] validation-logloss:0.20720 validation-auc:0.97124 validation-aucpr:0.97542
[81] validation-logloss:0.20658 validation-auc:0.97128 validation-aucpr:0.97544
[82] validation-logloss:0.20614 validation-auc:0.97127 validation-aucpr:0.97540
[83] validation-logloss:0.20568 validation-auc:0.97129 validation-aucpr:0.97540
{'best_iteration': '81', 'best_score': '0.9754383400541304'}
Trial 85, Fold 1: Log loss = 0.2056844300096283, Average precision = 0.9754078842769631, ROC-AUC = 0.9712908744497497, Elapsed Time = 6.905310800000734 seconds
Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 85, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66535 validation-auc:0.95775 validation-aucpr:0.96024
[1] validation-logloss:0.64158 validation-auc:0.96415 validation-aucpr:0.96716
[2] validation-logloss:0.62009 validation-auc:0.96397 validation-aucpr:0.96656
[3] validation-logloss:0.59731 validation-auc:0.96644 validation-aucpr:0.96914
[4] validation-logloss:0.57560 validation-auc:0.96853 validation-aucpr:0.97184
[5] validation-logloss:0.55564 validation-auc:0.96901 validation-aucpr:0.97219
[6] validation-logloss:0.53747 validation-auc:0.96931 validation-aucpr:0.97248
[7] validation-logloss:0.52029 validation-auc:0.96960 validation-aucpr:0.97292
[8] validation-logloss:0.50561 validation-auc:0.96987 validation-aucpr:0.97309
[9] validation-logloss:0.48998 validation-auc:0.97028 validation-aucpr:0.97339
[10] validation-logloss:0.47713 validation-auc:0.97004 validation-aucpr:0.97282
[11] validation-logloss:0.46337 validation-auc:0.97027 validation-aucpr:0.97328
[12] validation-logloss:0.45010 validation-auc:0.97069 validation-aucpr:0.97362
[13] validation-logloss:0.43780 validation-auc:0.97063 validation-aucpr:0.97382
[14] validation-logloss:0.42574 validation-auc:0.97101 validation-aucpr:0.97413
[15] validation-logloss:0.41449 validation-auc:0.97097 validation-aucpr:0.97411
[16] validation-logloss:0.40397 validation-auc:0.97113 validation-aucpr:0.97407
[17] validation-logloss:0.39391 validation-auc:0.97114 validation-aucpr:0.97419
[18] validation-logloss:0.38453 validation-auc:0.97126 validation-aucpr:0.97418
[19] validation-logloss:0.37542 validation-auc:0.97155 validation-aucpr:0.97435
[20] validation-logloss:0.36663 validation-auc:0.97182 validation-aucpr:0.97458
[21] validation-logloss:0.35879 validation-auc:0.97167 validation-aucpr:0.97446
[22] validation-logloss:0.35154 validation-auc:0.97183 validation-aucpr:0.97484
[23] validation-logloss:0.34468 validation-auc:0.97208 validation-aucpr:0.97500
[24] validation-logloss:0.33746 validation-auc:0.97216 validation-aucpr:0.97510
[25] validation-logloss:0.33049 validation-auc:0.97237 validation-aucpr:0.97523
[26] validation-logloss:0.32400 validation-auc:0.97229 validation-aucpr:0.97518
[27] validation-logloss:0.31795 validation-auc:0.97219 validation-aucpr:0.97511
[28] validation-logloss:0.31207 validation-auc:0.97225 validation-aucpr:0.97519
[29] validation-logloss:0.30725 validation-auc:0.97214 validation-aucpr:0.97506
[30] validation-logloss:0.30238 validation-auc:0.97221 validation-aucpr:0.97509
[31] validation-logloss:0.29768 validation-auc:0.97197 validation-aucpr:0.97489
[32] validation-logloss:0.29338 validation-auc:0.97192 validation-aucpr:0.97483
[33] validation-logloss:0.28842 validation-auc:0.97211 validation-aucpr:0.97497
[34] validation-logloss:0.28462 validation-auc:0.97222 validation-aucpr:0.97500
[35] validation-logloss:0.28087 validation-auc:0.97232 validation-aucpr:0.97508
[36] validation-logloss:0.27727 validation-auc:0.97222 validation-aucpr:0.97503
[37] validation-logloss:0.27310 validation-auc:0.97232 validation-aucpr:0.97511
[38] validation-logloss:0.26937 validation-auc:0.97230 validation-aucpr:0.97509
[39] validation-logloss:0.26574 validation-auc:0.97231 validation-aucpr:0.97510
[40] validation-logloss:0.26232 validation-auc:0.97225 validation-aucpr:0.97505
[41] validation-logloss:0.25924 validation-auc:0.97219 validation-aucpr:0.97500
[42] validation-logloss:0.25654 validation-auc:0.97229 validation-aucpr:0.97506
[43] validation-logloss:0.25356 validation-auc:0.97217 validation-aucpr:0.97495
[44] validation-logloss:0.25084 validation-auc:0.97230 validation-aucpr:0.97505
[45] validation-logloss:0.24788 validation-auc:0.97242 validation-aucpr:0.97516
[46] validation-logloss:0.24522 validation-auc:0.97240 validation-aucpr:0.97515
[47] validation-logloss:0.24277 validation-auc:0.97238 validation-aucpr:0.97511
[48] validation-logloss:0.24030 validation-auc:0.97241 validation-aucpr:0.97515
[49] validation-logloss:0.23828 validation-auc:0.97238 validation-aucpr:0.97510
[50] validation-logloss:0.23624 validation-auc:0.97227 validation-aucpr:0.97501
[51] validation-logloss:0.23450 validation-auc:0.97228 validation-aucpr:0.97541
[52] validation-logloss:0.23285 validation-auc:0.97230 validation-aucpr:0.97541
[53] validation-logloss:0.23072 validation-auc:0.97239 validation-aucpr:0.97548
[54] validation-logloss:0.22861 validation-auc:0.97241 validation-aucpr:0.97551
[55] validation-logloss:0.22704 validation-auc:0.97243 validation-aucpr:0.97555
[56] validation-logloss:0.22563 validation-auc:0.97237 validation-aucpr:0.97554
[57] validation-logloss:0.22427 validation-auc:0.97237 validation-aucpr:0.97554
[58] validation-logloss:0.22239 validation-auc:0.97241 validation-aucpr:0.97556
[59] validation-logloss:0.22064 validation-auc:0.97244 validation-aucpr:0.97559
[60] validation-logloss:0.21918 validation-auc:0.97245 validation-aucpr:0.97559
[61] validation-logloss:0.21791 validation-auc:0.97253 validation-aucpr:0.97564
[62] validation-logloss:0.21635 validation-auc:0.97253 validation-aucpr:0.97564
[63] validation-logloss:0.21503 validation-auc:0.97254 validation-aucpr:0.97567
[64] validation-logloss:0.21369 validation-auc:0.97260 validation-aucpr:0.97565
[65] validation-logloss:0.21264 validation-auc:0.97259 validation-aucpr:0.97564
[66] validation-logloss:0.21134 validation-auc:0.97271 validation-aucpr:0.97573
[67] validation-logloss:0.21016 validation-auc:0.97277 validation-aucpr:0.97576
[68] validation-logloss:0.20918 validation-auc:0.97270 validation-aucpr:0.97573
[69] validation-logloss:0.20826 validation-auc:0.97278 validation-aucpr:0.97577
[70] validation-logloss:0.20738 validation-auc:0.97280 validation-aucpr:0.97577
[71] validation-logloss:0.20604 validation-auc:0.97295 validation-aucpr:0.97588
[72] validation-logloss:0.20491 validation-auc:0.97301 validation-aucpr:0.97595
[73] validation-logloss:0.20423 validation-auc:0.97304 validation-aucpr:0.97597
[74] validation-logloss:0.20355 validation-auc:0.97306 validation-aucpr:0.97594
[75] validation-logloss:0.20263 validation-auc:0.97311 validation-aucpr:0.97596
[76] validation-logloss:0.20193 validation-auc:0.97308 validation-aucpr:0.97593
[77] validation-logloss:0.20097 validation-auc:0.97311 validation-aucpr:0.97586
[78] validation-logloss:0.20015 validation-auc:0.97319 validation-aucpr:0.97591
[79] validation-logloss:0.19919 validation-auc:0.97326 validation-aucpr:0.97592
[80] validation-logloss:0.19848 validation-auc:0.97334 validation-aucpr:0.97601
[81] validation-logloss:0.19798 validation-auc:0.97333 validation-aucpr:0.97611
[82] validation-logloss:0.19712 validation-auc:0.97345 validation-aucpr:0.97621
[83] validation-logloss:0.19638 validation-auc:0.97355 validation-aucpr:0.97628
{'best_iteration': '83', 'best_score': '0.9762840686470307'}
Trial 85, Fold 2: Log loss = 0.19637594776935594, Average precision = 0.9762886215576242, ROC-AUC = 0.9735509679839487, Elapsed Time = 6.715063700001338 seconds
Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 85, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66523 validation-auc:0.95901 validation-aucpr:0.96350
[1] validation-logloss:0.63919 validation-auc:0.96513 validation-aucpr:0.96909
[2] validation-logloss:0.61473 validation-auc:0.96702 validation-aucpr:0.97074
[3] validation-logloss:0.59218 validation-auc:0.96777 validation-aucpr:0.97130
[4] validation-logloss:0.57326 validation-auc:0.96813 validation-aucpr:0.97176
[5] validation-logloss:0.55355 validation-auc:0.96841 validation-aucpr:0.97198
[6] validation-logloss:0.53491 validation-auc:0.96908 validation-aucpr:0.97251
[7] validation-logloss:0.51802 validation-auc:0.96881 validation-aucpr:0.97268
[8] validation-logloss:0.50192 validation-auc:0.96876 validation-aucpr:0.97266
[9] validation-logloss:0.48642 validation-auc:0.96923 validation-aucpr:0.97301
[10] validation-logloss:0.47154 validation-auc:0.96972 validation-aucpr:0.97336
[11] validation-logloss:0.45916 validation-auc:0.96982 validation-aucpr:0.97407
[12] validation-logloss:0.44652 validation-auc:0.96991 validation-aucpr:0.97413
[13] validation-logloss:0.43456 validation-auc:0.96991 validation-aucpr:0.97409
[14] validation-logloss:0.42321 validation-auc:0.97017 validation-aucpr:0.97420
[15] validation-logloss:0.41307 validation-auc:0.97045 validation-aucpr:0.97458
[16] validation-logloss:0.40250 validation-auc:0.97057 validation-aucpr:0.97468
[17] validation-logloss:0.39391 validation-auc:0.97055 validation-aucpr:0.97471
[18] validation-logloss:0.38446 validation-auc:0.97079 validation-aucpr:0.97485
[19] validation-logloss:0.37529 validation-auc:0.97094 validation-aucpr:0.97500
[20] validation-logloss:0.36765 validation-auc:0.97105 validation-aucpr:0.97500
[21] validation-logloss:0.36030 validation-auc:0.97104 validation-aucpr:0.97497
[22] validation-logloss:0.35343 validation-auc:0.97111 validation-aucpr:0.97505
[23] validation-logloss:0.34599 validation-auc:0.97141 validation-aucpr:0.97545
[24] validation-logloss:0.33916 validation-auc:0.97129 validation-aucpr:0.97535
[25] validation-logloss:0.33269 validation-auc:0.97122 validation-aucpr:0.97530
[26] validation-logloss:0.32702 validation-auc:0.97099 validation-aucpr:0.97515
[27] validation-logloss:0.32076 validation-auc:0.97097 validation-aucpr:0.97513
[28] validation-logloss:0.31486 validation-auc:0.97112 validation-aucpr:0.97524
[29] validation-logloss:0.31010 validation-auc:0.97090 validation-aucpr:0.97512
[30] validation-logloss:0.30468 validation-auc:0.97095 validation-aucpr:0.97515
[31] validation-logloss:0.30025 validation-auc:0.97093 validation-aucpr:0.97511
[32] validation-logloss:0.29546 validation-auc:0.97074 validation-aucpr:0.97497
[33] validation-logloss:0.29164 validation-auc:0.97059 validation-aucpr:0.97486
[34] validation-logloss:0.28796 validation-auc:0.97042 validation-aucpr:0.97473
[35] validation-logloss:0.28364 validation-auc:0.97039 validation-aucpr:0.97470
[36] validation-logloss:0.27940 validation-auc:0.97054 validation-aucpr:0.97485
[37] validation-logloss:0.27543 validation-auc:0.97069 validation-aucpr:0.97495
[38] validation-logloss:0.27155 validation-auc:0.97064 validation-aucpr:0.97493
[39] validation-logloss:0.26881 validation-auc:0.97046 validation-aucpr:0.97478
[40] validation-logloss:0.26511 validation-auc:0.97061 validation-aucpr:0.97493
[41] validation-logloss:0.26161 validation-auc:0.97074 validation-aucpr:0.97502
[42] validation-logloss:0.25864 validation-auc:0.97074 validation-aucpr:0.97501
[43] validation-logloss:0.25553 validation-auc:0.97089 validation-aucpr:0.97510
[44] validation-logloss:0.25262 validation-auc:0.97098 validation-aucpr:0.97518
[45] validation-logloss:0.24986 validation-auc:0.97099 validation-aucpr:0.97517
[46] validation-logloss:0.24707 validation-auc:0.97111 validation-aucpr:0.97529
[47] validation-logloss:0.24456 validation-auc:0.97128 validation-aucpr:0.97539
[48] validation-logloss:0.24205 validation-auc:0.97138 validation-aucpr:0.97546
[49] validation-logloss:0.24024 validation-auc:0.97140 validation-aucpr:0.97541
[50] validation-logloss:0.23820 validation-auc:0.97129 validation-aucpr:0.97532
[51] validation-logloss:0.23576 validation-auc:0.97143 validation-aucpr:0.97544
[52] validation-logloss:0.23370 validation-auc:0.97142 validation-aucpr:0.97544
[53] validation-logloss:0.23225 validation-auc:0.97131 validation-aucpr:0.97533
[54] validation-logloss:0.23084 validation-auc:0.97124 validation-aucpr:0.97525
[55] validation-logloss:0.22915 validation-auc:0.97112 validation-aucpr:0.97507
[56] validation-logloss:0.22740 validation-auc:0.97120 validation-aucpr:0.97510
[57] validation-logloss:0.22569 validation-auc:0.97132 validation-aucpr:0.97518
[58] validation-logloss:0.22392 validation-auc:0.97141 validation-aucpr:0.97538
[59] validation-logloss:0.22256 validation-auc:0.97134 validation-aucpr:0.97533
[60] validation-logloss:0.22110 validation-auc:0.97138 validation-aucpr:0.97534
[61] validation-logloss:0.22003 validation-auc:0.97140 validation-aucpr:0.97537
[62] validation-logloss:0.21862 validation-auc:0.97146 validation-aucpr:0.97540
[63] validation-logloss:0.21755 validation-auc:0.97143 validation-aucpr:0.97538
[64] validation-logloss:0.21629 validation-auc:0.97149 validation-aucpr:0.97544
[65] validation-logloss:0.21495 validation-auc:0.97154 validation-aucpr:0.97550
[66] validation-logloss:0.21404 validation-auc:0.97152 validation-aucpr:0.97547
[67] validation-logloss:0.21306 validation-auc:0.97158 validation-aucpr:0.97552
[68] validation-logloss:0.21214 validation-auc:0.97158 validation-aucpr:0.97551
[69] validation-logloss:0.21110 validation-auc:0.97168 validation-aucpr:0.97558
[70] validation-logloss:0.21021 validation-auc:0.97167 validation-aucpr:0.97556
[71] validation-logloss:0.20915 validation-auc:0.97173 validation-aucpr:0.97558
[72] validation-logloss:0.20830 validation-auc:0.97176 validation-aucpr:0.97560
[73] validation-logloss:0.20741 validation-auc:0.97179 validation-aucpr:0.97561
[74] validation-logloss:0.20669 validation-auc:0.97180 validation-aucpr:0.97564
[75] validation-logloss:0.20578 validation-auc:0.97185 validation-aucpr:0.97568
[76] validation-logloss:0.20476 validation-auc:0.97194 validation-aucpr:0.97573
[77] validation-logloss:0.20396 validation-auc:0.97212 validation-aucpr:0.97599
[78] validation-logloss:0.20312 validation-auc:0.97216 validation-aucpr:0.97605
[79] validation-logloss:0.20250 validation-auc:0.97221 validation-aucpr:0.97608
[80] validation-logloss:0.20198 validation-auc:0.97216 validation-aucpr:0.97601
[81] validation-logloss:0.20152 validation-auc:0.97211 validation-aucpr:0.97595
[82] validation-logloss:0.20090 validation-auc:0.97213 validation-aucpr:0.97594
[83] validation-logloss:0.20036 validation-auc:0.97210 validation-aucpr:0.97588
{'best_iteration': '79', 'best_score': '0.976080442116701'}
Trial 85, Fold 3: Log loss = 0.20036022393956576, Average precision = 0.9758860880754837, ROC-AUC = 0.9720970104063333, Elapsed Time = 6.468014099998982 seconds
Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 85, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66541 validation-auc:0.95157 validation-aucpr:0.95421
[1] validation-logloss:0.63949 validation-auc:0.95963 validation-aucpr:0.96707
[2] validation-logloss:0.61516 validation-auc:0.96363 validation-aucpr:0.96952
[3] validation-logloss:0.59546 validation-auc:0.96362 validation-aucpr:0.96963
[4] validation-logloss:0.57432 validation-auc:0.96463 validation-aucpr:0.97042
[5] validation-logloss:0.55469 validation-auc:0.96526 validation-aucpr:0.97097
[6] validation-logloss:0.53592 validation-auc:0.96579 validation-aucpr:0.97145
[7] validation-logloss:0.51877 validation-auc:0.96653 validation-aucpr:0.97192
[8] validation-logloss:0.50209 validation-auc:0.96768 validation-aucpr:0.97263
[9] validation-logloss:0.48678 validation-auc:0.96755 validation-aucpr:0.97256
[10] validation-logloss:0.47250 validation-auc:0.96763 validation-aucpr:0.97276
[11] validation-logloss:0.46030 validation-auc:0.96731 validation-aucpr:0.97255
[12] validation-logloss:0.44766 validation-auc:0.96730 validation-aucpr:0.97251
[13] validation-logloss:0.43514 validation-auc:0.96773 validation-aucpr:0.97284
[14] validation-logloss:0.42346 validation-auc:0.96800 validation-aucpr:0.97303
[15] validation-logloss:0.41215 validation-auc:0.96844 validation-aucpr:0.97340
[16] validation-logloss:0.40150 validation-auc:0.96880 validation-aucpr:0.97367
[17] validation-logloss:0.39161 validation-auc:0.96903 validation-aucpr:0.97386
[18] validation-logloss:0.38340 validation-auc:0.96897 validation-aucpr:0.97382
[19] validation-logloss:0.37540 validation-auc:0.96910 validation-aucpr:0.97399
[20] validation-logloss:0.36845 validation-auc:0.96888 validation-aucpr:0.97382
[21] validation-logloss:0.36019 validation-auc:0.96925 validation-aucpr:0.97409
[22] validation-logloss:0.35359 validation-auc:0.96910 validation-aucpr:0.97396
[23] validation-logloss:0.34598 validation-auc:0.96935 validation-aucpr:0.97417
[24] validation-logloss:0.33907 validation-auc:0.96952 validation-aucpr:0.97438
[25] validation-logloss:0.33250 validation-auc:0.96963 validation-aucpr:0.97445
[26] validation-logloss:0.32712 validation-auc:0.96967 validation-aucpr:0.97447
[27] validation-logloss:0.32108 validation-auc:0.96973 validation-aucpr:0.97452
[28] validation-logloss:0.31627 validation-auc:0.96951 validation-aucpr:0.97436
[29] validation-logloss:0.31081 validation-auc:0.96986 validation-aucpr:0.97459
[30] validation-logloss:0.30565 validation-auc:0.96996 validation-aucpr:0.97467
[31] validation-logloss:0.30067 validation-auc:0.97002 validation-aucpr:0.97474
[32] validation-logloss:0.29584 validation-auc:0.97007 validation-aucpr:0.97477
[33] validation-logloss:0.29106 validation-auc:0.97024 validation-aucpr:0.97487
[34] validation-logloss:0.28775 validation-auc:0.96989 validation-aucpr:0.97461
[35] validation-logloss:0.28349 validation-auc:0.97003 validation-aucpr:0.97470
[36] validation-logloss:0.27938 validation-auc:0.97011 validation-aucpr:0.97477
[37] validation-logloss:0.27568 validation-auc:0.97010 validation-aucpr:0.97476
[38] validation-logloss:0.27219 validation-auc:0.97015 validation-aucpr:0.97480
[39] validation-logloss:0.26937 validation-auc:0.97009 validation-aucpr:0.97477
[40] validation-logloss:0.26583 validation-auc:0.97023 validation-aucpr:0.97487
[41] validation-logloss:0.26266 validation-auc:0.97018 validation-aucpr:0.97487
[42] validation-logloss:0.25958 validation-auc:0.97014 validation-aucpr:0.97486
[43] validation-logloss:0.25642 validation-auc:0.97016 validation-aucpr:0.97489
[44] validation-logloss:0.25359 validation-auc:0.97024 validation-aucpr:0.97498
[45] validation-logloss:0.25084 validation-auc:0.97029 validation-aucpr:0.97499
[46] validation-logloss:0.24858 validation-auc:0.97019 validation-aucpr:0.97492
[47] validation-logloss:0.24659 validation-auc:0.97017 validation-aucpr:0.97491
[48] validation-logloss:0.24419 validation-auc:0.97017 validation-aucpr:0.97492
[49] validation-logloss:0.24212 validation-auc:0.97021 validation-aucpr:0.97496
[50] validation-logloss:0.23987 validation-auc:0.97028 validation-aucpr:0.97501
[51] validation-logloss:0.23833 validation-auc:0.97017 validation-aucpr:0.97493
[52] validation-logloss:0.23640 validation-auc:0.97019 validation-aucpr:0.97494
[53] validation-logloss:0.23447 validation-auc:0.97020 validation-aucpr:0.97495
[54] validation-logloss:0.23278 validation-auc:0.97015 validation-aucpr:0.97492
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[59] validation-logloss:0.22503 validation-auc:0.97001 validation-aucpr:0.97483
[60] validation-logloss:0.22346 validation-auc:0.97004 validation-aucpr:0.97485
[61] validation-logloss:0.22208 validation-auc:0.97001 validation-aucpr:0.97486
[62] validation-logloss:0.22068 validation-auc:0.97004 validation-aucpr:0.97488
[63] validation-logloss:0.21967 validation-auc:0.96992 validation-aucpr:0.97480
[64] validation-logloss:0.21854 validation-auc:0.96987 validation-aucpr:0.97476
[65] validation-logloss:0.21752 validation-auc:0.96985 validation-aucpr:0.97473
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[71] validation-logloss:0.21224 validation-auc:0.96992 validation-aucpr:0.97477
[72] validation-logloss:0.21161 validation-auc:0.96980 validation-aucpr:0.97468
[73] validation-logloss:0.21064 validation-auc:0.96989 validation-aucpr:0.97475
[74] validation-logloss:0.20979 validation-auc:0.96997 validation-aucpr:0.97479
[75] validation-logloss:0.20932 validation-auc:0.96992 validation-aucpr:0.97476
[76] validation-logloss:0.20891 validation-auc:0.96984 validation-aucpr:0.97470
[77] validation-logloss:0.20819 validation-auc:0.96990 validation-aucpr:0.97474
[78] validation-logloss:0.20738 validation-auc:0.96997 validation-aucpr:0.97480
[79] validation-logloss:0.20662 validation-auc:0.97002 validation-aucpr:0.97485
[80] validation-logloss:0.20605 validation-auc:0.97004 validation-aucpr:0.97484
[81] validation-logloss:0.20540 validation-auc:0.97007 validation-aucpr:0.97487
[82] validation-logloss:0.20480 validation-auc:0.97011 validation-aucpr:0.97489
[83] validation-logloss:0.20433 validation-auc:0.97010 validation-aucpr:0.97488
{'best_iteration': '50', 'best_score': '0.9750077463971537'}
Trial 85, Fold 4: Log loss = 0.20432554100239475, Average precision = 0.9748823764951907, ROC-AUC = 0.9700994269071104, Elapsed Time = 6.552320899998449 seconds
Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 85, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66500 validation-auc:0.95722 validation-aucpr:0.95513
[1] validation-logloss:0.63950 validation-auc:0.95967 validation-aucpr:0.96068
[2] validation-logloss:0.61596 validation-auc:0.96211 validation-aucpr:0.96738
[3] validation-logloss:0.59631 validation-auc:0.96338 validation-aucpr:0.96852
[4] validation-logloss:0.57777 validation-auc:0.96342 validation-aucpr:0.96844
[5] validation-logloss:0.55803 validation-auc:0.96414 validation-aucpr:0.96916
[6] validation-logloss:0.54156 validation-auc:0.96437 validation-aucpr:0.96915
[7] validation-logloss:0.52352 validation-auc:0.96608 validation-aucpr:0.97071
[8] validation-logloss:0.50701 validation-auc:0.96609 validation-aucpr:0.97082
[9] validation-logloss:0.49140 validation-auc:0.96686 validation-aucpr:0.97133
[10] validation-logloss:0.47655 validation-auc:0.96729 validation-aucpr:0.97172
[11] validation-logloss:0.46279 validation-auc:0.96773 validation-aucpr:0.97207
[12] validation-logloss:0.44967 validation-auc:0.96819 validation-aucpr:0.97227
[13] validation-logloss:0.43699 validation-auc:0.96855 validation-aucpr:0.97264
[14] validation-logloss:0.42547 validation-auc:0.96880 validation-aucpr:0.97293
[15] validation-logloss:0.41474 validation-auc:0.96909 validation-aucpr:0.97325
[16] validation-logloss:0.40442 validation-auc:0.96941 validation-aucpr:0.97345
[17] validation-logloss:0.39472 validation-auc:0.96950 validation-aucpr:0.97357
[18] validation-logloss:0.38528 validation-auc:0.96980 validation-aucpr:0.97387
[19] validation-logloss:0.37643 validation-auc:0.96977 validation-aucpr:0.97384
[20] validation-logloss:0.36924 validation-auc:0.96958 validation-aucpr:0.97346
[21] validation-logloss:0.36220 validation-auc:0.96948 validation-aucpr:0.97336
[22] validation-logloss:0.35551 validation-auc:0.96956 validation-aucpr:0.97347
[23] validation-logloss:0.34865 validation-auc:0.96950 validation-aucpr:0.97345
[24] validation-logloss:0.34173 validation-auc:0.96962 validation-aucpr:0.97354
[25] validation-logloss:0.33500 validation-auc:0.96971 validation-aucpr:0.97362
[26] validation-logloss:0.32866 validation-auc:0.96982 validation-aucpr:0.97370
[27] validation-logloss:0.32326 validation-auc:0.96987 validation-aucpr:0.97371
[28] validation-logloss:0.31759 validation-auc:0.96991 validation-aucpr:0.97378
[29] validation-logloss:0.31331 validation-auc:0.96965 validation-aucpr:0.97383
[30] validation-logloss:0.30814 validation-auc:0.96975 validation-aucpr:0.97390
[31] validation-logloss:0.30307 validation-auc:0.96980 validation-aucpr:0.97394
[32] validation-logloss:0.29867 validation-auc:0.96975 validation-aucpr:0.97391
[33] validation-logloss:0.29406 validation-auc:0.96978 validation-aucpr:0.97393
[34] validation-logloss:0.28974 validation-auc:0.96969 validation-aucpr:0.97391
[35] validation-logloss:0.28623 validation-auc:0.96958 validation-aucpr:0.97383
[36] validation-logloss:0.28243 validation-auc:0.96954 validation-aucpr:0.97382
[37] validation-logloss:0.27896 validation-auc:0.96963 validation-aucpr:0.97387
[38] validation-logloss:0.27601 validation-auc:0.96959 validation-aucpr:0.97386
[39] validation-logloss:0.27224 validation-auc:0.96974 validation-aucpr:0.97398
[40] validation-logloss:0.26951 validation-auc:0.96946 validation-aucpr:0.97378
[41] validation-logloss:0.26624 validation-auc:0.96957 validation-aucpr:0.97388
[42] validation-logloss:0.26330 validation-auc:0.96954 validation-aucpr:0.97383
[43] validation-logloss:0.26032 validation-auc:0.96954 validation-aucpr:0.97383
[44] validation-logloss:0.25768 validation-auc:0.96952 validation-aucpr:0.97380
[45] validation-logloss:0.25477 validation-auc:0.96986 validation-aucpr:0.97397
[46] validation-logloss:0.25230 validation-auc:0.96988 validation-aucpr:0.97399
[47] validation-logloss:0.24977 validation-auc:0.96996 validation-aucpr:0.97404
[48] validation-logloss:0.24706 validation-auc:0.97020 validation-aucpr:0.97424
[49] validation-logloss:0.24498 validation-auc:0.97021 validation-aucpr:0.97423
[50] validation-logloss:0.24267 validation-auc:0.97024 validation-aucpr:0.97427
[51] validation-logloss:0.24086 validation-auc:0.97017 validation-aucpr:0.97419
[52] validation-logloss:0.23876 validation-auc:0.97035 validation-aucpr:0.97434
[53] validation-logloss:0.23742 validation-auc:0.97024 validation-aucpr:0.97426
[54] validation-logloss:0.23543 validation-auc:0.97032 validation-aucpr:0.97431
[55] validation-logloss:0.23389 validation-auc:0.97039 validation-aucpr:0.97437
[56] validation-logloss:0.23212 validation-auc:0.97045 validation-aucpr:0.97445
[57] validation-logloss:0.23032 validation-auc:0.97051 validation-aucpr:0.97439
[58] validation-logloss:0.22839 validation-auc:0.97086 validation-aucpr:0.97465
[59] validation-logloss:0.22725 validation-auc:0.97071 validation-aucpr:0.97454
[60] validation-logloss:0.22578 validation-auc:0.97072 validation-aucpr:0.97453
[61] validation-logloss:0.22435 validation-auc:0.97079 validation-aucpr:0.97460
[62] validation-logloss:0.22316 validation-auc:0.97083 validation-aucpr:0.97454
[63] validation-logloss:0.22186 validation-auc:0.97087 validation-aucpr:0.97454
[64] validation-logloss:0.22047 validation-auc:0.97094 validation-aucpr:0.97460
[65] validation-logloss:0.21933 validation-auc:0.97091 validation-aucpr:0.97455
[66] validation-logloss:0.21814 validation-auc:0.97107 validation-aucpr:0.97467
[67] validation-logloss:0.21723 validation-auc:0.97104 validation-aucpr:0.97465
[68] validation-logloss:0.21630 validation-auc:0.97106 validation-aucpr:0.97463
[69] validation-logloss:0.21524 validation-auc:0.97107 validation-aucpr:0.97464
[70] validation-logloss:0.21436 validation-auc:0.97103 validation-aucpr:0.97454
[71] validation-logloss:0.21352 validation-auc:0.97095 validation-aucpr:0.97447
[72] validation-logloss:0.21269 validation-auc:0.97091 validation-aucpr:0.97445
[73] validation-logloss:0.21162 validation-auc:0.97101 validation-aucpr:0.97454
[74] validation-logloss:0.21098 validation-auc:0.97099 validation-aucpr:0.97451
[75] validation-logloss:0.21021 validation-auc:0.97099 validation-aucpr:0.97441
[76] validation-logloss:0.20960 validation-auc:0.97096 validation-aucpr:0.97448
[77] validation-logloss:0.20921 validation-auc:0.97090 validation-aucpr:0.97435
[78] validation-logloss:0.20891 validation-auc:0.97073 validation-aucpr:0.97421
[79] validation-logloss:0.20821 validation-auc:0.97078 validation-aucpr:0.97422
[80] validation-logloss:0.20735 validation-auc:0.97097 validation-aucpr:0.97440
[81] validation-logloss:0.20709 validation-auc:0.97078 validation-aucpr:0.97424
[82] validation-logloss:0.20644 validation-auc:0.97083 validation-aucpr:0.97434
[83] validation-logloss:0.20587 validation-auc:0.97086 validation-aucpr:0.97437
{'best_iteration': '66', 'best_score': '0.9746692647212228'}
Trial 85, Fold 5: Log loss = 0.20586828511529295, Average precision = 0.9743731212573705, ROC-AUC = 0.9708630882708136, Elapsed Time = 6.65404910000143 seconds
Optimization Progress: 86%|########6 | 86/100 [3:45:22<14:14, 61.02s/it]
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 86, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68238 validation-auc:0.95468 validation-aucpr:0.94200
[1] validation-logloss:0.67214 validation-auc:0.96377 validation-aucpr:0.96689
[2] validation-logloss:0.66215 validation-auc:0.96635 validation-aucpr:0.97169
[3] validation-logloss:0.65219 validation-auc:0.96699 validation-aucpr:0.97231
[4] validation-logloss:0.64346 validation-auc:0.96786 validation-aucpr:0.97295
[5] validation-logloss:0.63442 validation-auc:0.96695 validation-aucpr:0.97228
[6] validation-logloss:0.62549 validation-auc:0.96680 validation-aucpr:0.97217
[7] validation-logloss:0.61751 validation-auc:0.96697 validation-aucpr:0.97218
[8] validation-logloss:0.60864 validation-auc:0.96780 validation-aucpr:0.97290
[9] validation-logloss:0.60004 validation-auc:0.96856 validation-aucpr:0.97349
[10] validation-logloss:0.59168 validation-auc:0.96890 validation-aucpr:0.97374
[11] validation-logloss:0.58369 validation-auc:0.96919 validation-aucpr:0.97400
[12] validation-logloss:0.57608 validation-auc:0.96917 validation-aucpr:0.97423
[13] validation-logloss:0.56913 validation-auc:0.96895 validation-aucpr:0.97402
[14] validation-logloss:0.56172 validation-auc:0.96891 validation-aucpr:0.97399
[15] validation-logloss:0.55456 validation-auc:0.96899 validation-aucpr:0.97398
[16] validation-logloss:0.54756 validation-auc:0.96892 validation-aucpr:0.97398
[17] validation-logloss:0.54063 validation-auc:0.96912 validation-aucpr:0.97407
[18] validation-logloss:0.53414 validation-auc:0.96895 validation-aucpr:0.97176
[19] validation-logloss:0.52825 validation-auc:0.96885 validation-aucpr:0.97163
[20] validation-logloss:0.52179 validation-auc:0.96891 validation-aucpr:0.97168
[21] validation-logloss:0.51549 validation-auc:0.96915 validation-aucpr:0.97184
[22] validation-logloss:0.50928 validation-auc:0.96928 validation-aucpr:0.97193
[23] validation-logloss:0.50358 validation-auc:0.96926 validation-aucpr:0.97231
[24] validation-logloss:0.49826 validation-auc:0.96936 validation-aucpr:0.97236
[25] validation-logloss:0.49247 validation-auc:0.96961 validation-aucpr:0.97448
[26] validation-logloss:0.48699 validation-auc:0.96962 validation-aucpr:0.97449
[27] validation-logloss:0.48139 validation-auc:0.96967 validation-aucpr:0.97456
[28] validation-logloss:0.47650 validation-auc:0.96960 validation-aucpr:0.97447
[29] validation-logloss:0.47115 validation-auc:0.96964 validation-aucpr:0.97449
[30] validation-logloss:0.46648 validation-auc:0.96957 validation-aucpr:0.97443
[31] validation-logloss:0.46157 validation-auc:0.96956 validation-aucpr:0.97443
[32] validation-logloss:0.45649 validation-auc:0.96960 validation-aucpr:0.97448
[33] validation-logloss:0.45168 validation-auc:0.96965 validation-aucpr:0.97454
[34] validation-logloss:0.44754 validation-auc:0.96966 validation-aucpr:0.97450
[35] validation-logloss:0.44278 validation-auc:0.96976 validation-aucpr:0.97456
[36] validation-logloss:0.43811 validation-auc:0.96976 validation-aucpr:0.97458
[37] validation-logloss:0.43363 validation-auc:0.96983 validation-aucpr:0.97462
[38] validation-logloss:0.42915 validation-auc:0.96996 validation-aucpr:0.97471
[39] validation-logloss:0.42494 validation-auc:0.96998 validation-aucpr:0.97472
[40] validation-logloss:0.42107 validation-auc:0.97002 validation-aucpr:0.97475
[41] validation-logloss:0.41738 validation-auc:0.96998 validation-aucpr:0.97470
[42] validation-logloss:0.41391 validation-auc:0.96993 validation-aucpr:0.97463
[43] validation-logloss:0.40992 validation-auc:0.97006 validation-aucpr:0.97473
[44] validation-logloss:0.40655 validation-auc:0.96998 validation-aucpr:0.97467
[45] validation-logloss:0.40311 validation-auc:0.96998 validation-aucpr:0.97464
[46] validation-logloss:0.39980 validation-auc:0.97003 validation-aucpr:0.97464
[47] validation-logloss:0.39655 validation-auc:0.96998 validation-aucpr:0.97460
[48] validation-logloss:0.39290 validation-auc:0.96992 validation-aucpr:0.97456
{'best_iteration': '40', 'best_score': '0.9747540236047042'}
Trial 86, Fold 1: Log loss = 0.39290141609750506, Average precision = 0.9745635372166829, ROC-AUC = 0.9699236878832633, Elapsed Time = 56.130409400000644 seconds
Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 86, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68258 validation-auc:0.95691 validation-aucpr:0.95557
[1] validation-logloss:0.67317 validation-auc:0.96309 validation-aucpr:0.96389
[2] validation-logloss:0.66402 validation-auc:0.96405 validation-aucpr:0.96728
[3] validation-logloss:0.65427 validation-auc:0.96643 validation-aucpr:0.96952
[4] validation-logloss:0.64592 validation-auc:0.96592 validation-aucpr:0.96893
[5] validation-logloss:0.63759 validation-auc:0.96605 validation-aucpr:0.96898
[6] validation-logloss:0.62865 validation-auc:0.96662 validation-aucpr:0.96968
[7] validation-logloss:0.61963 validation-auc:0.96732 validation-aucpr:0.97036
[8] validation-logloss:0.61080 validation-auc:0.96795 validation-aucpr:0.97096
[9] validation-logloss:0.60216 validation-auc:0.96867 validation-aucpr:0.97164
[10] validation-logloss:0.59405 validation-auc:0.96902 validation-aucpr:0.97189
[11] validation-logloss:0.58687 validation-auc:0.96899 validation-aucpr:0.97202
[12] validation-logloss:0.57899 validation-auc:0.96915 validation-aucpr:0.97222
[13] validation-logloss:0.57124 validation-auc:0.96935 validation-aucpr:0.97242
[14] validation-logloss:0.56392 validation-auc:0.96961 validation-aucpr:0.97262
[15] validation-logloss:0.55725 validation-auc:0.96971 validation-aucpr:0.97268
[16] validation-logloss:0.54994 validation-auc:0.97002 validation-aucpr:0.97298
[17] validation-logloss:0.54368 validation-auc:0.96982 validation-aucpr:0.97271
[18] validation-logloss:0.53699 validation-auc:0.96978 validation-aucpr:0.97272
[19] validation-logloss:0.53080 validation-auc:0.96971 validation-aucpr:0.97264
[20] validation-logloss:0.52509 validation-auc:0.96961 validation-aucpr:0.97240
[21] validation-logloss:0.51955 validation-auc:0.96922 validation-aucpr:0.97204
[22] validation-logloss:0.51404 validation-auc:0.96914 validation-aucpr:0.97196
[23] validation-logloss:0.50846 validation-auc:0.96918 validation-aucpr:0.97199
[24] validation-logloss:0.50314 validation-auc:0.96914 validation-aucpr:0.97199
[25] validation-logloss:0.49712 validation-auc:0.96922 validation-aucpr:0.97208
[26] validation-logloss:0.49138 validation-auc:0.96926 validation-aucpr:0.97209
[27] validation-logloss:0.48618 validation-auc:0.96932 validation-aucpr:0.97212
[28] validation-logloss:0.48078 validation-auc:0.96951 validation-aucpr:0.97226
[29] validation-logloss:0.47600 validation-auc:0.96951 validation-aucpr:0.97225
[30] validation-logloss:0.47142 validation-auc:0.96946 validation-aucpr:0.97222
[31] validation-logloss:0.46679 validation-auc:0.96945 validation-aucpr:0.97217
[32] validation-logloss:0.46153 validation-auc:0.96961 validation-aucpr:0.97233
[33] validation-logloss:0.45652 validation-auc:0.96964 validation-aucpr:0.97235
[34] validation-logloss:0.45157 validation-auc:0.96976 validation-aucpr:0.97247
[35] validation-logloss:0.44679 validation-auc:0.96985 validation-aucpr:0.97254
[36] validation-logloss:0.44212 validation-auc:0.97000 validation-aucpr:0.97263
[37] validation-logloss:0.43752 validation-auc:0.97014 validation-aucpr:0.97275
[38] validation-logloss:0.43302 validation-auc:0.97031 validation-aucpr:0.97286
[39] validation-logloss:0.42914 validation-auc:0.97028 validation-aucpr:0.97286
[40] validation-logloss:0.42530 validation-auc:0.97023 validation-aucpr:0.97282
[41] validation-logloss:0.42159 validation-auc:0.97020 validation-aucpr:0.97280
[42] validation-logloss:0.41725 validation-auc:0.97033 validation-aucpr:0.97287
[43] validation-logloss:0.41332 validation-auc:0.97033 validation-aucpr:0.97296
[44] validation-logloss:0.40983 validation-auc:0.97028 validation-aucpr:0.97292
[45] validation-logloss:0.40579 validation-auc:0.97053 validation-aucpr:0.97311
[46] validation-logloss:0.40189 validation-auc:0.97056 validation-aucpr:0.97315
[47] validation-logloss:0.39825 validation-auc:0.97062 validation-aucpr:0.97320
[48] validation-logloss:0.39500 validation-auc:0.97064 validation-aucpr:0.97320
{'best_iteration': '48', 'best_score': '0.9731980560776368'}
Trial 86, Fold 2: Log loss = 0.39500035361280744, Average precision = 0.9732003530395197, ROC-AUC = 0.9706408248281229, Elapsed Time = 56.37032370000088 seconds
Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 86, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68239 validation-auc:0.94580 validation-aucpr:0.92497
[1] validation-logloss:0.67194 validation-auc:0.96398 validation-aucpr:0.95526
[2] validation-logloss:0.66194 validation-auc:0.96750 validation-aucpr:0.96994
[3] validation-logloss:0.65197 validation-auc:0.96821 validation-aucpr:0.97268
[4] validation-logloss:0.64325 validation-auc:0.96979 validation-aucpr:0.97371
[5] validation-logloss:0.63491 validation-auc:0.96951 validation-aucpr:0.97344
[6] validation-logloss:0.62639 validation-auc:0.96986 validation-aucpr:0.97396
[7] validation-logloss:0.61829 validation-auc:0.96972 validation-aucpr:0.97367
[8] validation-logloss:0.60951 validation-auc:0.96980 validation-aucpr:0.97394
[9] validation-logloss:0.60181 validation-auc:0.96954 validation-aucpr:0.97371
[10] validation-logloss:0.59435 validation-auc:0.96917 validation-aucpr:0.97337
[11] validation-logloss:0.58726 validation-auc:0.96914 validation-aucpr:0.97327
[12] validation-logloss:0.57920 validation-auc:0.96940 validation-aucpr:0.97358
[13] validation-logloss:0.57214 validation-auc:0.96961 validation-aucpr:0.97369
[14] validation-logloss:0.56552 validation-auc:0.96965 validation-aucpr:0.97384
[15] validation-logloss:0.55889 validation-auc:0.96961 validation-aucpr:0.97383
[16] validation-logloss:0.55168 validation-auc:0.96976 validation-aucpr:0.97400
[17] validation-logloss:0.54465 validation-auc:0.96999 validation-aucpr:0.97431
[18] validation-logloss:0.53780 validation-auc:0.97008 validation-aucpr:0.97439
[19] validation-logloss:0.53178 validation-auc:0.96996 validation-aucpr:0.97429
[20] validation-logloss:0.52609 validation-auc:0.96980 validation-aucpr:0.97413
[21] validation-logloss:0.51956 validation-auc:0.96996 validation-aucpr:0.97429
[22] validation-logloss:0.51331 validation-auc:0.96994 validation-aucpr:0.97428
[23] validation-logloss:0.50723 validation-auc:0.97010 validation-aucpr:0.97440
[24] validation-logloss:0.50165 validation-auc:0.97034 validation-aucpr:0.97457
[25] validation-logloss:0.49566 validation-auc:0.97049 validation-aucpr:0.97470
[26] validation-logloss:0.48998 validation-auc:0.97065 validation-aucpr:0.97483
[27] validation-logloss:0.48432 validation-auc:0.97070 validation-aucpr:0.97487
[28] validation-logloss:0.47936 validation-auc:0.97068 validation-aucpr:0.97482
[29] validation-logloss:0.47379 validation-auc:0.97094 validation-aucpr:0.97504
[30] validation-logloss:0.46916 validation-auc:0.97097 validation-aucpr:0.97502
[31] validation-logloss:0.46389 validation-auc:0.97105 validation-aucpr:0.97512
[32] validation-logloss:0.45932 validation-auc:0.97111 validation-aucpr:0.97513
[33] validation-logloss:0.45428 validation-auc:0.97124 validation-aucpr:0.97525
[34] validation-logloss:0.45006 validation-auc:0.97123 validation-aucpr:0.97524
[35] validation-logloss:0.44600 validation-auc:0.97121 validation-aucpr:0.97523
[36] validation-logloss:0.44156 validation-auc:0.97124 validation-aucpr:0.97524
[37] validation-logloss:0.43693 validation-auc:0.97129 validation-aucpr:0.97532
[38] validation-logloss:0.43235 validation-auc:0.97132 validation-aucpr:0.97536
[39] validation-logloss:0.42781 validation-auc:0.97139 validation-aucpr:0.97544
[40] validation-logloss:0.42341 validation-auc:0.97145 validation-aucpr:0.97548
[41] validation-logloss:0.41932 validation-auc:0.97140 validation-aucpr:0.97547
[42] validation-logloss:0.41559 validation-auc:0.97140 validation-aucpr:0.97546
[43] validation-logloss:0.41204 validation-auc:0.97130 validation-aucpr:0.97536
[44] validation-logloss:0.40842 validation-auc:0.97130 validation-aucpr:0.97538
[45] validation-logloss:0.40508 validation-auc:0.97120 validation-aucpr:0.97528
[46] validation-logloss:0.40181 validation-auc:0.97112 validation-aucpr:0.97520
[47] validation-logloss:0.39836 validation-auc:0.97122 validation-aucpr:0.97527
[48] validation-logloss:0.39475 validation-auc:0.97128 validation-aucpr:0.97533
{'best_iteration': '40', 'best_score': '0.9754833863268657'}
Trial 86, Fold 3: Log loss = 0.3947504659735634, Average precision = 0.9753386299852591, ROC-AUC = 0.971284316581753, Elapsed Time = 52.0949412000009 seconds
Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 86, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68240 validation-auc:0.95348 validation-aucpr:0.94323
[1] validation-logloss:0.67235 validation-auc:0.96156 validation-aucpr:0.96249
[2] validation-logloss:0.66228 validation-auc:0.96536 validation-aucpr:0.97044
[3] validation-logloss:0.65339 validation-auc:0.96512 validation-aucpr:0.97043
[4] validation-logloss:0.64514 validation-auc:0.96469 validation-aucpr:0.97004
[5] validation-logloss:0.63685 validation-auc:0.96557 validation-aucpr:0.97062
[6] validation-logloss:0.62772 validation-auc:0.96670 validation-aucpr:0.97179
[7] validation-logloss:0.61874 validation-auc:0.96722 validation-aucpr:0.97235
[8] validation-logloss:0.61037 validation-auc:0.96784 validation-aucpr:0.97279
[9] validation-logloss:0.60194 validation-auc:0.96816 validation-aucpr:0.97308
[10] validation-logloss:0.59368 validation-auc:0.96834 validation-aucpr:0.97323
[11] validation-logloss:0.58573 validation-auc:0.96869 validation-aucpr:0.97350
[12] validation-logloss:0.57783 validation-auc:0.96891 validation-aucpr:0.97367
[13] validation-logloss:0.57040 validation-auc:0.96904 validation-aucpr:0.97376
[14] validation-logloss:0.56286 validation-auc:0.96928 validation-aucpr:0.97400
[15] validation-logloss:0.55632 validation-auc:0.96924 validation-aucpr:0.97398
[16] validation-logloss:0.55019 validation-auc:0.96889 validation-aucpr:0.97367
[17] validation-logloss:0.54361 validation-auc:0.96891 validation-aucpr:0.97369
[18] validation-logloss:0.53745 validation-auc:0.96902 validation-aucpr:0.97387
[19] validation-logloss:0.53066 validation-auc:0.96924 validation-aucpr:0.97406
[20] validation-logloss:0.52420 validation-auc:0.96929 validation-aucpr:0.97413
[21] validation-logloss:0.51790 validation-auc:0.96928 validation-aucpr:0.97416
[22] validation-logloss:0.51187 validation-auc:0.96939 validation-aucpr:0.97427
[23] validation-logloss:0.50574 validation-auc:0.96949 validation-aucpr:0.97442
[24] validation-logloss:0.50050 validation-auc:0.96932 validation-aucpr:0.97426
[25] validation-logloss:0.49460 validation-auc:0.96944 validation-aucpr:0.97437
[26] validation-logloss:0.48879 validation-auc:0.96960 validation-aucpr:0.97449
[27] validation-logloss:0.48385 validation-auc:0.96961 validation-aucpr:0.97447
[28] validation-logloss:0.47907 validation-auc:0.96955 validation-aucpr:0.97440
[29] validation-logloss:0.47425 validation-auc:0.96971 validation-aucpr:0.97451
[30] validation-logloss:0.46976 validation-auc:0.96958 validation-aucpr:0.97440
[31] validation-logloss:0.46513 validation-auc:0.96951 validation-aucpr:0.97433
[32] validation-logloss:0.46006 validation-auc:0.96958 validation-aucpr:0.97439
[33] validation-logloss:0.45500 validation-auc:0.96971 validation-aucpr:0.97451
[34] validation-logloss:0.45015 validation-auc:0.96976 validation-aucpr:0.97455
[35] validation-logloss:0.44534 validation-auc:0.96985 validation-aucpr:0.97460
[36] validation-logloss:0.44133 validation-auc:0.96966 validation-aucpr:0.97444
[37] validation-logloss:0.43667 validation-auc:0.96976 validation-aucpr:0.97452
[38] validation-logloss:0.43223 validation-auc:0.96968 validation-aucpr:0.97448
[39] validation-logloss:0.42835 validation-auc:0.96964 validation-aucpr:0.97443
[40] validation-logloss:0.42448 validation-auc:0.96964 validation-aucpr:0.97443
[41] validation-logloss:0.42085 validation-auc:0.96959 validation-aucpr:0.97438
[42] validation-logloss:0.41670 validation-auc:0.96966 validation-aucpr:0.97445
[43] validation-logloss:0.41249 validation-auc:0.96981 validation-aucpr:0.97457
[44] validation-logloss:0.40898 validation-auc:0.96985 validation-aucpr:0.97459
[45] validation-logloss:0.40564 validation-auc:0.96973 validation-aucpr:0.97449
[46] validation-logloss:0.40173 validation-auc:0.96991 validation-aucpr:0.97467
[47] validation-logloss:0.39821 validation-auc:0.96996 validation-aucpr:0.97471
[48] validation-logloss:0.39452 validation-auc:0.97001 validation-aucpr:0.97476
{'best_iteration': '48', 'best_score': '0.9747625947591851'}
Trial 86, Fold 4: Log loss = 0.39452383486087683, Average precision = 0.9747594994211715, ROC-AUC = 0.9700139607192333, Elapsed Time = 50.71849939999811 seconds
Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 86, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68288 validation-auc:0.94632 validation-aucpr:0.93265
[1] validation-logloss:0.67243 validation-auc:0.96482 validation-aucpr:0.96976
[2] validation-logloss:0.66338 validation-auc:0.96409 validation-aucpr:0.96862
[3] validation-logloss:0.65344 validation-auc:0.96560 validation-aucpr:0.97002
[4] validation-logloss:0.64390 validation-auc:0.96642 validation-aucpr:0.97075
[5] validation-logloss:0.63465 validation-auc:0.96676 validation-aucpr:0.97093
[6] validation-logloss:0.62646 validation-auc:0.96666 validation-aucpr:0.97083
[7] validation-logloss:0.61769 validation-auc:0.96701 validation-aucpr:0.97117
[8] validation-logloss:0.60998 validation-auc:0.96640 validation-aucpr:0.97056
[9] validation-logloss:0.60208 validation-auc:0.96633 validation-aucpr:0.97050
[10] validation-logloss:0.59372 validation-auc:0.96698 validation-aucpr:0.97104
[11] validation-logloss:0.58569 validation-auc:0.96741 validation-aucpr:0.97134
[12] validation-logloss:0.57868 validation-auc:0.96760 validation-aucpr:0.97159
[13] validation-logloss:0.57115 validation-auc:0.96781 validation-aucpr:0.97175
[14] validation-logloss:0.56439 validation-auc:0.96759 validation-aucpr:0.97158
[15] validation-logloss:0.55718 validation-auc:0.96769 validation-aucpr:0.97170
[16] validation-logloss:0.55008 validation-auc:0.96778 validation-aucpr:0.97180
[17] validation-logloss:0.54395 validation-auc:0.96767 validation-aucpr:0.97169
[18] validation-logloss:0.53751 validation-auc:0.96769 validation-aucpr:0.97175
[19] validation-logloss:0.53157 validation-auc:0.96755 validation-aucpr:0.97162
[20] validation-logloss:0.52516 validation-auc:0.96753 validation-aucpr:0.97162
[21] validation-logloss:0.51906 validation-auc:0.96770 validation-aucpr:0.97177
[22] validation-logloss:0.51274 validation-auc:0.96781 validation-aucpr:0.97185
[23] validation-logloss:0.50679 validation-auc:0.96797 validation-aucpr:0.97199
[24] validation-logloss:0.50167 validation-auc:0.96789 validation-aucpr:0.97192
[25] validation-logloss:0.49644 validation-auc:0.96792 validation-aucpr:0.97190
[26] validation-logloss:0.49094 validation-auc:0.96794 validation-aucpr:0.97192
[27] validation-logloss:0.48530 validation-auc:0.96807 validation-aucpr:0.97205
[28] validation-logloss:0.48056 validation-auc:0.96800 validation-aucpr:0.97198
[29] validation-logloss:0.47596 validation-auc:0.96781 validation-aucpr:0.97182
[30] validation-logloss:0.47056 validation-auc:0.96812 validation-aucpr:0.97207
[31] validation-logloss:0.46552 validation-auc:0.96821 validation-aucpr:0.97210
[32] validation-logloss:0.46083 validation-auc:0.96823 validation-aucpr:0.97211
[33] validation-logloss:0.45666 validation-auc:0.96815 validation-aucpr:0.97203
[34] validation-logloss:0.45176 validation-auc:0.96836 validation-aucpr:0.97221
[35] validation-logloss:0.44698 validation-auc:0.96857 validation-aucpr:0.97237
[36] validation-logloss:0.44255 validation-auc:0.96858 validation-aucpr:0.97236
[37] validation-logloss:0.43884 validation-auc:0.96836 validation-aucpr:0.97034
[38] validation-logloss:0.43492 validation-auc:0.96833 validation-aucpr:0.97058
[39] validation-logloss:0.43117 validation-auc:0.96823 validation-aucpr:0.97024
[40] validation-logloss:0.42692 validation-auc:0.96834 validation-aucpr:0.97058
[41] validation-logloss:0.42295 validation-auc:0.96839 validation-aucpr:0.97076
[42] validation-logloss:0.41936 validation-auc:0.96829 validation-aucpr:0.97066
[43] validation-logloss:0.41520 validation-auc:0.96848 validation-aucpr:0.97084
[44] validation-logloss:0.41170 validation-auc:0.96858 validation-aucpr:0.97090
[45] validation-logloss:0.40771 validation-auc:0.96867 validation-aucpr:0.97098
[46] validation-logloss:0.40385 validation-auc:0.96866 validation-aucpr:0.97099
[47] validation-logloss:0.40064 validation-auc:0.96860 validation-aucpr:0.97087
[48] validation-logloss:0.39730 validation-auc:0.96861 validation-aucpr:0.97091
{'best_iteration': '35', 'best_score': '0.9723703196133963'}
Trial 86, Fold 5: Log loss = 0.39729713898022145, Average precision = 0.9716444141942279, ROC-AUC = 0.9686050201586683, Elapsed Time = 53.13384550000046 seconds
Optimization Progress: 87%|########7 | 87/100 [3:49:59<27:13, 125.67s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 87, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66841 validation-auc:0.91973 validation-aucpr:0.87848
[1] validation-logloss:0.64508 validation-auc:0.95583 validation-aucpr:0.94072
[2] validation-logloss:0.62419 validation-auc:0.96261 validation-aucpr:0.96576
[3] validation-logloss:0.60364 validation-auc:0.96458 validation-aucpr:0.96851
[4] validation-logloss:0.58423 validation-auc:0.96598 validation-aucpr:0.96908
[5] validation-logloss:0.56593 validation-auc:0.96685 validation-aucpr:0.96984
[6] validation-logloss:0.54867 validation-auc:0.96743 validation-aucpr:0.96912
[7] validation-logloss:0.53240 validation-auc:0.96778 validation-aucpr:0.96876
[8] validation-logloss:0.51698 validation-auc:0.96802 validation-aucpr:0.96898
[9] validation-logloss:0.50226 validation-auc:0.96867 validation-aucpr:0.97061
[10] validation-logloss:0.48878 validation-auc:0.96896 validation-aucpr:0.97042
[11] validation-logloss:0.47621 validation-auc:0.96886 validation-aucpr:0.97023
[12] validation-logloss:0.46388 validation-auc:0.96936 validation-aucpr:0.97278
[13] validation-logloss:0.45209 validation-auc:0.96940 validation-aucpr:0.97279
[14] validation-logloss:0.44108 validation-auc:0.96937 validation-aucpr:0.97398
[15] validation-logloss:0.43087 validation-auc:0.96972 validation-aucpr:0.97432
[16] validation-logloss:0.42079 validation-auc:0.96986 validation-aucpr:0.97436
[17] validation-logloss:0.41123 validation-auc:0.96982 validation-aucpr:0.97432
[18] validation-logloss:0.40217 validation-auc:0.96979 validation-aucpr:0.97222
[19] validation-logloss:0.39323 validation-auc:0.97004 validation-aucpr:0.97239
[20] validation-logloss:0.38504 validation-auc:0.97046 validation-aucpr:0.97496
[21] validation-logloss:0.37726 validation-auc:0.97047 validation-aucpr:0.97492
[22] validation-logloss:0.36956 validation-auc:0.97073 validation-aucpr:0.97514
[23] validation-logloss:0.36231 validation-auc:0.97065 validation-aucpr:0.97506
[24] validation-logloss:0.35534 validation-auc:0.97064 validation-aucpr:0.97504
[25] validation-logloss:0.34881 validation-auc:0.97083 validation-aucpr:0.97515
[26] validation-logloss:0.34228 validation-auc:0.97104 validation-aucpr:0.97534
[27] validation-logloss:0.33620 validation-auc:0.97112 validation-aucpr:0.97535
[28] validation-logloss:0.33030 validation-auc:0.97138 validation-aucpr:0.97564
[29] validation-logloss:0.32487 validation-auc:0.97133 validation-aucpr:0.97562
[30] validation-logloss:0.31951 validation-auc:0.97137 validation-aucpr:0.97564
[31] validation-logloss:0.31445 validation-auc:0.97145 validation-aucpr:0.97570
[32] validation-logloss:0.31010 validation-auc:0.97150 validation-aucpr:0.97576
[33] validation-logloss:0.30558 validation-auc:0.97137 validation-aucpr:0.97566
[34] validation-logloss:0.30094 validation-auc:0.97147 validation-aucpr:0.97574
[35] validation-logloss:0.29664 validation-auc:0.97157 validation-aucpr:0.97575
[36] validation-logloss:0.29248 validation-auc:0.97165 validation-aucpr:0.97581
[37] validation-logloss:0.28851 validation-auc:0.97172 validation-aucpr:0.97588
[38] validation-logloss:0.28479 validation-auc:0.97169 validation-aucpr:0.97586
[39] validation-logloss:0.28150 validation-auc:0.97174 validation-aucpr:0.97592
[40] validation-logloss:0.27796 validation-auc:0.97175 validation-aucpr:0.97592
[41] validation-logloss:0.27447 validation-auc:0.97187 validation-aucpr:0.97601
{'best_iteration': '41', 'best_score': '0.9760065105274797'}
Trial 87, Fold 1: Log loss = 0.2744740229206771, Average precision = 0.9760110559976684, ROC-AUC = 0.9718659934615219, Elapsed Time = 1.948451500000374 seconds
Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 87, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.66807 validation-auc:0.92792 validation-aucpr:0.88731
[1] validation-logloss:0.64453 validation-auc:0.95960 validation-aucpr:0.94918
[2] validation-logloss:0.62291 validation-auc:0.96457 validation-aucpr:0.96300
[3] validation-logloss:0.60414 validation-auc:0.96694 validation-aucpr:0.97097
[4] validation-logloss:0.58510 validation-auc:0.96733 validation-aucpr:0.97137
[5] validation-logloss:0.56695 validation-auc:0.96736 validation-aucpr:0.97152
[6] validation-logloss:0.54976 validation-auc:0.96734 validation-aucpr:0.97136
[7] validation-logloss:0.53352 validation-auc:0.96769 validation-aucpr:0.97165
[8] validation-logloss:0.51795 validation-auc:0.96808 validation-aucpr:0.97208
[9] validation-logloss:0.50358 validation-auc:0.96837 validation-aucpr:0.97216
[10] validation-logloss:0.48987 validation-auc:0.96834 validation-aucpr:0.97210
[11] validation-logloss:0.47681 validation-auc:0.96817 validation-aucpr:0.97203
[12] validation-logloss:0.46467 validation-auc:0.96838 validation-aucpr:0.97230
[13] validation-logloss:0.45303 validation-auc:0.96826 validation-aucpr:0.97222
[14] validation-logloss:0.44183 validation-auc:0.96840 validation-aucpr:0.97186
[15] validation-logloss:0.43098 validation-auc:0.96881 validation-aucpr:0.97230
[16] validation-logloss:0.42095 validation-auc:0.96910 validation-aucpr:0.97251
[17] validation-logloss:0.41125 validation-auc:0.96930 validation-aucpr:0.97265
[18] validation-logloss:0.40237 validation-auc:0.96935 validation-aucpr:0.97256
[19] validation-logloss:0.39360 validation-auc:0.96960 validation-aucpr:0.97276
[20] validation-logloss:0.38512 validation-auc:0.96982 validation-aucpr:0.97296
[21] validation-logloss:0.37739 validation-auc:0.96967 validation-aucpr:0.97293
[22] validation-logloss:0.36977 validation-auc:0.96977 validation-aucpr:0.97294
[23] validation-logloss:0.36256 validation-auc:0.96999 validation-aucpr:0.97285
[24] validation-logloss:0.35628 validation-auc:0.97005 validation-aucpr:0.97275
[25] validation-logloss:0.35010 validation-auc:0.97005 validation-aucpr:0.97293
[26] validation-logloss:0.34346 validation-auc:0.97029 validation-aucpr:0.97312
[27] validation-logloss:0.33727 validation-auc:0.97028 validation-aucpr:0.97315
[28] validation-logloss:0.33114 validation-auc:0.97043 validation-aucpr:0.97330
[29] validation-logloss:0.32588 validation-auc:0.97032 validation-aucpr:0.97326
[30] validation-logloss:0.32041 validation-auc:0.97029 validation-aucpr:0.97321
[31] validation-logloss:0.31485 validation-auc:0.97054 validation-aucpr:0.97347
[32] validation-logloss:0.30969 validation-auc:0.97084 validation-aucpr:0.97358
[33] validation-logloss:0.30478 validation-auc:0.97107 validation-aucpr:0.97364
[34] validation-logloss:0.30002 validation-auc:0.97119 validation-aucpr:0.97368
[35] validation-logloss:0.29577 validation-auc:0.97122 validation-aucpr:0.97370
[36] validation-logloss:0.29218 validation-auc:0.97119 validation-aucpr:0.97446
[37] validation-logloss:0.28816 validation-auc:0.97109 validation-aucpr:0.97437
[38] validation-logloss:0.28417 validation-auc:0.97119 validation-aucpr:0.97443
[39] validation-logloss:0.28046 validation-auc:0.97114 validation-aucpr:0.97441
[40] validation-logloss:0.27673 validation-auc:0.97131 validation-aucpr:0.97459
[41] validation-logloss:0.27316 validation-auc:0.97142 validation-aucpr:0.97465
{'best_iteration': '41', 'best_score': '0.9746504882779937'}
Trial 87, Fold 2: Log loss = 0.27315787486291515, Average precision = 0.97459003043263, ROC-AUC = 0.9714154972729405, Elapsed Time = 2.1508365999980015 seconds
Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 87, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66822 validation-auc:0.91816 validation-aucpr:0.87591
[1] validation-logloss:0.64466 validation-auc:0.95738 validation-aucpr:0.94106
[2] validation-logloss:0.62257 validation-auc:0.96645 validation-aucpr:0.96665
[3] validation-logloss:0.60396 validation-auc:0.96735 validation-aucpr:0.97051
[4] validation-logloss:0.58440 validation-auc:0.96790 validation-aucpr:0.97252
[5] validation-logloss:0.56591 validation-auc:0.96850 validation-aucpr:0.97304
[6] validation-logloss:0.54841 validation-auc:0.96944 validation-aucpr:0.97378
[7] validation-logloss:0.53217 validation-auc:0.96951 validation-aucpr:0.97405
[8] validation-logloss:0.51695 validation-auc:0.96940 validation-aucpr:0.97397
[9] validation-logloss:0.50214 validation-auc:0.96948 validation-aucpr:0.97315
[10] validation-logloss:0.48805 validation-auc:0.96932 validation-aucpr:0.97288
[11] validation-logloss:0.47528 validation-auc:0.96947 validation-aucpr:0.97360
[12] validation-logloss:0.46293 validation-auc:0.97000 validation-aucpr:0.97374
[13] validation-logloss:0.45131 validation-auc:0.97006 validation-aucpr:0.97375
[14] validation-logloss:0.44097 validation-auc:0.97010 validation-aucpr:0.97371
[15] validation-logloss:0.42999 validation-auc:0.97050 validation-aucpr:0.97402
[16] validation-logloss:0.42006 validation-auc:0.97081 validation-aucpr:0.97423
[17] validation-logloss:0.41021 validation-auc:0.97118 validation-aucpr:0.97543
[18] validation-logloss:0.40159 validation-auc:0.97119 validation-aucpr:0.97540
[19] validation-logloss:0.39248 validation-auc:0.97125 validation-aucpr:0.97534
[20] validation-logloss:0.38402 validation-auc:0.97118 validation-aucpr:0.97531
[21] validation-logloss:0.37603 validation-auc:0.97128 validation-aucpr:0.97534
[22] validation-logloss:0.36869 validation-auc:0.97109 validation-aucpr:0.97520
[23] validation-logloss:0.36126 validation-auc:0.97121 validation-aucpr:0.97531
[24] validation-logloss:0.35453 validation-auc:0.97098 validation-aucpr:0.97512
[25] validation-logloss:0.34807 validation-auc:0.97083 validation-aucpr:0.97500
[26] validation-logloss:0.34165 validation-auc:0.97083 validation-aucpr:0.97498
[27] validation-logloss:0.33542 validation-auc:0.97114 validation-aucpr:0.97533
[28] validation-logloss:0.32992 validation-auc:0.97130 validation-aucpr:0.97543
[29] validation-logloss:0.32410 validation-auc:0.97140 validation-aucpr:0.97549
[30] validation-logloss:0.31842 validation-auc:0.97152 validation-aucpr:0.97555
[31] validation-logloss:0.31315 validation-auc:0.97155 validation-aucpr:0.97557
[32] validation-logloss:0.30862 validation-auc:0.97166 validation-aucpr:0.97571
[33] validation-logloss:0.30371 validation-auc:0.97177 validation-aucpr:0.97584
[34] validation-logloss:0.29900 validation-auc:0.97195 validation-aucpr:0.97599
[35] validation-logloss:0.29472 validation-auc:0.97178 validation-aucpr:0.97586
[36] validation-logloss:0.29034 validation-auc:0.97190 validation-aucpr:0.97610
[37] validation-logloss:0.28630 validation-auc:0.97191 validation-aucpr:0.97613
[38] validation-logloss:0.28245 validation-auc:0.97193 validation-aucpr:0.97614
[39] validation-logloss:0.27878 validation-auc:0.97198 validation-aucpr:0.97617
[40] validation-logloss:0.27513 validation-auc:0.97194 validation-aucpr:0.97617
[41] validation-logloss:0.27177 validation-auc:0.97190 validation-aucpr:0.97610
{'best_iteration': '40', 'best_score': '0.9761697992665844'}
Trial 87, Fold 3: Log loss = 0.27177006105110235, Average precision = 0.976105101470461, ROC-AUC = 0.9718986712783272, Elapsed Time = 2.32190780000019 seconds
Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 87, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66814 validation-auc:0.91552 validation-aucpr:0.88203
[1] validation-logloss:0.64555 validation-auc:0.94976 validation-aucpr:0.94123
[2] validation-logloss:0.62354 validation-auc:0.96001 validation-aucpr:0.96171
[3] validation-logloss:0.60277 validation-auc:0.96264 validation-aucpr:0.96326
[4] validation-logloss:0.58355 validation-auc:0.96383 validation-aucpr:0.96660
[5] validation-logloss:0.56655 validation-auc:0.96375 validation-aucpr:0.96597
[6] validation-logloss:0.55093 validation-auc:0.96487 validation-aucpr:0.97111
[7] validation-logloss:0.53626 validation-auc:0.96556 validation-aucpr:0.97145
[8] validation-logloss:0.52066 validation-auc:0.96600 validation-aucpr:0.97183
[9] validation-logloss:0.50599 validation-auc:0.96686 validation-aucpr:0.97244
[10] validation-logloss:0.49231 validation-auc:0.96712 validation-aucpr:0.97259
[11] validation-logloss:0.47915 validation-auc:0.96704 validation-aucpr:0.97256
[12] validation-logloss:0.46657 validation-auc:0.96721 validation-aucpr:0.97274
[13] validation-logloss:0.45487 validation-auc:0.96747 validation-aucpr:0.97294
[14] validation-logloss:0.44465 validation-auc:0.96782 validation-aucpr:0.97314
[15] validation-logloss:0.43398 validation-auc:0.96795 validation-aucpr:0.97330
[16] validation-logloss:0.42347 validation-auc:0.96829 validation-aucpr:0.97356
[17] validation-logloss:0.41386 validation-auc:0.96799 validation-aucpr:0.97334
[18] validation-logloss:0.40456 validation-auc:0.96804 validation-aucpr:0.97341
[19] validation-logloss:0.39541 validation-auc:0.96832 validation-aucpr:0.97364
[20] validation-logloss:0.38787 validation-auc:0.96853 validation-aucpr:0.97375
[21] validation-logloss:0.37969 validation-auc:0.96879 validation-aucpr:0.97394
[22] validation-logloss:0.37189 validation-auc:0.96881 validation-aucpr:0.97397
[23] validation-logloss:0.36457 validation-auc:0.96893 validation-aucpr:0.97407
[24] validation-logloss:0.35758 validation-auc:0.96891 validation-aucpr:0.97405
[25] validation-logloss:0.35160 validation-auc:0.96884 validation-aucpr:0.97398
[26] validation-logloss:0.34503 validation-auc:0.96917 validation-aucpr:0.97419
[27] validation-logloss:0.33866 validation-auc:0.96950 validation-aucpr:0.97442
[28] validation-logloss:0.33275 validation-auc:0.96964 validation-aucpr:0.97454
[29] validation-logloss:0.32750 validation-auc:0.96964 validation-aucpr:0.97454
[30] validation-logloss:0.32197 validation-auc:0.96959 validation-aucpr:0.97452
[31] validation-logloss:0.31669 validation-auc:0.96968 validation-aucpr:0.97460
[32] validation-logloss:0.31238 validation-auc:0.96958 validation-aucpr:0.97453
[33] validation-logloss:0.30742 validation-auc:0.96966 validation-aucpr:0.97458
[34] validation-logloss:0.30340 validation-auc:0.96969 validation-aucpr:0.97459
[35] validation-logloss:0.29881 validation-auc:0.96976 validation-aucpr:0.97466
[36] validation-logloss:0.29451 validation-auc:0.96976 validation-aucpr:0.97468
[37] validation-logloss:0.29028 validation-auc:0.96984 validation-aucpr:0.97477
[38] validation-logloss:0.28635 validation-auc:0.96993 validation-aucpr:0.97483
[39] validation-logloss:0.28254 validation-auc:0.97000 validation-aucpr:0.97491
[40] validation-logloss:0.27890 validation-auc:0.96989 validation-aucpr:0.97487
[41] validation-logloss:0.27548 validation-auc:0.96993 validation-aucpr:0.97489
{'best_iteration': '39', 'best_score': '0.974910544350713'}
Trial 87, Fold 4: Log loss = 0.2754796669893886, Average precision = 0.9748833936863853, ROC-AUC = 0.9699267062240249, Elapsed Time = 2.1952992000005906 seconds
Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 87, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66835 validation-auc:0.91932 validation-aucpr:0.88248
[1] validation-logloss:0.64550 validation-auc:0.95531 validation-aucpr:0.94997
[2] validation-logloss:0.62387 validation-auc:0.96068 validation-aucpr:0.96237
[3] validation-logloss:0.60353 validation-auc:0.96315 validation-aucpr:0.96870
[4] validation-logloss:0.58473 validation-auc:0.96522 validation-aucpr:0.97014
[5] validation-logloss:0.56741 validation-auc:0.96485 validation-aucpr:0.96944
[6] validation-logloss:0.55094 validation-auc:0.96529 validation-aucpr:0.96948
[7] validation-logloss:0.53694 validation-auc:0.96464 validation-aucpr:0.96877
[8] validation-logloss:0.52136 validation-auc:0.96520 validation-aucpr:0.96929
[9] validation-logloss:0.50686 validation-auc:0.96604 validation-aucpr:0.96986
[10] validation-logloss:0.49325 validation-auc:0.96631 validation-aucpr:0.97006
[11] validation-logloss:0.48042 validation-auc:0.96698 validation-aucpr:0.97007
[12] validation-logloss:0.46817 validation-auc:0.96733 validation-aucpr:0.97028
[13] validation-logloss:0.45616 validation-auc:0.96780 validation-aucpr:0.97075
[14] validation-logloss:0.44472 validation-auc:0.96828 validation-aucpr:0.97113
[15] validation-logloss:0.43416 validation-auc:0.96869 validation-aucpr:0.97138
[16] validation-logloss:0.42421 validation-auc:0.96863 validation-aucpr:0.97148
[17] validation-logloss:0.41465 validation-auc:0.96885 validation-aucpr:0.97167
[18] validation-logloss:0.40545 validation-auc:0.96921 validation-aucpr:0.97188
[19] validation-logloss:0.39687 validation-auc:0.96908 validation-aucpr:0.97181
[20] validation-logloss:0.38885 validation-auc:0.96902 validation-aucpr:0.97189
[21] validation-logloss:0.38101 validation-auc:0.96899 validation-aucpr:0.97187
[22] validation-logloss:0.37384 validation-auc:0.96914 validation-aucpr:0.97195
[23] validation-logloss:0.36661 validation-auc:0.96919 validation-aucpr:0.97201
[24] validation-logloss:0.35959 validation-auc:0.96957 validation-aucpr:0.97231
[25] validation-logloss:0.35332 validation-auc:0.96975 validation-aucpr:0.97244
[26] validation-logloss:0.34690 validation-auc:0.96993 validation-aucpr:0.97229
[27] validation-logloss:0.34128 validation-auc:0.96999 validation-aucpr:0.97235
[28] validation-logloss:0.33538 validation-auc:0.97006 validation-aucpr:0.97237
[29] validation-logloss:0.32988 validation-auc:0.97002 validation-aucpr:0.97240
[30] validation-logloss:0.32452 validation-auc:0.97013 validation-aucpr:0.97266
[31] validation-logloss:0.31997 validation-auc:0.96998 validation-aucpr:0.97249
[32] validation-logloss:0.31541 validation-auc:0.96995 validation-aucpr:0.97246
[33] validation-logloss:0.31069 validation-auc:0.96985 validation-aucpr:0.97239
[34] validation-logloss:0.30615 validation-auc:0.96996 validation-aucpr:0.97230
[35] validation-logloss:0.30192 validation-auc:0.96995 validation-aucpr:0.97230
[36] validation-logloss:0.29763 validation-auc:0.97012 validation-aucpr:0.97244
[37] validation-logloss:0.29347 validation-auc:0.97024 validation-aucpr:0.97254
[38] validation-logloss:0.28982 validation-auc:0.97040 validation-aucpr:0.97265
[39] validation-logloss:0.28612 validation-auc:0.97038 validation-aucpr:0.97278
[40] validation-logloss:0.28272 validation-auc:0.97038 validation-aucpr:0.97286
[41] validation-logloss:0.27934 validation-auc:0.97038 validation-aucpr:0.97296
{'best_iteration': '41', 'best_score': '0.9729616450654627'}
Trial 87, Fold 5: Log loss = 0.2793432269406746, Average precision = 0.9731349045573595, ROC-AUC = 0.9703788343273322, Elapsed Time = 2.298222099998384 seconds
Optimization Progress: 88%|########8 | 88/100 [3:50:18<18:44, 93.73s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 88, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67879 validation-auc:0.94026 validation-aucpr:0.94310
[1] validation-logloss:0.66553 validation-auc:0.94625 validation-aucpr:0.94913
[2] validation-logloss:0.65201 validation-auc:0.94978 validation-aucpr:0.95335
[3] validation-logloss:0.63958 validation-auc:0.95177 validation-aucpr:0.95584
[4] validation-logloss:0.62735 validation-auc:0.95260 validation-aucpr:0.95705
[5] validation-logloss:0.61310 validation-auc:0.96017 validation-aucpr:0.96567
[6] validation-logloss:0.60127 validation-auc:0.96091 validation-aucpr:0.96615
[7] validation-logloss:0.58892 validation-auc:0.96204 validation-aucpr:0.96761
[8] validation-logloss:0.57856 validation-auc:0.96218 validation-aucpr:0.96769
[9] validation-logloss:0.56692 validation-auc:0.96295 validation-aucpr:0.96882
[10] validation-logloss:0.55737 validation-auc:0.96287 validation-aucpr:0.96867
[11] validation-logloss:0.54626 validation-auc:0.96343 validation-aucpr:0.96941
[12] validation-logloss:0.53608 validation-auc:0.96331 validation-aucpr:0.96943
[13] validation-logloss:0.52713 validation-auc:0.96336 validation-aucpr:0.96943
[14] validation-logloss:0.51890 validation-auc:0.96317 validation-aucpr:0.96923
[15] validation-logloss:0.51083 validation-auc:0.96314 validation-aucpr:0.96908
[16] validation-logloss:0.50245 validation-auc:0.96317 validation-aucpr:0.96911
[17] validation-logloss:0.49529 validation-auc:0.96306 validation-aucpr:0.96898
[18] validation-logloss:0.48806 validation-auc:0.96298 validation-aucpr:0.96893
[19] validation-logloss:0.47969 validation-auc:0.96339 validation-aucpr:0.96935
[20] validation-logloss:0.47139 validation-auc:0.96365 validation-aucpr:0.96962
[21] validation-logloss:0.46376 validation-auc:0.96387 validation-aucpr:0.96986
[22] validation-logloss:0.45609 validation-auc:0.96400 validation-aucpr:0.97005
[23] validation-logloss:0.45027 validation-auc:0.96386 validation-aucpr:0.96991
[24] validation-logloss:0.44469 validation-auc:0.96375 validation-aucpr:0.96979
[25] validation-logloss:0.43922 validation-auc:0.96369 validation-aucpr:0.96970
[26] validation-logloss:0.43375 validation-auc:0.96371 validation-aucpr:0.96964
[27] validation-logloss:0.42869 validation-auc:0.96365 validation-aucpr:0.96956
[28] validation-logloss:0.42344 validation-auc:0.96372 validation-aucpr:0.96962
[29] validation-logloss:0.41871 validation-auc:0.96366 validation-aucpr:0.96955
[30] validation-logloss:0.41273 validation-auc:0.96383 validation-aucpr:0.96972
[31] validation-logloss:0.40783 validation-auc:0.96398 validation-aucpr:0.96983
[32] validation-logloss:0.40183 validation-auc:0.96426 validation-aucpr:0.97008
[33] validation-logloss:0.39593 validation-auc:0.96455 validation-aucpr:0.97036
[34] validation-logloss:0.39195 validation-auc:0.96446 validation-aucpr:0.97029
[35] validation-logloss:0.38791 validation-auc:0.96443 validation-aucpr:0.97026
[36] validation-logloss:0.38274 validation-auc:0.96452 validation-aucpr:0.97039
[37] validation-logloss:0.37730 validation-auc:0.96479 validation-aucpr:0.97063
[38] validation-logloss:0.37219 validation-auc:0.96499 validation-aucpr:0.97083
[39] validation-logloss:0.36870 validation-auc:0.96496 validation-aucpr:0.97080
[40] validation-logloss:0.36393 validation-auc:0.96517 validation-aucpr:0.97096
[41] validation-logloss:0.36027 validation-auc:0.96522 validation-aucpr:0.97098
[42] validation-logloss:0.35569 validation-auc:0.96538 validation-aucpr:0.97115
[43] validation-logloss:0.35282 validation-auc:0.96529 validation-aucpr:0.97107
[44] validation-logloss:0.34946 validation-auc:0.96536 validation-aucpr:0.97110
[45] validation-logloss:0.34551 validation-auc:0.96547 validation-aucpr:0.97123
[46] validation-logloss:0.34134 validation-auc:0.96565 validation-aucpr:0.97138
{'best_iteration': '46', 'best_score': '0.9713768990895306'}
Trial 88, Fold 1: Log loss = 0.3413363609274223, Average precision = 0.9713801334846242, ROC-AUC = 0.9656482706251225, Elapsed Time = 1.1811795000030543 seconds
Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 88, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67841 validation-auc:0.94098 validation-aucpr:0.94155
[1] validation-logloss:0.66472 validation-auc:0.94942 validation-aucpr:0.95172
[2] validation-logloss:0.64888 validation-auc:0.96139 validation-aucpr:0.96578
[3] validation-logloss:0.63668 validation-auc:0.96151 validation-aucpr:0.96570
[4] validation-logloss:0.62391 validation-auc:0.96223 validation-aucpr:0.96659
[5] validation-logloss:0.61175 validation-auc:0.96246 validation-aucpr:0.96654
[6] validation-logloss:0.59849 validation-auc:0.96350 validation-aucpr:0.96765
[7] validation-logloss:0.58573 validation-auc:0.96443 validation-aucpr:0.96850
[8] validation-logloss:0.57492 validation-auc:0.96434 validation-aucpr:0.96842
[9] validation-logloss:0.56313 validation-auc:0.96482 validation-aucpr:0.96891
[10] validation-logloss:0.55358 validation-auc:0.96462 validation-aucpr:0.96869
[11] validation-logloss:0.54242 validation-auc:0.96504 validation-aucpr:0.96914
[12] validation-logloss:0.53391 validation-auc:0.96483 validation-aucpr:0.96930
[13] validation-logloss:0.52351 validation-auc:0.96505 validation-aucpr:0.96951
[14] validation-logloss:0.51369 validation-auc:0.96539 validation-aucpr:0.96986
[15] validation-logloss:0.50569 validation-auc:0.96529 validation-aucpr:0.96975
[16] validation-logloss:0.49620 validation-auc:0.96572 validation-aucpr:0.97016
[17] validation-logloss:0.48717 validation-auc:0.96610 validation-aucpr:0.97047
[18] validation-logloss:0.47857 validation-auc:0.96645 validation-aucpr:0.97080
[19] validation-logloss:0.47169 validation-auc:0.96632 validation-aucpr:0.97062
[20] validation-logloss:0.46523 validation-auc:0.96597 validation-aucpr:0.97032
[21] validation-logloss:0.45770 validation-auc:0.96628 validation-aucpr:0.97061
[22] validation-logloss:0.45128 validation-auc:0.96622 validation-aucpr:0.97053
[23] validation-logloss:0.44510 validation-auc:0.96626 validation-aucpr:0.97047
[24] validation-logloss:0.43910 validation-auc:0.96626 validation-aucpr:0.97045
[25] validation-logloss:0.43247 validation-auc:0.96640 validation-aucpr:0.97060
[26] validation-logloss:0.42556 validation-auc:0.96667 validation-aucpr:0.97084
[27] validation-logloss:0.42026 validation-auc:0.96653 validation-aucpr:0.97066
[28] validation-logloss:0.41500 validation-auc:0.96640 validation-aucpr:0.97053
[29] validation-logloss:0.41002 validation-auc:0.96632 validation-aucpr:0.97043
[30] validation-logloss:0.40533 validation-auc:0.96632 validation-aucpr:0.97034
[31] validation-logloss:0.40071 validation-auc:0.96629 validation-aucpr:0.97029
[32] validation-logloss:0.39475 validation-auc:0.96659 validation-aucpr:0.97056
[33] validation-logloss:0.39062 validation-auc:0.96650 validation-aucpr:0.97048
[34] validation-logloss:0.38651 validation-auc:0.96645 validation-aucpr:0.97040
[35] validation-logloss:0.38135 validation-auc:0.96666 validation-aucpr:0.97051
[36] validation-logloss:0.37598 validation-auc:0.96688 validation-aucpr:0.97072
[37] validation-logloss:0.37226 validation-auc:0.96677 validation-aucpr:0.97061
[38] validation-logloss:0.36845 validation-auc:0.96682 validation-aucpr:0.97063
[39] validation-logloss:0.36363 validation-auc:0.96703 validation-aucpr:0.97084
[40] validation-logloss:0.35914 validation-auc:0.96715 validation-aucpr:0.97096
[41] validation-logloss:0.35458 validation-auc:0.96729 validation-aucpr:0.97115
[42] validation-logloss:0.35017 validation-auc:0.96747 validation-aucpr:0.97132
[43] validation-logloss:0.34612 validation-auc:0.96760 validation-aucpr:0.97135
[44] validation-logloss:0.34296 validation-auc:0.96757 validation-aucpr:0.97131
[45] validation-logloss:0.33931 validation-auc:0.96762 validation-aucpr:0.97136
[46] validation-logloss:0.33617 validation-auc:0.96765 validation-aucpr:0.97138
{'best_iteration': '46', 'best_score': '0.9713759033775468'}
Trial 88, Fold 2: Log loss = 0.33617017571275093, Average precision = 0.9713869132696409, ROC-AUC = 0.967648434372573, Elapsed Time = 1.597771599997941 seconds
Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 88, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67819 validation-auc:0.94612 validation-aucpr:0.94908
[1] validation-logloss:0.66173 validation-auc:0.96430 validation-aucpr:0.96780
[2] validation-logloss:0.64828 validation-auc:0.96401 validation-aucpr:0.96794
[3] validation-logloss:0.63366 validation-auc:0.96519 validation-aucpr:0.96896
[4] validation-logloss:0.62120 validation-auc:0.96508 validation-aucpr:0.96889
[5] validation-logloss:0.60920 validation-auc:0.96536 validation-aucpr:0.96945
[6] validation-logloss:0.59791 validation-auc:0.96511 validation-aucpr:0.96914
[7] validation-logloss:0.58509 validation-auc:0.96580 validation-aucpr:0.96985
[8] validation-logloss:0.57464 validation-auc:0.96604 validation-aucpr:0.96995
[9] validation-logloss:0.56292 validation-auc:0.96636 validation-aucpr:0.97038
[10] validation-logloss:0.55346 validation-auc:0.96628 validation-aucpr:0.97060
[11] validation-logloss:0.54364 validation-auc:0.96639 validation-aucpr:0.97067
[12] validation-logloss:0.53489 validation-auc:0.96599 validation-aucpr:0.97032
[13] validation-logloss:0.52466 validation-auc:0.96628 validation-aucpr:0.97061
[14] validation-logloss:0.51639 validation-auc:0.96599 validation-aucpr:0.97036
[15] validation-logloss:0.50808 validation-auc:0.96601 validation-aucpr:0.97040
[16] validation-logloss:0.49875 validation-auc:0.96649 validation-aucpr:0.97119
[17] validation-logloss:0.48964 validation-auc:0.96666 validation-aucpr:0.97139
[18] validation-logloss:0.48105 validation-auc:0.96685 validation-aucpr:0.97157
[19] validation-logloss:0.47438 validation-auc:0.96659 validation-aucpr:0.97135
[20] validation-logloss:0.46646 validation-auc:0.96683 validation-aucpr:0.97162
[21] validation-logloss:0.46013 validation-auc:0.96678 validation-aucpr:0.97160
[22] validation-logloss:0.45380 validation-auc:0.96675 validation-aucpr:0.97152
[23] validation-logloss:0.44629 validation-auc:0.96693 validation-aucpr:0.97170
[24] validation-logloss:0.44003 validation-auc:0.96705 validation-aucpr:0.97180
[25] validation-logloss:0.43426 validation-auc:0.96698 validation-aucpr:0.97175
[26] validation-logloss:0.42826 validation-auc:0.96706 validation-aucpr:0.97179
[27] validation-logloss:0.42306 validation-auc:0.96704 validation-aucpr:0.97174
[28] validation-logloss:0.41803 validation-auc:0.96695 validation-aucpr:0.97164
[29] validation-logloss:0.41309 validation-auc:0.96689 validation-aucpr:0.97158
[30] validation-logloss:0.40831 validation-auc:0.96677 validation-aucpr:0.97146
[31] validation-logloss:0.40237 validation-auc:0.96682 validation-aucpr:0.97156
[32] validation-logloss:0.39755 validation-auc:0.96698 validation-aucpr:0.97169
[33] validation-logloss:0.39309 validation-auc:0.96707 validation-aucpr:0.97177
[34] validation-logloss:0.38855 validation-auc:0.96722 validation-aucpr:0.97191
[35] validation-logloss:0.38443 validation-auc:0.96721 validation-aucpr:0.97190
[36] validation-logloss:0.38017 validation-auc:0.96722 validation-aucpr:0.97187
[37] validation-logloss:0.37500 validation-auc:0.96729 validation-aucpr:0.97195
[38] validation-logloss:0.36976 validation-auc:0.96752 validation-aucpr:0.97216
[39] validation-logloss:0.36500 validation-auc:0.96762 validation-aucpr:0.97232
[40] validation-logloss:0.36145 validation-auc:0.96755 validation-aucpr:0.97225
[41] validation-logloss:0.35791 validation-auc:0.96758 validation-aucpr:0.97225
[42] validation-logloss:0.35440 validation-auc:0.96760 validation-aucpr:0.97225
[43] validation-logloss:0.34966 validation-auc:0.96779 validation-aucpr:0.97244
[44] validation-logloss:0.34549 validation-auc:0.96786 validation-aucpr:0.97251
[45] validation-logloss:0.34244 validation-auc:0.96786 validation-aucpr:0.97252
[46] validation-logloss:0.33934 validation-auc:0.96789 validation-aucpr:0.97254
{'best_iteration': '46', 'best_score': '0.9725354782972834'}
Trial 88, Fold 3: Log loss = 0.33934060727163606, Average precision = 0.9725391929748513, ROC-AUC = 0.9678902796371026, Elapsed Time = 1.4755301000004692 seconds
Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 88, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67847 validation-auc:0.93894 validation-aucpr:0.94389
[1] validation-logloss:0.66476 validation-auc:0.94607 validation-aucpr:0.95173
[2] validation-logloss:0.65105 validation-auc:0.95285 validation-aucpr:0.95696
[3] validation-logloss:0.63844 validation-auc:0.95362 validation-aucpr:0.95798
[4] validation-logloss:0.62411 validation-auc:0.96066 validation-aucpr:0.96642
[5] validation-logloss:0.61000 validation-auc:0.96201 validation-aucpr:0.96803
[6] validation-logloss:0.59770 validation-auc:0.96247 validation-aucpr:0.96858
[7] validation-logloss:0.58557 validation-auc:0.96268 validation-aucpr:0.96880
[8] validation-logloss:0.57522 validation-auc:0.96253 validation-aucpr:0.96862
[9] validation-logloss:0.56321 validation-auc:0.96291 validation-aucpr:0.96912
[10] validation-logloss:0.55170 validation-auc:0.96324 validation-aucpr:0.96944
[11] validation-logloss:0.54062 validation-auc:0.96351 validation-aucpr:0.96982
[12] validation-logloss:0.53121 validation-auc:0.96378 validation-aucpr:0.96994
[13] validation-logloss:0.52250 validation-auc:0.96363 validation-aucpr:0.96979
[14] validation-logloss:0.51428 validation-auc:0.96369 validation-aucpr:0.96976
[15] validation-logloss:0.50623 validation-auc:0.96360 validation-aucpr:0.96964
[16] validation-logloss:0.49861 validation-auc:0.96358 validation-aucpr:0.96962
[17] validation-logloss:0.49103 validation-auc:0.96376 validation-aucpr:0.96979
[18] validation-logloss:0.48222 validation-auc:0.96403 validation-aucpr:0.97005
[19] validation-logloss:0.47372 validation-auc:0.96430 validation-aucpr:0.97033
[20] validation-logloss:0.46676 validation-auc:0.96431 validation-aucpr:0.97032
[21] validation-logloss:0.46007 validation-auc:0.96441 validation-aucpr:0.97043
[22] validation-logloss:0.45395 validation-auc:0.96434 validation-aucpr:0.97035
[23] validation-logloss:0.44625 validation-auc:0.96469 validation-aucpr:0.97066
[24] validation-logloss:0.44035 validation-auc:0.96473 validation-aucpr:0.97064
[25] validation-logloss:0.43315 validation-auc:0.96489 validation-aucpr:0.97083
[26] validation-logloss:0.42625 validation-auc:0.96501 validation-aucpr:0.97097
[27] validation-logloss:0.41948 validation-auc:0.96526 validation-aucpr:0.97118
[28] validation-logloss:0.41340 validation-auc:0.96533 validation-aucpr:0.97126
[29] validation-logloss:0.40845 validation-auc:0.96524 validation-aucpr:0.97117
[30] validation-logloss:0.40244 validation-auc:0.96537 validation-aucpr:0.97131
[31] validation-logloss:0.39779 validation-auc:0.96542 validation-aucpr:0.97131
[32] validation-logloss:0.39229 validation-auc:0.96550 validation-aucpr:0.97140
[33] validation-logloss:0.38790 validation-auc:0.96557 validation-aucpr:0.97145
[34] validation-logloss:0.38359 validation-auc:0.96559 validation-aucpr:0.97143
[35] validation-logloss:0.37826 validation-auc:0.96564 validation-aucpr:0.97149
[36] validation-logloss:0.37438 validation-auc:0.96565 validation-aucpr:0.97149
[37] validation-logloss:0.37026 validation-auc:0.96576 validation-aucpr:0.97156
[38] validation-logloss:0.36665 validation-auc:0.96566 validation-aucpr:0.97149
[39] validation-logloss:0.36220 validation-auc:0.96577 validation-aucpr:0.97160
[40] validation-logloss:0.35866 validation-auc:0.96574 validation-aucpr:0.97155
[41] validation-logloss:0.35544 validation-auc:0.96568 validation-aucpr:0.97149
[42] validation-logloss:0.35186 validation-auc:0.96570 validation-aucpr:0.97149
[43] validation-logloss:0.34775 validation-auc:0.96579 validation-aucpr:0.97158
[44] validation-logloss:0.34356 validation-auc:0.96596 validation-aucpr:0.97174
[45] validation-logloss:0.34071 validation-auc:0.96587 validation-aucpr:0.97166
[46] validation-logloss:0.33759 validation-auc:0.96586 validation-aucpr:0.97163
{'best_iteration': '44', 'best_score': '0.9717392143244231'}
Trial 88, Fold 4: Log loss = 0.3375868960426251, Average precision = 0.9716230766946939, ROC-AUC = 0.9658556990970241, Elapsed Time = 1.6040769999999611 seconds
Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 88, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67878 validation-auc:0.93635 validation-aucpr:0.93760
[1] validation-logloss:0.66563 validation-auc:0.94369 validation-aucpr:0.94688
[2] validation-logloss:0.64986 validation-auc:0.95830 validation-aucpr:0.96298
[3] validation-logloss:0.63732 validation-auc:0.95859 validation-aucpr:0.96372
[4] validation-logloss:0.62287 validation-auc:0.95984 validation-aucpr:0.96491
[5] validation-logloss:0.61064 validation-auc:0.96044 validation-aucpr:0.96541
[6] validation-logloss:0.59754 validation-auc:0.96110 validation-aucpr:0.96619
[7] validation-logloss:0.58545 validation-auc:0.96139 validation-aucpr:0.96667
[8] validation-logloss:0.57514 validation-auc:0.96133 validation-aucpr:0.96681
[9] validation-logloss:0.56583 validation-auc:0.96118 validation-aucpr:0.96652
[10] validation-logloss:0.55442 validation-auc:0.96139 validation-aucpr:0.96679
[11] validation-logloss:0.54397 validation-auc:0.96133 validation-aucpr:0.96670
[12] validation-logloss:0.53485 validation-auc:0.96127 validation-aucpr:0.96674
[13] validation-logloss:0.52590 validation-auc:0.96140 validation-aucpr:0.96681
[14] validation-logloss:0.51768 validation-auc:0.96132 validation-aucpr:0.96673
[15] validation-logloss:0.50971 validation-auc:0.96144 validation-aucpr:0.96679
[16] validation-logloss:0.50240 validation-auc:0.96129 validation-aucpr:0.96667
[17] validation-logloss:0.49305 validation-auc:0.96169 validation-aucpr:0.96702
[18] validation-logloss:0.48464 validation-auc:0.96182 validation-aucpr:0.96716
[19] validation-logloss:0.47638 validation-auc:0.96204 validation-aucpr:0.96739
[20] validation-logloss:0.46811 validation-auc:0.96239 validation-aucpr:0.96771
[21] validation-logloss:0.46176 validation-auc:0.96234 validation-aucpr:0.96751
[22] validation-logloss:0.45577 validation-auc:0.96218 validation-aucpr:0.96736
[23] validation-logloss:0.44881 validation-auc:0.96242 validation-aucpr:0.96757
[24] validation-logloss:0.44186 validation-auc:0.96251 validation-aucpr:0.96764
[25] validation-logloss:0.43626 validation-auc:0.96247 validation-aucpr:0.96756
[26] validation-logloss:0.42946 validation-auc:0.96261 validation-aucpr:0.96771
[27] validation-logloss:0.42423 validation-auc:0.96260 validation-aucpr:0.96766
[28] validation-logloss:0.41916 validation-auc:0.96259 validation-aucpr:0.96764
[29] validation-logloss:0.41298 validation-auc:0.96276 validation-aucpr:0.96791
[30] validation-logloss:0.40677 validation-auc:0.96300 validation-aucpr:0.96810
[31] validation-logloss:0.40195 validation-auc:0.96308 validation-aucpr:0.96814
[32] validation-logloss:0.39767 validation-auc:0.96303 validation-aucpr:0.96804
[33] validation-logloss:0.39322 validation-auc:0.96306 validation-aucpr:0.96802
[34] validation-logloss:0.38900 validation-auc:0.96301 validation-aucpr:0.96794
[35] validation-logloss:0.38513 validation-auc:0.96294 validation-aucpr:0.96788
[36] validation-logloss:0.37973 validation-auc:0.96321 validation-aucpr:0.96812
[37] validation-logloss:0.37584 validation-auc:0.96326 validation-aucpr:0.96815
[38] validation-logloss:0.37208 validation-auc:0.96328 validation-aucpr:0.96812
[39] validation-logloss:0.36733 validation-auc:0.96343 validation-aucpr:0.96828
[40] validation-logloss:0.36311 validation-auc:0.96352 validation-aucpr:0.96877
[41] validation-logloss:0.35959 validation-auc:0.96365 validation-aucpr:0.96885
[42] validation-logloss:0.35602 validation-auc:0.96377 validation-aucpr:0.96892
[43] validation-logloss:0.35273 validation-auc:0.96383 validation-aucpr:0.96895
[44] validation-logloss:0.34956 validation-auc:0.96384 validation-aucpr:0.96893
[45] validation-logloss:0.34679 validation-auc:0.96376 validation-aucpr:0.96884
[46] validation-logloss:0.34271 validation-auc:0.96390 validation-aucpr:0.96899
{'best_iteration': '46', 'best_score': '0.968991717837239'}
Trial 88, Fold 5: Log loss = 0.34270535412207304, Average precision = 0.9689859086742532, ROC-AUC = 0.9639023465804581, Elapsed Time = 1.4417032999990624 seconds
Optimization Progress: 89%|########9 | 89/100 [3:50:33<12:51, 70.17s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 89, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65288 validation-auc:0.95789 validation-aucpr:0.96334
[1] validation-logloss:0.61737 validation-auc:0.96161 validation-aucpr:0.96669
[2] validation-logloss:0.58472 validation-auc:0.96400 validation-aucpr:0.96993
[3] validation-logloss:0.55552 validation-auc:0.96476 validation-aucpr:0.97077
[4] validation-logloss:0.52917 validation-auc:0.96521 validation-aucpr:0.97137
[5] validation-logloss:0.50523 validation-auc:0.96574 validation-aucpr:0.97169
[6] validation-logloss:0.48312 validation-auc:0.96599 validation-aucpr:0.97131
[7] validation-logloss:0.46302 validation-auc:0.96659 validation-aucpr:0.97149
[8] validation-logloss:0.44414 validation-auc:0.96723 validation-aucpr:0.97191
[9] validation-logloss:0.42642 validation-auc:0.96779 validation-aucpr:0.97241
[10] validation-logloss:0.41255 validation-auc:0.96788 validation-aucpr:0.97247
[11] validation-logloss:0.39786 validation-auc:0.96819 validation-aucpr:0.97258
[12] validation-logloss:0.38413 validation-auc:0.96844 validation-aucpr:0.97283
[13] validation-logloss:0.37098 validation-auc:0.96881 validation-aucpr:0.97304
[14] validation-logloss:0.35929 validation-auc:0.96888 validation-aucpr:0.97312
[15] validation-logloss:0.34832 validation-auc:0.96906 validation-aucpr:0.97326
[16] validation-logloss:0.33861 validation-auc:0.96906 validation-aucpr:0.97326
[17] validation-logloss:0.32923 validation-auc:0.96936 validation-aucpr:0.97341
[18] validation-logloss:0.32011 validation-auc:0.96954 validation-aucpr:0.97390
[19] validation-logloss:0.31192 validation-auc:0.96963 validation-aucpr:0.97405
[20] validation-logloss:0.30384 validation-auc:0.97019 validation-aucpr:0.97441
[21] validation-logloss:0.29739 validation-auc:0.97022 validation-aucpr:0.97440
[22] validation-logloss:0.29085 validation-auc:0.97030 validation-aucpr:0.97449
[23] validation-logloss:0.28552 validation-auc:0.97027 validation-aucpr:0.97446
[24] validation-logloss:0.27984 validation-auc:0.97043 validation-aucpr:0.97524
[25] validation-logloss:0.27456 validation-auc:0.97047 validation-aucpr:0.97525
[26] validation-logloss:0.26977 validation-auc:0.97040 validation-aucpr:0.97523
[27] validation-logloss:0.26472 validation-auc:0.97053 validation-aucpr:0.97531
[28] validation-logloss:0.26056 validation-auc:0.97032 validation-aucpr:0.97517
[29] validation-logloss:0.25613 validation-auc:0.97055 validation-aucpr:0.97534
[30] validation-logloss:0.25199 validation-auc:0.97079 validation-aucpr:0.97548
[31] validation-logloss:0.24875 validation-auc:0.97052 validation-aucpr:0.97526
[32] validation-logloss:0.24518 validation-auc:0.97066 validation-aucpr:0.97534
[33] validation-logloss:0.24210 validation-auc:0.97060 validation-aucpr:0.97530
[34] validation-logloss:0.23881 validation-auc:0.97084 validation-aucpr:0.97546
[35] validation-logloss:0.23616 validation-auc:0.97073 validation-aucpr:0.97537
[36] validation-logloss:0.23347 validation-auc:0.97075 validation-aucpr:0.97543
[37] validation-logloss:0.23094 validation-auc:0.97092 validation-aucpr:0.97553
[38] validation-logloss:0.22854 validation-auc:0.97092 validation-aucpr:0.97552
[39] validation-logloss:0.22634 validation-auc:0.97110 validation-aucpr:0.97571
[40] validation-logloss:0.22474 validation-auc:0.97100 validation-aucpr:0.97562
[41] validation-logloss:0.22347 validation-auc:0.97079 validation-aucpr:0.97540
[42] validation-logloss:0.22140 validation-auc:0.97086 validation-aucpr:0.97543
[43] validation-logloss:0.21945 validation-auc:0.97101 validation-aucpr:0.97557
[44] validation-logloss:0.21773 validation-auc:0.97115 validation-aucpr:0.97565
[45] validation-logloss:0.21650 validation-auc:0.97113 validation-aucpr:0.97563
[46] validation-logloss:0.21513 validation-auc:0.97116 validation-aucpr:0.97564
[47] validation-logloss:0.21385 validation-auc:0.97124 validation-aucpr:0.97570
[48] validation-logloss:0.21293 validation-auc:0.97119 validation-aucpr:0.97574
[49] validation-logloss:0.21136 validation-auc:0.97131 validation-aucpr:0.97585
[50] validation-logloss:0.20978 validation-auc:0.97149 validation-aucpr:0.97599
[51] validation-logloss:0.20852 validation-auc:0.97156 validation-aucpr:0.97606
[52] validation-logloss:0.20741 validation-auc:0.97172 validation-aucpr:0.97618
[53] validation-logloss:0.20668 validation-auc:0.97163 validation-aucpr:0.97613
[54] validation-logloss:0.20607 validation-auc:0.97148 validation-aucpr:0.97598
[55] validation-logloss:0.20509 validation-auc:0.97156 validation-aucpr:0.97605
[56] validation-logloss:0.20398 validation-auc:0.97178 validation-aucpr:0.97618
{'best_iteration': '52', 'best_score': '0.976176684221807'}
Trial 89, Fold 1: Log loss = 0.2039760299158349, Average precision = 0.9761799411108688, ROC-AUC = 0.9717819025632937, Elapsed Time = 12.004220999999234 seconds
Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 89, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65220 validation-auc:0.95974 validation-aucpr:0.96371
[1] validation-logloss:0.61616 validation-auc:0.96514 validation-aucpr:0.96885
[2] validation-logloss:0.58510 validation-auc:0.96508 validation-aucpr:0.96870
[3] validation-logloss:0.55633 validation-auc:0.96588 validation-aucpr:0.96964
[4] validation-logloss:0.52896 validation-auc:0.96729 validation-aucpr:0.97087
[5] validation-logloss:0.50677 validation-auc:0.96826 validation-aucpr:0.97161
[6] validation-logloss:0.48432 validation-auc:0.96853 validation-aucpr:0.97176
[7] validation-logloss:0.46352 validation-auc:0.96885 validation-aucpr:0.97255
[8] validation-logloss:0.44485 validation-auc:0.96899 validation-aucpr:0.97262
[9] validation-logloss:0.42724 validation-auc:0.96924 validation-aucpr:0.97290
[10] validation-logloss:0.41329 validation-auc:0.96942 validation-aucpr:0.97302
[11] validation-logloss:0.39979 validation-auc:0.96983 validation-aucpr:0.97328
[12] validation-logloss:0.38577 validation-auc:0.97014 validation-aucpr:0.97357
[13] validation-logloss:0.37266 validation-auc:0.97043 validation-aucpr:0.97379
[14] validation-logloss:0.36108 validation-auc:0.97041 validation-aucpr:0.97377
[15] validation-logloss:0.35131 validation-auc:0.97065 validation-aucpr:0.97392
[16] validation-logloss:0.34075 validation-auc:0.97058 validation-aucpr:0.97378
[17] validation-logloss:0.33208 validation-auc:0.97061 validation-aucpr:0.97379
[18] validation-logloss:0.32282 validation-auc:0.97096 validation-aucpr:0.97406
[19] validation-logloss:0.31413 validation-auc:0.97111 validation-aucpr:0.97413
[20] validation-logloss:0.30582 validation-auc:0.97140 validation-aucpr:0.97430
[21] validation-logloss:0.29973 validation-auc:0.97113 validation-aucpr:0.97411
[22] validation-logloss:0.29394 validation-auc:0.97124 validation-aucpr:0.97419
[23] validation-logloss:0.28759 validation-auc:0.97116 validation-aucpr:0.97409
[24] validation-logloss:0.28124 validation-auc:0.97130 validation-aucpr:0.97420
[25] validation-logloss:0.27554 validation-auc:0.97120 validation-aucpr:0.97416
[26] validation-logloss:0.26983 validation-auc:0.97138 validation-aucpr:0.97432
[27] validation-logloss:0.26468 validation-auc:0.97138 validation-aucpr:0.97432
[28] validation-logloss:0.25982 validation-auc:0.97129 validation-aucpr:0.97433
[29] validation-logloss:0.25532 validation-auc:0.97125 validation-aucpr:0.97433
[30] validation-logloss:0.25155 validation-auc:0.97133 validation-aucpr:0.97441
[31] validation-logloss:0.24752 validation-auc:0.97137 validation-aucpr:0.97439
[32] validation-logloss:0.24386 validation-auc:0.97142 validation-aucpr:0.97441
[33] validation-logloss:0.24081 validation-auc:0.97167 validation-aucpr:0.97459
[34] validation-logloss:0.23747 validation-auc:0.97160 validation-aucpr:0.97455
[35] validation-logloss:0.23508 validation-auc:0.97168 validation-aucpr:0.97490
[36] validation-logloss:0.23257 validation-auc:0.97162 validation-aucpr:0.97486
[37] validation-logloss:0.22964 validation-auc:0.97166 validation-aucpr:0.97487
[38] validation-logloss:0.22697 validation-auc:0.97180 validation-aucpr:0.97497
[39] validation-logloss:0.22466 validation-auc:0.97195 validation-aucpr:0.97506
[40] validation-logloss:0.22219 validation-auc:0.97202 validation-aucpr:0.97508
[41] validation-logloss:0.21982 validation-auc:0.97192 validation-aucpr:0.97499
[42] validation-logloss:0.21823 validation-auc:0.97195 validation-aucpr:0.97497
[43] validation-logloss:0.21677 validation-auc:0.97195 validation-aucpr:0.97493
[44] validation-logloss:0.21478 validation-auc:0.97222 validation-aucpr:0.97515
[45] validation-logloss:0.21297 validation-auc:0.97216 validation-aucpr:0.97501
[46] validation-logloss:0.21145 validation-auc:0.97223 validation-aucpr:0.97505
[47] validation-logloss:0.20975 validation-auc:0.97228 validation-aucpr:0.97509
[48] validation-logloss:0.20793 validation-auc:0.97245 validation-aucpr:0.97521
[49] validation-logloss:0.20650 validation-auc:0.97238 validation-aucpr:0.97513
[50] validation-logloss:0.20518 validation-auc:0.97234 validation-aucpr:0.97503
[51] validation-logloss:0.20395 validation-auc:0.97231 validation-aucpr:0.97499
[52] validation-logloss:0.20237 validation-auc:0.97254 validation-aucpr:0.97517
[53] validation-logloss:0.20136 validation-auc:0.97249 validation-aucpr:0.97508
[54] validation-logloss:0.20000 validation-auc:0.97266 validation-aucpr:0.97520
[55] validation-logloss:0.19878 validation-auc:0.97278 validation-aucpr:0.97531
[56] validation-logloss:0.19779 validation-auc:0.97278 validation-aucpr:0.97531
{'best_iteration': '56', 'best_score': '0.9753079018133185'}
Trial 89, Fold 2: Log loss = 0.19778888192740518, Average precision = 0.975314491837502, ROC-AUC = 0.9727828206939062, Elapsed Time = 11.682237000000896 seconds
Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 89, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65183 validation-auc:0.96410 validation-aucpr:0.96861
[1] validation-logloss:0.61589 validation-auc:0.96421 validation-aucpr:0.96958
[2] validation-logloss:0.58678 validation-auc:0.96644 validation-aucpr:0.97093
[3] validation-logloss:0.55724 validation-auc:0.96706 validation-aucpr:0.97161
[4] validation-logloss:0.53337 validation-auc:0.96768 validation-aucpr:0.97198
[5] validation-logloss:0.50880 validation-auc:0.96797 validation-aucpr:0.97228
[6] validation-logloss:0.48597 validation-auc:0.96861 validation-aucpr:0.97261
[7] validation-logloss:0.46724 validation-auc:0.96905 validation-aucpr:0.97301
[8] validation-logloss:0.45022 validation-auc:0.96924 validation-aucpr:0.97352
[9] validation-logloss:0.43204 validation-auc:0.96919 validation-aucpr:0.97345
[10] validation-logloss:0.41515 validation-auc:0.96941 validation-aucpr:0.97356
[11] validation-logloss:0.40154 validation-auc:0.96924 validation-aucpr:0.97334
[12] validation-logloss:0.38915 validation-auc:0.96927 validation-aucpr:0.97335
[13] validation-logloss:0.37579 validation-auc:0.96958 validation-aucpr:0.97354
[14] validation-logloss:0.36525 validation-auc:0.96956 validation-aucpr:0.97351
[15] validation-logloss:0.35341 validation-auc:0.96972 validation-aucpr:0.97362
[16] validation-logloss:0.34239 validation-auc:0.97035 validation-aucpr:0.97406
[17] validation-logloss:0.33251 validation-auc:0.97046 validation-aucpr:0.97413
[18] validation-logloss:0.32270 validation-auc:0.97089 validation-aucpr:0.97449
[19] validation-logloss:0.31366 validation-auc:0.97116 validation-aucpr:0.97471
[20] validation-logloss:0.30569 validation-auc:0.97119 validation-aucpr:0.97472
[21] validation-logloss:0.29800 validation-auc:0.97127 validation-aucpr:0.97491
[22] validation-logloss:0.29141 validation-auc:0.97158 validation-aucpr:0.97512
[23] validation-logloss:0.28484 validation-auc:0.97176 validation-aucpr:0.97535
[24] validation-logloss:0.27937 validation-auc:0.97168 validation-aucpr:0.97526
[25] validation-logloss:0.27333 validation-auc:0.97175 validation-aucpr:0.97524
[26] validation-logloss:0.26864 validation-auc:0.97169 validation-aucpr:0.97517
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[28] validation-logloss:0.25820 validation-auc:0.97207 validation-aucpr:0.97541
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[31] validation-logloss:0.24684 validation-auc:0.97203 validation-aucpr:0.97472
[32] validation-logloss:0.24296 validation-auc:0.97205 validation-aucpr:0.97475
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[37] validation-logloss:0.22796 validation-auc:0.97219 validation-aucpr:0.97518
[38] validation-logloss:0.22504 validation-auc:0.97227 validation-aucpr:0.97523
[39] validation-logloss:0.22276 validation-auc:0.97222 validation-aucpr:0.97516
[40] validation-logloss:0.22057 validation-auc:0.97235 validation-aucpr:0.97525
[41] validation-logloss:0.21832 validation-auc:0.97230 validation-aucpr:0.97519
[42] validation-logloss:0.21607 validation-auc:0.97238 validation-aucpr:0.97524
[43] validation-logloss:0.21482 validation-auc:0.97227 validation-aucpr:0.97590
[44] validation-logloss:0.21324 validation-auc:0.97234 validation-aucpr:0.97593
[45] validation-logloss:0.21161 validation-auc:0.97226 validation-aucpr:0.97587
[46] validation-logloss:0.21051 validation-auc:0.97225 validation-aucpr:0.97584
[47] validation-logloss:0.20868 validation-auc:0.97241 validation-aucpr:0.97605
[48] validation-logloss:0.20670 validation-auc:0.97270 validation-aucpr:0.97628
[49] validation-logloss:0.20550 validation-auc:0.97269 validation-aucpr:0.97626
[50] validation-logloss:0.20409 validation-auc:0.97273 validation-aucpr:0.97625
[51] validation-logloss:0.20264 validation-auc:0.97279 validation-aucpr:0.97634
[52] validation-logloss:0.20146 validation-auc:0.97287 validation-aucpr:0.97640
[53] validation-logloss:0.20027 validation-auc:0.97293 validation-aucpr:0.97645
[54] validation-logloss:0.19940 validation-auc:0.97298 validation-aucpr:0.97646
[55] validation-logloss:0.19850 validation-auc:0.97290 validation-aucpr:0.97639
[56] validation-logloss:0.19759 validation-auc:0.97294 validation-aucpr:0.97639
{'best_iteration': '54', 'best_score': '0.9764551443514747'}
Trial 89, Fold 3: Log loss = 0.19758623619424412, Average precision = 0.9763968490881044, ROC-AUC = 0.9729429613988114, Elapsed Time = 10.560710200003086 seconds
Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 89, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65225 validation-auc:0.95540 validation-aucpr:0.95811
[1] validation-logloss:0.61561 validation-auc:0.96157 validation-aucpr:0.96507
[2] validation-logloss:0.58339 validation-auc:0.96360 validation-aucpr:0.96762
[3] validation-logloss:0.55708 validation-auc:0.96406 validation-aucpr:0.96979
[4] validation-logloss:0.52991 validation-auc:0.96531 validation-aucpr:0.97079
[5] validation-logloss:0.50509 validation-auc:0.96687 validation-aucpr:0.97220
[6] validation-logloss:0.48227 validation-auc:0.96739 validation-aucpr:0.97261
[7] validation-logloss:0.46261 validation-auc:0.96778 validation-aucpr:0.97287
[8] validation-logloss:0.44365 validation-auc:0.96797 validation-aucpr:0.97301
[9] validation-logloss:0.42648 validation-auc:0.96851 validation-aucpr:0.97344
[10] validation-logloss:0.41246 validation-auc:0.96883 validation-aucpr:0.97357
[11] validation-logloss:0.39939 validation-auc:0.96829 validation-aucpr:0.97318
[12] validation-logloss:0.38724 validation-auc:0.96827 validation-aucpr:0.97314
[13] validation-logloss:0.37425 validation-auc:0.96837 validation-aucpr:0.97329
[14] validation-logloss:0.36229 validation-auc:0.96863 validation-aucpr:0.97351
[15] validation-logloss:0.35095 validation-auc:0.96890 validation-aucpr:0.97369
[16] validation-logloss:0.34195 validation-auc:0.96873 validation-aucpr:0.97356
[17] validation-logloss:0.33226 validation-auc:0.96899 validation-aucpr:0.97384
[18] validation-logloss:0.32354 validation-auc:0.96899 validation-aucpr:0.97385
[19] validation-logloss:0.31471 validation-auc:0.96921 validation-aucpr:0.97406
[20] validation-logloss:0.30803 validation-auc:0.96954 validation-aucpr:0.97436
[21] validation-logloss:0.30076 validation-auc:0.96948 validation-aucpr:0.97432
[22] validation-logloss:0.29405 validation-auc:0.96950 validation-aucpr:0.97431
[23] validation-logloss:0.28809 validation-auc:0.96957 validation-aucpr:0.97436
[24] validation-logloss:0.28198 validation-auc:0.96971 validation-aucpr:0.97448
[25] validation-logloss:0.27607 validation-auc:0.96981 validation-aucpr:0.97456
[26] validation-logloss:0.27111 validation-auc:0.96986 validation-aucpr:0.97461
[27] validation-logloss:0.26569 validation-auc:0.97013 validation-aucpr:0.97481
[28] validation-logloss:0.26117 validation-auc:0.96996 validation-aucpr:0.97470
[29] validation-logloss:0.25733 validation-auc:0.96998 validation-aucpr:0.97467
[30] validation-logloss:0.25292 validation-auc:0.97025 validation-aucpr:0.97487
[31] validation-logloss:0.24986 validation-auc:0.97007 validation-aucpr:0.97470
[32] validation-logloss:0.24660 validation-auc:0.97020 validation-aucpr:0.97479
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[34] validation-logloss:0.23945 validation-auc:0.97069 validation-aucpr:0.97522
[35] validation-logloss:0.23636 validation-auc:0.97073 validation-aucpr:0.97528
[36] validation-logloss:0.23332 validation-auc:0.97094 validation-aucpr:0.97543
[37] validation-logloss:0.23088 validation-auc:0.97093 validation-aucpr:0.97541
[38] validation-logloss:0.22898 validation-auc:0.97084 validation-aucpr:0.97536
[39] validation-logloss:0.22634 validation-auc:0.97107 validation-aucpr:0.97552
[40] validation-logloss:0.22398 validation-auc:0.97124 validation-aucpr:0.97562
[41] validation-logloss:0.22257 validation-auc:0.97111 validation-aucpr:0.97554
[42] validation-logloss:0.22034 validation-auc:0.97129 validation-aucpr:0.97568
[43] validation-logloss:0.21859 validation-auc:0.97130 validation-aucpr:0.97570
[44] validation-logloss:0.21661 validation-auc:0.97148 validation-aucpr:0.97583
[45] validation-logloss:0.21468 validation-auc:0.97156 validation-aucpr:0.97590
[46] validation-logloss:0.21352 validation-auc:0.97146 validation-aucpr:0.97584
[47] validation-logloss:0.21203 validation-auc:0.97139 validation-aucpr:0.97583
[48] validation-logloss:0.21031 validation-auc:0.97157 validation-aucpr:0.97595
[49] validation-logloss:0.20906 validation-auc:0.97153 validation-aucpr:0.97592
[50] validation-logloss:0.20791 validation-auc:0.97149 validation-aucpr:0.97589
[51] validation-logloss:0.20664 validation-auc:0.97174 validation-aucpr:0.97605
[52] validation-logloss:0.20545 validation-auc:0.97183 validation-aucpr:0.97609
[53] validation-logloss:0.20491 validation-auc:0.97169 validation-aucpr:0.97599
[54] validation-logloss:0.20397 validation-auc:0.97160 validation-aucpr:0.97595
[55] validation-logloss:0.20282 validation-auc:0.97164 validation-aucpr:0.97599
[56] validation-logloss:0.20183 validation-auc:0.97157 validation-aucpr:0.97595
{'best_iteration': '52', 'best_score': '0.9760938284206304'}
Trial 89, Fold 4: Log loss = 0.20183458227168163, Average precision = 0.9759502113850549, ROC-AUC = 0.9715703096871415, Elapsed Time = 10.825298800002201 seconds
Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 89, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65276 validation-auc:0.95644 validation-aucpr:0.96185
[1] validation-logloss:0.62152 validation-auc:0.95868 validation-aucpr:0.96409
[2] validation-logloss:0.59298 validation-auc:0.95954 validation-aucpr:0.96431
[3] validation-logloss:0.56285 validation-auc:0.96259 validation-aucpr:0.96699
[4] validation-logloss:0.53503 validation-auc:0.96485 validation-aucpr:0.96886
[5] validation-logloss:0.51198 validation-auc:0.96489 validation-aucpr:0.96878
[6] validation-logloss:0.48924 validation-auc:0.96562 validation-aucpr:0.96947
[7] validation-logloss:0.46824 validation-auc:0.96644 validation-aucpr:0.97020
[8] validation-logloss:0.45220 validation-auc:0.96629 validation-aucpr:0.96987
[9] validation-logloss:0.43381 validation-auc:0.96712 validation-aucpr:0.97046
[10] validation-logloss:0.41953 validation-auc:0.96696 validation-aucpr:0.97030
[11] validation-logloss:0.40397 validation-auc:0.96768 validation-aucpr:0.97087
[12] validation-logloss:0.39028 validation-auc:0.96815 validation-aucpr:0.97148
[13] validation-logloss:0.37725 validation-auc:0.96809 validation-aucpr:0.97029
[14] validation-logloss:0.36693 validation-auc:0.96791 validation-aucpr:0.97011
[15] validation-logloss:0.35704 validation-auc:0.96823 validation-aucpr:0.97223
[16] validation-logloss:0.34606 validation-auc:0.96880 validation-aucpr:0.97277
[17] validation-logloss:0.33817 validation-auc:0.96842 validation-aucpr:0.97228
[18] validation-logloss:0.33036 validation-auc:0.96823 validation-aucpr:0.97208
[19] validation-logloss:0.32150 validation-auc:0.96855 validation-aucpr:0.97230
[20] validation-logloss:0.31339 validation-auc:0.96881 validation-aucpr:0.97250
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[22] validation-logloss:0.29955 validation-auc:0.96898 validation-aucpr:0.97264
[23] validation-logloss:0.29436 validation-auc:0.96890 validation-aucpr:0.97282
[24] validation-logloss:0.28795 validation-auc:0.96909 validation-aucpr:0.97292
[25] validation-logloss:0.28333 validation-auc:0.96901 validation-aucpr:0.97283
[26] validation-logloss:0.27758 validation-auc:0.96919 validation-aucpr:0.97298
[27] validation-logloss:0.27353 validation-auc:0.96904 validation-aucpr:0.97306
[28] validation-logloss:0.26843 validation-auc:0.96929 validation-aucpr:0.97320
[29] validation-logloss:0.26456 validation-auc:0.96932 validation-aucpr:0.97321
[30] validation-logloss:0.26005 validation-auc:0.96955 validation-aucpr:0.97341
[31] validation-logloss:0.25677 validation-auc:0.96946 validation-aucpr:0.97331
[32] validation-logloss:0.25262 validation-auc:0.96982 validation-aucpr:0.97365
[33] validation-logloss:0.24985 validation-auc:0.96966 validation-aucpr:0.97350
[34] validation-logloss:0.24712 validation-auc:0.96966 validation-aucpr:0.97347
[35] validation-logloss:0.24399 validation-auc:0.96955 validation-aucpr:0.97344
[36] validation-logloss:0.24083 validation-auc:0.96971 validation-aucpr:0.97354
[37] validation-logloss:0.23755 validation-auc:0.97002 validation-aucpr:0.97382
[38] validation-logloss:0.23485 validation-auc:0.97013 validation-aucpr:0.97390
[39] validation-logloss:0.23222 validation-auc:0.97032 validation-aucpr:0.97403
[40] validation-logloss:0.23036 validation-auc:0.97023 validation-aucpr:0.97393
[41] validation-logloss:0.22854 validation-auc:0.97036 validation-aucpr:0.97406
[42] validation-logloss:0.22604 validation-auc:0.97055 validation-aucpr:0.97419
[43] validation-logloss:0.22468 validation-auc:0.97039 validation-aucpr:0.97404
[44] validation-logloss:0.22264 validation-auc:0.97054 validation-aucpr:0.97415
[45] validation-logloss:0.22064 validation-auc:0.97064 validation-aucpr:0.97422
[46] validation-logloss:0.21937 validation-auc:0.97071 validation-aucpr:0.97425
[47] validation-logloss:0.21739 validation-auc:0.97095 validation-aucpr:0.97438
[48] validation-logloss:0.21560 validation-auc:0.97111 validation-aucpr:0.97446
[49] validation-logloss:0.21391 validation-auc:0.97135 validation-aucpr:0.97465
[50] validation-logloss:0.21264 validation-auc:0.97131 validation-aucpr:0.97461
[51] validation-logloss:0.21130 validation-auc:0.97140 validation-aucpr:0.97473
[52] validation-logloss:0.20998 validation-auc:0.97157 validation-aucpr:0.97492
[53] validation-logloss:0.20877 validation-auc:0.97167 validation-aucpr:0.97497
[54] validation-logloss:0.20782 validation-auc:0.97171 validation-aucpr:0.97492
[55] validation-logloss:0.20683 validation-auc:0.97173 validation-aucpr:0.97493
[56] validation-logloss:0.20575 validation-auc:0.97182 validation-aucpr:0.97479
{'best_iteration': '53', 'best_score': '0.9749698823576478'}
Trial 89, Fold 5: Log loss = 0.20574513483962278, Average precision = 0.9748008098075681, ROC-AUC = 0.9718218605428905, Elapsed Time = 11.012917900003231 seconds
Optimization Progress: 90%|######### | 90/100 [3:51:38<11:27, 68.71s/it]
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 90, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68354 validation-auc:0.92709 validation-aucpr:0.92888
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67487 validation-auc:0.95282 validation-aucpr:0.94079
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66557 validation-auc:0.96054 validation-aucpr:0.95758
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65668 validation-auc:0.96288 validation-aucpr:0.96216
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64834 validation-auc:0.96400 validation-aucpr:0.96566
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63979 validation-auc:0.96555 validation-aucpr:0.97077
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63156 validation-auc:0.96603 validation-aucpr:0.97110
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62432 validation-auc:0.96592 validation-aucpr:0.97096
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.61660 validation-auc:0.96646 validation-aucpr:0.97132
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.60976 validation-auc:0.96623 validation-aucpr:0.97109
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60202 validation-auc:0.96670 validation-aucpr:0.97103
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.59529 validation-auc:0.96675 validation-aucpr:0.97129
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.58876 validation-auc:0.96668 validation-aucpr:0.97118
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58162 validation-auc:0.96673 validation-aucpr:0.97120
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.57436 validation-auc:0.96723 validation-aucpr:0.97022
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.56836 validation-auc:0.96765 validation-aucpr:0.97103
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56162 validation-auc:0.96798 validation-aucpr:0.97305
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.55592 validation-auc:0.96783 validation-aucpr:0.97289
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.54955 validation-auc:0.96822 validation-aucpr:0.97321
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.54312 validation-auc:0.96861 validation-aucpr:0.97346
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.53768 validation-auc:0.96851 validation-aucpr:0.97336
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.53159 validation-auc:0.96872 validation-aucpr:0.97357
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.52656 validation-auc:0.96852 validation-aucpr:0.97339
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52076 validation-auc:0.96870 validation-aucpr:0.97354
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.51577 validation-auc:0.96864 validation-aucpr:0.97345
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51095 validation-auc:0.96845 validation-aucpr:0.97329
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.50561 validation-auc:0.96839 validation-aucpr:0.97325
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.50098 validation-auc:0.96833 validation-aucpr:0.97317
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.49566 validation-auc:0.96840 validation-aucpr:0.97322
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.49130 validation-auc:0.96818 validation-aucpr:0.97306
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.48694 validation-auc:0.96796 validation-aucpr:0.97286
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.48257 validation-auc:0.96796 validation-aucpr:0.97284
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.47842 validation-auc:0.96776 validation-aucpr:0.97269
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.47353 validation-auc:0.96797 validation-aucpr:0.97288
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.46935 validation-auc:0.96797 validation-aucpr:0.97289
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.46466 validation-auc:0.96811 validation-aucpr:0.97301
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.46080 validation-auc:0.96792 validation-aucpr:0.97286
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.45619 validation-auc:0.96814 validation-aucpr:0.97305
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45172 validation-auc:0.96828 validation-aucpr:0.97315
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.44734 validation-auc:0.96829 validation-aucpr:0.97319
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.44310 validation-auc:0.96844 validation-aucpr:0.97339
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.43887 validation-auc:0.96872 validation-aucpr:0.97361
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.43468 validation-auc:0.96901 validation-aucpr:0.97381
[21:50:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.43078 validation-auc:0.96897 validation-aucpr:0.97379
{'best_iteration': '42', 'best_score': '0.9738097543498954'}
Trial 90, Fold 1: Log loss = 0.430776386850286, Average precision = 0.9737941693851264, ROC-AUC = 0.9689733711159862, Elapsed Time = 6.182111800000712 seconds
Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 90, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68314 validation-auc:0.93332 validation-aucpr:0.93694
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67376 validation-auc:0.95557 validation-aucpr:0.94053
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66562 validation-auc:0.96129 validation-aucpr:0.95678
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65646 validation-auc:0.96455 validation-aucpr:0.96381
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64867 validation-auc:0.96572 validation-aucpr:0.96888
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.63997 validation-auc:0.96678 validation-aucpr:0.97098
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63256 validation-auc:0.96687 validation-aucpr:0.97096
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62427 validation-auc:0.96759 validation-aucpr:0.97164
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.61730 validation-auc:0.96759 validation-aucpr:0.97148
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61039 validation-auc:0.96742 validation-aucpr:0.97125
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60353 validation-auc:0.96722 validation-aucpr:0.97102
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.59681 validation-auc:0.96745 validation-aucpr:0.97112
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.58935 validation-auc:0.96774 validation-aucpr:0.97148
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58212 validation-auc:0.96801 validation-aucpr:0.97167
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.57526 validation-auc:0.96837 validation-aucpr:0.97198
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.56850 validation-auc:0.96853 validation-aucpr:0.97210
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56171 validation-auc:0.96867 validation-aucpr:0.97230
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.55550 validation-auc:0.96859 validation-aucpr:0.97223
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.54988 validation-auc:0.96842 validation-aucpr:0.97193
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.54354 validation-auc:0.96854 validation-aucpr:0.97202
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.53726 validation-auc:0.96894 validation-aucpr:0.97231
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.53186 validation-auc:0.96876 validation-aucpr:0.97224
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.52656 validation-auc:0.96868 validation-aucpr:0.97214
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52155 validation-auc:0.96863 validation-aucpr:0.97204
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.51588 validation-auc:0.96859 validation-aucpr:0.97196
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51013 validation-auc:0.96890 validation-aucpr:0.97221
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.50525 validation-auc:0.96880 validation-aucpr:0.97212
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.50035 validation-auc:0.96898 validation-aucpr:0.97227
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.49581 validation-auc:0.96917 validation-aucpr:0.97236
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.49056 validation-auc:0.96934 validation-aucpr:0.97250
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.48597 validation-auc:0.96937 validation-aucpr:0.97245
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.48173 validation-auc:0.96945 validation-aucpr:0.97251
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.47746 validation-auc:0.96930 validation-aucpr:0.97241
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.47315 validation-auc:0.96950 validation-aucpr:0.97253
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.46909 validation-auc:0.96949 validation-aucpr:0.97241
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.46441 validation-auc:0.96963 validation-aucpr:0.97353
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.45973 validation-auc:0.96976 validation-aucpr:0.97367
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.45586 validation-auc:0.96978 validation-aucpr:0.97365
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45133 validation-auc:0.96992 validation-aucpr:0.97377
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.44703 validation-auc:0.96998 validation-aucpr:0.97382
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.44321 validation-auc:0.97006 validation-aucpr:0.97386
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.43898 validation-auc:0.97013 validation-aucpr:0.97390
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.43478 validation-auc:0.97035 validation-aucpr:0.97408
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.43077 validation-auc:0.97038 validation-aucpr:0.97411
{'best_iteration': '43', 'best_score': '0.9741106364338346'}
Trial 90, Fold 2: Log loss = 0.43076507082274706, Average precision = 0.9741145879228299, ROC-AUC = 0.9703752662035527, Elapsed Time = 6.197583100001793 seconds
Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 90, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:50:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68327 validation-auc:0.93318 validation-aucpr:0.94252
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67480 validation-auc:0.95431 validation-aucpr:0.93696
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66570 validation-auc:0.96271 validation-aucpr:0.95550
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65667 validation-auc:0.96478 validation-aucpr:0.96069
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64886 validation-auc:0.96514 validation-aucpr:0.96758
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64028 validation-auc:0.96608 validation-aucpr:0.96940
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63192 validation-auc:0.96657 validation-aucpr:0.97014
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62368 validation-auc:0.96688 validation-aucpr:0.97021
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.61653 validation-auc:0.96736 validation-aucpr:0.96978
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.60849 validation-auc:0.96858 validation-aucpr:0.97065
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60163 validation-auc:0.96898 validation-aucpr:0.97107
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.59481 validation-auc:0.96932 validation-aucpr:0.97121
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.58772 validation-auc:0.96964 validation-aucpr:0.97144
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58129 validation-auc:0.96967 validation-aucpr:0.97148
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.57427 validation-auc:0.96969 validation-aucpr:0.97096
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.56741 validation-auc:0.97002 validation-aucpr:0.97094
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56137 validation-auc:0.96998 validation-aucpr:0.97085
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.55562 validation-auc:0.96998 validation-aucpr:0.97125
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.54918 validation-auc:0.97008 validation-aucpr:0.97160
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.54356 validation-auc:0.97043 validation-aucpr:0.97363
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.53805 validation-auc:0.97042 validation-aucpr:0.97353
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.53201 validation-auc:0.97039 validation-aucpr:0.97342
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.52677 validation-auc:0.97045 validation-aucpr:0.97337
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52095 validation-auc:0.97062 validation-aucpr:0.97436
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.51588 validation-auc:0.97047 validation-aucpr:0.97335
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51030 validation-auc:0.97053 validation-aucpr:0.97340
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.50515 validation-auc:0.97051 validation-aucpr:0.97407
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.49958 validation-auc:0.97073 validation-aucpr:0.97423
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.49435 validation-auc:0.97062 validation-aucpr:0.97414
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.48971 validation-auc:0.97055 validation-aucpr:0.97407
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.48441 validation-auc:0.97059 validation-aucpr:0.97349
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.47994 validation-auc:0.97067 validation-aucpr:0.97339
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.47596 validation-auc:0.97047 validation-aucpr:0.97314
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.47157 validation-auc:0.97044 validation-aucpr:0.97314
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.46679 validation-auc:0.97068 validation-aucpr:0.97359
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.46251 validation-auc:0.97086 validation-aucpr:0.97372
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.45795 validation-auc:0.97082 validation-aucpr:0.97367
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.45336 validation-auc:0.97099 validation-aucpr:0.97400
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.44901 validation-auc:0.97113 validation-aucpr:0.97521
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.44506 validation-auc:0.97108 validation-aucpr:0.97518
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.44146 validation-auc:0.97110 validation-aucpr:0.97519
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.43724 validation-auc:0.97116 validation-aucpr:0.97526
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.43318 validation-auc:0.97120 validation-aucpr:0.97531
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.42915 validation-auc:0.97126 validation-aucpr:0.97536
{'best_iteration': '43', 'best_score': '0.9753572218732132'}
Trial 90, Fold 3: Log loss = 0.42914536504407685, Average precision = 0.9753615635108435, ROC-AUC = 0.9712575302655276, Elapsed Time = 6.2764660000029835 seconds
Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 90, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68309 validation-auc:0.92522 validation-aucpr:0.91691
[21:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67472 validation-auc:0.95181 validation-aucpr:0.94043
[21:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66553 validation-auc:0.95816 validation-aucpr:0.95189
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65765 validation-auc:0.96004 validation-aucpr:0.95955
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64888 validation-auc:0.96237 validation-aucpr:0.96545
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64020 validation-auc:0.96373 validation-aucpr:0.96673
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63268 validation-auc:0.96432 validation-aucpr:0.96678
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62543 validation-auc:0.96487 validation-aucpr:0.97023
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.61842 validation-auc:0.96460 validation-aucpr:0.96989
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61055 validation-auc:0.96503 validation-aucpr:0.97049
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60274 validation-auc:0.96561 validation-aucpr:0.97107
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.59510 validation-auc:0.96584 validation-aucpr:0.97128
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.58775 validation-auc:0.96621 validation-aucpr:0.97162
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58155 validation-auc:0.96587 validation-aucpr:0.97134
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.57447 validation-auc:0.96625 validation-aucpr:0.97160
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.56840 validation-auc:0.96655 validation-aucpr:0.97190
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56165 validation-auc:0.96700 validation-aucpr:0.97225
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.55580 validation-auc:0.96722 validation-aucpr:0.97241
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.54934 validation-auc:0.96726 validation-aucpr:0.97245
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.54309 validation-auc:0.96748 validation-aucpr:0.97263
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.53701 validation-auc:0.96758 validation-aucpr:0.97272
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.53165 validation-auc:0.96760 validation-aucpr:0.97271
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.52629 validation-auc:0.96775 validation-aucpr:0.97281
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52036 validation-auc:0.96801 validation-aucpr:0.97300
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.51513 validation-auc:0.96815 validation-aucpr:0.97313
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51019 validation-auc:0.96802 validation-aucpr:0.97300
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.50538 validation-auc:0.96797 validation-aucpr:0.97292
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.49998 validation-auc:0.96812 validation-aucpr:0.97308
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.49476 validation-auc:0.96830 validation-aucpr:0.97330
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.49034 validation-auc:0.96818 validation-aucpr:0.97319
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.48579 validation-auc:0.96821 validation-aucpr:0.97322
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.48071 validation-auc:0.96839 validation-aucpr:0.97337
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.47636 validation-auc:0.96841 validation-aucpr:0.97338
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.47151 validation-auc:0.96858 validation-aucpr:0.97351
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.46675 validation-auc:0.96867 validation-aucpr:0.97365
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.46282 validation-auc:0.96857 validation-aucpr:0.97356
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.45816 validation-auc:0.96869 validation-aucpr:0.97364
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.45415 validation-auc:0.96868 validation-aucpr:0.97362
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45045 validation-auc:0.96851 validation-aucpr:0.97350
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.44606 validation-auc:0.96851 validation-aucpr:0.97353
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.44240 validation-auc:0.96855 validation-aucpr:0.97357
[21:50:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.43830 validation-auc:0.96858 validation-aucpr:0.97359
[21:50:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.43406 validation-auc:0.96870 validation-aucpr:0.97368
[21:50:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.43003 validation-auc:0.96879 validation-aucpr:0.97378
{'best_iteration': '43', 'best_score': '0.9737783677406094'}
Trial 90, Fold 4: Log loss = 0.4300305917885512, Average precision = 0.9737804054317507, ROC-AUC = 0.9687914291198428, Elapsed Time = 6.178327800000261 seconds
Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 90, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68339 validation-auc:0.93084 validation-aucpr:0.92778
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67511 validation-auc:0.95050 validation-aucpr:0.93833
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.66581 validation-auc:0.95928 validation-aucpr:0.95885
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.65679 validation-auc:0.96248 validation-aucpr:0.96825
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.64912 validation-auc:0.96295 validation-aucpr:0.96555
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64146 validation-auc:0.96371 validation-aucpr:0.96758
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.63312 validation-auc:0.96474 validation-aucpr:0.96983
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.62501 validation-auc:0.96503 validation-aucpr:0.96929
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.61720 validation-auc:0.96575 validation-aucpr:0.97081
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.61011 validation-auc:0.96632 validation-aucpr:0.97131
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.60262 validation-auc:0.96657 validation-aucpr:0.97153
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.59530 validation-auc:0.96642 validation-aucpr:0.97157
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.58891 validation-auc:0.96656 validation-aucpr:0.97166
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.58163 validation-auc:0.96705 validation-aucpr:0.97204
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.57459 validation-auc:0.96745 validation-aucpr:0.97235
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.56772 validation-auc:0.96807 validation-aucpr:0.97271
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.56197 validation-auc:0.96793 validation-aucpr:0.97251
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.55629 validation-auc:0.96797 validation-aucpr:0.97248
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.55004 validation-auc:0.96777 validation-aucpr:0.97238
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.54456 validation-auc:0.96758 validation-aucpr:0.97220
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.53833 validation-auc:0.96766 validation-aucpr:0.97227
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.53304 validation-auc:0.96743 validation-aucpr:0.97207
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.52802 validation-auc:0.96721 validation-aucpr:0.97190
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.52291 validation-auc:0.96701 validation-aucpr:0.97171
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.51717 validation-auc:0.96715 validation-aucpr:0.97183
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.51222 validation-auc:0.96709 validation-aucpr:0.97176
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.50671 validation-auc:0.96737 validation-aucpr:0.97197
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.50172 validation-auc:0.96733 validation-aucpr:0.97199
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.49724 validation-auc:0.96712 validation-aucpr:0.97184
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.49264 validation-auc:0.96714 validation-aucpr:0.97186
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.48747 validation-auc:0.96747 validation-aucpr:0.97215
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.48312 validation-auc:0.96738 validation-aucpr:0.97196
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.47811 validation-auc:0.96757 validation-aucpr:0.97210
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.47324 validation-auc:0.96776 validation-aucpr:0.97226
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.46913 validation-auc:0.96783 validation-aucpr:0.97231
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.46449 validation-auc:0.96797 validation-aucpr:0.97243
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.45998 validation-auc:0.96801 validation-aucpr:0.97246
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.45613 validation-auc:0.96800 validation-aucpr:0.97245
[21:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.45181 validation-auc:0.96820 validation-aucpr:0.97259
[21:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.44759 validation-auc:0.96828 validation-aucpr:0.97268
[21:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.44329 validation-auc:0.96833 validation-aucpr:0.97275
[21:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.43971 validation-auc:0.96832 validation-aucpr:0.97274
[21:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.43615 validation-auc:0.96837 validation-aucpr:0.97281
[21:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.43208 validation-auc:0.96858 validation-aucpr:0.97299
{'best_iteration': '43', 'best_score': '0.9729927994550973'}
Trial 90, Fold 5: Log loss = 0.43207551940990796, Average precision = 0.9729978877294114, ROC-AUC = 0.9685836909871245, Elapsed Time = 6.390422000000399 seconds
Optimization Progress: 91%|#########1| 91/100 [3:52:18<08:59, 59.98s/it]
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 91, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:51:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68534 validation-auc:0.95976 validation-aucpr:0.96326
[21:51:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67859 validation-auc:0.96192 validation-aucpr:0.96653
[21:51:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67123 validation-auc:0.96368 validation-aucpr:0.96761
[21:51:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66410 validation-auc:0.96501 validation-aucpr:0.96989
[21:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65765 validation-auc:0.96517 validation-aucpr:0.96987
[21:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65063 validation-auc:0.96550 validation-aucpr:0.97027
[21:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64455 validation-auc:0.96508 validation-aucpr:0.96975
[21:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63845 validation-auc:0.96501 validation-aucpr:0.96999
[21:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63181 validation-auc:0.96566 validation-aucpr:0.97066
[21:51:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62605 validation-auc:0.96561 validation-aucpr:0.97054
[21:51:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.62051 validation-auc:0.96524 validation-aucpr:0.97017
[21:51:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61508 validation-auc:0.96516 validation-aucpr:0.97011
[21:51:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60994 validation-auc:0.96489 validation-aucpr:0.96987
[21:51:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60474 validation-auc:0.96469 validation-aucpr:0.96974
[21:51:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59873 validation-auc:0.96485 validation-aucpr:0.96999
[21:51:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59354 validation-auc:0.96493 validation-aucpr:0.97003
[21:51:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58782 validation-auc:0.96522 validation-aucpr:0.97033
[21:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58203 validation-auc:0.96567 validation-aucpr:0.97071
[21:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57715 validation-auc:0.96577 validation-aucpr:0.97075
[21:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57167 validation-auc:0.96612 validation-aucpr:0.97120
[21:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56626 validation-auc:0.96626 validation-aucpr:0.97133
[21:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.56096 validation-auc:0.96639 validation-aucpr:0.97147
[21:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55584 validation-auc:0.96644 validation-aucpr:0.97161
[21:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55078 validation-auc:0.96661 validation-aucpr:0.97230
[21:51:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54588 validation-auc:0.96670 validation-aucpr:0.97237
[21:51:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54104 validation-auc:0.96688 validation-aucpr:0.97246
[21:51:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53619 validation-auc:0.96709 validation-aucpr:0.97266
[21:51:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53146 validation-auc:0.96728 validation-aucpr:0.97280
[21:51:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52680 validation-auc:0.96731 validation-aucpr:0.97283
[21:51:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52228 validation-auc:0.96742 validation-aucpr:0.97298
[21:51:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51851 validation-auc:0.96735 validation-aucpr:0.97283
[21:51:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51415 validation-auc:0.96738 validation-aucpr:0.97288
[21:51:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51023 validation-auc:0.96736 validation-aucpr:0.97279
[21:51:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50652 validation-auc:0.96727 validation-aucpr:0.97267
[21:51:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50265 validation-auc:0.96736 validation-aucpr:0.97273
[21:51:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49893 validation-auc:0.96740 validation-aucpr:0.97276
[21:51:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49487 validation-auc:0.96748 validation-aucpr:0.97283
[21:51:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49070 validation-auc:0.96773 validation-aucpr:0.97302
[21:51:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48711 validation-auc:0.96784 validation-aucpr:0.97306
[21:51:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48309 validation-auc:0.96787 validation-aucpr:0.97311
[21:52:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47914 validation-auc:0.96801 validation-aucpr:0.97325
[21:52:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47539 validation-auc:0.96810 validation-aucpr:0.97334
[21:52:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47161 validation-auc:0.96810 validation-aucpr:0.97336
[21:52:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46834 validation-auc:0.96810 validation-aucpr:0.97333
{'best_iteration': '42', 'best_score': '0.9733574134621086'}
Trial 91, Fold 1: Log loss = 0.4683359849337515, Average precision = 0.9733385803221706, ROC-AUC = 0.9681011707603847, Elapsed Time = 51.88532340000165 seconds
Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 91, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:52:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68550 validation-auc:0.96132 validation-aucpr:0.96534
[21:52:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67810 validation-auc:0.96501 validation-aucpr:0.96922
[21:52:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67070 validation-auc:0.96661 validation-aucpr:0.97081
[21:52:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66416 validation-auc:0.96751 validation-aucpr:0.97149
[21:52:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65707 validation-auc:0.96772 validation-aucpr:0.97172
[21:52:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65085 validation-auc:0.96768 validation-aucpr:0.97168
[21:52:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64462 validation-auc:0.96776 validation-aucpr:0.97164
[21:52:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63780 validation-auc:0.96829 validation-aucpr:0.97202
[21:52:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63113 validation-auc:0.96837 validation-aucpr:0.97210
[21:52:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62530 validation-auc:0.96858 validation-aucpr:0.97215
[21:52:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61892 validation-auc:0.96934 validation-aucpr:0.97274
[21:52:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61328 validation-auc:0.96963 validation-aucpr:0.97291
[21:52:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60700 validation-auc:0.97004 validation-aucpr:0.97329
[21:52:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60169 validation-auc:0.96959 validation-aucpr:0.97285
[21:52:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59664 validation-auc:0.96923 validation-aucpr:0.97253
[21:52:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59145 validation-auc:0.96913 validation-aucpr:0.97246
[21:52:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58579 validation-auc:0.96941 validation-aucpr:0.97273
[21:52:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58017 validation-auc:0.96944 validation-aucpr:0.97276
[21:52:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57477 validation-auc:0.96953 validation-aucpr:0.97284
[21:52:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57001 validation-auc:0.96932 validation-aucpr:0.97268
[21:52:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56456 validation-auc:0.96963 validation-aucpr:0.97292
[21:52:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55988 validation-auc:0.96969 validation-aucpr:0.97294
[21:52:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55477 validation-auc:0.96967 validation-aucpr:0.97297
[21:52:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55032 validation-auc:0.96962 validation-aucpr:0.97292
[21:52:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54588 validation-auc:0.96958 validation-aucpr:0.97286
[21:52:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54098 validation-auc:0.96966 validation-aucpr:0.97297
[21:52:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53674 validation-auc:0.96949 validation-aucpr:0.97269
[21:52:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53193 validation-auc:0.96952 validation-aucpr:0.97276
[21:52:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52717 validation-auc:0.96967 validation-aucpr:0.97290
[21:52:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52242 validation-auc:0.96980 validation-aucpr:0.97302
[21:52:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51848 validation-auc:0.96976 validation-aucpr:0.97296
[21:52:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51381 validation-auc:0.97003 validation-aucpr:0.97321
[21:52:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51004 validation-auc:0.96987 validation-aucpr:0.97305
[21:52:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50585 validation-auc:0.96986 validation-aucpr:0.97307
[21:52:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50151 validation-auc:0.96995 validation-aucpr:0.97316
[21:52:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49782 validation-auc:0.97001 validation-aucpr:0.97277
[21:52:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49422 validation-auc:0.96996 validation-aucpr:0.97272
[21:52:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49066 validation-auc:0.96987 validation-aucpr:0.97264
[21:52:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48705 validation-auc:0.96992 validation-aucpr:0.97268
[21:52:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48357 validation-auc:0.96991 validation-aucpr:0.97265
[21:52:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47958 validation-auc:0.97004 validation-aucpr:0.97278
[21:52:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47636 validation-auc:0.96999 validation-aucpr:0.97275
[21:52:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47245 validation-auc:0.97010 validation-aucpr:0.97283
[21:52:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46912 validation-auc:0.97005 validation-aucpr:0.97279
{'best_iteration': '12', 'best_score': '0.9732866427723065'}
Trial 91, Fold 2: Log loss = 0.469120277958387, Average precision = 0.9727538310090776, ROC-AUC = 0.9700462011307639, Elapsed Time = 50.572803199997 seconds
Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 91, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:52:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68535 validation-auc:0.96075 validation-aucpr:0.96245
[21:52:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67866 validation-auc:0.96244 validation-aucpr:0.96594
[21:52:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67133 validation-auc:0.96557 validation-aucpr:0.96968
[21:53:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66397 validation-auc:0.96694 validation-aucpr:0.97038
[21:53:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65708 validation-auc:0.96715 validation-aucpr:0.97051
[21:53:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65020 validation-auc:0.96761 validation-aucpr:0.97109
[21:53:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64349 validation-auc:0.96723 validation-aucpr:0.97087
[21:53:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63665 validation-auc:0.96774 validation-aucpr:0.97129
[21:53:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63011 validation-auc:0.96757 validation-aucpr:0.97131
[21:53:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62363 validation-auc:0.96714 validation-aucpr:0.97135
[21:53:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61802 validation-auc:0.96746 validation-aucpr:0.97168
[21:53:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61255 validation-auc:0.96742 validation-aucpr:0.97158
[21:53:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60652 validation-auc:0.96778 validation-aucpr:0.97180
[21:53:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60050 validation-auc:0.96842 validation-aucpr:0.97197
[21:53:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59530 validation-auc:0.96843 validation-aucpr:0.97192
[21:53:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59008 validation-auc:0.96865 validation-aucpr:0.97201
[21:53:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58447 validation-auc:0.96863 validation-aucpr:0.97185
[21:53:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57895 validation-auc:0.96871 validation-aucpr:0.97190
[21:53:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57421 validation-auc:0.96870 validation-aucpr:0.97342
[21:53:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56936 validation-auc:0.96882 validation-aucpr:0.97352
[21:53:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56463 validation-auc:0.96862 validation-aucpr:0.97336
[21:53:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55947 validation-auc:0.96865 validation-aucpr:0.97343
[21:53:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55444 validation-auc:0.96852 validation-aucpr:0.97332
[21:53:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54919 validation-auc:0.96897 validation-aucpr:0.97368
[21:53:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54473 validation-auc:0.96889 validation-aucpr:0.97359
[21:53:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54042 validation-auc:0.96888 validation-aucpr:0.97357
[21:53:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53552 validation-auc:0.96909 validation-aucpr:0.97372
[21:53:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53073 validation-auc:0.96925 validation-aucpr:0.97387
[21:53:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52643 validation-auc:0.96938 validation-aucpr:0.97399
[21:53:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52180 validation-auc:0.96956 validation-aucpr:0.97415
[21:53:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51775 validation-auc:0.96953 validation-aucpr:0.97411
[21:53:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51384 validation-auc:0.96944 validation-aucpr:0.97404
[21:53:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50960 validation-auc:0.96940 validation-aucpr:0.97399
[21:53:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50590 validation-auc:0.96932 validation-aucpr:0.97390
[21:53:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50196 validation-auc:0.96946 validation-aucpr:0.97401
[21:53:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49766 validation-auc:0.96960 validation-aucpr:0.97413
[21:53:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49416 validation-auc:0.96947 validation-aucpr:0.97398
[21:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49068 validation-auc:0.96949 validation-aucpr:0.97396
[21:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48651 validation-auc:0.96969 validation-aucpr:0.97414
[21:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48232 validation-auc:0.96997 validation-aucpr:0.97435
[21:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47912 validation-auc:0.96990 validation-aucpr:0.97428
[21:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47584 validation-auc:0.96986 validation-aucpr:0.97421
[21:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47191 validation-auc:0.97000 validation-aucpr:0.97435
[21:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46860 validation-auc:0.97007 validation-aucpr:0.97441
{'best_iteration': '43', 'best_score': '0.9744061651856183'}
Trial 91, Fold 3: Log loss = 0.4686027267876157, Average precision = 0.9744106591318193, ROC-AUC = 0.9700656896784198, Elapsed Time = 50.93511260000014 seconds
Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 91, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68538 validation-auc:0.95451 validation-aucpr:0.95174
[21:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67861 validation-auc:0.96242 validation-aucpr:0.96761
[21:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67120 validation-auc:0.96456 validation-aucpr:0.97007
[21:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66400 validation-auc:0.96577 validation-aucpr:0.97157
[21:53:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65773 validation-auc:0.96489 validation-aucpr:0.97086
[21:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.65065 validation-auc:0.96518 validation-aucpr:0.97125
[21:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64453 validation-auc:0.96506 validation-aucpr:0.97099
[21:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63779 validation-auc:0.96587 validation-aucpr:0.97173
[21:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63123 validation-auc:0.96651 validation-aucpr:0.97228
[21:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62539 validation-auc:0.96680 validation-aucpr:0.97228
[21:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61905 validation-auc:0.96684 validation-aucpr:0.97234
[21:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61302 validation-auc:0.96725 validation-aucpr:0.97271
[21:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60685 validation-auc:0.96748 validation-aucpr:0.97299
[21:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60171 validation-auc:0.96696 validation-aucpr:0.97256
[21:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59576 validation-auc:0.96704 validation-aucpr:0.97267
[21:54:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59002 validation-auc:0.96728 validation-aucpr:0.97284
[21:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58489 validation-auc:0.96723 validation-aucpr:0.97280
[21:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.57987 validation-auc:0.96723 validation-aucpr:0.97273
[21:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57529 validation-auc:0.96689 validation-aucpr:0.97245
[21:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.57048 validation-auc:0.96683 validation-aucpr:0.97238
[21:54:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56510 validation-auc:0.96697 validation-aucpr:0.97253
[21:54:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55993 validation-auc:0.96708 validation-aucpr:0.97265
[21:54:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55501 validation-auc:0.96721 validation-aucpr:0.97275
[21:54:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.55052 validation-auc:0.96709 validation-aucpr:0.97265
[21:54:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54615 validation-auc:0.96699 validation-aucpr:0.97253
[21:54:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.54128 validation-auc:0.96696 validation-aucpr:0.97253
[21:54:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53644 validation-auc:0.96712 validation-aucpr:0.97268
[21:54:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53221 validation-auc:0.96707 validation-aucpr:0.97262
[21:54:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52816 validation-auc:0.96703 validation-aucpr:0.97256
[21:54:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52350 validation-auc:0.96732 validation-aucpr:0.97281
[21:54:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51949 validation-auc:0.96726 validation-aucpr:0.97274
[21:54:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51520 validation-auc:0.96726 validation-aucpr:0.97274
[21:54:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.51089 validation-auc:0.96734 validation-aucpr:0.97281
[21:54:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50696 validation-auc:0.96735 validation-aucpr:0.97281
[21:54:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.50271 validation-auc:0.96737 validation-aucpr:0.97284
[21:54:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49847 validation-auc:0.96736 validation-aucpr:0.97285
[21:54:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49489 validation-auc:0.96730 validation-aucpr:0.97278
[21:54:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.49083 validation-auc:0.96740 validation-aucpr:0.97287
[21:54:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48739 validation-auc:0.96739 validation-aucpr:0.97285
[21:54:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48348 validation-auc:0.96742 validation-aucpr:0.97288
[21:54:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47952 validation-auc:0.96759 validation-aucpr:0.97302
[21:54:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47573 validation-auc:0.96764 validation-aucpr:0.97307
[21:54:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.47246 validation-auc:0.96752 validation-aucpr:0.97298
[21:54:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46871 validation-auc:0.96771 validation-aucpr:0.97314
{'best_iteration': '43', 'best_score': '0.9731358137383671'}
Trial 91, Fold 4: Log loss = 0.46871306628357956, Average precision = 0.9731291862601925, ROC-AUC = 0.967713228824653, Elapsed Time = 50.220442699999694 seconds
Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 91, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:54:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.68537 validation-auc:0.95408 validation-aucpr:0.95892
[21:54:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.67777 validation-auc:0.96220 validation-aucpr:0.96781
[21:54:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.67069 validation-auc:0.96237 validation-aucpr:0.96809
[21:54:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.66347 validation-auc:0.96512 validation-aucpr:0.97000
[21:54:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.65654 validation-auc:0.96510 validation-aucpr:0.96975
[21:54:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.64959 validation-auc:0.96544 validation-aucpr:0.97009
[21:54:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.64344 validation-auc:0.96634 validation-aucpr:0.97102
[21:54:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.63701 validation-auc:0.96590 validation-aucpr:0.97096
[21:54:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.63054 validation-auc:0.96633 validation-aucpr:0.97125
[21:54:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.62486 validation-auc:0.96644 validation-aucpr:0.97131
[21:54:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.61852 validation-auc:0.96666 validation-aucpr:0.97149
[21:54:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.61320 validation-auc:0.96647 validation-aucpr:0.97128
[21:54:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.60776 validation-auc:0.96679 validation-aucpr:0.97156
[21:54:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.60259 validation-auc:0.96615 validation-aucpr:0.97103
[21:54:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.59728 validation-auc:0.96645 validation-aucpr:0.97112
[21:54:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.59146 validation-auc:0.96702 validation-aucpr:0.97165
[21:54:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.58584 validation-auc:0.96717 validation-aucpr:0.97180
[21:54:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.58048 validation-auc:0.96706 validation-aucpr:0.97172
[21:54:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.57512 validation-auc:0.96709 validation-aucpr:0.97181
[21:54:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.56974 validation-auc:0.96736 validation-aucpr:0.97203
[21:54:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.56443 validation-auc:0.96746 validation-aucpr:0.97213
[21:55:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.55926 validation-auc:0.96761 validation-aucpr:0.97223
[21:55:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.55413 validation-auc:0.96774 validation-aucpr:0.97233
[21:55:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.54914 validation-auc:0.96782 validation-aucpr:0.97243
[21:55:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.54408 validation-auc:0.96801 validation-aucpr:0.97256
[21:55:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.53985 validation-auc:0.96774 validation-aucpr:0.97234
[21:55:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.53498 validation-auc:0.96795 validation-aucpr:0.97253
[21:55:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.53036 validation-auc:0.96817 validation-aucpr:0.97269
[21:55:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.52563 validation-auc:0.96840 validation-aucpr:0.97284
[21:55:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.52102 validation-auc:0.96853 validation-aucpr:0.97295
[21:55:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.51648 validation-auc:0.96873 validation-aucpr:0.97308
[21:55:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.51208 validation-auc:0.96869 validation-aucpr:0.97304
[21:55:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.50828 validation-auc:0.96871 validation-aucpr:0.97303
[21:55:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.50406 validation-auc:0.96875 validation-aucpr:0.97307
[21:55:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.49979 validation-auc:0.96888 validation-aucpr:0.97319
[21:55:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.49576 validation-auc:0.96890 validation-aucpr:0.97320
[21:55:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.49208 validation-auc:0.96882 validation-aucpr:0.97314
[21:55:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.48805 validation-auc:0.96889 validation-aucpr:0.97318
[21:55:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.48457 validation-auc:0.96868 validation-aucpr:0.97301
[21:55:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.48065 validation-auc:0.96876 validation-aucpr:0.97308
[21:55:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.47679 validation-auc:0.96890 validation-aucpr:0.97317
[21:55:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.47291 validation-auc:0.96908 validation-aucpr:0.97331
[21:55:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.46930 validation-auc:0.96918 validation-aucpr:0.97338
[21:55:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.46572 validation-auc:0.96916 validation-aucpr:0.97336
{'best_iteration': '42', 'best_score': '0.9733813409437337'}
Trial 91, Fold 5: Log loss = 0.46572245851236477, Average precision = 0.9733665370275528, ROC-AUC = 0.9691603961131858, Elapsed Time = 46.25501720000102 seconds
Optimization Progress: 92%|#########2| 92/100 [3:56:36<15:55, 119.39s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 92, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.65897 validation-auc:0.94764 validation-aucpr:0.95389
[1] validation-logloss:0.63078 validation-auc:0.91919 validation-aucpr:0.88950
[2] validation-logloss:0.60254 validation-auc:0.94006 validation-aucpr:0.92849
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[75] validation-logloss:0.20955 validation-auc:0.96906 validation-aucpr:0.97373
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[78] validation-logloss:0.20851 validation-auc:0.96930 validation-aucpr:0.97399
[79] validation-logloss:0.20822 validation-auc:0.96928 validation-aucpr:0.97390
[80] validation-logloss:0.20784 validation-auc:0.96930 validation-aucpr:0.97391
[81] validation-logloss:0.20813 validation-auc:0.96909 validation-aucpr:0.97383
[82] validation-logloss:0.20794 validation-auc:0.96907 validation-aucpr:0.97375
[83] validation-logloss:0.20795 validation-auc:0.96889 validation-aucpr:0.97362
[84] validation-logloss:0.20751 validation-auc:0.96899 validation-aucpr:0.97367
[85] validation-logloss:0.20741 validation-auc:0.96900 validation-aucpr:0.97362
[86] validation-logloss:0.20704 validation-auc:0.96910 validation-aucpr:0.97361
[87] validation-logloss:0.20675 validation-auc:0.96911 validation-aucpr:0.97358
[88] validation-logloss:0.20663 validation-auc:0.96911 validation-aucpr:0.97356
[89] validation-logloss:0.20621 validation-auc:0.96925 validation-aucpr:0.97368
[90] validation-logloss:0.20562 validation-auc:0.96947 validation-aucpr:0.97384
[91] validation-logloss:0.20572 validation-auc:0.96949 validation-aucpr:0.97390
[92] validation-logloss:0.20559 validation-auc:0.96957 validation-aucpr:0.97399
[93] validation-logloss:0.20570 validation-auc:0.96955 validation-aucpr:0.97396
{'best_iteration': '92', 'best_score': '0.9739895790503507'}
Trial 92, Fold 1: Log loss = 0.20569699593105253, Average precision = 0.9739613171371575, ROC-AUC = 0.9695516267331985, Elapsed Time = 2.5761713000028976 seconds
Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 92, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.65786 validation-auc:0.94682 validation-aucpr:0.95072
[1] validation-logloss:0.62898 validation-auc:0.93550 validation-aucpr:0.91127
[2] validation-logloss:0.60339 validation-auc:0.94895 validation-aucpr:0.93533
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[4] validation-logloss:0.55491 validation-auc:0.95707 validation-aucpr:0.95339
[5] validation-logloss:0.53235 validation-auc:0.96108 validation-aucpr:0.96298
[6] validation-logloss:0.51335 validation-auc:0.96108 validation-aucpr:0.96320
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[12] validation-logloss:0.42295 validation-auc:0.96408 validation-aucpr:0.96641
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[14] validation-logloss:0.39876 validation-auc:0.96479 validation-aucpr:0.96676
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[78] validation-logloss:0.20192 validation-auc:0.96994 validation-aucpr:0.97334
[79] validation-logloss:0.20179 validation-auc:0.96996 validation-aucpr:0.97335
[80] validation-logloss:0.20137 validation-auc:0.96998 validation-aucpr:0.97333
[81] validation-logloss:0.20077 validation-auc:0.97010 validation-aucpr:0.97344
[82] validation-logloss:0.20011 validation-auc:0.97012 validation-aucpr:0.97341
[83] validation-logloss:0.19931 validation-auc:0.97026 validation-aucpr:0.97352
[84] validation-logloss:0.19866 validation-auc:0.97039 validation-aucpr:0.97373
[85] validation-logloss:0.19850 validation-auc:0.97041 validation-aucpr:0.97369
[86] validation-logloss:0.19866 validation-auc:0.97031 validation-aucpr:0.97362
[87] validation-logloss:0.19824 validation-auc:0.97042 validation-aucpr:0.97366
[88] validation-logloss:0.19778 validation-auc:0.97044 validation-aucpr:0.97367
[89] validation-logloss:0.19746 validation-auc:0.97044 validation-aucpr:0.97365
[90] validation-logloss:0.19752 validation-auc:0.97038 validation-aucpr:0.97364
[91] validation-logloss:0.19731 validation-auc:0.97039 validation-aucpr:0.97362
[92] validation-logloss:0.19745 validation-auc:0.97026 validation-aucpr:0.97357
[93] validation-logloss:0.19740 validation-auc:0.97028 validation-aucpr:0.97344
{'best_iteration': '84', 'best_score': '0.9737342955246244'}
Trial 92, Fold 2: Log loss = 0.19739752275694591, Average precision = 0.9734464038101037, ROC-AUC = 0.9702782234947456, Elapsed Time = 3.030435299999226 seconds
Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 92, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.65806 validation-auc:0.95150 validation-aucpr:0.95838
[1] validation-logloss:0.62595 validation-auc:0.93582 validation-aucpr:0.91588
[2] validation-logloss:0.60060 validation-auc:0.94687 validation-aucpr:0.93455
[3] validation-logloss:0.57280 validation-auc:0.95316 validation-aucpr:0.94356
[4] validation-logloss:0.54954 validation-auc:0.95733 validation-aucpr:0.95671
[5] validation-logloss:0.52884 validation-auc:0.96035 validation-aucpr:0.96441
[6] validation-logloss:0.51051 validation-auc:0.96097 validation-aucpr:0.96329
[7] validation-logloss:0.49281 validation-auc:0.96233 validation-aucpr:0.96561
[8] validation-logloss:0.47540 validation-auc:0.96236 validation-aucpr:0.96586
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[10] validation-logloss:0.44741 validation-auc:0.96266 validation-aucpr:0.96567
[11] validation-logloss:0.43436 validation-auc:0.96308 validation-aucpr:0.96683
[12] validation-logloss:0.42042 validation-auc:0.96354 validation-aucpr:0.96733
[13] validation-logloss:0.40775 validation-auc:0.96422 validation-aucpr:0.96749
[14] validation-logloss:0.39649 validation-auc:0.96475 validation-aucpr:0.96771
[15] validation-logloss:0.38641 validation-auc:0.96485 validation-aucpr:0.96742
[16] validation-logloss:0.37466 validation-auc:0.96519 validation-aucpr:0.96728
[17] validation-logloss:0.36418 validation-auc:0.96536 validation-aucpr:0.96733
[18] validation-logloss:0.35620 validation-auc:0.96588 validation-aucpr:0.96717
[19] validation-logloss:0.34672 validation-auc:0.96626 validation-aucpr:0.96751
[20] validation-logloss:0.33830 validation-auc:0.96717 validation-aucpr:0.96868
[21] validation-logloss:0.32976 validation-auc:0.96725 validation-aucpr:0.96868
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[30] validation-logloss:0.27597 validation-auc:0.96827 validation-aucpr:0.97076
[31] validation-logloss:0.27178 validation-auc:0.96823 validation-aucpr:0.97071
[32] validation-logloss:0.26830 validation-auc:0.96830 validation-aucpr:0.97067
[33] validation-logloss:0.26442 validation-auc:0.96821 validation-aucpr:0.97056
[34] validation-logloss:0.26110 validation-auc:0.96836 validation-aucpr:0.97060
[35] validation-logloss:0.25783 validation-auc:0.96843 validation-aucpr:0.97045
[36] validation-logloss:0.25490 validation-auc:0.96847 validation-aucpr:0.97042
[37] validation-logloss:0.25188 validation-auc:0.96879 validation-aucpr:0.97079
[38] validation-logloss:0.24820 validation-auc:0.96890 validation-aucpr:0.97076
[39] validation-logloss:0.24605 validation-auc:0.96884 validation-aucpr:0.97169
[40] validation-logloss:0.24308 validation-auc:0.96884 validation-aucpr:0.97169
[41] validation-logloss:0.24045 validation-auc:0.96879 validation-aucpr:0.97167
[42] validation-logloss:0.23764 validation-auc:0.96905 validation-aucpr:0.97326
[43] validation-logloss:0.23497 validation-auc:0.96928 validation-aucpr:0.97366
[44] validation-logloss:0.23320 validation-auc:0.96932 validation-aucpr:0.97375
[45] validation-logloss:0.23145 validation-auc:0.96922 validation-aucpr:0.97365
[46] validation-logloss:0.22915 validation-auc:0.96938 validation-aucpr:0.97378
[47] validation-logloss:0.22713 validation-auc:0.96942 validation-aucpr:0.97377
[48] validation-logloss:0.22538 validation-auc:0.96944 validation-aucpr:0.97369
[49] validation-logloss:0.22387 validation-auc:0.96961 validation-aucpr:0.97389
[50] validation-logloss:0.22213 validation-auc:0.96953 validation-aucpr:0.97385
[51] validation-logloss:0.22070 validation-auc:0.96947 validation-aucpr:0.97382
[52] validation-logloss:0.21948 validation-auc:0.96952 validation-aucpr:0.97380
[53] validation-logloss:0.21815 validation-auc:0.96970 validation-aucpr:0.97388
[54] validation-logloss:0.21731 validation-auc:0.96959 validation-aucpr:0.97378
[55] validation-logloss:0.21673 validation-auc:0.96944 validation-aucpr:0.97364
[56] validation-logloss:0.21554 validation-auc:0.96956 validation-aucpr:0.97383
[57] validation-logloss:0.21422 validation-auc:0.96977 validation-aucpr:0.97264
[58] validation-logloss:0.21324 validation-auc:0.96989 validation-aucpr:0.97269
[59] validation-logloss:0.21249 validation-auc:0.96998 validation-aucpr:0.97407
[60] validation-logloss:0.21153 validation-auc:0.97009 validation-aucpr:0.97416
[61] validation-logloss:0.21049 validation-auc:0.97021 validation-aucpr:0.97425
[62] validation-logloss:0.20957 validation-auc:0.97039 validation-aucpr:0.97440
[63] validation-logloss:0.20870 validation-auc:0.97041 validation-aucpr:0.97437
[64] validation-logloss:0.20815 validation-auc:0.97028 validation-aucpr:0.97428
[65] validation-logloss:0.20753 validation-auc:0.97033 validation-aucpr:0.97426
[66] validation-logloss:0.20691 validation-auc:0.97031 validation-aucpr:0.97412
[67] validation-logloss:0.20600 validation-auc:0.97046 validation-aucpr:0.97422
[68] validation-logloss:0.20559 validation-auc:0.97040 validation-aucpr:0.97420
[69] validation-logloss:0.20519 validation-auc:0.97036 validation-aucpr:0.97417
[70] validation-logloss:0.20435 validation-auc:0.97050 validation-aucpr:0.97405
[71] validation-logloss:0.20372 validation-auc:0.97059 validation-aucpr:0.97413
[72] validation-logloss:0.20364 validation-auc:0.97048 validation-aucpr:0.97400
[73] validation-logloss:0.20346 validation-auc:0.97040 validation-aucpr:0.97402
[74] validation-logloss:0.20317 validation-auc:0.97034 validation-aucpr:0.97394
[75] validation-logloss:0.20315 validation-auc:0.97017 validation-aucpr:0.97376
[76] validation-logloss:0.20270 validation-auc:0.97023 validation-aucpr:0.97377
[77] validation-logloss:0.20225 validation-auc:0.97023 validation-aucpr:0.97375
[78] validation-logloss:0.20193 validation-auc:0.97028 validation-aucpr:0.97361
[79] validation-logloss:0.20169 validation-auc:0.97029 validation-aucpr:0.97360
[80] validation-logloss:0.20089 validation-auc:0.97050 validation-aucpr:0.97374
[81] validation-logloss:0.20081 validation-auc:0.97052 validation-aucpr:0.97367
[82] validation-logloss:0.20059 validation-auc:0.97050 validation-aucpr:0.97361
[83] validation-logloss:0.20015 validation-auc:0.97053 validation-aucpr:0.97361
[84] validation-logloss:0.19965 validation-auc:0.97063 validation-aucpr:0.97412
[85] validation-logloss:0.19945 validation-auc:0.97061 validation-aucpr:0.97409
[86] validation-logloss:0.19947 validation-auc:0.97061 validation-aucpr:0.97429
[87] validation-logloss:0.19900 validation-auc:0.97067 validation-aucpr:0.97433
[88] validation-logloss:0.19925 validation-auc:0.97060 validation-aucpr:0.97421
[89] validation-logloss:0.19932 validation-auc:0.97046 validation-aucpr:0.97407
[90] validation-logloss:0.19930 validation-auc:0.97043 validation-aucpr:0.97407
[91] validation-logloss:0.19913 validation-auc:0.97051 validation-aucpr:0.97412
[92] validation-logloss:0.19883 validation-auc:0.97062 validation-aucpr:0.97417
[93] validation-logloss:0.19853 validation-auc:0.97075 validation-aucpr:0.97425
{'best_iteration': '62', 'best_score': '0.9743978942246921'}
Trial 92, Fold 3: Log loss = 0.19853437848993327, Average precision = 0.9742558475354604, ROC-AUC = 0.9707452795889475, Elapsed Time = 3.047268400001485 seconds
Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 92, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.65883 validation-auc:0.94222 validation-aucpr:0.94345
[1] validation-logloss:0.63025 validation-auc:0.92396 validation-aucpr:0.90743
[2] validation-logloss:0.60046 validation-auc:0.94528 validation-aucpr:0.93742
[3] validation-logloss:0.57344 validation-auc:0.95397 validation-aucpr:0.95632
[4] validation-logloss:0.54915 validation-auc:0.95825 validation-aucpr:0.96297
[5] validation-logloss:0.52948 validation-auc:0.95884 validation-aucpr:0.96315
[6] validation-logloss:0.51112 validation-auc:0.95977 validation-aucpr:0.96139
[7] validation-logloss:0.49180 validation-auc:0.96052 validation-aucpr:0.96205
[8] validation-logloss:0.47487 validation-auc:0.96095 validation-aucpr:0.96420
[9] validation-logloss:0.45835 validation-auc:0.96175 validation-aucpr:0.96449
[10] validation-logloss:0.44416 validation-auc:0.96284 validation-aucpr:0.96510
[11] validation-logloss:0.43141 validation-auc:0.96255 validation-aucpr:0.96483
[12] validation-logloss:0.41914 validation-auc:0.96271 validation-aucpr:0.96632
[13] validation-logloss:0.40800 validation-auc:0.96283 validation-aucpr:0.96617
[14] validation-logloss:0.39789 validation-auc:0.96359 validation-aucpr:0.96666
[15] validation-logloss:0.38799 validation-auc:0.96414 validation-aucpr:0.96988
[16] validation-logloss:0.37700 validation-auc:0.96456 validation-aucpr:0.97024
[17] validation-logloss:0.36799 validation-auc:0.96513 validation-aucpr:0.97065
[18] validation-logloss:0.35797 validation-auc:0.96581 validation-aucpr:0.97116
[19] validation-logloss:0.34997 validation-auc:0.96594 validation-aucpr:0.97124
[20] validation-logloss:0.34169 validation-auc:0.96614 validation-aucpr:0.97142
[21] validation-logloss:0.33346 validation-auc:0.96642 validation-aucpr:0.97164
[22] validation-logloss:0.32697 validation-auc:0.96641 validation-aucpr:0.97159
[23] validation-logloss:0.32174 validation-auc:0.96589 validation-aucpr:0.97132
[24] validation-logloss:0.31443 validation-auc:0.96630 validation-aucpr:0.97158
[25] validation-logloss:0.30950 validation-auc:0.96608 validation-aucpr:0.97142
[26] validation-logloss:0.30400 validation-auc:0.96586 validation-aucpr:0.97128
[27] validation-logloss:0.29982 validation-auc:0.96573 validation-aucpr:0.97116
[28] validation-logloss:0.29378 validation-auc:0.96602 validation-aucpr:0.97146
[29] validation-logloss:0.28977 validation-auc:0.96584 validation-aucpr:0.97128
[30] validation-logloss:0.28444 validation-auc:0.96608 validation-aucpr:0.97147
[31] validation-logloss:0.28020 validation-auc:0.96588 validation-aucpr:0.97130
[32] validation-logloss:0.27539 validation-auc:0.96627 validation-aucpr:0.97158
[33] validation-logloss:0.27085 validation-auc:0.96648 validation-aucpr:0.97174
[34] validation-logloss:0.26674 validation-auc:0.96665 validation-aucpr:0.97188
[35] validation-logloss:0.26392 validation-auc:0.96651 validation-aucpr:0.97170
[36] validation-logloss:0.26099 validation-auc:0.96666 validation-aucpr:0.97179
[37] validation-logloss:0.25869 validation-auc:0.96662 validation-aucpr:0.97178
[38] validation-logloss:0.25569 validation-auc:0.96647 validation-aucpr:0.97168
[39] validation-logloss:0.25216 validation-auc:0.96684 validation-aucpr:0.97194
[40] validation-logloss:0.24950 validation-auc:0.96704 validation-aucpr:0.97209
[41] validation-logloss:0.24734 validation-auc:0.96706 validation-aucpr:0.97205
[42] validation-logloss:0.24560 validation-auc:0.96698 validation-aucpr:0.97202
[43] validation-logloss:0.24310 validation-auc:0.96725 validation-aucpr:0.97221
[44] validation-logloss:0.24011 validation-auc:0.96757 validation-aucpr:0.97245
[45] validation-logloss:0.23805 validation-auc:0.96755 validation-aucpr:0.97244
[46] validation-logloss:0.23591 validation-auc:0.96759 validation-aucpr:0.97248
[47] validation-logloss:0.23413 validation-auc:0.96740 validation-aucpr:0.97238
[48] validation-logloss:0.23154 validation-auc:0.96770 validation-aucpr:0.97266
[49] validation-logloss:0.23015 validation-auc:0.96758 validation-aucpr:0.97252
[50] validation-logloss:0.22850 validation-auc:0.96764 validation-aucpr:0.97258
[51] validation-logloss:0.22674 validation-auc:0.96766 validation-aucpr:0.97258
[52] validation-logloss:0.22505 validation-auc:0.96772 validation-aucpr:0.97264
[53] validation-logloss:0.22425 validation-auc:0.96762 validation-aucpr:0.97257
[54] validation-logloss:0.22350 validation-auc:0.96763 validation-aucpr:0.97256
[55] validation-logloss:0.22213 validation-auc:0.96761 validation-aucpr:0.97258
[56] validation-logloss:0.22134 validation-auc:0.96735 validation-aucpr:0.97240
[57] validation-logloss:0.22029 validation-auc:0.96752 validation-aucpr:0.97250
[58] validation-logloss:0.21929 validation-auc:0.96769 validation-aucpr:0.97262
[59] validation-logloss:0.21853 validation-auc:0.96756 validation-aucpr:0.97252
[60] validation-logloss:0.21784 validation-auc:0.96767 validation-aucpr:0.97262
[61] validation-logloss:0.21760 validation-auc:0.96748 validation-aucpr:0.97253
[62] validation-logloss:0.21661 validation-auc:0.96768 validation-aucpr:0.97268
[63] validation-logloss:0.21559 validation-auc:0.96782 validation-aucpr:0.97280
[64] validation-logloss:0.21434 validation-auc:0.96802 validation-aucpr:0.97294
[65] validation-logloss:0.21325 validation-auc:0.96807 validation-aucpr:0.97299
[66] validation-logloss:0.21252 validation-auc:0.96828 validation-aucpr:0.97314
[67] validation-logloss:0.21195 validation-auc:0.96832 validation-aucpr:0.97317
[68] validation-logloss:0.21133 validation-auc:0.96830 validation-aucpr:0.97319
[69] validation-logloss:0.21055 validation-auc:0.96844 validation-aucpr:0.97328
[70] validation-logloss:0.20974 validation-auc:0.96853 validation-aucpr:0.97336
[71] validation-logloss:0.20872 validation-auc:0.96876 validation-aucpr:0.97352
[72] validation-logloss:0.20825 validation-auc:0.96884 validation-aucpr:0.97358
[73] validation-logloss:0.20796 validation-auc:0.96885 validation-aucpr:0.97357
[74] validation-logloss:0.20747 validation-auc:0.96895 validation-aucpr:0.97363
[75] validation-logloss:0.20696 validation-auc:0.96886 validation-aucpr:0.97356
[76] validation-logloss:0.20670 validation-auc:0.96889 validation-aucpr:0.97360
[77] validation-logloss:0.20627 validation-auc:0.96888 validation-aucpr:0.97362
[78] validation-logloss:0.20627 validation-auc:0.96880 validation-aucpr:0.97357
[79] validation-logloss:0.20597 validation-auc:0.96885 validation-aucpr:0.97363
[80] validation-logloss:0.20588 validation-auc:0.96882 validation-aucpr:0.97360
[81] validation-logloss:0.20513 validation-auc:0.96903 validation-aucpr:0.97375
[82] validation-logloss:0.20475 validation-auc:0.96921 validation-aucpr:0.97385
[83] validation-logloss:0.20477 validation-auc:0.96919 validation-aucpr:0.97382
[84] validation-logloss:0.20484 validation-auc:0.96910 validation-aucpr:0.97375
[85] validation-logloss:0.20515 validation-auc:0.96906 validation-aucpr:0.97370
[86] validation-logloss:0.20475 validation-auc:0.96908 validation-aucpr:0.97373
[87] validation-logloss:0.20439 validation-auc:0.96922 validation-aucpr:0.97382
[88] validation-logloss:0.20413 validation-auc:0.96929 validation-aucpr:0.97385
[89] validation-logloss:0.20389 validation-auc:0.96935 validation-aucpr:0.97391
[90] validation-logloss:0.20397 validation-auc:0.96929 validation-aucpr:0.97390
[91] validation-logloss:0.20370 validation-auc:0.96948 validation-aucpr:0.97404
[92] validation-logloss:0.20376 validation-auc:0.96945 validation-aucpr:0.97401
[93] validation-logloss:0.20354 validation-auc:0.96953 validation-aucpr:0.97407
{'best_iteration': '93', 'best_score': '0.9740735088385705'}
Trial 92, Fold 4: Log loss = 0.2035398037214788, Average precision = 0.9740776692600727, ROC-AUC = 0.9695270195354693, Elapsed Time = 2.9379043000008096 seconds
Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 92, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.65963 validation-auc:0.93324 validation-aucpr:0.93882
[1] validation-logloss:0.62811 validation-auc:0.92946 validation-aucpr:0.89337
[2] validation-logloss:0.60297 validation-auc:0.93860 validation-aucpr:0.92625
[3] validation-logloss:0.57963 validation-auc:0.94628 validation-aucpr:0.94370
[4] validation-logloss:0.55489 validation-auc:0.95300 validation-aucpr:0.95294
[5] validation-logloss:0.53307 validation-auc:0.95457 validation-aucpr:0.95439
[6] validation-logloss:0.51465 validation-auc:0.95653 validation-aucpr:0.95882
[7] validation-logloss:0.49524 validation-auc:0.95835 validation-aucpr:0.96185
[8] validation-logloss:0.47709 validation-auc:0.95907 validation-aucpr:0.96305
[9] validation-logloss:0.45976 validation-auc:0.96133 validation-aucpr:0.96504
[10] validation-logloss:0.44577 validation-auc:0.96230 validation-aucpr:0.96667
[11] validation-logloss:0.43105 validation-auc:0.96279 validation-aucpr:0.96725
[12] validation-logloss:0.41809 validation-auc:0.96312 validation-aucpr:0.96711
[13] validation-logloss:0.40710 validation-auc:0.96311 validation-aucpr:0.96705
[14] validation-logloss:0.39695 validation-auc:0.96308 validation-aucpr:0.96692
[15] validation-logloss:0.38748 validation-auc:0.96286 validation-aucpr:0.96677
[16] validation-logloss:0.37668 validation-auc:0.96302 validation-aucpr:0.96689
[17] validation-logloss:0.36744 validation-auc:0.96362 validation-aucpr:0.96718
[18] validation-logloss:0.35783 validation-auc:0.96383 validation-aucpr:0.96721
[19] validation-logloss:0.34907 validation-auc:0.96368 validation-aucpr:0.96711
[20] validation-logloss:0.34101 validation-auc:0.96384 validation-aucpr:0.96727
[21] validation-logloss:0.33271 validation-auc:0.96412 validation-aucpr:0.96749
[22] validation-logloss:0.32511 validation-auc:0.96464 validation-aucpr:0.96921
[23] validation-logloss:0.31960 validation-auc:0.96454 validation-aucpr:0.96900
[24] validation-logloss:0.31529 validation-auc:0.96416 validation-aucpr:0.96862
[25] validation-logloss:0.30935 validation-auc:0.96434 validation-aucpr:0.96878
[26] validation-logloss:0.30332 validation-auc:0.96493 validation-aucpr:0.96910
[27] validation-logloss:0.29860 validation-auc:0.96490 validation-aucpr:0.96909
[28] validation-logloss:0.29322 validation-auc:0.96494 validation-aucpr:0.96915
[29] validation-logloss:0.28849 validation-auc:0.96561 validation-aucpr:0.96944
[30] validation-logloss:0.28400 validation-auc:0.96552 validation-aucpr:0.96943
[31] validation-logloss:0.27918 validation-auc:0.96572 validation-aucpr:0.96958
[32] validation-logloss:0.27451 validation-auc:0.96605 validation-aucpr:0.96970
[33] validation-logloss:0.27018 validation-auc:0.96617 validation-aucpr:0.96947
[34] validation-logloss:0.26600 validation-auc:0.96624 validation-aucpr:0.96955
[35] validation-logloss:0.26224 validation-auc:0.96661 validation-aucpr:0.96976
[36] validation-logloss:0.25914 validation-auc:0.96664 validation-aucpr:0.96978
[37] validation-logloss:0.25582 validation-auc:0.96667 validation-aucpr:0.96983
[38] validation-logloss:0.25332 validation-auc:0.96671 validation-aucpr:0.96980
[39] validation-logloss:0.25141 validation-auc:0.96638 validation-aucpr:0.96957
[40] validation-logloss:0.24965 validation-auc:0.96615 validation-aucpr:0.96938
[41] validation-logloss:0.24845 validation-auc:0.96589 validation-aucpr:0.96890
[42] validation-logloss:0.24674 validation-auc:0.96570 validation-aucpr:0.96868
[43] validation-logloss:0.24445 validation-auc:0.96576 validation-aucpr:0.96878
[44] validation-logloss:0.24187 validation-auc:0.96592 validation-aucpr:0.96897
[45] validation-logloss:0.24005 validation-auc:0.96606 validation-aucpr:0.96903
[46] validation-logloss:0.23808 validation-auc:0.96606 validation-aucpr:0.96902
[47] validation-logloss:0.23622 validation-auc:0.96604 validation-aucpr:0.96889
[48] validation-logloss:0.23492 validation-auc:0.96582 validation-aucpr:0.96866
[49] validation-logloss:0.23305 validation-auc:0.96612 validation-aucpr:0.97033
[50] validation-logloss:0.23195 validation-auc:0.96622 validation-aucpr:0.97033
[51] validation-logloss:0.23047 validation-auc:0.96640 validation-aucpr:0.97037
[52] validation-logloss:0.22896 validation-auc:0.96645 validation-aucpr:0.97043
[53] validation-logloss:0.22733 validation-auc:0.96672 validation-aucpr:0.97071
[54] validation-logloss:0.22602 validation-auc:0.96667 validation-aucpr:0.97074
[55] validation-logloss:0.22517 validation-auc:0.96683 validation-aucpr:0.97083
[56] validation-logloss:0.22372 validation-auc:0.96711 validation-aucpr:0.97106
[57] validation-logloss:0.22310 validation-auc:0.96704 validation-aucpr:0.97091
[58] validation-logloss:0.22217 validation-auc:0.96711 validation-aucpr:0.97097
[59] validation-logloss:0.22131 validation-auc:0.96710 validation-aucpr:0.97085
[60] validation-logloss:0.22071 validation-auc:0.96712 validation-aucpr:0.97081
[61] validation-logloss:0.21939 validation-auc:0.96726 validation-aucpr:0.97092
[62] validation-logloss:0.21898 validation-auc:0.96722 validation-aucpr:0.97091
[63] validation-logloss:0.21842 validation-auc:0.96718 validation-aucpr:0.97092
[64] validation-logloss:0.21779 validation-auc:0.96731 validation-aucpr:0.97096
[65] validation-logloss:0.21666 validation-auc:0.96737 validation-aucpr:0.97103
[66] validation-logloss:0.21628 validation-auc:0.96740 validation-aucpr:0.97101
[67] validation-logloss:0.21578 validation-auc:0.96739 validation-aucpr:0.97098
[68] validation-logloss:0.21468 validation-auc:0.96751 validation-aucpr:0.97111
[69] validation-logloss:0.21406 validation-auc:0.96759 validation-aucpr:0.97143
[70] validation-logloss:0.21365 validation-auc:0.96759 validation-aucpr:0.97172
[71] validation-logloss:0.21305 validation-auc:0.96761 validation-aucpr:0.97175
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[73] validation-logloss:0.21231 validation-auc:0.96767 validation-aucpr:0.97170
[74] validation-logloss:0.21183 validation-auc:0.96767 validation-aucpr:0.97169
[75] validation-logloss:0.21161 validation-auc:0.96767 validation-aucpr:0.97187
[76] validation-logloss:0.21082 validation-auc:0.96789 validation-aucpr:0.97189
[77] validation-logloss:0.21112 validation-auc:0.96770 validation-aucpr:0.97173
[78] validation-logloss:0.21121 validation-auc:0.96765 validation-aucpr:0.97164
[79] validation-logloss:0.21103 validation-auc:0.96766 validation-aucpr:0.97203
[80] validation-logloss:0.21044 validation-auc:0.96787 validation-aucpr:0.97215
[81] validation-logloss:0.21048 validation-auc:0.96771 validation-aucpr:0.97207
[82] validation-logloss:0.21019 validation-auc:0.96777 validation-aucpr:0.97215
[83] validation-logloss:0.21025 validation-auc:0.96766 validation-aucpr:0.97219
[84] validation-logloss:0.21046 validation-auc:0.96768 validation-aucpr:0.97222
[85] validation-logloss:0.21004 validation-auc:0.96784 validation-aucpr:0.97229
[86] validation-logloss:0.21040 validation-auc:0.96772 validation-aucpr:0.97213
[87] validation-logloss:0.21030 validation-auc:0.96771 validation-aucpr:0.97214
[88] validation-logloss:0.20992 validation-auc:0.96781 validation-aucpr:0.97230
[89] validation-logloss:0.21021 validation-auc:0.96773 validation-aucpr:0.97227
[90] validation-logloss:0.21028 validation-auc:0.96768 validation-aucpr:0.97225
[91] validation-logloss:0.21062 validation-auc:0.96760 validation-aucpr:0.97220
[92] validation-logloss:0.21034 validation-auc:0.96771 validation-aucpr:0.97226
[93] validation-logloss:0.21025 validation-auc:0.96775 validation-aucpr:0.97225
{'best_iteration': '88', 'best_score': '0.9722974589530706'}
Trial 92, Fold 5: Log loss = 0.21024819867975683, Average precision = 0.9722566510920048, ROC-AUC = 0.9677525221559555, Elapsed Time = 3.7220796999972663 seconds
Optimization Progress: 93%|#########3| 93/100 [3:56:59<10:34, 90.62s/it]
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 93, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.67575 validation-auc:0.93460 validation-aucpr:0.93800
[1] validation-logloss:0.65965 validation-auc:0.93703 validation-aucpr:0.93943
[2] validation-logloss:0.64457 validation-auc:0.94314 validation-aucpr:0.94762
[3] validation-logloss:0.63090 validation-auc:0.94449 validation-aucpr:0.94784
[4] validation-logloss:0.61637 validation-auc:0.94767 validation-aucpr:0.95120
[5] validation-logloss:0.59997 validation-auc:0.95603 validation-aucpr:0.96103
[6] validation-logloss:0.58508 validation-auc:0.95694 validation-aucpr:0.96212
[7] validation-logloss:0.57333 validation-auc:0.95698 validation-aucpr:0.96369
[8] validation-logloss:0.55955 validation-auc:0.95805 validation-aucpr:0.96472
[9] validation-logloss:0.54840 validation-auc:0.95826 validation-aucpr:0.96488
[10] validation-logloss:0.53508 validation-auc:0.95968 validation-aucpr:0.96614
[11] validation-logloss:0.52519 validation-auc:0.95954 validation-aucpr:0.96584
[12] validation-logloss:0.51566 validation-auc:0.95980 validation-aucpr:0.96604
[13] validation-logloss:0.50712 validation-auc:0.95945 validation-aucpr:0.96557
[14] validation-logloss:0.49760 validation-auc:0.95993 validation-aucpr:0.96593
[15] validation-logloss:0.48704 validation-auc:0.96048 validation-aucpr:0.96685
[16] validation-logloss:0.47644 validation-auc:0.96104 validation-aucpr:0.96734
[17] validation-logloss:0.46889 validation-auc:0.96102 validation-aucpr:0.96723
[18] validation-logloss:0.46130 validation-auc:0.96094 validation-aucpr:0.96708
[19] validation-logloss:0.45403 validation-auc:0.96093 validation-aucpr:0.96697
[20] validation-logloss:0.44503 validation-auc:0.96119 validation-aucpr:0.96724
[21] validation-logloss:0.43896 validation-auc:0.96120 validation-aucpr:0.96718
[22] validation-logloss:0.43254 validation-auc:0.96112 validation-aucpr:0.96720
[23] validation-logloss:0.42405 validation-auc:0.96143 validation-aucpr:0.96745
[24] validation-logloss:0.41801 validation-auc:0.96163 validation-aucpr:0.96762
[25] validation-logloss:0.41218 validation-auc:0.96157 validation-aucpr:0.96755
[26] validation-logloss:0.40492 validation-auc:0.96186 validation-aucpr:0.96783
[27] validation-logloss:0.39955 validation-auc:0.96184 validation-aucpr:0.96777
[28] validation-logloss:0.39321 validation-auc:0.96198 validation-aucpr:0.96798
[29] validation-logloss:0.38867 validation-auc:0.96207 validation-aucpr:0.96802
[30] validation-logloss:0.38450 validation-auc:0.96191 validation-aucpr:0.96783
[31] validation-logloss:0.37859 validation-auc:0.96231 validation-aucpr:0.96822
[32] validation-logloss:0.37464 validation-auc:0.96225 validation-aucpr:0.96813
[33] validation-logloss:0.37083 validation-auc:0.96224 validation-aucpr:0.96813
[34] validation-logloss:0.36682 validation-auc:0.96219 validation-aucpr:0.96804
[35] validation-logloss:0.36285 validation-auc:0.96213 validation-aucpr:0.96800
[36] validation-logloss:0.35731 validation-auc:0.96238 validation-aucpr:0.96828
[37] validation-logloss:0.35363 validation-auc:0.96240 validation-aucpr:0.96827
[38] validation-logloss:0.34861 validation-auc:0.96251 validation-aucpr:0.96838
[39] validation-logloss:0.34446 validation-auc:0.96265 validation-aucpr:0.96852
[40] validation-logloss:0.34107 validation-auc:0.96277 validation-aucpr:0.96867
[41] validation-logloss:0.33793 validation-auc:0.96279 validation-aucpr:0.96865
[42] validation-logloss:0.33492 validation-auc:0.96290 validation-aucpr:0.96874
[43] validation-logloss:0.33197 validation-auc:0.96285 validation-aucpr:0.96864
[44] validation-logloss:0.32773 validation-auc:0.96311 validation-aucpr:0.96892
[45] validation-logloss:0.32496 validation-auc:0.96307 validation-aucpr:0.96881
[46] validation-logloss:0.32238 validation-auc:0.96310 validation-aucpr:0.96880
[47] validation-logloss:0.31998 validation-auc:0.96308 validation-aucpr:0.96877
[48] validation-logloss:0.31758 validation-auc:0.96318 validation-aucpr:0.96888
[49] validation-logloss:0.31513 validation-auc:0.96329 validation-aucpr:0.96892
[50] validation-logloss:0.31285 validation-auc:0.96332 validation-aucpr:0.96890
[51] validation-logloss:0.30887 validation-auc:0.96356 validation-aucpr:0.96914
[52] validation-logloss:0.30669 validation-auc:0.96355 validation-aucpr:0.96913
[53] validation-logloss:0.30469 validation-auc:0.96343 validation-aucpr:0.96897
[54] validation-logloss:0.30108 validation-auc:0.96358 validation-aucpr:0.96912
[55] validation-logloss:0.29922 validation-auc:0.96361 validation-aucpr:0.96915
[56] validation-logloss:0.29742 validation-auc:0.96360 validation-aucpr:0.96908
[57] validation-logloss:0.29432 validation-auc:0.96375 validation-aucpr:0.96921
[58] validation-logloss:0.29225 validation-auc:0.96381 validation-aucpr:0.96924
[59] validation-logloss:0.29060 validation-auc:0.96383 validation-aucpr:0.96923
[60] validation-logloss:0.28796 validation-auc:0.96394 validation-aucpr:0.96941
[61] validation-logloss:0.28544 validation-auc:0.96412 validation-aucpr:0.96957
[62] validation-logloss:0.28292 validation-auc:0.96422 validation-aucpr:0.96971
[63] validation-logloss:0.28004 validation-auc:0.96440 validation-aucpr:0.96987
[64] validation-logloss:0.27858 validation-auc:0.96445 validation-aucpr:0.96988
[65] validation-logloss:0.27607 validation-auc:0.96456 validation-aucpr:0.97002
[66] validation-logloss:0.27367 validation-auc:0.96469 validation-aucpr:0.97011
[67] validation-logloss:0.27142 validation-auc:0.96482 validation-aucpr:0.97027
[68] validation-logloss:0.26915 validation-auc:0.96490 validation-aucpr:0.97034
[69] validation-logloss:0.26776 validation-auc:0.96494 validation-aucpr:0.97037
[70] validation-logloss:0.26636 validation-auc:0.96504 validation-aucpr:0.97046
[71] validation-logloss:0.26506 validation-auc:0.96511 validation-aucpr:0.97054
[72] validation-logloss:0.26302 validation-auc:0.96518 validation-aucpr:0.97061
[73] validation-logloss:0.26105 validation-auc:0.96526 validation-aucpr:0.97072
[74] validation-logloss:0.25909 validation-auc:0.96536 validation-aucpr:0.97081
{'best_iteration': '74', 'best_score': '0.9708118262327414'}
Trial 93, Fold 1: Log loss = 0.2590858465904124, Average precision = 0.9707767569767338, ROC-AUC = 0.9653620927192518, Elapsed Time = 7.22263490000114 seconds
Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 93, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67552 validation-auc:0.93147 validation-aucpr:0.93164
[1] validation-logloss:0.65929 validation-auc:0.93648 validation-aucpr:0.93653
[2] validation-logloss:0.64406 validation-auc:0.94359 validation-aucpr:0.94518
[3] validation-logloss:0.63037 validation-auc:0.94403 validation-aucpr:0.94610
[4] validation-logloss:0.61580 validation-auc:0.94694 validation-aucpr:0.94908
[5] validation-logloss:0.59967 validation-auc:0.95573 validation-aucpr:0.95969
[6] validation-logloss:0.58490 validation-auc:0.95757 validation-aucpr:0.96145
[7] validation-logloss:0.57307 validation-auc:0.95758 validation-aucpr:0.96143
[8] validation-logloss:0.55913 validation-auc:0.95846 validation-aucpr:0.96222
[9] validation-logloss:0.54799 validation-auc:0.95884 validation-aucpr:0.96275
[10] validation-logloss:0.53493 validation-auc:0.95985 validation-aucpr:0.96389
[11] validation-logloss:0.52482 validation-auc:0.95980 validation-aucpr:0.96390
[12] validation-logloss:0.51530 validation-auc:0.96003 validation-aucpr:0.96395
[13] validation-logloss:0.50636 validation-auc:0.96003 validation-aucpr:0.96436
[14] validation-logloss:0.49693 validation-auc:0.96040 validation-aucpr:0.96455
[15] validation-logloss:0.48635 validation-auc:0.96074 validation-aucpr:0.96515
[16] validation-logloss:0.47848 validation-auc:0.96044 validation-aucpr:0.96475
[17] validation-logloss:0.47090 validation-auc:0.96034 validation-aucpr:0.96451
[18] validation-logloss:0.46342 validation-auc:0.96032 validation-aucpr:0.96446
[19] validation-logloss:0.45657 validation-auc:0.96020 validation-aucpr:0.96419
[20] validation-logloss:0.44748 validation-auc:0.96045 validation-aucpr:0.96444
[21] validation-logloss:0.44127 validation-auc:0.96059 validation-aucpr:0.96456
[22] validation-logloss:0.43488 validation-auc:0.96069 validation-aucpr:0.96461
[23] validation-logloss:0.42640 validation-auc:0.96103 validation-aucpr:0.96501
[24] validation-logloss:0.42039 validation-auc:0.96114 validation-aucpr:0.96504
[25] validation-logloss:0.41446 validation-auc:0.96114 validation-aucpr:0.96500
[26] validation-logloss:0.40700 validation-auc:0.96146 validation-aucpr:0.96539
[27] validation-logloss:0.40175 validation-auc:0.96154 validation-aucpr:0.96543
[28] validation-logloss:0.39504 validation-auc:0.96179 validation-aucpr:0.96559
[29] validation-logloss:0.39022 validation-auc:0.96189 validation-aucpr:0.96568
[30] validation-logloss:0.38600 validation-auc:0.96178 validation-aucpr:0.96546
[31] validation-logloss:0.37998 validation-auc:0.96242 validation-aucpr:0.96637
[32] validation-logloss:0.37601 validation-auc:0.96237 validation-aucpr:0.96626
[33] validation-logloss:0.37189 validation-auc:0.96235 validation-aucpr:0.96620
[34] validation-logloss:0.36605 validation-auc:0.96257 validation-aucpr:0.96643
[35] validation-logloss:0.36041 validation-auc:0.96282 validation-aucpr:0.96673
[36] validation-logloss:0.35533 validation-auc:0.96308 validation-aucpr:0.96707
[37] validation-logloss:0.35145 validation-auc:0.96310 validation-aucpr:0.96708
[38] validation-logloss:0.34773 validation-auc:0.96328 validation-aucpr:0.96723
[39] validation-logloss:0.34426 validation-auc:0.96323 validation-aucpr:0.96717
[40] validation-logloss:0.34093 validation-auc:0.96336 validation-aucpr:0.96727
[41] validation-logloss:0.33764 validation-auc:0.96337 validation-aucpr:0.96720
[42] validation-logloss:0.33435 validation-auc:0.96344 validation-aucpr:0.96721
[43] validation-logloss:0.33164 validation-auc:0.96334 validation-aucpr:0.96711
[44] validation-logloss:0.32910 validation-auc:0.96327 validation-aucpr:0.96704
[45] validation-logloss:0.32584 validation-auc:0.96343 validation-aucpr:0.96718
[46] validation-logloss:0.32170 validation-auc:0.96359 validation-aucpr:0.96738
[47] validation-logloss:0.31890 validation-auc:0.96371 validation-aucpr:0.96747
[48] validation-logloss:0.31489 validation-auc:0.96389 validation-aucpr:0.96770
[49] validation-logloss:0.31214 validation-auc:0.96404 validation-aucpr:0.96781
[50] validation-logloss:0.30974 validation-auc:0.96404 validation-aucpr:0.96777
[51] validation-logloss:0.30757 validation-auc:0.96400 validation-aucpr:0.96768
[52] validation-logloss:0.30527 validation-auc:0.96406 validation-aucpr:0.96767
[53] validation-logloss:0.30169 validation-auc:0.96415 validation-aucpr:0.96781
[54] validation-logloss:0.29958 validation-auc:0.96418 validation-aucpr:0.96782
[55] validation-logloss:0.29762 validation-auc:0.96419 validation-aucpr:0.96772
[56] validation-logloss:0.29573 validation-auc:0.96413 validation-aucpr:0.96764
[57] validation-logloss:0.29374 validation-auc:0.96413 validation-aucpr:0.96759
[58] validation-logloss:0.29199 validation-auc:0.96416 validation-aucpr:0.96762
[59] validation-logloss:0.29017 validation-auc:0.96422 validation-aucpr:0.96804
[60] validation-logloss:0.28849 validation-auc:0.96420 validation-aucpr:0.96797
[61] validation-logloss:0.28557 validation-auc:0.96437 validation-aucpr:0.96826
[62] validation-logloss:0.28398 validation-auc:0.96436 validation-aucpr:0.96826
[63] validation-logloss:0.28118 validation-auc:0.96443 validation-aucpr:0.96835
[64] validation-logloss:0.27939 validation-auc:0.96452 validation-aucpr:0.96841
[65] validation-logloss:0.27792 validation-auc:0.96459 validation-aucpr:0.96849
[66] validation-logloss:0.27524 validation-auc:0.96474 validation-aucpr:0.96864
[67] validation-logloss:0.27267 validation-auc:0.96487 validation-aucpr:0.96879
[68] validation-logloss:0.27138 validation-auc:0.96483 validation-aucpr:0.96875
[69] validation-logloss:0.27017 validation-auc:0.96483 validation-aucpr:0.96858
[70] validation-logloss:0.26891 validation-auc:0.96482 validation-aucpr:0.96852
[71] validation-logloss:0.26643 validation-auc:0.96499 validation-aucpr:0.96869
[72] validation-logloss:0.26511 validation-auc:0.96502 validation-aucpr:0.96870
[73] validation-logloss:0.26364 validation-auc:0.96515 validation-aucpr:0.96879
[74] validation-logloss:0.26242 validation-auc:0.96521 validation-aucpr:0.96882
{'best_iteration': '74', 'best_score': '0.9688176110599449'}
Trial 93, Fold 2: Log loss = 0.26241678949069214, Average precision = 0.9687837016793314, ROC-AUC = 0.9652092404689492, Elapsed Time = 7.36766390000048 seconds
Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 93, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67562 validation-auc:0.93375 validation-aucpr:0.93331
[1] validation-logloss:0.65914 validation-auc:0.94029 validation-aucpr:0.94233
[2] validation-logloss:0.64383 validation-auc:0.94486 validation-aucpr:0.94795
[3] validation-logloss:0.62994 validation-auc:0.94719 validation-aucpr:0.94897
[4] validation-logloss:0.61579 validation-auc:0.94976 validation-aucpr:0.95056
[5] validation-logloss:0.60285 validation-auc:0.95017 validation-aucpr:0.95187
[6] validation-logloss:0.59043 validation-auc:0.94965 validation-aucpr:0.95346
[7] validation-logloss:0.57838 validation-auc:0.95054 validation-aucpr:0.95439
[8] validation-logloss:0.56478 validation-auc:0.95896 validation-aucpr:0.96397
[9] validation-logloss:0.55409 validation-auc:0.95874 validation-aucpr:0.96359
[10] validation-logloss:0.54148 validation-auc:0.96012 validation-aucpr:0.96522
[11] validation-logloss:0.53196 validation-auc:0.95982 validation-aucpr:0.96494
[12] validation-logloss:0.52050 validation-auc:0.96050 validation-aucpr:0.96571
[13] validation-logloss:0.51118 validation-auc:0.96056 validation-aucpr:0.96587
[14] validation-logloss:0.50231 validation-auc:0.96062 validation-aucpr:0.96591
[15] validation-logloss:0.49072 validation-auc:0.96142 validation-aucpr:0.96664
[16] validation-logloss:0.48241 validation-auc:0.96148 validation-aucpr:0.96686
[17] validation-logloss:0.47191 validation-auc:0.96211 validation-aucpr:0.96738
[18] validation-logloss:0.46184 validation-auc:0.96274 validation-aucpr:0.96796
[19] validation-logloss:0.45473 validation-auc:0.96270 validation-aucpr:0.96791
[20] validation-logloss:0.44779 validation-auc:0.96282 validation-aucpr:0.96802
[21] validation-logloss:0.43924 validation-auc:0.96285 validation-aucpr:0.96807
[22] validation-logloss:0.43073 validation-auc:0.96311 validation-aucpr:0.96826
[23] validation-logloss:0.42466 validation-auc:0.96336 validation-aucpr:0.96849
[24] validation-logloss:0.41874 validation-auc:0.96339 validation-aucpr:0.96857
[25] validation-logloss:0.41322 validation-auc:0.96340 validation-aucpr:0.96852
[26] validation-logloss:0.40718 validation-auc:0.96350 validation-aucpr:0.96865
[27] validation-logloss:0.40188 validation-auc:0.96352 validation-aucpr:0.96866
[28] validation-logloss:0.39690 validation-auc:0.96366 validation-aucpr:0.96872
[29] validation-logloss:0.39034 validation-auc:0.96384 validation-aucpr:0.96892
[30] validation-logloss:0.38582 validation-auc:0.96375 validation-aucpr:0.96880
[31] validation-logloss:0.38153 validation-auc:0.96355 validation-aucpr:0.96863
[32] validation-logloss:0.37718 validation-auc:0.96367 validation-aucpr:0.96874
[33] validation-logloss:0.37125 validation-auc:0.96389 validation-aucpr:0.96895
[34] validation-logloss:0.36717 validation-auc:0.96393 validation-aucpr:0.96899
[35] validation-logloss:0.36174 validation-auc:0.96414 validation-aucpr:0.96918
[36] validation-logloss:0.35791 validation-auc:0.96428 validation-aucpr:0.96929
[37] validation-logloss:0.35397 validation-auc:0.96435 validation-aucpr:0.96934
[38] validation-logloss:0.35023 validation-auc:0.96443 validation-aucpr:0.96938
[39] validation-logloss:0.34481 validation-auc:0.96473 validation-aucpr:0.96966
[40] validation-logloss:0.33988 validation-auc:0.96491 validation-aucpr:0.96987
[41] validation-logloss:0.33683 validation-auc:0.96481 validation-aucpr:0.96977
[42] validation-logloss:0.33204 validation-auc:0.96510 validation-aucpr:0.97003
[43] validation-logloss:0.32732 validation-auc:0.96534 validation-aucpr:0.97024
[44] validation-logloss:0.32403 validation-auc:0.96545 validation-aucpr:0.97037
[45] validation-logloss:0.32103 validation-auc:0.96559 validation-aucpr:0.97046
[46] validation-logloss:0.31719 validation-auc:0.96566 validation-aucpr:0.97056
[47] validation-logloss:0.31354 validation-auc:0.96565 validation-aucpr:0.97058
[48] validation-logloss:0.31104 validation-auc:0.96563 validation-aucpr:0.97055
[49] validation-logloss:0.30809 validation-auc:0.96586 validation-aucpr:0.97076
[50] validation-logloss:0.30587 validation-auc:0.96576 validation-aucpr:0.97066
[51] validation-logloss:0.30320 validation-auc:0.96594 validation-aucpr:0.97083
[52] validation-logloss:0.29968 validation-auc:0.96603 validation-aucpr:0.97093
[53] validation-logloss:0.29646 validation-auc:0.96622 validation-aucpr:0.97113
[54] validation-logloss:0.29446 validation-auc:0.96612 validation-aucpr:0.97101
[55] validation-logloss:0.29243 validation-auc:0.96613 validation-aucpr:0.97099
[56] validation-logloss:0.29065 validation-auc:0.96607 validation-aucpr:0.97092
[57] validation-logloss:0.28875 validation-auc:0.96607 validation-aucpr:0.97095
[58] validation-logloss:0.28695 validation-auc:0.96602 validation-aucpr:0.97093
[59] validation-logloss:0.28521 validation-auc:0.96603 validation-aucpr:0.97092
[60] validation-logloss:0.28349 validation-auc:0.96604 validation-aucpr:0.97089
[61] validation-logloss:0.28066 validation-auc:0.96612 validation-aucpr:0.97098
[62] validation-logloss:0.27898 validation-auc:0.96614 validation-aucpr:0.97098
[63] validation-logloss:0.27632 validation-auc:0.96623 validation-aucpr:0.97108
[64] validation-logloss:0.27445 validation-auc:0.96636 validation-aucpr:0.97119
[65] validation-logloss:0.27293 validation-auc:0.96641 validation-aucpr:0.97123
[66] validation-logloss:0.27048 validation-auc:0.96648 validation-aucpr:0.97132
[67] validation-logloss:0.26800 validation-auc:0.96657 validation-aucpr:0.97143
[68] validation-logloss:0.26655 validation-auc:0.96667 validation-aucpr:0.97149
[69] validation-logloss:0.26469 validation-auc:0.96671 validation-aucpr:0.97152
[70] validation-logloss:0.26335 validation-auc:0.96671 validation-aucpr:0.97151
[71] validation-logloss:0.26100 validation-auc:0.96683 validation-aucpr:0.97162
[72] validation-logloss:0.25969 validation-auc:0.96681 validation-aucpr:0.97158
[73] validation-logloss:0.25820 validation-auc:0.96693 validation-aucpr:0.97167
[74] validation-logloss:0.25698 validation-auc:0.96698 validation-aucpr:0.97171
{'best_iteration': '74', 'best_score': '0.971711828125484'}
Trial 93, Fold 3: Log loss = 0.256981879734254, Average precision = 0.971697582692562, ROC-AUC = 0.9669770618842157, Elapsed Time = 7.480115800000931 seconds
Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 93, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.67552 validation-auc:0.93217 validation-aucpr:0.93573
[1] validation-logloss:0.65921 validation-auc:0.93524 validation-aucpr:0.93838
[2] validation-logloss:0.64398 validation-auc:0.94157 validation-aucpr:0.94618
[3] validation-logloss:0.63029 validation-auc:0.94242 validation-aucpr:0.94709
[4] validation-logloss:0.61586 validation-auc:0.94668 validation-aucpr:0.95153
[5] validation-logloss:0.59969 validation-auc:0.95483 validation-aucpr:0.96118
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[10] validation-logloss:0.53478 validation-auc:0.95881 validation-aucpr:0.96540
[11] validation-logloss:0.52485 validation-auc:0.95844 validation-aucpr:0.96493
[12] validation-logloss:0.51541 validation-auc:0.95842 validation-aucpr:0.96488
[13] validation-logloss:0.50631 validation-auc:0.95866 validation-aucpr:0.96510
[14] validation-logloss:0.49697 validation-auc:0.95906 validation-aucpr:0.96539
[15] validation-logloss:0.48650 validation-auc:0.95953 validation-aucpr:0.96598
[16] validation-logloss:0.47601 validation-auc:0.96003 validation-aucpr:0.96653
[17] validation-logloss:0.46840 validation-auc:0.96016 validation-aucpr:0.96655
[18] validation-logloss:0.46090 validation-auc:0.96022 validation-aucpr:0.96654
[19] validation-logloss:0.45360 validation-auc:0.96011 validation-aucpr:0.96637
[20] validation-logloss:0.44483 validation-auc:0.96022 validation-aucpr:0.96657
[21] validation-logloss:0.43851 validation-auc:0.96035 validation-aucpr:0.96660
[22] validation-logloss:0.43218 validation-auc:0.96030 validation-aucpr:0.96648
[23] validation-logloss:0.42367 validation-auc:0.96067 validation-aucpr:0.96687
[24] validation-logloss:0.41788 validation-auc:0.96063 validation-aucpr:0.96672
[25] validation-logloss:0.41219 validation-auc:0.96056 validation-aucpr:0.96664
[26] validation-logloss:0.40504 validation-auc:0.96086 validation-aucpr:0.96709
[27] validation-logloss:0.39992 validation-auc:0.96090 validation-aucpr:0.96708
[28] validation-logloss:0.39347 validation-auc:0.96105 validation-aucpr:0.96729
[29] validation-logloss:0.38863 validation-auc:0.96121 validation-aucpr:0.96748
[30] validation-logloss:0.38436 validation-auc:0.96117 validation-aucpr:0.96738
[31] validation-logloss:0.37837 validation-auc:0.96156 validation-aucpr:0.96787
[32] validation-logloss:0.37431 validation-auc:0.96152 validation-aucpr:0.96779
[33] validation-logloss:0.37005 validation-auc:0.96149 validation-aucpr:0.96777
[34] validation-logloss:0.36437 validation-auc:0.96177 validation-aucpr:0.96801
[35] validation-logloss:0.35888 validation-auc:0.96184 validation-aucpr:0.96816
[36] validation-logloss:0.35398 validation-auc:0.96209 validation-aucpr:0.96842
[37] validation-logloss:0.35019 validation-auc:0.96209 validation-aucpr:0.96842
[38] validation-logloss:0.34673 validation-auc:0.96212 validation-aucpr:0.96841
[39] validation-logloss:0.34343 validation-auc:0.96209 validation-aucpr:0.96840
[40] validation-logloss:0.34016 validation-auc:0.96220 validation-aucpr:0.96845
[41] validation-logloss:0.33693 validation-auc:0.96217 validation-aucpr:0.96835
[42] validation-logloss:0.33371 validation-auc:0.96219 validation-aucpr:0.96835
[43] validation-logloss:0.33096 validation-auc:0.96220 validation-aucpr:0.96834
[44] validation-logloss:0.32841 validation-auc:0.96217 validation-aucpr:0.96833
[45] validation-logloss:0.32433 validation-auc:0.96236 validation-aucpr:0.96854
[46] validation-logloss:0.32009 validation-auc:0.96256 validation-aucpr:0.96873
[47] validation-logloss:0.31735 validation-auc:0.96265 validation-aucpr:0.96875
[48] validation-logloss:0.31361 validation-auc:0.96281 validation-aucpr:0.96892
[49] validation-logloss:0.31080 validation-auc:0.96290 validation-aucpr:0.96895
[50] validation-logloss:0.30849 validation-auc:0.96294 validation-aucpr:0.96897
[51] validation-logloss:0.30478 validation-auc:0.96312 validation-aucpr:0.96917
[52] validation-logloss:0.30244 validation-auc:0.96310 validation-aucpr:0.96912
[53] validation-logloss:0.29939 validation-auc:0.96332 validation-aucpr:0.96934
[54] validation-logloss:0.29730 validation-auc:0.96332 validation-aucpr:0.96929
[55] validation-logloss:0.29524 validation-auc:0.96337 validation-aucpr:0.96932
[56] validation-logloss:0.29351 validation-auc:0.96328 validation-aucpr:0.96923
[57] validation-logloss:0.29166 validation-auc:0.96333 validation-aucpr:0.96924
[58] validation-logloss:0.28992 validation-auc:0.96344 validation-aucpr:0.96935
[59] validation-logloss:0.28808 validation-auc:0.96340 validation-aucpr:0.96928
[60] validation-logloss:0.28639 validation-auc:0.96347 validation-aucpr:0.96930
[61] validation-logloss:0.28362 validation-auc:0.96362 validation-aucpr:0.96947
[62] validation-logloss:0.28195 validation-auc:0.96369 validation-aucpr:0.96953
[63] validation-logloss:0.27926 validation-auc:0.96388 validation-aucpr:0.96971
[64] validation-logloss:0.27737 validation-auc:0.96400 validation-aucpr:0.96980
[65] validation-logloss:0.27580 validation-auc:0.96403 validation-aucpr:0.96986
[66] validation-logloss:0.27329 validation-auc:0.96413 validation-aucpr:0.97000
[67] validation-logloss:0.27084 validation-auc:0.96425 validation-aucpr:0.97014
[68] validation-logloss:0.26946 validation-auc:0.96424 validation-aucpr:0.97014
[69] validation-logloss:0.26750 validation-auc:0.96439 validation-aucpr:0.97031
[70] validation-logloss:0.26623 validation-auc:0.96435 validation-aucpr:0.97022
[71] validation-logloss:0.26400 validation-auc:0.96445 validation-aucpr:0.97037
[72] validation-logloss:0.26267 validation-auc:0.96448 validation-aucpr:0.97037
[73] validation-logloss:0.26116 validation-auc:0.96463 validation-aucpr:0.97049
[74] validation-logloss:0.25995 validation-auc:0.96471 validation-aucpr:0.97053
{'best_iteration': '74', 'best_score': '0.970527887052778'}
Trial 93, Fold 4: Log loss = 0.2599548907235923, Average precision = 0.9704986765151592, ROC-AUC = 0.9647127471738784, Elapsed Time = 7.700011400000221 seconds
Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 93, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.67591 validation-auc:0.92772 validation-aucpr:0.92825
[1] validation-logloss:0.65975 validation-auc:0.93253 validation-aucpr:0.93482
[2] validation-logloss:0.64457 validation-auc:0.94027 validation-aucpr:0.94445
[3] validation-logloss:0.63095 validation-auc:0.94259 validation-aucpr:0.94718
[4] validation-logloss:0.61670 validation-auc:0.94487 validation-aucpr:0.94922
[5] validation-logloss:0.60374 validation-auc:0.94523 validation-aucpr:0.94979
[6] validation-logloss:0.59138 validation-auc:0.94542 validation-aucpr:0.95025
[7] validation-logloss:0.57952 validation-auc:0.94576 validation-aucpr:0.95051
[8] validation-logloss:0.56519 validation-auc:0.95392 validation-aucpr:0.96006
[9] validation-logloss:0.55464 validation-auc:0.95459 validation-aucpr:0.96061
[10] validation-logloss:0.54426 validation-auc:0.95467 validation-aucpr:0.96084
[11] validation-logloss:0.53326 validation-auc:0.95499 validation-aucpr:0.96118
[12] validation-logloss:0.52150 validation-auc:0.95611 validation-aucpr:0.96241
[13] validation-logloss:0.51267 validation-auc:0.95605 validation-aucpr:0.96222
[14] validation-logloss:0.50409 validation-auc:0.95607 validation-aucpr:0.96217
[15] validation-logloss:0.49301 validation-auc:0.95652 validation-aucpr:0.96259
[16] validation-logloss:0.48500 validation-auc:0.95653 validation-aucpr:0.96258
[17] validation-logloss:0.47458 validation-auc:0.95698 validation-aucpr:0.96300
[18] validation-logloss:0.46469 validation-auc:0.95734 validation-aucpr:0.96341
[19] validation-logloss:0.45776 validation-auc:0.95720 validation-aucpr:0.96329
[20] validation-logloss:0.44897 validation-auc:0.95749 validation-aucpr:0.96364
[21] validation-logloss:0.44265 validation-auc:0.95760 validation-aucpr:0.96373
[22] validation-logloss:0.43639 validation-auc:0.95772 validation-aucpr:0.96382
[23] validation-logloss:0.42799 validation-auc:0.95809 validation-aucpr:0.96417
[24] validation-logloss:0.42227 validation-auc:0.95820 validation-aucpr:0.96420
[25] validation-logloss:0.41635 validation-auc:0.95841 validation-aucpr:0.96429
[26] validation-logloss:0.40914 validation-auc:0.95901 validation-aucpr:0.96492
[27] validation-logloss:0.40393 validation-auc:0.95903 validation-aucpr:0.96488
[28] validation-logloss:0.39741 validation-auc:0.95928 validation-aucpr:0.96512
[29] validation-logloss:0.39271 validation-auc:0.95943 validation-aucpr:0.96523
[30] validation-logloss:0.38858 validation-auc:0.95945 validation-aucpr:0.96519
[31] validation-logloss:0.38258 validation-auc:0.95992 validation-aucpr:0.96576
[32] validation-logloss:0.37857 validation-auc:0.95984 validation-aucpr:0.96567
[33] validation-logloss:0.37420 validation-auc:0.96007 validation-aucpr:0.96584
[34] validation-logloss:0.36854 validation-auc:0.96030 validation-aucpr:0.96609
[35] validation-logloss:0.36307 validation-auc:0.96049 validation-aucpr:0.96631
[36] validation-logloss:0.35815 validation-auc:0.96066 validation-aucpr:0.96644
[37] validation-logloss:0.35432 validation-auc:0.96070 validation-aucpr:0.96645
[38] validation-logloss:0.35070 validation-auc:0.96089 validation-aucpr:0.96655
[39] validation-logloss:0.34745 validation-auc:0.96087 validation-aucpr:0.96651
[40] validation-logloss:0.34411 validation-auc:0.96095 validation-aucpr:0.96655
[41] validation-logloss:0.34092 validation-auc:0.96110 validation-aucpr:0.96671
[42] validation-logloss:0.33780 validation-auc:0.96114 validation-aucpr:0.96677
[43] validation-logloss:0.33507 validation-auc:0.96113 validation-aucpr:0.96672
[44] validation-logloss:0.33256 validation-auc:0.96100 validation-aucpr:0.96662
[45] validation-logloss:0.32860 validation-auc:0.96118 validation-aucpr:0.96679
[46] validation-logloss:0.32459 validation-auc:0.96133 validation-aucpr:0.96690
[47] validation-logloss:0.32182 validation-auc:0.96136 validation-aucpr:0.96687
[48] validation-logloss:0.31802 validation-auc:0.96143 validation-aucpr:0.96697
[49] validation-logloss:0.31520 validation-auc:0.96157 validation-aucpr:0.96705
[50] validation-logloss:0.31293 validation-auc:0.96160 validation-aucpr:0.96707
[51] validation-logloss:0.31069 validation-auc:0.96165 validation-aucpr:0.96710
[52] validation-logloss:0.30847 validation-auc:0.96164 validation-aucpr:0.96706
[53] validation-logloss:0.30628 validation-auc:0.96171 validation-aucpr:0.96707
[54] validation-logloss:0.30286 validation-auc:0.96187 validation-aucpr:0.96727
[55] validation-logloss:0.30086 validation-auc:0.96190 validation-aucpr:0.96727
[56] validation-logloss:0.29918 validation-auc:0.96188 validation-aucpr:0.96723
[57] validation-logloss:0.29727 validation-auc:0.96196 validation-aucpr:0.96729
[58] validation-logloss:0.29552 validation-auc:0.96199 validation-aucpr:0.96734
[59] validation-logloss:0.29375 validation-auc:0.96197 validation-aucpr:0.96729
[60] validation-logloss:0.29217 validation-auc:0.96200 validation-aucpr:0.96731
[61] validation-logloss:0.28923 validation-auc:0.96204 validation-aucpr:0.96736
[62] validation-logloss:0.28747 validation-auc:0.96214 validation-aucpr:0.96745
[63] validation-logloss:0.28480 validation-auc:0.96223 validation-aucpr:0.96756
[64] validation-logloss:0.28298 validation-auc:0.96231 validation-aucpr:0.96761
[65] validation-logloss:0.28147 validation-auc:0.96242 validation-aucpr:0.96769
[66] validation-logloss:0.27893 validation-auc:0.96261 validation-aucpr:0.96787
[67] validation-logloss:0.27647 validation-auc:0.96271 validation-aucpr:0.96799
[68] validation-logloss:0.27376 validation-auc:0.96294 validation-aucpr:0.96820
[69] validation-logloss:0.27137 validation-auc:0.96304 validation-aucpr:0.96829
[70] validation-logloss:0.26897 validation-auc:0.96326 validation-aucpr:0.96846
[71] validation-logloss:0.26679 validation-auc:0.96335 validation-aucpr:0.96856
[72] validation-logloss:0.26525 validation-auc:0.96352 validation-aucpr:0.96870
[73] validation-logloss:0.26413 validation-auc:0.96361 validation-aucpr:0.96875
[74] validation-logloss:0.26266 validation-auc:0.96378 validation-aucpr:0.96890
{'best_iteration': '74', 'best_score': '0.9689014911357019'}
Trial 93, Fold 5: Log loss = 0.2626649744827682, Average precision = 0.9689000073615628, ROC-AUC = 0.9637839585307395, Elapsed Time = 8.099983399999473 seconds
Optimization Progress: 94%|#########3| 94/100 [3:57:46<07:43, 77.27s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 94, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68325 validation-auc:0.94038 validation-aucpr:0.94328
[1] validation-logloss:0.67350 validation-auc:0.94740 validation-aucpr:0.95020
[2] validation-logloss:0.66237 validation-auc:0.95695 validation-aucpr:0.96034
[3] validation-logloss:0.65320 validation-auc:0.95779 validation-aucpr:0.96079
[4] validation-logloss:0.64298 validation-auc:0.95912 validation-aucpr:0.96398
[5] validation-logloss:0.63455 validation-auc:0.95918 validation-aucpr:0.96426
[6] validation-logloss:0.62506 validation-auc:0.96062 validation-aucpr:0.96583
[7] validation-logloss:0.61546 validation-auc:0.96104 validation-aucpr:0.96626
[8] validation-logloss:0.60785 validation-auc:0.96098 validation-aucpr:0.96606
[9] validation-logloss:0.60007 validation-auc:0.96089 validation-aucpr:0.96589
[10] validation-logloss:0.59154 validation-auc:0.96141 validation-aucpr:0.96645
[11] validation-logloss:0.58289 validation-auc:0.96152 validation-aucpr:0.96662
[12] validation-logloss:0.57562 validation-auc:0.96166 validation-aucpr:0.96667
[13] validation-logloss:0.56903 validation-auc:0.96141 validation-aucpr:0.96637
[14] validation-logloss:0.56089 validation-auc:0.96187 validation-aucpr:0.96681
[15] validation-logloss:0.55308 validation-auc:0.96218 validation-aucpr:0.96694
[16] validation-logloss:0.54550 validation-auc:0.96239 validation-aucpr:0.96724
[17] validation-logloss:0.53854 validation-auc:0.96250 validation-aucpr:0.96808
[18] validation-logloss:0.53288 validation-auc:0.96231 validation-aucpr:0.96792
[19] validation-logloss:0.52578 validation-auc:0.96246 validation-aucpr:0.96811
[20] validation-logloss:0.52031 validation-auc:0.96243 validation-aucpr:0.96805
[21] validation-logloss:0.51489 validation-auc:0.96262 validation-aucpr:0.96842
[22] validation-logloss:0.50830 validation-auc:0.96269 validation-aucpr:0.96854
[23] validation-logloss:0.50312 validation-auc:0.96274 validation-aucpr:0.96860
[24] validation-logloss:0.49695 validation-auc:0.96289 validation-aucpr:0.96876
[25] validation-logloss:0.49177 validation-auc:0.96298 validation-aucpr:0.96879
[26] validation-logloss:0.48570 validation-auc:0.96312 validation-aucpr:0.96894
[27] validation-logloss:0.48023 validation-auc:0.96316 validation-aucpr:0.96901
[28] validation-logloss:0.47471 validation-auc:0.96321 validation-aucpr:0.96907
[29] validation-logloss:0.46901 validation-auc:0.96335 validation-aucpr:0.96922
[30] validation-logloss:0.46391 validation-auc:0.96349 validation-aucpr:0.96934
[31] validation-logloss:0.45936 validation-auc:0.96358 validation-aucpr:0.96935
[32] validation-logloss:0.45401 validation-auc:0.96367 validation-aucpr:0.96946
[33] validation-logloss:0.44971 validation-auc:0.96370 validation-aucpr:0.96945
[34] validation-logloss:0.44454 validation-auc:0.96386 validation-aucpr:0.96958
[35] validation-logloss:0.44055 validation-auc:0.96388 validation-aucpr:0.96959
[36] validation-logloss:0.43579 validation-auc:0.96394 validation-aucpr:0.96966
[37] validation-logloss:0.43123 validation-auc:0.96401 validation-aucpr:0.96976
[38] validation-logloss:0.42736 validation-auc:0.96397 validation-aucpr:0.96969
[39] validation-logloss:0.42292 validation-auc:0.96398 validation-aucpr:0.96971
[40] validation-logloss:0.41918 validation-auc:0.96413 validation-aucpr:0.96978
[41] validation-logloss:0.41485 validation-auc:0.96425 validation-aucpr:0.96989
[42] validation-logloss:0.41109 validation-auc:0.96427 validation-aucpr:0.96991
[43] validation-logloss:0.40702 validation-auc:0.96431 validation-aucpr:0.96997
[44] validation-logloss:0.40285 validation-auc:0.96449 validation-aucpr:0.97010
[45] validation-logloss:0.39945 validation-auc:0.96451 validation-aucpr:0.97009
[46] validation-logloss:0.39562 validation-auc:0.96457 validation-aucpr:0.97015
[47] validation-logloss:0.39265 validation-auc:0.96460 validation-aucpr:0.97013
[48] validation-logloss:0.38894 validation-auc:0.96470 validation-aucpr:0.97020
[49] validation-logloss:0.38521 validation-auc:0.96488 validation-aucpr:0.97035
[50] validation-logloss:0.38236 validation-auc:0.96478 validation-aucpr:0.97024
[51] validation-logloss:0.37939 validation-auc:0.96484 validation-aucpr:0.97024
[52] validation-logloss:0.37605 validation-auc:0.96486 validation-aucpr:0.97026
[53] validation-logloss:0.37322 validation-auc:0.96483 validation-aucpr:0.97020
[54] validation-logloss:0.36994 validation-auc:0.96490 validation-aucpr:0.97027
[55] validation-logloss:0.36737 validation-auc:0.96490 validation-aucpr:0.97026
{'best_iteration': '49', 'best_score': '0.9703483133017742'}
Trial 94, Fold 1: Log loss = 0.3673695350323967, Average precision = 0.9698315584733633, ROC-AUC = 0.9649038496006654, Elapsed Time = 1.0445719999988796 seconds
Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 94, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68219 validation-auc:0.95006 validation-aucpr:0.94543
[1] validation-logloss:0.67232 validation-auc:0.95474 validation-aucpr:0.95743
[2] validation-logloss:0.66131 validation-auc:0.96087 validation-aucpr:0.96474
[3] validation-logloss:0.65200 validation-auc:0.96064 validation-aucpr:0.96456
[4] validation-logloss:0.64192 validation-auc:0.96278 validation-aucpr:0.96663
[5] validation-logloss:0.63331 validation-auc:0.96275 validation-aucpr:0.96643
[6] validation-logloss:0.62387 validation-auc:0.96392 validation-aucpr:0.96778
[7] validation-logloss:0.61423 validation-auc:0.96490 validation-aucpr:0.96888
[8] validation-logloss:0.60653 validation-auc:0.96465 validation-aucpr:0.96844
[9] validation-logloss:0.59880 validation-auc:0.96410 validation-aucpr:0.96780
[10] validation-logloss:0.59021 validation-auc:0.96461 validation-aucpr:0.96830
[11] validation-logloss:0.58151 validation-auc:0.96514 validation-aucpr:0.96886
[12] validation-logloss:0.57430 validation-auc:0.96500 validation-aucpr:0.96863
[13] validation-logloss:0.56776 validation-auc:0.96477 validation-aucpr:0.96840
[14] validation-logloss:0.55966 validation-auc:0.96514 validation-aucpr:0.96880
[15] validation-logloss:0.55189 validation-auc:0.96534 validation-aucpr:0.96919
[16] validation-logloss:0.54425 validation-auc:0.96553 validation-aucpr:0.96944
[17] validation-logloss:0.53733 validation-auc:0.96539 validation-aucpr:0.96938
[18] validation-logloss:0.53163 validation-auc:0.96521 validation-aucpr:0.96927
[19] validation-logloss:0.52454 validation-auc:0.96534 validation-aucpr:0.96943
[20] validation-logloss:0.51810 validation-auc:0.96534 validation-aucpr:0.96931
[21] validation-logloss:0.51290 validation-auc:0.96508 validation-aucpr:0.96909
[22] validation-logloss:0.50751 validation-auc:0.96501 validation-aucpr:0.96898
[23] validation-logloss:0.50199 validation-auc:0.96481 validation-aucpr:0.96881
[24] validation-logloss:0.49670 validation-auc:0.96476 validation-aucpr:0.96872
[25] validation-logloss:0.49169 validation-auc:0.96465 validation-aucpr:0.96862
[26] validation-logloss:0.48680 validation-auc:0.96458 validation-aucpr:0.96855
[27] validation-logloss:0.48107 validation-auc:0.96469 validation-aucpr:0.96868
[28] validation-logloss:0.47515 validation-auc:0.96486 validation-aucpr:0.96883
[29] validation-logloss:0.47047 validation-auc:0.96488 validation-aucpr:0.96880
[30] validation-logloss:0.46590 validation-auc:0.96491 validation-aucpr:0.96878
[31] validation-logloss:0.46132 validation-auc:0.96483 validation-aucpr:0.96868
[32] validation-logloss:0.45608 validation-auc:0.96499 validation-aucpr:0.96882
[33] validation-logloss:0.45082 validation-auc:0.96506 validation-aucpr:0.96887
[34] validation-logloss:0.44580 validation-auc:0.96518 validation-aucpr:0.96898
[35] validation-logloss:0.44096 validation-auc:0.96540 validation-aucpr:0.96922
[36] validation-logloss:0.43630 validation-auc:0.96553 validation-aucpr:0.96934
[37] validation-logloss:0.43243 validation-auc:0.96548 validation-aucpr:0.96928
[38] validation-logloss:0.42879 validation-auc:0.96544 validation-aucpr:0.96911
[39] validation-logloss:0.42499 validation-auc:0.96546 validation-aucpr:0.96909
[40] validation-logloss:0.42161 validation-auc:0.96537 validation-aucpr:0.96902
[41] validation-logloss:0.41722 validation-auc:0.96544 validation-aucpr:0.96911
[42] validation-logloss:0.41289 validation-auc:0.96545 validation-aucpr:0.96913
[43] validation-logloss:0.40868 validation-auc:0.96545 validation-aucpr:0.96913
[44] validation-logloss:0.40455 validation-auc:0.96548 validation-aucpr:0.96918
[45] validation-logloss:0.40130 validation-auc:0.96549 validation-aucpr:0.96917
[46] validation-logloss:0.39802 validation-auc:0.96557 validation-aucpr:0.96922
[47] validation-logloss:0.39421 validation-auc:0.96567 validation-aucpr:0.96932
[48] validation-logloss:0.39034 validation-auc:0.96574 validation-aucpr:0.96940
[49] validation-logloss:0.38654 validation-auc:0.96582 validation-aucpr:0.96945
[50] validation-logloss:0.38368 validation-auc:0.96587 validation-aucpr:0.96950
[51] validation-logloss:0.38074 validation-auc:0.96580 validation-aucpr:0.96939
[52] validation-logloss:0.37734 validation-auc:0.96590 validation-aucpr:0.96949
[53] validation-logloss:0.37451 validation-auc:0.96584 validation-aucpr:0.96941
[54] validation-logloss:0.37098 validation-auc:0.96595 validation-aucpr:0.96951
[55] validation-logloss:0.36825 validation-auc:0.96597 validation-aucpr:0.96953
{'best_iteration': '55', 'best_score': '0.9695323887159993'}
Trial 94, Fold 2: Log loss = 0.3682469709595792, Average precision = 0.9692905878067982, ROC-AUC = 0.9659652479361542, Elapsed Time = 1.2049114000001282 seconds
Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 94, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68261 validation-auc:0.94370 validation-aucpr:0.93423
[1] validation-logloss:0.67270 validation-auc:0.95496 validation-aucpr:0.95408
[2] validation-logloss:0.66175 validation-auc:0.96100 validation-aucpr:0.96347
[3] validation-logloss:0.65249 validation-auc:0.96148 validation-aucpr:0.96356
[4] validation-logloss:0.64201 validation-auc:0.96287 validation-aucpr:0.96673
[5] validation-logloss:0.63347 validation-auc:0.96293 validation-aucpr:0.96672
[6] validation-logloss:0.62390 validation-auc:0.96422 validation-aucpr:0.96796
[7] validation-logloss:0.61428 validation-auc:0.96461 validation-aucpr:0.96834
[8] validation-logloss:0.60650 validation-auc:0.96487 validation-aucpr:0.96877
[9] validation-logloss:0.59873 validation-auc:0.96458 validation-aucpr:0.96841
[10] validation-logloss:0.59016 validation-auc:0.96495 validation-aucpr:0.96889
[11] validation-logloss:0.58160 validation-auc:0.96495 validation-aucpr:0.96891
[12] validation-logloss:0.57443 validation-auc:0.96475 validation-aucpr:0.96869
[13] validation-logloss:0.56776 validation-auc:0.96458 validation-aucpr:0.96852
[14] validation-logloss:0.55981 validation-auc:0.96463 validation-aucpr:0.96858
[15] validation-logloss:0.55199 validation-auc:0.96487 validation-aucpr:0.96912
[16] validation-logloss:0.54431 validation-auc:0.96513 validation-aucpr:0.96939
[17] validation-logloss:0.53742 validation-auc:0.96520 validation-aucpr:0.96980
[18] validation-logloss:0.53170 validation-auc:0.96500 validation-aucpr:0.96963
[19] validation-logloss:0.52455 validation-auc:0.96520 validation-aucpr:0.96978
[20] validation-logloss:0.51806 validation-auc:0.96543 validation-aucpr:0.96996
[21] validation-logloss:0.51271 validation-auc:0.96512 validation-aucpr:0.96967
[22] validation-logloss:0.50720 validation-auc:0.96501 validation-aucpr:0.96955
[23] validation-logloss:0.50168 validation-auc:0.96490 validation-aucpr:0.96945
[24] validation-logloss:0.49634 validation-auc:0.96496 validation-aucpr:0.96946
[25] validation-logloss:0.49109 validation-auc:0.96510 validation-aucpr:0.96962
[26] validation-logloss:0.48608 validation-auc:0.96517 validation-aucpr:0.96971
[27] validation-logloss:0.48041 validation-auc:0.96527 validation-aucpr:0.97023
[28] validation-logloss:0.47460 validation-auc:0.96532 validation-aucpr:0.97025
[29] validation-logloss:0.46980 validation-auc:0.96544 validation-aucpr:0.97031
[30] validation-logloss:0.46521 validation-auc:0.96537 validation-aucpr:0.97020
[31] validation-logloss:0.46058 validation-auc:0.96532 validation-aucpr:0.97016
[32] validation-logloss:0.45538 validation-auc:0.96534 validation-aucpr:0.97018
[33] validation-logloss:0.45009 validation-auc:0.96542 validation-aucpr:0.97026
[34] validation-logloss:0.44511 validation-auc:0.96555 validation-aucpr:0.97041
[35] validation-logloss:0.44020 validation-auc:0.96563 validation-aucpr:0.97051
[36] validation-logloss:0.43544 validation-auc:0.96567 validation-aucpr:0.97057
[37] validation-logloss:0.43154 validation-auc:0.96573 validation-aucpr:0.97062
[38] validation-logloss:0.42789 validation-auc:0.96571 validation-aucpr:0.97064
[39] validation-logloss:0.42405 validation-auc:0.96564 validation-aucpr:0.97056
[40] validation-logloss:0.42061 validation-auc:0.96550 validation-aucpr:0.97046
[41] validation-logloss:0.41616 validation-auc:0.96565 validation-aucpr:0.97061
[42] validation-logloss:0.41180 validation-auc:0.96577 validation-aucpr:0.97073
[43] validation-logloss:0.40752 validation-auc:0.96587 validation-aucpr:0.97080
[44] validation-logloss:0.40319 validation-auc:0.96601 validation-aucpr:0.97091
[45] validation-logloss:0.39978 validation-auc:0.96613 validation-aucpr:0.97098
[46] validation-logloss:0.39575 validation-auc:0.96620 validation-aucpr:0.97105
[47] validation-logloss:0.39274 validation-auc:0.96621 validation-aucpr:0.97103
[48] validation-logloss:0.38890 validation-auc:0.96639 validation-aucpr:0.97120
[49] validation-logloss:0.38515 validation-auc:0.96639 validation-aucpr:0.97122
[50] validation-logloss:0.38228 validation-auc:0.96640 validation-aucpr:0.97121
[51] validation-logloss:0.37940 validation-auc:0.96644 validation-aucpr:0.97122
[52] validation-logloss:0.37584 validation-auc:0.96651 validation-aucpr:0.97130
[53] validation-logloss:0.37301 validation-auc:0.96660 validation-aucpr:0.97135
[54] validation-logloss:0.37023 validation-auc:0.96665 validation-aucpr:0.97139
[55] validation-logloss:0.36678 validation-auc:0.96674 validation-aucpr:0.97149
{'best_iteration': '55', 'best_score': '0.9714851566138424'}
Trial 94, Fold 3: Log loss = 0.36677932299082194, Average precision = 0.971296260368586, ROC-AUC = 0.9667447131636988, Elapsed Time = 1.1741581999995105 seconds
Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 94, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68171 validation-auc:0.95216 validation-aucpr:0.94627
[1] validation-logloss:0.67187 validation-auc:0.95852 validation-aucpr:0.96196
[2] validation-logloss:0.66088 validation-auc:0.96030 validation-aucpr:0.96401
[3] validation-logloss:0.65158 validation-auc:0.96063 validation-aucpr:0.96492
[4] validation-logloss:0.64159 validation-auc:0.96117 validation-aucpr:0.96443
[5] validation-logloss:0.63308 validation-auc:0.96155 validation-aucpr:0.96729
[6] validation-logloss:0.62359 validation-auc:0.96292 validation-aucpr:0.96869
[7] validation-logloss:0.61399 validation-auc:0.96356 validation-aucpr:0.96931
[8] validation-logloss:0.60637 validation-auc:0.96324 validation-aucpr:0.96878
[9] validation-logloss:0.59852 validation-auc:0.96321 validation-aucpr:0.96876
[10] validation-logloss:0.59094 validation-auc:0.96308 validation-aucpr:0.96858
[11] validation-logloss:0.58245 validation-auc:0.96351 validation-aucpr:0.96903
[12] validation-logloss:0.57394 validation-auc:0.96372 validation-aucpr:0.96923
[13] validation-logloss:0.56578 validation-auc:0.96376 validation-aucpr:0.96941
[14] validation-logloss:0.55927 validation-auc:0.96367 validation-aucpr:0.96936
[15] validation-logloss:0.55267 validation-auc:0.96361 validation-aucpr:0.96924
[16] validation-logloss:0.54623 validation-auc:0.96368 validation-aucpr:0.96934
[17] validation-logloss:0.53994 validation-auc:0.96367 validation-aucpr:0.96923
[18] validation-logloss:0.53435 validation-auc:0.96329 validation-aucpr:0.96892
[19] validation-logloss:0.52839 validation-auc:0.96327 validation-aucpr:0.96881
[20] validation-logloss:0.52255 validation-auc:0.96318 validation-aucpr:0.96867
[21] validation-logloss:0.51693 validation-auc:0.96342 validation-aucpr:0.96883
[22] validation-logloss:0.51024 validation-auc:0.96354 validation-aucpr:0.96899
[23] validation-logloss:0.50503 validation-auc:0.96349 validation-aucpr:0.96897
[24] validation-logloss:0.49910 validation-auc:0.96356 validation-aucpr:0.96921
[25] validation-logloss:0.49387 validation-auc:0.96358 validation-aucpr:0.96915
[26] validation-logloss:0.48759 validation-auc:0.96391 validation-aucpr:0.96947
[27] validation-logloss:0.48150 validation-auc:0.96407 validation-aucpr:0.96963
[28] validation-logloss:0.47705 validation-auc:0.96400 validation-aucpr:0.96956
[29] validation-logloss:0.47235 validation-auc:0.96399 validation-aucpr:0.96950
[30] validation-logloss:0.46815 validation-auc:0.96370 validation-aucpr:0.96927
[31] validation-logloss:0.46275 validation-auc:0.96391 validation-aucpr:0.96951
[32] validation-logloss:0.45831 validation-auc:0.96394 validation-aucpr:0.96951
[33] validation-logloss:0.45295 validation-auc:0.96408 validation-aucpr:0.96966
[34] validation-logloss:0.44881 validation-auc:0.96413 validation-aucpr:0.96967
[35] validation-logloss:0.44473 validation-auc:0.96417 validation-aucpr:0.96967
[36] validation-logloss:0.44003 validation-auc:0.96431 validation-aucpr:0.96990
[37] validation-logloss:0.43514 validation-auc:0.96443 validation-aucpr:0.97005
[38] validation-logloss:0.43044 validation-auc:0.96455 validation-aucpr:0.97017
[39] validation-logloss:0.42587 validation-auc:0.96470 validation-aucpr:0.97028
[40] validation-logloss:0.42159 validation-auc:0.96477 validation-aucpr:0.97036
[41] validation-logloss:0.41721 validation-auc:0.96486 validation-aucpr:0.97043
[42] validation-logloss:0.41302 validation-auc:0.96480 validation-aucpr:0.97042
[43] validation-logloss:0.40869 validation-auc:0.96483 validation-aucpr:0.97047
[44] validation-logloss:0.40560 validation-auc:0.96486 validation-aucpr:0.97049
[45] validation-logloss:0.40161 validation-auc:0.96501 validation-aucpr:0.97065
[46] validation-logloss:0.39794 validation-auc:0.96490 validation-aucpr:0.97062
[47] validation-logloss:0.39487 validation-auc:0.96482 validation-aucpr:0.97053
[48] validation-logloss:0.39181 validation-auc:0.96475 validation-aucpr:0.97044
[49] validation-logloss:0.38896 validation-auc:0.96471 validation-aucpr:0.97041
[50] validation-logloss:0.38539 validation-auc:0.96470 validation-aucpr:0.97042
[51] validation-logloss:0.38167 validation-auc:0.96480 validation-aucpr:0.97054
[52] validation-logloss:0.37867 validation-auc:0.96486 validation-aucpr:0.97055
[53] validation-logloss:0.37512 validation-auc:0.96503 validation-aucpr:0.97072
[54] validation-logloss:0.37190 validation-auc:0.96505 validation-aucpr:0.97079
[55] validation-logloss:0.36850 validation-auc:0.96516 validation-aucpr:0.97089
{'best_iteration': '55', 'best_score': '0.9708877542981628'}
Trial 94, Fold 4: Log loss = 0.3685043068161344, Average precision = 0.9707699630531544, ROC-AUC = 0.9651570670329119, Elapsed Time = 1.1587060000019846 seconds
Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 94, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68254 validation-auc:0.94775 validation-aucpr:0.94739
[1] validation-logloss:0.67277 validation-auc:0.95156 validation-aucpr:0.95367
[2] validation-logloss:0.66178 validation-auc:0.95803 validation-aucpr:0.96181
[3] validation-logloss:0.65278 validation-auc:0.95746 validation-aucpr:0.96125
[4] validation-logloss:0.64278 validation-auc:0.95835 validation-aucpr:0.96195
[5] validation-logloss:0.63412 validation-auc:0.95922 validation-aucpr:0.96406
[6] validation-logloss:0.62476 validation-auc:0.96033 validation-aucpr:0.96525
[7] validation-logloss:0.61521 validation-auc:0.96062 validation-aucpr:0.96575
[8] validation-logloss:0.60764 validation-auc:0.96039 validation-aucpr:0.96528
[9] validation-logloss:0.59995 validation-auc:0.96039 validation-aucpr:0.96506
[10] validation-logloss:0.59142 validation-auc:0.96095 validation-aucpr:0.96557
[11] validation-logloss:0.58288 validation-auc:0.96130 validation-aucpr:0.96584
[12] validation-logloss:0.57568 validation-auc:0.96161 validation-aucpr:0.96605
[13] validation-logloss:0.56751 validation-auc:0.96177 validation-aucpr:0.96629
[14] validation-logloss:0.56083 validation-auc:0.96189 validation-aucpr:0.96661
[15] validation-logloss:0.55341 validation-auc:0.96192 validation-aucpr:0.96697
[16] validation-logloss:0.54587 validation-auc:0.96192 validation-aucpr:0.96700
[17] validation-logloss:0.53896 validation-auc:0.96196 validation-aucpr:0.96705
[18] validation-logloss:0.53303 validation-auc:0.96192 validation-aucpr:0.96701
[19] validation-logloss:0.52716 validation-auc:0.96195 validation-aucpr:0.96700
[20] validation-logloss:0.52159 validation-auc:0.96190 validation-aucpr:0.96693
[21] validation-logloss:0.51483 validation-auc:0.96202 validation-aucpr:0.96704
[22] validation-logloss:0.50949 validation-auc:0.96192 validation-aucpr:0.96700
[23] validation-logloss:0.50299 validation-auc:0.96202 validation-aucpr:0.96713
[24] validation-logloss:0.49681 validation-auc:0.96206 validation-aucpr:0.96720
[25] validation-logloss:0.49223 validation-auc:0.96178 validation-aucpr:0.96695
[26] validation-logloss:0.48627 validation-auc:0.96191 validation-aucpr:0.96710
[27] validation-logloss:0.48164 validation-auc:0.96189 validation-aucpr:0.96707
[28] validation-logloss:0.47588 validation-auc:0.96191 validation-aucpr:0.96708
[29] validation-logloss:0.47153 validation-auc:0.96177 validation-aucpr:0.96699
[30] validation-logloss:0.46627 validation-auc:0.96177 validation-aucpr:0.96697
[31] validation-logloss:0.46102 validation-auc:0.96194 validation-aucpr:0.96712
[32] validation-logloss:0.45563 validation-auc:0.96207 validation-aucpr:0.96725
[33] validation-logloss:0.45147 validation-auc:0.96203 validation-aucpr:0.96717
[34] validation-logloss:0.44760 validation-auc:0.96206 validation-aucpr:0.96717
[35] validation-logloss:0.44346 validation-auc:0.96207 validation-aucpr:0.96716
[36] validation-logloss:0.43976 validation-auc:0.96206 validation-aucpr:0.96718
[37] validation-logloss:0.43591 validation-auc:0.96210 validation-aucpr:0.96721
[38] validation-logloss:0.43203 validation-auc:0.96223 validation-aucpr:0.96727
[39] validation-logloss:0.42753 validation-auc:0.96232 validation-aucpr:0.96735
[40] validation-logloss:0.42402 validation-auc:0.96234 validation-aucpr:0.96735
[41] validation-logloss:0.42048 validation-auc:0.96227 validation-aucpr:0.96730
[42] validation-logloss:0.41703 validation-auc:0.96234 validation-aucpr:0.96733
[43] validation-logloss:0.41286 validation-auc:0.96244 validation-aucpr:0.96741
[44] validation-logloss:0.40972 validation-auc:0.96234 validation-aucpr:0.96732
[45] validation-logloss:0.40572 validation-auc:0.96250 validation-aucpr:0.96752
[46] validation-logloss:0.40232 validation-auc:0.96249 validation-aucpr:0.96753
[47] validation-logloss:0.39931 validation-auc:0.96245 validation-aucpr:0.96749
[48] validation-logloss:0.39548 validation-auc:0.96249 validation-aucpr:0.96755
[49] validation-logloss:0.39177 validation-auc:0.96256 validation-aucpr:0.96762
[50] validation-logloss:0.38813 validation-auc:0.96270 validation-aucpr:0.96776
[51] validation-logloss:0.38447 validation-auc:0.96287 validation-aucpr:0.96790
[52] validation-logloss:0.38095 validation-auc:0.96296 validation-aucpr:0.96798
[53] validation-logloss:0.37832 validation-auc:0.96300 validation-aucpr:0.96798
[54] validation-logloss:0.37496 validation-auc:0.96308 validation-aucpr:0.96807
[55] validation-logloss:0.37261 validation-auc:0.96304 validation-aucpr:0.96803
{'best_iteration': '54', 'best_score': '0.968069327592028'}
Trial 94, Fold 5: Log loss = 0.3726133849620998, Average precision = 0.967905009267924, ROC-AUC = 0.9630352823142523, Elapsed Time = 1.158533500001795 seconds
Optimization Progress: 95%|#########5| 95/100 [3:57:59<04:51, 58.20s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 95, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66971 validation-auc:0.93383 validation-aucpr:0.90166
[1] validation-logloss:0.64720 validation-auc:0.95771 validation-aucpr:0.95242
[2] validation-logloss:0.62615 validation-auc:0.96304 validation-aucpr:0.96622
[3] validation-logloss:0.60645 validation-auc:0.96451 validation-aucpr:0.96875
[4] validation-logloss:0.58754 validation-auc:0.96530 validation-aucpr:0.96909
[5] validation-logloss:0.56988 validation-auc:0.96587 validation-aucpr:0.96997
[6] validation-logloss:0.55326 validation-auc:0.96596 validation-aucpr:0.97020
[7] validation-logloss:0.53816 validation-auc:0.96587 validation-aucpr:0.96927
[8] validation-logloss:0.52323 validation-auc:0.96686 validation-aucpr:0.97025
[9] validation-logloss:0.50994 validation-auc:0.96737 validation-aucpr:0.97291
[10] validation-logloss:0.49618 validation-auc:0.96782 validation-aucpr:0.97323
[11] validation-logloss:0.48370 validation-auc:0.96760 validation-aucpr:0.97308
[12] validation-logloss:0.47155 validation-auc:0.96753 validation-aucpr:0.97303
[13] validation-logloss:0.46132 validation-auc:0.96767 validation-aucpr:0.97322
[14] validation-logloss:0.45019 validation-auc:0.96793 validation-aucpr:0.97336
[15] validation-logloss:0.43988 validation-auc:0.96801 validation-aucpr:0.97345
[16] validation-logloss:0.42974 validation-auc:0.96806 validation-aucpr:0.97355
[17] validation-logloss:0.42043 validation-auc:0.96814 validation-aucpr:0.97356
[18] validation-logloss:0.41199 validation-auc:0.96843 validation-aucpr:0.97388
{'best_iteration': '18', 'best_score': '0.9738836899662131'}
Trial 95, Fold 1: Log loss = 0.41199033206300667, Average precision = 0.9738432506185263, ROC-AUC = 0.968429170072124, Elapsed Time = 1.558035199999722 seconds
Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 95, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67021 validation-auc:0.93318 validation-aucpr:0.90261
[1] validation-logloss:0.64763 validation-auc:0.95870 validation-aucpr:0.95098
[2] validation-logloss:0.62867 validation-auc:0.96013 validation-aucpr:0.96485
[3] validation-logloss:0.60860 validation-auc:0.96347 validation-aucpr:0.96772
[4] validation-logloss:0.59048 validation-auc:0.96454 validation-aucpr:0.96876
[5] validation-logloss:0.57283 validation-auc:0.96522 validation-aucpr:0.96957
[6] validation-logloss:0.55635 validation-auc:0.96588 validation-aucpr:0.96786
[7] validation-logloss:0.54047 validation-auc:0.96648 validation-aucpr:0.96858
[8] validation-logloss:0.52704 validation-auc:0.96655 validation-aucpr:0.96837
[9] validation-logloss:0.51461 validation-auc:0.96694 validation-aucpr:0.96877
[10] validation-logloss:0.50246 validation-auc:0.96691 validation-aucpr:0.97035
[11] validation-logloss:0.49007 validation-auc:0.96708 validation-aucpr:0.97046
[12] validation-logloss:0.47778 validation-auc:0.96763 validation-aucpr:0.97091
[13] validation-logloss:0.46588 validation-auc:0.96786 validation-aucpr:0.96931
[14] validation-logloss:0.45537 validation-auc:0.96822 validation-aucpr:0.97142
[15] validation-logloss:0.44450 validation-auc:0.96842 validation-aucpr:0.97170
[16] validation-logloss:0.43455 validation-auc:0.96907 validation-aucpr:0.97230
[17] validation-logloss:0.42465 validation-auc:0.96941 validation-aucpr:0.97255
[18] validation-logloss:0.41603 validation-auc:0.96964 validation-aucpr:0.97270
{'best_iteration': '18', 'best_score': '0.9727045585634997'}
Trial 95, Fold 2: Log loss = 0.4160298962320222, Average precision = 0.9725908240025432, ROC-AUC = 0.9696389859484585, Elapsed Time = 1.7594687999990128 seconds
Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 95, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.67059 validation-auc:0.92465 validation-aucpr:0.87755
[1] validation-logloss:0.64834 validation-auc:0.95652 validation-aucpr:0.94821
[2] validation-logloss:0.62705 validation-auc:0.96279 validation-aucpr:0.96143
[3] validation-logloss:0.60711 validation-auc:0.96491 validation-aucpr:0.96733
[4] validation-logloss:0.58878 validation-auc:0.96567 validation-aucpr:0.97121
[5] validation-logloss:0.57091 validation-auc:0.96607 validation-aucpr:0.97160
[6] validation-logloss:0.55419 validation-auc:0.96724 validation-aucpr:0.97252
[7] validation-logloss:0.53819 validation-auc:0.96767 validation-aucpr:0.97286
[8] validation-logloss:0.52335 validation-auc:0.96806 validation-aucpr:0.97308
[9] validation-logloss:0.50879 validation-auc:0.96848 validation-aucpr:0.97332
[10] validation-logloss:0.49531 validation-auc:0.96870 validation-aucpr:0.97356
[11] validation-logloss:0.48245 validation-auc:0.96891 validation-aucpr:0.97376
[12] validation-logloss:0.47038 validation-auc:0.96865 validation-aucpr:0.97356
[13] validation-logloss:0.45897 validation-auc:0.96903 validation-aucpr:0.97387
[14] validation-logloss:0.44787 validation-auc:0.96908 validation-aucpr:0.97391
[15] validation-logloss:0.43733 validation-auc:0.96935 validation-aucpr:0.97406
[16] validation-logloss:0.42764 validation-auc:0.96970 validation-aucpr:0.97423
[17] validation-logloss:0.41846 validation-auc:0.96952 validation-aucpr:0.97413
[18] validation-logloss:0.40935 validation-auc:0.96984 validation-aucpr:0.97438
{'best_iteration': '18', 'best_score': '0.974380799626537'}
Trial 95, Fold 3: Log loss = 0.40934568100738855, Average precision = 0.9743398942778332, ROC-AUC = 0.9698392098698848, Elapsed Time = 1.8469833000017388 seconds
Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 95, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66999 validation-auc:0.93093 validation-aucpr:0.91191
[1] validation-logloss:0.64894 validation-auc:0.95747 validation-aucpr:0.95670
[2] validation-logloss:0.62772 validation-auc:0.96340 validation-aucpr:0.96424
[3] validation-logloss:0.60792 validation-auc:0.96536 validation-aucpr:0.97088
[4] validation-logloss:0.58997 validation-auc:0.96549 validation-aucpr:0.97109
[5] validation-logloss:0.57242 validation-auc:0.96663 validation-aucpr:0.97201
[6] validation-logloss:0.55560 validation-auc:0.96702 validation-aucpr:0.97243
[7] validation-logloss:0.53998 validation-auc:0.96754 validation-aucpr:0.97284
[8] validation-logloss:0.52548 validation-auc:0.96762 validation-aucpr:0.97291
[9] validation-logloss:0.51100 validation-auc:0.96805 validation-aucpr:0.97322
[10] validation-logloss:0.49800 validation-auc:0.96792 validation-aucpr:0.97315
[11] validation-logloss:0.48483 validation-auc:0.96846 validation-aucpr:0.97360
[12] validation-logloss:0.47264 validation-auc:0.96897 validation-aucpr:0.97391
[13] validation-logloss:0.46065 validation-auc:0.96928 validation-aucpr:0.97415
[14] validation-logloss:0.45103 validation-auc:0.96935 validation-aucpr:0.97417
[15] validation-logloss:0.44033 validation-auc:0.96935 validation-aucpr:0.97423
[16] validation-logloss:0.43028 validation-auc:0.96944 validation-aucpr:0.97433
[17] validation-logloss:0.42070 validation-auc:0.96969 validation-aucpr:0.97452
[18] validation-logloss:0.41148 validation-auc:0.96961 validation-aucpr:0.97450
{'best_iteration': '17', 'best_score': '0.974523158907407'}
Trial 95, Fold 4: Log loss = 0.4114788840127488, Average precision = 0.9744901295180513, ROC-AUC = 0.969611815108097, Elapsed Time = 1.8002224000010756 seconds
Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 95, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66984 validation-auc:0.92772 validation-aucpr:0.90006
[1] validation-logloss:0.64844 validation-auc:0.95360 validation-aucpr:0.94183
[2] validation-logloss:0.62801 validation-auc:0.95915 validation-aucpr:0.95796
[3] validation-logloss:0.61014 validation-auc:0.96151 validation-aucpr:0.96392
[4] validation-logloss:0.59133 validation-auc:0.96352 validation-aucpr:0.96524
[5] validation-logloss:0.57388 validation-auc:0.96433 validation-aucpr:0.96541
[6] validation-logloss:0.55786 validation-auc:0.96487 validation-aucpr:0.96603
[7] validation-logloss:0.54292 validation-auc:0.96506 validation-aucpr:0.96654
[8] validation-logloss:0.52797 validation-auc:0.96524 validation-aucpr:0.96763
[9] validation-logloss:0.51360 validation-auc:0.96594 validation-aucpr:0.96824
[10] validation-logloss:0.50009 validation-auc:0.96632 validation-aucpr:0.96853
[11] validation-logloss:0.48888 validation-auc:0.96624 validation-aucpr:0.97111
[12] validation-logloss:0.47679 validation-auc:0.96649 validation-aucpr:0.97128
[13] validation-logloss:0.46527 validation-auc:0.96644 validation-aucpr:0.97130
[14] validation-logloss:0.45418 validation-auc:0.96691 validation-aucpr:0.97171
[15] validation-logloss:0.44343 validation-auc:0.96740 validation-aucpr:0.97206
[16] validation-logloss:0.43343 validation-auc:0.96774 validation-aucpr:0.97232
[17] validation-logloss:0.42382 validation-auc:0.96786 validation-aucpr:0.97262
[18] validation-logloss:0.41446 validation-auc:0.96809 validation-aucpr:0.97280
{'best_iteration': '18', 'best_score': '0.9727953837135653'}
Trial 95, Fold 5: Log loss = 0.4144598869762319, Average precision = 0.9727891260068996, ROC-AUC = 0.9680885123460231, Elapsed Time = 1.8103469999987283 seconds
Optimization Progress: 96%|#########6| 96/100 [3:58:16<03:03, 45.79s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 96, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:57:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62898 validation-auc:0.92845 validation-aucpr:0.86784
[21:57:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57505 validation-auc:0.95687 validation-aucpr:0.93799
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.52993 validation-auc:0.96239 validation-aucpr:0.95987
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49115 validation-auc:0.96512 validation-aucpr:0.97003
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.45687 validation-auc:0.96635 validation-aucpr:0.97089
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.42798 validation-auc:0.96647 validation-aucpr:0.96934
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40235 validation-auc:0.96705 validation-aucpr:0.97122
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.37945 validation-auc:0.96804 validation-aucpr:0.97221
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36011 validation-auc:0.96831 validation-aucpr:0.97283
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34476 validation-auc:0.96885 validation-aucpr:0.97334
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.32943 validation-auc:0.96865 validation-aucpr:0.97322
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31572 validation-auc:0.96909 validation-aucpr:0.97378
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30299 validation-auc:0.96932 validation-aucpr:0.97399
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29170 validation-auc:0.96966 validation-aucpr:0.97423
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28176 validation-auc:0.96966 validation-aucpr:0.97339
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27283 validation-auc:0.97016 validation-aucpr:0.97369
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26482 validation-auc:0.97009 validation-aucpr:0.97365
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25797 validation-auc:0.96999 validation-aucpr:0.97359
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.25221 validation-auc:0.97009 validation-aucpr:0.97370
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24652 validation-auc:0.97007 validation-aucpr:0.97402
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.24136 validation-auc:0.97014 validation-aucpr:0.97474
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23666 validation-auc:0.97009 validation-aucpr:0.97473
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.23231 validation-auc:0.97018 validation-aucpr:0.97479
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22895 validation-auc:0.97015 validation-aucpr:0.97475
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22533 validation-auc:0.97037 validation-aucpr:0.97489
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.22221 validation-auc:0.97050 validation-aucpr:0.97497
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21949 validation-auc:0.97068 validation-aucpr:0.97510
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21704 validation-auc:0.97080 validation-aucpr:0.97522
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21484 validation-auc:0.97089 validation-aucpr:0.97528
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.21329 validation-auc:0.97078 validation-aucpr:0.97520
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21159 validation-auc:0.97083 validation-aucpr:0.97523
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21008 validation-auc:0.97092 validation-aucpr:0.97533
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20848 validation-auc:0.97097 validation-aucpr:0.97538
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20707 validation-auc:0.97112 validation-aucpr:0.97549
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20581 validation-auc:0.97122 validation-aucpr:0.97555
[21:57:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20488 validation-auc:0.97135 validation-aucpr:0.97565
[21:57:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20417 validation-auc:0.97134 validation-aucpr:0.97563
[21:57:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20362 validation-auc:0.97126 validation-aucpr:0.97556
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20317 validation-auc:0.97126 validation-aucpr:0.97558
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20304 validation-auc:0.97128 validation-aucpr:0.97555
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20285 validation-auc:0.97125 validation-aucpr:0.97553
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20225 validation-auc:0.97144 validation-aucpr:0.97573
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20190 validation-auc:0.97153 validation-aucpr:0.97578
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20163 validation-auc:0.97153 validation-aucpr:0.97576
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20201 validation-auc:0.97151 validation-aucpr:0.97590
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20150 validation-auc:0.97169 validation-aucpr:0.97592
[21:57:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20141 validation-auc:0.97170 validation-aucpr:0.97597
[21:57:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20115 validation-auc:0.97188 validation-aucpr:0.97636
[21:57:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20116 validation-auc:0.97195 validation-aucpr:0.97644
[21:57:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20118 validation-auc:0.97204 validation-aucpr:0.97651
[21:57:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20172 validation-auc:0.97192 validation-aucpr:0.97641
[21:57:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20206 validation-auc:0.97195 validation-aucpr:0.97642
[21:57:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20201 validation-auc:0.97208 validation-aucpr:0.97651
[21:57:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20252 validation-auc:0.97204 validation-aucpr:0.97642
[21:57:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20308 validation-auc:0.97207 validation-aucpr:0.97642
[21:57:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20359 validation-auc:0.97204 validation-aucpr:0.97637
[21:57:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20420 validation-auc:0.97201 validation-aucpr:0.97639
[21:57:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20423 validation-auc:0.97213 validation-aucpr:0.97646
[21:57:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20441 validation-auc:0.97222 validation-aucpr:0.97657
[21:57:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20509 validation-auc:0.97218 validation-aucpr:0.97652
[21:57:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20573 validation-auc:0.97213 validation-aucpr:0.97648
{'best_iteration': '58', 'best_score': '0.976570721942885'}
Trial 96, Fold 1: Log loss = 0.20572811828880266, Average precision = 0.9764804945313532, ROC-AUC = 0.9721327542860967, Elapsed Time = 27.369219000000157 seconds
Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 96, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:57:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62831 validation-auc:0.93587 validation-aucpr:0.89205
[21:57:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57441 validation-auc:0.95918 validation-aucpr:0.95031
[21:57:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.52866 validation-auc:0.96432 validation-aucpr:0.96373
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49263 validation-auc:0.96662 validation-aucpr:0.97075
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.46241 validation-auc:0.96662 validation-aucpr:0.97057
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.43262 validation-auc:0.96697 validation-aucpr:0.97100
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40614 validation-auc:0.96731 validation-aucpr:0.97136
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38442 validation-auc:0.96831 validation-aucpr:0.97204
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36339 validation-auc:0.96896 validation-aucpr:0.97258
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34686 validation-auc:0.96926 validation-aucpr:0.97276
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.33063 validation-auc:0.96945 validation-aucpr:0.97292
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31610 validation-auc:0.96943 validation-aucpr:0.97303
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30338 validation-auc:0.96930 validation-aucpr:0.97298
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29096 validation-auc:0.97006 validation-aucpr:0.97358
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28065 validation-auc:0.97018 validation-aucpr:0.97366
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27226 validation-auc:0.97035 validation-aucpr:0.97372
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26480 validation-auc:0.97053 validation-aucpr:0.97379
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25711 validation-auc:0.97060 validation-aucpr:0.97385
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.25078 validation-auc:0.97074 validation-aucpr:0.97398
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24541 validation-auc:0.97073 validation-aucpr:0.97394
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.23953 validation-auc:0.97084 validation-aucpr:0.97398
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23407 validation-auc:0.97096 validation-aucpr:0.97404
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.22953 validation-auc:0.97085 validation-aucpr:0.97397
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22547 validation-auc:0.97080 validation-aucpr:0.97390
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22134 validation-auc:0.97101 validation-aucpr:0.97409
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.21862 validation-auc:0.97092 validation-aucpr:0.97405
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21540 validation-auc:0.97110 validation-aucpr:0.97442
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21330 validation-auc:0.97107 validation-aucpr:0.97439
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21082 validation-auc:0.97105 validation-aucpr:0.97439
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.20811 validation-auc:0.97129 validation-aucpr:0.97457
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.20601 validation-auc:0.97126 validation-aucpr:0.97451
[21:57:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.20387 validation-auc:0.97137 validation-aucpr:0.97463
[21:57:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20179 validation-auc:0.97161 validation-aucpr:0.97477
[21:57:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20015 validation-auc:0.97167 validation-aucpr:0.97478
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.19895 validation-auc:0.97179 validation-aucpr:0.97490
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.19818 validation-auc:0.97176 validation-aucpr:0.97485
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.19718 validation-auc:0.97179 validation-aucpr:0.97484
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.19581 validation-auc:0.97194 validation-aucpr:0.97496
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19497 validation-auc:0.97199 validation-aucpr:0.97501
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19419 validation-auc:0.97212 validation-aucpr:0.97505
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19376 validation-auc:0.97200 validation-aucpr:0.97491
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19364 validation-auc:0.97192 validation-aucpr:0.97486
[21:58:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19339 validation-auc:0.97192 validation-aucpr:0.97485
[21:58:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19278 validation-auc:0.97199 validation-aucpr:0.97488
[21:58:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19220 validation-auc:0.97210 validation-aucpr:0.97501
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19166 validation-auc:0.97213 validation-aucpr:0.97504
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19107 validation-auc:0.97227 validation-aucpr:0.97506
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19066 validation-auc:0.97237 validation-aucpr:0.97511
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19054 validation-auc:0.97244 validation-aucpr:0.97527
[21:58:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19061 validation-auc:0.97227 validation-aucpr:0.97513
[21:58:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19042 validation-auc:0.97234 validation-aucpr:0.97516
[21:58:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19050 validation-auc:0.97231 validation-aucpr:0.97485
[21:58:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19038 validation-auc:0.97233 validation-aucpr:0.97484
[21:58:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19033 validation-auc:0.97233 validation-aucpr:0.97482
[21:58:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19070 validation-auc:0.97230 validation-aucpr:0.97433
[21:58:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19102 validation-auc:0.97228 validation-aucpr:0.97429
[21:58:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19081 validation-auc:0.97232 validation-aucpr:0.97433
[21:58:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19116 validation-auc:0.97240 validation-aucpr:0.97426
[21:58:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19138 validation-auc:0.97238 validation-aucpr:0.97422
[21:58:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19171 validation-auc:0.97244 validation-aucpr:0.97412
[21:58:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19156 validation-auc:0.97249 validation-aucpr:0.97413
{'best_iteration': '48', 'best_score': '0.9752665860623102'}
Trial 96, Fold 2: Log loss = 0.19155986050609108, Average precision = 0.9742061023984951, ROC-AUC = 0.9724941205320521, Elapsed Time = 27.559297799998603 seconds
Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 96, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62854 validation-auc:0.93540 validation-aucpr:0.88474
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57500 validation-auc:0.95578 validation-aucpr:0.93764
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.52904 validation-auc:0.96384 validation-aucpr:0.96444
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.49038 validation-auc:0.96541 validation-aucpr:0.96817
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.45652 validation-auc:0.96621 validation-aucpr:0.96876
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.42740 validation-auc:0.96648 validation-aucpr:0.96873
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40194 validation-auc:0.96676 validation-aucpr:0.96886
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38083 validation-auc:0.96736 validation-aucpr:0.96932
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36028 validation-auc:0.96849 validation-aucpr:0.97213
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34389 validation-auc:0.96909 validation-aucpr:0.97342
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.32781 validation-auc:0.96912 validation-aucpr:0.97350
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31350 validation-auc:0.96944 validation-aucpr:0.97375
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30032 validation-auc:0.96981 validation-aucpr:0.97406
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29026 validation-auc:0.96994 validation-aucpr:0.97406
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28002 validation-auc:0.97009 validation-aucpr:0.97423
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27121 validation-auc:0.97002 validation-aucpr:0.97415
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26270 validation-auc:0.97040 validation-aucpr:0.97438
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25534 validation-auc:0.97050 validation-aucpr:0.97452
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.24836 validation-auc:0.97073 validation-aucpr:0.97474
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24211 validation-auc:0.97080 validation-aucpr:0.97482
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.23651 validation-auc:0.97122 validation-aucpr:0.97512
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23182 validation-auc:0.97106 validation-aucpr:0.97509
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.22748 validation-auc:0.97122 validation-aucpr:0.97522
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22372 validation-auc:0.97140 validation-aucpr:0.97528
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22066 validation-auc:0.97142 validation-aucpr:0.97528
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.21760 validation-auc:0.97133 validation-aucpr:0.97526
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21453 validation-auc:0.97147 validation-aucpr:0.97535
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21188 validation-auc:0.97150 validation-aucpr:0.97543
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.20981 validation-auc:0.97151 validation-aucpr:0.97541
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.20792 validation-auc:0.97158 validation-aucpr:0.97575
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.20574 validation-auc:0.97180 validation-aucpr:0.97591
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.20442 validation-auc:0.97167 validation-aucpr:0.97582
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20297 validation-auc:0.97176 validation-aucpr:0.97588
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20209 validation-auc:0.97167 validation-aucpr:0.97581
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20057 validation-auc:0.97190 validation-aucpr:0.97604
[21:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.19954 validation-auc:0.97202 validation-aucpr:0.97611
[21:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.19862 validation-auc:0.97211 validation-aucpr:0.97612
[21:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.19766 validation-auc:0.97230 validation-aucpr:0.97617
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19688 validation-auc:0.97236 validation-aucpr:0.97618
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19656 validation-auc:0.97238 validation-aucpr:0.97618
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19588 validation-auc:0.97251 validation-aucpr:0.97624
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19546 validation-auc:0.97249 validation-aucpr:0.97621
[21:58:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19532 validation-auc:0.97250 validation-aucpr:0.97622
[21:58:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19526 validation-auc:0.97249 validation-aucpr:0.97618
[21:58:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19488 validation-auc:0.97251 validation-aucpr:0.97613
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19493 validation-auc:0.97243 validation-aucpr:0.97608
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19438 validation-auc:0.97255 validation-aucpr:0.97609
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19498 validation-auc:0.97243 validation-aucpr:0.97598
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19492 validation-auc:0.97250 validation-aucpr:0.97599
[21:58:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19502 validation-auc:0.97247 validation-aucpr:0.97593
[21:58:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19527 validation-auc:0.97252 validation-aucpr:0.97612
[21:58:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19560 validation-auc:0.97250 validation-aucpr:0.97613
[21:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19595 validation-auc:0.97254 validation-aucpr:0.97608
[21:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19647 validation-auc:0.97245 validation-aucpr:0.97596
[21:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19678 validation-auc:0.97246 validation-aucpr:0.97599
[21:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19660 validation-auc:0.97256 validation-aucpr:0.97606
[21:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19673 validation-auc:0.97264 validation-aucpr:0.97612
[21:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19687 validation-auc:0.97275 validation-aucpr:0.97621
[21:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19717 validation-auc:0.97275 validation-aucpr:0.97613
[21:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19796 validation-auc:0.97272 validation-aucpr:0.97626
[21:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.19811 validation-auc:0.97276 validation-aucpr:0.97623
{'best_iteration': '59', 'best_score': '0.9762626557997987'}
Trial 96, Fold 3: Log loss = 0.19811367177543784, Average precision = 0.9762333554119768, ROC-AUC = 0.972757263004305, Elapsed Time = 27.47824279999986 seconds
Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 96, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62844 validation-auc:0.93617 validation-aucpr:0.88997
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57530 validation-auc:0.96105 validation-aucpr:0.95438
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.52950 validation-auc:0.96479 validation-aucpr:0.96434
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.48986 validation-auc:0.96819 validation-aucpr:0.97292
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.45614 validation-auc:0.96935 validation-aucpr:0.97402
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.42734 validation-auc:0.96886 validation-aucpr:0.97372
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40166 validation-auc:0.96878 validation-aucpr:0.97374
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.37911 validation-auc:0.96889 validation-aucpr:0.97389
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36122 validation-auc:0.96896 validation-aucpr:0.97394
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.34385 validation-auc:0.96920 validation-aucpr:0.97411
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.32770 validation-auc:0.96989 validation-aucpr:0.97454
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.31396 validation-auc:0.96993 validation-aucpr:0.97454
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.30194 validation-auc:0.97043 validation-aucpr:0.97492
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.29033 validation-auc:0.97090 validation-aucpr:0.97527
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28015 validation-auc:0.97092 validation-aucpr:0.97528
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.27242 validation-auc:0.97066 validation-aucpr:0.97516
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.26367 validation-auc:0.97080 validation-aucpr:0.97531
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.25596 validation-auc:0.97106 validation-aucpr:0.97549
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.24916 validation-auc:0.97124 validation-aucpr:0.97560
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.24322 validation-auc:0.97136 validation-aucpr:0.97570
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.23784 validation-auc:0.97153 validation-aucpr:0.97582
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.23272 validation-auc:0.97171 validation-aucpr:0.97601
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.22845 validation-auc:0.97165 validation-aucpr:0.97600
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.22476 validation-auc:0.97162 validation-aucpr:0.97599
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22102 validation-auc:0.97168 validation-aucpr:0.97606
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.21834 validation-auc:0.97150 validation-aucpr:0.97594
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.21518 validation-auc:0.97172 validation-aucpr:0.97607
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.21267 validation-auc:0.97174 validation-aucpr:0.97607
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21011 validation-auc:0.97193 validation-aucpr:0.97618
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.20854 validation-auc:0.97174 validation-aucpr:0.97607
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.20717 validation-auc:0.97173 validation-aucpr:0.97605
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.20533 validation-auc:0.97175 validation-aucpr:0.97608
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.20430 validation-auc:0.97156 validation-aucpr:0.97595
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20294 validation-auc:0.97159 validation-aucpr:0.97597
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20175 validation-auc:0.97165 validation-aucpr:0.97602
[21:58:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20075 validation-auc:0.97173 validation-aucpr:0.97606
[21:58:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.19985 validation-auc:0.97174 validation-aucpr:0.97606
[21:58:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.19920 validation-auc:0.97178 validation-aucpr:0.97607
[21:58:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.19827 validation-auc:0.97193 validation-aucpr:0.97618
[21:58:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.19832 validation-auc:0.97170 validation-aucpr:0.97601
[21:58:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.19779 validation-auc:0.97173 validation-aucpr:0.97603
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.19740 validation-auc:0.97178 validation-aucpr:0.97610
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.19753 validation-auc:0.97178 validation-aucpr:0.97606
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.19697 validation-auc:0.97188 validation-aucpr:0.97614
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.19634 validation-auc:0.97200 validation-aucpr:0.97623
[21:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.19627 validation-auc:0.97203 validation-aucpr:0.97624
[21:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.19603 validation-auc:0.97218 validation-aucpr:0.97634
[21:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.19614 validation-auc:0.97222 validation-aucpr:0.97638
[21:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19644 validation-auc:0.97220 validation-aucpr:0.97637
[21:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19654 validation-auc:0.97220 validation-aucpr:0.97635
[21:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.19653 validation-auc:0.97217 validation-aucpr:0.97632
[21:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.19668 validation-auc:0.97212 validation-aucpr:0.97629
[21:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.19677 validation-auc:0.97214 validation-aucpr:0.97631
[21:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.19705 validation-auc:0.97209 validation-aucpr:0.97627
[21:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.19765 validation-auc:0.97196 validation-aucpr:0.97618
[21:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.19801 validation-auc:0.97197 validation-aucpr:0.97619
[21:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.19842 validation-auc:0.97192 validation-aucpr:0.97617
[21:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.19873 validation-auc:0.97190 validation-aucpr:0.97617
[21:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.19920 validation-auc:0.97196 validation-aucpr:0.97618
[21:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.19961 validation-auc:0.97193 validation-aucpr:0.97615
[21:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20026 validation-auc:0.97191 validation-aucpr:0.97612
{'best_iteration': '47', 'best_score': '0.9763758129206098'}
Trial 96, Fold 4: Log loss = 0.20025557918199285, Average precision = 0.9761245672747666, ROC-AUC = 0.9719141862843981, Elapsed Time = 27.80174470000202 seconds
Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 96, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.62839 validation-auc:0.93519 validation-aucpr:0.89789
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.57898 validation-auc:0.95845 validation-aucpr:0.95465
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.53731 validation-auc:0.96030 validation-aucpr:0.95839
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.50217 validation-auc:0.96126 validation-aucpr:0.96048
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.46734 validation-auc:0.96368 validation-aucpr:0.96446
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.43656 validation-auc:0.96572 validation-aucpr:0.96920
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.40993 validation-auc:0.96666 validation-aucpr:0.97110
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.38940 validation-auc:0.96721 validation-aucpr:0.97134
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.36860 validation-auc:0.96778 validation-aucpr:0.97199
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.35279 validation-auc:0.96756 validation-aucpr:0.97173
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.33625 validation-auc:0.96782 validation-aucpr:0.97193
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.32411 validation-auc:0.96761 validation-aucpr:0.97159
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.31280 validation-auc:0.96772 validation-aucpr:0.97156
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.30078 validation-auc:0.96806 validation-aucpr:0.97185
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.28999 validation-auc:0.96819 validation-aucpr:0.97223
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.28010 validation-auc:0.96840 validation-aucpr:0.97247
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.27136 validation-auc:0.96857 validation-aucpr:0.97265
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.26364 validation-auc:0.96877 validation-aucpr:0.97228
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.25789 validation-auc:0.96888 validation-aucpr:0.97228
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.25197 validation-auc:0.96863 validation-aucpr:0.97214
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.24590 validation-auc:0.96918 validation-aucpr:0.97317
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.24144 validation-auc:0.96919 validation-aucpr:0.97317
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.23699 validation-auc:0.96914 validation-aucpr:0.97344
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.23361 validation-auc:0.96923 validation-aucpr:0.97343
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.22980 validation-auc:0.96946 validation-aucpr:0.97355
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.22724 validation-auc:0.96939 validation-aucpr:0.97343
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.22416 validation-auc:0.96956 validation-aucpr:0.97354
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.22090 validation-auc:0.96982 validation-aucpr:0.97376
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.21810 validation-auc:0.96998 validation-aucpr:0.97389
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.21607 validation-auc:0.97000 validation-aucpr:0.97390
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.21424 validation-auc:0.97009 validation-aucpr:0.97395
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.21229 validation-auc:0.97030 validation-aucpr:0.97412
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.21049 validation-auc:0.97051 validation-aucpr:0.97440
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.20905 validation-auc:0.97053 validation-aucpr:0.97448
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.20765 validation-auc:0.97069 validation-aucpr:0.97458
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.20723 validation-auc:0.97056 validation-aucpr:0.97443
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.20677 validation-auc:0.97055 validation-aucpr:0.97436
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.20554 validation-auc:0.97072 validation-aucpr:0.97451
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.20505 validation-auc:0.97066 validation-aucpr:0.97444
[21:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.20449 validation-auc:0.97077 validation-aucpr:0.97470
[21:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.20403 validation-auc:0.97081 validation-aucpr:0.97471
[21:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20342 validation-auc:0.97087 validation-aucpr:0.97480
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20301 validation-auc:0.97095 validation-aucpr:0.97486
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20243 validation-auc:0.97106 validation-aucpr:0.97490
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20235 validation-auc:0.97109 validation-aucpr:0.97488
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20210 validation-auc:0.97118 validation-aucpr:0.97494
[21:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20198 validation-auc:0.97124 validation-aucpr:0.97493
[21:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20192 validation-auc:0.97125 validation-aucpr:0.97495
[21:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20199 validation-auc:0.97121 validation-aucpr:0.97482
[21:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20212 validation-auc:0.97118 validation-aucpr:0.97478
[21:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50] validation-logloss:0.20192 validation-auc:0.97131 validation-aucpr:0.97489
[21:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51] validation-logloss:0.20181 validation-auc:0.97134 validation-aucpr:0.97492
[21:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52] validation-logloss:0.20187 validation-auc:0.97145 validation-aucpr:0.97512
[21:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53] validation-logloss:0.20251 validation-auc:0.97136 validation-aucpr:0.97501
[21:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54] validation-logloss:0.20274 validation-auc:0.97143 validation-aucpr:0.97504
[21:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55] validation-logloss:0.20294 validation-auc:0.97145 validation-aucpr:0.97506
[21:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56] validation-logloss:0.20285 validation-auc:0.97156 validation-aucpr:0.97494
[21:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57] validation-logloss:0.20294 validation-auc:0.97162 validation-aucpr:0.97496
[21:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58] validation-logloss:0.20318 validation-auc:0.97163 validation-aucpr:0.97491
[21:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59] validation-logloss:0.20374 validation-auc:0.97159 validation-aucpr:0.97474
[21:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60] validation-logloss:0.20419 validation-auc:0.97162 validation-aucpr:0.97477
{'best_iteration': '52', 'best_score': '0.9751215601216484'}
Trial 96, Fold 5: Log loss = 0.2041886193925169, Average precision = 0.9747760514099615, ROC-AUC = 0.9716203853371236, Elapsed Time = 28.233958600001642 seconds
Optimization Progress: 97%|#########7| 97/100 [4:00:43<03:47, 76.00s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 97, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64867 validation-auc:0.95670 validation-aucpr:0.96258
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60951 validation-auc:0.96225 validation-aucpr:0.96166
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.57441 validation-auc:0.96412 validation-aucpr:0.96074
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.54647 validation-auc:0.96526 validation-aucpr:0.96692
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.51790 validation-auc:0.96650 validation-aucpr:0.96834
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.49202 validation-auc:0.96625 validation-aucpr:0.96698
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.47174 validation-auc:0.96654 validation-aucpr:0.97127
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.45055 validation-auc:0.96698 validation-aucpr:0.97175
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.43051 validation-auc:0.96747 validation-aucpr:0.97218
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.41292 validation-auc:0.96788 validation-aucpr:0.97253
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.39664 validation-auc:0.96821 validation-aucpr:0.97272
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.38360 validation-auc:0.96824 validation-aucpr:0.97275
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.36961 validation-auc:0.96868 validation-aucpr:0.97313
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35663 validation-auc:0.96895 validation-aucpr:0.97330
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34479 validation-auc:0.96915 validation-aucpr:0.97342
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33570 validation-auc:0.96905 validation-aucpr:0.97362
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.32540 validation-auc:0.96930 validation-aucpr:0.97387
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.31689 validation-auc:0.96926 validation-aucpr:0.97379
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.30786 validation-auc:0.96959 validation-aucpr:0.97406
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.30076 validation-auc:0.96983 validation-aucpr:0.97422
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.29305 validation-auc:0.96999 validation-aucpr:0.97434
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.28598 validation-auc:0.97010 validation-aucpr:0.97441
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.28064 validation-auc:0.97001 validation-aucpr:0.97431
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.27448 validation-auc:0.97014 validation-aucpr:0.97441
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.26983 validation-auc:0.97001 validation-aucpr:0.97424
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.26474 validation-auc:0.96998 validation-aucpr:0.97419
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.25992 validation-auc:0.97000 validation-aucpr:0.97424
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.25517 validation-auc:0.97028 validation-aucpr:0.97445
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.25077 validation-auc:0.97034 validation-aucpr:0.97451
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24746 validation-auc:0.97047 validation-aucpr:0.97460
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.24383 validation-auc:0.97054 validation-aucpr:0.97458
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.24001 validation-auc:0.97078 validation-aucpr:0.97480
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23669 validation-auc:0.97088 validation-aucpr:0.97487
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.23377 validation-auc:0.97089 validation-aucpr:0.97489
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.23160 validation-auc:0.97095 validation-aucpr:0.97490
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22891 validation-auc:0.97113 validation-aucpr:0.97503
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22629 validation-auc:0.97121 validation-aucpr:0.97507
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.22400 validation-auc:0.97135 validation-aucpr:0.97516
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.22147 validation-auc:0.97161 validation-aucpr:0.97534
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21930 validation-auc:0.97184 validation-aucpr:0.97598
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21776 validation-auc:0.97188 validation-aucpr:0.97597
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21644 validation-auc:0.97178 validation-aucpr:0.97587
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21440 validation-auc:0.97195 validation-aucpr:0.97601
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.21275 validation-auc:0.97206 validation-aucpr:0.97609
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.21126 validation-auc:0.97210 validation-aucpr:0.97616
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20968 validation-auc:0.97213 validation-aucpr:0.97617
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20811 validation-auc:0.97223 validation-aucpr:0.97625
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20666 validation-auc:0.97239 validation-aucpr:0.97637
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20540 validation-auc:0.97243 validation-aucpr:0.97639
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20408 validation-auc:0.97258 validation-aucpr:0.97649
{'best_iteration': '49', 'best_score': '0.9764873601218161'}
Trial 97, Fold 1: Log loss = 0.20407838779868182, Average precision = 0.9764898931492989, ROC-AUC = 0.9725778535342673, Elapsed Time = 5.513618299999507 seconds
Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 97, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64888 validation-auc:0.95914 validation-aucpr:0.95894
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60917 validation-auc:0.96414 validation-aucpr:0.96715
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.57409 validation-auc:0.96652 validation-aucpr:0.96728
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.54269 validation-auc:0.96667 validation-aucpr:0.97064
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.51440 validation-auc:0.96649 validation-aucpr:0.97065
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.48842 validation-auc:0.96777 validation-aucpr:0.97157
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.46542 validation-auc:0.96869 validation-aucpr:0.97244
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.44628 validation-auc:0.96878 validation-aucpr:0.97248
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.42715 validation-auc:0.96921 validation-aucpr:0.97277
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.40905 validation-auc:0.97001 validation-aucpr:0.97332
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.39284 validation-auc:0.97031 validation-aucpr:0.97344
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.37946 validation-auc:0.97019 validation-aucpr:0.97326
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.36566 validation-auc:0.97009 validation-aucpr:0.97321
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35292 validation-auc:0.96986 validation-aucpr:0.97306
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34118 validation-auc:0.96988 validation-aucpr:0.97308
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33134 validation-auc:0.97014 validation-aucpr:0.97329
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.32116 validation-auc:0.97019 validation-aucpr:0.97324
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.31305 validation-auc:0.97037 validation-aucpr:0.97339
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.30537 validation-auc:0.97060 validation-aucpr:0.97359
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.29727 validation-auc:0.97052 validation-aucpr:0.97368
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.28970 validation-auc:0.97057 validation-aucpr:0.97368
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.28236 validation-auc:0.97089 validation-aucpr:0.97393
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.27561 validation-auc:0.97115 validation-aucpr:0.97411
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.26958 validation-auc:0.97099 validation-aucpr:0.97400
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.26461 validation-auc:0.97087 validation-aucpr:0.97393
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.25924 validation-auc:0.97110 validation-aucpr:0.97409
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.25420 validation-auc:0.97118 validation-aucpr:0.97415
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.24962 validation-auc:0.97113 validation-aucpr:0.97414
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.24538 validation-auc:0.97123 validation-aucpr:0.97422
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24131 validation-auc:0.97120 validation-aucpr:0.97419
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.23747 validation-auc:0.97146 validation-aucpr:0.97439
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.23397 validation-auc:0.97163 validation-aucpr:0.97452
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23092 validation-auc:0.97200 validation-aucpr:0.97477
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22804 validation-auc:0.97195 validation-aucpr:0.97469
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22526 validation-auc:0.97187 validation-aucpr:0.97463
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22267 validation-auc:0.97214 validation-aucpr:0.97483
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22086 validation-auc:0.97213 validation-aucpr:0.97519
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21832 validation-auc:0.97220 validation-aucpr:0.97521
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21595 validation-auc:0.97222 validation-aucpr:0.97522
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21371 validation-auc:0.97232 validation-aucpr:0.97530
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21160 validation-auc:0.97248 validation-aucpr:0.97540
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.20973 validation-auc:0.97247 validation-aucpr:0.97539
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20802 validation-auc:0.97240 validation-aucpr:0.97533
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20638 validation-auc:0.97250 validation-aucpr:0.97540
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20478 validation-auc:0.97251 validation-aucpr:0.97538
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20331 validation-auc:0.97258 validation-aucpr:0.97545
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20178 validation-auc:0.97263 validation-aucpr:0.97548
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20034 validation-auc:0.97273 validation-aucpr:0.97559
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.19908 validation-auc:0.97287 validation-aucpr:0.97565
[21:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.19801 validation-auc:0.97283 validation-aucpr:0.97561
{'best_iteration': '48', 'best_score': '0.975645861673028'}
Trial 97, Fold 2: Log loss = 0.19801219240028642, Average precision = 0.9755632458595235, ROC-AUC = 0.972825461821623, Elapsed Time = 5.8320394000002125 seconds
Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 97, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64838 validation-auc:0.96097 validation-aucpr:0.96283
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60883 validation-auc:0.96486 validation-aucpr:0.96844
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.57343 validation-auc:0.96625 validation-aucpr:0.96988
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.54120 validation-auc:0.96950 validation-aucpr:0.97233
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.51260 validation-auc:0.96927 validation-aucpr:0.97209
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.48677 validation-auc:0.97006 validation-aucpr:0.97259
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.46385 validation-auc:0.96987 validation-aucpr:0.97202
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.44239 validation-auc:0.97010 validation-aucpr:0.97299
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.42575 validation-auc:0.97042 validation-aucpr:0.97321
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.40816 validation-auc:0.97024 validation-aucpr:0.97312
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.39209 validation-auc:0.97014 validation-aucpr:0.97305
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.37705 validation-auc:0.97041 validation-aucpr:0.97322
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.36479 validation-auc:0.97031 validation-aucpr:0.97326
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35187 validation-auc:0.97049 validation-aucpr:0.97340
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34143 validation-auc:0.97043 validation-aucpr:0.97342
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33037 validation-auc:0.97067 validation-aucpr:0.97361
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.32055 validation-auc:0.97066 validation-aucpr:0.97378
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.31226 validation-auc:0.97086 validation-aucpr:0.97393
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.30338 validation-auc:0.97095 validation-aucpr:0.97401
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.29539 validation-auc:0.97060 validation-aucpr:0.97273
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.28877 validation-auc:0.97077 validation-aucpr:0.97321
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.28252 validation-auc:0.97102 validation-aucpr:0.97336
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.27570 validation-auc:0.97129 validation-aucpr:0.97359
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.26972 validation-auc:0.97145 validation-aucpr:0.97550
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.26388 validation-auc:0.97141 validation-aucpr:0.97547
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.25876 validation-auc:0.97140 validation-aucpr:0.97546
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.25394 validation-auc:0.97148 validation-aucpr:0.97552
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.25000 validation-auc:0.97155 validation-aucpr:0.97563
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.24648 validation-auc:0.97162 validation-aucpr:0.97566
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24217 validation-auc:0.97176 validation-aucpr:0.97573
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.23862 validation-auc:0.97199 validation-aucpr:0.97588
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.23512 validation-auc:0.97202 validation-aucpr:0.97590
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23244 validation-auc:0.97208 validation-aucpr:0.97585
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22980 validation-auc:0.97205 validation-aucpr:0.97579
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22733 validation-auc:0.97208 validation-aucpr:0.97577
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22464 validation-auc:0.97212 validation-aucpr:0.97579
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22217 validation-auc:0.97205 validation-aucpr:0.97571
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21975 validation-auc:0.97214 validation-aucpr:0.97577
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21761 validation-auc:0.97212 validation-aucpr:0.97576
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21561 validation-auc:0.97215 validation-aucpr:0.97578
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21386 validation-auc:0.97230 validation-aucpr:0.97606
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21162 validation-auc:0.97247 validation-aucpr:0.97621
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.20973 validation-auc:0.97258 validation-aucpr:0.97636
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20819 validation-auc:0.97256 validation-aucpr:0.97632
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20656 validation-auc:0.97265 validation-aucpr:0.97640
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20502 validation-auc:0.97272 validation-aucpr:0.97646
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20365 validation-auc:0.97272 validation-aucpr:0.97645
[21:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20246 validation-auc:0.97283 validation-aucpr:0.97653
[21:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20146 validation-auc:0.97279 validation-aucpr:0.97650
[21:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20023 validation-auc:0.97291 validation-aucpr:0.97657
{'best_iteration': '49', 'best_score': '0.9765698410238164'}
Trial 97, Fold 3: Log loss = 0.20022721945089825, Average precision = 0.9765723226286877, ROC-AUC = 0.9729139930512083, Elapsed Time = 7.785095100000035 seconds
Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 97, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64852 validation-auc:0.95775 validation-aucpr:0.95917
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60866 validation-auc:0.96676 validation-aucpr:0.96997
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.57367 validation-auc:0.96795 validation-aucpr:0.97094
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.54182 validation-auc:0.96924 validation-aucpr:0.97401
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.51680 validation-auc:0.96821 validation-aucpr:0.97303
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.49102 validation-auc:0.96826 validation-aucpr:0.97310
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.46785 validation-auc:0.96834 validation-aucpr:0.97341
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.44666 validation-auc:0.96850 validation-aucpr:0.97370
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.42706 validation-auc:0.96885 validation-aucpr:0.97398
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.40939 validation-auc:0.96900 validation-aucpr:0.97412
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.39326 validation-auc:0.96917 validation-aucpr:0.97425
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.37855 validation-auc:0.96917 validation-aucpr:0.97427
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.36467 validation-auc:0.96949 validation-aucpr:0.97453
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35205 validation-auc:0.96994 validation-aucpr:0.97487
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34049 validation-auc:0.97003 validation-aucpr:0.97490
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33047 validation-auc:0.97035 validation-aucpr:0.97515
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.32075 validation-auc:0.97027 validation-aucpr:0.97514
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.31266 validation-auc:0.97038 validation-aucpr:0.97526
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.30524 validation-auc:0.97050 validation-aucpr:0.97536
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.29699 validation-auc:0.97069 validation-aucpr:0.97549
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.28937 validation-auc:0.97075 validation-aucpr:0.97552
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.28228 validation-auc:0.97089 validation-aucpr:0.97563
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.27684 validation-auc:0.97061 validation-aucpr:0.97541
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.27060 validation-auc:0.97059 validation-aucpr:0.97538
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.26491 validation-auc:0.97063 validation-aucpr:0.97538
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.25937 validation-auc:0.97075 validation-aucpr:0.97548
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.25437 validation-auc:0.97090 validation-aucpr:0.97558
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.24977 validation-auc:0.97098 validation-aucpr:0.97563
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.24538 validation-auc:0.97092 validation-aucpr:0.97561
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24167 validation-auc:0.97087 validation-aucpr:0.97555
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.23820 validation-auc:0.97075 validation-aucpr:0.97545
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.23533 validation-auc:0.97054 validation-aucpr:0.97529
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23236 validation-auc:0.97047 validation-aucpr:0.97526
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.22965 validation-auc:0.97046 validation-aucpr:0.97525
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.22683 validation-auc:0.97052 validation-aucpr:0.97528
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.22451 validation-auc:0.97066 validation-aucpr:0.97537
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22199 validation-auc:0.97082 validation-aucpr:0.97547
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.21962 validation-auc:0.97099 validation-aucpr:0.97560
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.21745 validation-auc:0.97103 validation-aucpr:0.97563
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.21588 validation-auc:0.97105 validation-aucpr:0.97565
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.21385 validation-auc:0.97119 validation-aucpr:0.97574
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21231 validation-auc:0.97105 validation-aucpr:0.97563
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21059 validation-auc:0.97114 validation-aucpr:0.97570
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.20902 validation-auc:0.97126 validation-aucpr:0.97579
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.20771 validation-auc:0.97114 validation-aucpr:0.97570
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.20642 validation-auc:0.97124 validation-aucpr:0.97579
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.20477 validation-auc:0.97150 validation-aucpr:0.97596
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20348 validation-auc:0.97154 validation-aucpr:0.97599
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20223 validation-auc:0.97162 validation-aucpr:0.97604
[22:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20124 validation-auc:0.97161 validation-aucpr:0.97604
{'best_iteration': '48', 'best_score': '0.9760388955289463'}
Trial 97, Fold 4: Log loss = 0.20123741217815116, Average precision = 0.9760271817156592, ROC-AUC = 0.9716141232167597, Elapsed Time = 7.109914699998626 seconds
Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 97, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0] validation-logloss:0.64889 validation-auc:0.95644 validation-aucpr:0.96122
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1] validation-logloss:0.60933 validation-auc:0.96394 validation-aucpr:0.96845
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2] validation-logloss:0.57441 validation-auc:0.96582 validation-aucpr:0.97007
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3] validation-logloss:0.54731 validation-auc:0.96355 validation-aucpr:0.96828
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4] validation-logloss:0.51876 validation-auc:0.96506 validation-aucpr:0.96967
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5] validation-logloss:0.49593 validation-auc:0.96520 validation-aucpr:0.96963
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6] validation-logloss:0.47211 validation-auc:0.96584 validation-aucpr:0.97025
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7] validation-logloss:0.45092 validation-auc:0.96602 validation-aucpr:0.97038
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8] validation-logloss:0.43155 validation-auc:0.96675 validation-aucpr:0.97088
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9] validation-logloss:0.41429 validation-auc:0.96716 validation-aucpr:0.97126
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10] validation-logloss:0.39916 validation-auc:0.96746 validation-aucpr:0.97141
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11] validation-logloss:0.38598 validation-auc:0.96724 validation-aucpr:0.97111
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12] validation-logloss:0.37172 validation-auc:0.96784 validation-aucpr:0.97166
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13] validation-logloss:0.35967 validation-auc:0.96785 validation-aucpr:0.97176
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14] validation-logloss:0.34969 validation-auc:0.96773 validation-aucpr:0.97161
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15] validation-logloss:0.33863 validation-auc:0.96797 validation-aucpr:0.97181
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16] validation-logloss:0.32863 validation-auc:0.96818 validation-aucpr:0.97197
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17] validation-logloss:0.32070 validation-auc:0.96803 validation-aucpr:0.97179
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18] validation-logloss:0.31323 validation-auc:0.96804 validation-aucpr:0.97171
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19] validation-logloss:0.30485 validation-auc:0.96831 validation-aucpr:0.97193
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20] validation-logloss:0.29723 validation-auc:0.96871 validation-aucpr:0.97225
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21] validation-logloss:0.29003 validation-auc:0.96888 validation-aucpr:0.97241
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22] validation-logloss:0.28468 validation-auc:0.96872 validation-aucpr:0.97229
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23] validation-logloss:0.27832 validation-auc:0.96904 validation-aucpr:0.97262
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24] validation-logloss:0.27266 validation-auc:0.96907 validation-aucpr:0.97264
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25] validation-logloss:0.26737 validation-auc:0.96925 validation-aucpr:0.97282
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26] validation-logloss:0.26238 validation-auc:0.96937 validation-aucpr:0.97291
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27] validation-logloss:0.25755 validation-auc:0.96961 validation-aucpr:0.97311
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28] validation-logloss:0.25348 validation-auc:0.96985 validation-aucpr:0.97323
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29] validation-logloss:0.24905 validation-auc:0.97013 validation-aucpr:0.97349
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30] validation-logloss:0.24522 validation-auc:0.97025 validation-aucpr:0.97363
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31] validation-logloss:0.24230 validation-auc:0.97016 validation-aucpr:0.97353
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32] validation-logloss:0.23978 validation-auc:0.97010 validation-aucpr:0.97347
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33] validation-logloss:0.23681 validation-auc:0.97008 validation-aucpr:0.97343
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34] validation-logloss:0.23389 validation-auc:0.97015 validation-aucpr:0.97349
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35] validation-logloss:0.23120 validation-auc:0.97017 validation-aucpr:0.97349
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36] validation-logloss:0.22894 validation-auc:0.97026 validation-aucpr:0.97341
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37] validation-logloss:0.22674 validation-auc:0.97026 validation-aucpr:0.97341
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38] validation-logloss:0.22480 validation-auc:0.97037 validation-aucpr:0.97352
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39] validation-logloss:0.22316 validation-auc:0.97028 validation-aucpr:0.97341
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40] validation-logloss:0.22100 validation-auc:0.97046 validation-aucpr:0.97361
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41] validation-logloss:0.21895 validation-auc:0.97067 validation-aucpr:0.97409
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42] validation-logloss:0.21693 validation-auc:0.97090 validation-aucpr:0.97427
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43] validation-logloss:0.21514 validation-auc:0.97095 validation-aucpr:0.97429
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44] validation-logloss:0.21363 validation-auc:0.97112 validation-aucpr:0.97445
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45] validation-logloss:0.21242 validation-auc:0.97120 validation-aucpr:0.97451
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46] validation-logloss:0.21067 validation-auc:0.97142 validation-aucpr:0.97469
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47] validation-logloss:0.20927 validation-auc:0.97149 validation-aucpr:0.97478
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48] validation-logloss:0.20784 validation-auc:0.97162 validation-aucpr:0.97485
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49] validation-logloss:0.20652 validation-auc:0.97173 validation-aucpr:0.97498
{'best_iteration': '49', 'best_score': '0.9749812712453916'}
Trial 97, Fold 5: Log loss = 0.20652109625057113, Average precision = 0.9749294362547176, ROC-AUC = 0.9717331995615258, Elapsed Time = 7.842935299999226 seconds
Optimization Progress: 98%|#########8| 98/100 [4:01:25<02:12, 66.03s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 98, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.66884 validation-auc:0.94051 validation-aucpr:0.94034
[1] validation-logloss:0.64799 validation-auc:0.95079 validation-aucpr:0.95078
[2] validation-logloss:0.62482 validation-auc:0.95751 validation-aucpr:0.95860
[3] validation-logloss:0.60614 validation-auc:0.95857 validation-aucpr:0.96514
[4] validation-logloss:0.58710 validation-auc:0.95926 validation-aucpr:0.96610
[5] validation-logloss:0.57165 validation-auc:0.96036 validation-aucpr:0.96675
[6] validation-logloss:0.55371 validation-auc:0.96162 validation-aucpr:0.96842
[7] validation-logloss:0.53786 validation-auc:0.96269 validation-aucpr:0.96927
[8] validation-logloss:0.52480 validation-auc:0.96279 validation-aucpr:0.96925
[9] validation-logloss:0.50960 validation-auc:0.96317 validation-aucpr:0.96967
[10] validation-logloss:0.49487 validation-auc:0.96389 validation-aucpr:0.97028
[11] validation-logloss:0.48348 validation-auc:0.96459 validation-aucpr:0.97087
[12] validation-logloss:0.47409 validation-auc:0.96409 validation-aucpr:0.97044
[13] validation-logloss:0.46168 validation-auc:0.96412 validation-aucpr:0.97067
[14] validation-logloss:0.45211 validation-auc:0.96373 validation-aucpr:0.97028
[15] validation-logloss:0.44119 validation-auc:0.96388 validation-aucpr:0.97045
[16] validation-logloss:0.43201 validation-auc:0.96395 validation-aucpr:0.97052
[17] validation-logloss:0.42372 validation-auc:0.96375 validation-aucpr:0.97036
[18] validation-logloss:0.41592 validation-auc:0.96355 validation-aucpr:0.97012
[19] validation-logloss:0.40686 validation-auc:0.96354 validation-aucpr:0.97013
[20] validation-logloss:0.39940 validation-auc:0.96360 validation-aucpr:0.97009
[21] validation-logloss:0.39086 validation-auc:0.96375 validation-aucpr:0.97029
[22] validation-logloss:0.38466 validation-auc:0.96361 validation-aucpr:0.97019
[23] validation-logloss:0.37694 validation-auc:0.96374 validation-aucpr:0.97036
[24] validation-logloss:0.37103 validation-auc:0.96374 validation-aucpr:0.97031
[25] validation-logloss:0.36507 validation-auc:0.96399 validation-aucpr:0.97047
[26] validation-logloss:0.35950 validation-auc:0.96379 validation-aucpr:0.97037
[27] validation-logloss:0.35412 validation-auc:0.96366 validation-aucpr:0.97028
[28] validation-logloss:0.34911 validation-auc:0.96360 validation-aucpr:0.97020
[29] validation-logloss:0.34321 validation-auc:0.96368 validation-aucpr:0.97027
[30] validation-logloss:0.33869 validation-auc:0.96365 validation-aucpr:0.97021
[31] validation-logloss:0.33474 validation-auc:0.96387 validation-aucpr:0.97029
[32] validation-logloss:0.33052 validation-auc:0.96389 validation-aucpr:0.97029
[33] validation-logloss:0.32658 validation-auc:0.96375 validation-aucpr:0.97010
[34] validation-logloss:0.32243 validation-auc:0.96376 validation-aucpr:0.97013
[35] validation-logloss:0.31939 validation-auc:0.96354 validation-aucpr:0.96992
[36] validation-logloss:0.31576 validation-auc:0.96353 validation-aucpr:0.96985
[37] validation-logloss:0.31113 validation-auc:0.96393 validation-aucpr:0.97022
[38] validation-logloss:0.30638 validation-auc:0.96428 validation-aucpr:0.97051
[39] validation-logloss:0.30234 validation-auc:0.96429 validation-aucpr:0.97052
[40] validation-logloss:0.29918 validation-auc:0.96440 validation-aucpr:0.97055
[41] validation-logloss:0.29528 validation-auc:0.96468 validation-aucpr:0.97081
[42] validation-logloss:0.29164 validation-auc:0.96483 validation-aucpr:0.97096
[43] validation-logloss:0.28894 validation-auc:0.96478 validation-aucpr:0.97090
[44] validation-logloss:0.28677 validation-auc:0.96463 validation-aucpr:0.97075
{'best_iteration': '42', 'best_score': '0.9709633010271024'}
Trial 98, Fold 1: Log loss = 0.2867742175548801, Average precision = 0.9707397207243473, ROC-AUC = 0.9646308902462923, Elapsed Time = 0.839261200002511 seconds
Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 98, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.67039 validation-auc:0.93017 validation-aucpr:0.91660
[1] validation-logloss:0.64871 validation-auc:0.95007 validation-aucpr:0.95231
[2] validation-logloss:0.62938 validation-auc:0.95374 validation-aucpr:0.95551
[3] validation-logloss:0.60925 validation-auc:0.95643 validation-aucpr:0.96082
[4] validation-logloss:0.59263 validation-auc:0.95691 validation-aucpr:0.96111
[5] validation-logloss:0.57408 validation-auc:0.95835 validation-aucpr:0.96276
[6] validation-logloss:0.55616 validation-auc:0.95922 validation-aucpr:0.96345
[7] validation-logloss:0.54222 validation-auc:0.95911 validation-aucpr:0.96359
[8] validation-logloss:0.52724 validation-auc:0.95964 validation-aucpr:0.96396
[9] validation-logloss:0.51176 validation-auc:0.96018 validation-aucpr:0.96461
[10] validation-logloss:0.49829 validation-auc:0.96090 validation-aucpr:0.96574
[11] validation-logloss:0.48504 validation-auc:0.96133 validation-aucpr:0.96633
[12] validation-logloss:0.47425 validation-auc:0.96134 validation-aucpr:0.96626
[13] validation-logloss:0.46392 validation-auc:0.96193 validation-aucpr:0.96661
[14] validation-logloss:0.45399 validation-auc:0.96206 validation-aucpr:0.96676
[15] validation-logloss:0.44308 validation-auc:0.96231 validation-aucpr:0.96696
[16] validation-logloss:0.43509 validation-auc:0.96218 validation-aucpr:0.96669
[17] validation-logloss:0.42479 validation-auc:0.96248 validation-aucpr:0.96685
[18] validation-logloss:0.41703 validation-auc:0.96264 validation-aucpr:0.96688
[19] validation-logloss:0.40777 validation-auc:0.96331 validation-aucpr:0.96742
[20] validation-logloss:0.39873 validation-auc:0.96391 validation-aucpr:0.96794
[21] validation-logloss:0.39183 validation-auc:0.96395 validation-aucpr:0.96772
[22] validation-logloss:0.38513 validation-auc:0.96396 validation-aucpr:0.96758
[23] validation-logloss:0.37734 validation-auc:0.96422 validation-aucpr:0.96783
[24] validation-logloss:0.36972 validation-auc:0.96465 validation-aucpr:0.96829
[25] validation-logloss:0.36281 validation-auc:0.96491 validation-aucpr:0.96845
[26] validation-logloss:0.35695 validation-auc:0.96501 validation-aucpr:0.96851
[27] validation-logloss:0.35008 validation-auc:0.96540 validation-aucpr:0.96893
[28] validation-logloss:0.34507 validation-auc:0.96559 validation-aucpr:0.96914
[29] validation-logloss:0.33898 validation-auc:0.96559 validation-aucpr:0.96921
[30] validation-logloss:0.33309 validation-auc:0.96577 validation-aucpr:0.96936
[31] validation-logloss:0.32756 validation-auc:0.96596 validation-aucpr:0.96954
[32] validation-logloss:0.32204 validation-auc:0.96629 validation-aucpr:0.96984
[33] validation-logloss:0.31816 validation-auc:0.96642 validation-aucpr:0.96991
[34] validation-logloss:0.31330 validation-auc:0.96659 validation-aucpr:0.97017
[35] validation-logloss:0.30971 validation-auc:0.96645 validation-aucpr:0.97010
[36] validation-logloss:0.30558 validation-auc:0.96661 validation-aucpr:0.97032
[37] validation-logloss:0.30148 validation-auc:0.96665 validation-aucpr:0.97034
[38] validation-logloss:0.29827 validation-auc:0.96668 validation-aucpr:0.97034
[39] validation-logloss:0.29526 validation-auc:0.96672 validation-aucpr:0.97040
[40] validation-logloss:0.29280 validation-auc:0.96673 validation-aucpr:0.97073
[41] validation-logloss:0.28926 validation-auc:0.96685 validation-aucpr:0.97076
[42] validation-logloss:0.28663 validation-auc:0.96683 validation-aucpr:0.97074
[43] validation-logloss:0.28313 validation-auc:0.96696 validation-aucpr:0.97083
[44] validation-logloss:0.27967 validation-auc:0.96714 validation-aucpr:0.97099
{'best_iteration': '44', 'best_score': '0.9709925090997215'}
Trial 98, Fold 2: Log loss = 0.2796652386858184, Average precision = 0.9709801210620819, ROC-AUC = 0.9671362855966161, Elapsed Time = 1.1067725000029895 seconds
Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 98, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.66926 validation-auc:0.93339 validation-aucpr:0.91513
[1] validation-logloss:0.64911 validation-auc:0.94741 validation-aucpr:0.94176
[2] validation-logloss:0.62933 validation-auc:0.95574 validation-aucpr:0.95849
[3] validation-logloss:0.60876 validation-auc:0.95955 validation-aucpr:0.96526
[4] validation-logloss:0.59157 validation-auc:0.96022 validation-aucpr:0.96597
[5] validation-logloss:0.57577 validation-auc:0.95997 validation-aucpr:0.96580
[6] validation-logloss:0.55910 validation-auc:0.95986 validation-aucpr:0.96602
[7] validation-logloss:0.54490 validation-auc:0.96022 validation-aucpr:0.96609
[8] validation-logloss:0.52932 validation-auc:0.96093 validation-aucpr:0.96658
[9] validation-logloss:0.51402 validation-auc:0.96148 validation-aucpr:0.96713
[10] validation-logloss:0.50185 validation-auc:0.96141 validation-aucpr:0.96731
[11] validation-logloss:0.48753 validation-auc:0.96242 validation-aucpr:0.96822
[12] validation-logloss:0.47425 validation-auc:0.96278 validation-aucpr:0.96854
[13] validation-logloss:0.46183 validation-auc:0.96284 validation-aucpr:0.96874
[14] validation-logloss:0.45221 validation-auc:0.96289 validation-aucpr:0.96866
[15] validation-logloss:0.44315 validation-auc:0.96287 validation-aucpr:0.96860
[16] validation-logloss:0.43438 validation-auc:0.96334 validation-aucpr:0.96871
[17] validation-logloss:0.42426 validation-auc:0.96357 validation-aucpr:0.96901
[18] validation-logloss:0.41664 validation-auc:0.96364 validation-aucpr:0.96900
[19] validation-logloss:0.40722 validation-auc:0.96369 validation-aucpr:0.96917
[20] validation-logloss:0.39961 validation-auc:0.96397 validation-aucpr:0.96936
[21] validation-logloss:0.39028 validation-auc:0.96478 validation-aucpr:0.97005
[22] validation-logloss:0.38212 validation-auc:0.96487 validation-aucpr:0.97011
[23] validation-logloss:0.37402 validation-auc:0.96515 validation-aucpr:0.97047
[24] validation-logloss:0.36725 validation-auc:0.96515 validation-aucpr:0.97060
[25] validation-logloss:0.36014 validation-auc:0.96541 validation-aucpr:0.97078
[26] validation-logloss:0.35408 validation-auc:0.96537 validation-aucpr:0.97081
[27] validation-logloss:0.34867 validation-auc:0.96557 validation-aucpr:0.97092
[28] validation-logloss:0.34361 validation-auc:0.96555 validation-aucpr:0.97088
[29] validation-logloss:0.33740 validation-auc:0.96588 validation-aucpr:0.97111
[30] validation-logloss:0.33286 validation-auc:0.96584 validation-aucpr:0.97101
[31] validation-logloss:0.32842 validation-auc:0.96589 validation-aucpr:0.97108
[32] validation-logloss:0.32427 validation-auc:0.96607 validation-aucpr:0.97120
[33] validation-logloss:0.32029 validation-auc:0.96605 validation-aucpr:0.97115
[34] validation-logloss:0.31545 validation-auc:0.96607 validation-aucpr:0.97121
[35] validation-logloss:0.31171 validation-auc:0.96607 validation-aucpr:0.97122
[36] validation-logloss:0.30840 validation-auc:0.96601 validation-aucpr:0.97114
[37] validation-logloss:0.30413 validation-auc:0.96618 validation-aucpr:0.97128
[38] validation-logloss:0.29924 validation-auc:0.96633 validation-aucpr:0.97141
[39] validation-logloss:0.29613 validation-auc:0.96648 validation-aucpr:0.97149
[40] validation-logloss:0.29314 validation-auc:0.96653 validation-aucpr:0.97156
[41] validation-logloss:0.28938 validation-auc:0.96647 validation-aucpr:0.97153
[42] validation-logloss:0.28565 validation-auc:0.96650 validation-aucpr:0.97153
[43] validation-logloss:0.28295 validation-auc:0.96672 validation-aucpr:0.97172
[44] validation-logloss:0.27939 validation-auc:0.96678 validation-aucpr:0.97179
{'best_iteration': '44', 'best_score': '0.9717887583922987'}
Trial 98, Fold 3: Log loss = 0.2793851269635118, Average precision = 0.9717884446967304, ROC-AUC = 0.9667844411832689, Elapsed Time = 1.0817418000006 seconds
Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 98, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.66794 validation-auc:0.93771 validation-aucpr:0.92727
[1] validation-logloss:0.64542 validation-auc:0.95095 validation-aucpr:0.95402
[2] validation-logloss:0.62701 validation-auc:0.95216 validation-aucpr:0.95924
[3] validation-logloss:0.60977 validation-auc:0.95322 validation-aucpr:0.96004
[4] validation-logloss:0.59292 validation-auc:0.95518 validation-aucpr:0.96135
[5] validation-logloss:0.57694 validation-auc:0.95516 validation-aucpr:0.96170
[6] validation-logloss:0.55957 validation-auc:0.95681 validation-aucpr:0.96365
[7] validation-logloss:0.54466 validation-auc:0.95690 validation-aucpr:0.96394
[8] validation-logloss:0.53135 validation-auc:0.95738 validation-aucpr:0.96429
[9] validation-logloss:0.51857 validation-auc:0.95879 validation-aucpr:0.96504
[10] validation-logloss:0.50427 validation-auc:0.95977 validation-aucpr:0.96613
[11] validation-logloss:0.49322 validation-auc:0.95992 validation-aucpr:0.96620
[12] validation-logloss:0.48257 validation-auc:0.95986 validation-aucpr:0.96613
[13] validation-logloss:0.47021 validation-auc:0.96038 validation-aucpr:0.96676
[14] validation-logloss:0.45778 validation-auc:0.96110 validation-aucpr:0.96735
[15] validation-logloss:0.44832 validation-auc:0.96162 validation-aucpr:0.96771
[16] validation-logloss:0.43948 validation-auc:0.96199 validation-aucpr:0.96799
[17] validation-logloss:0.43147 validation-auc:0.96180 validation-aucpr:0.96788
[18] validation-logloss:0.42168 validation-auc:0.96230 validation-aucpr:0.96841
[19] validation-logloss:0.41173 validation-auc:0.96261 validation-aucpr:0.96871
[20] validation-logloss:0.40307 validation-auc:0.96273 validation-aucpr:0.96892
[21] validation-logloss:0.39647 validation-auc:0.96250 validation-aucpr:0.96867
[22] validation-logloss:0.38995 validation-auc:0.96266 validation-aucpr:0.96884
[23] validation-logloss:0.38394 validation-auc:0.96266 validation-aucpr:0.96883
[24] validation-logloss:0.37825 validation-auc:0.96270 validation-aucpr:0.96882
[25] validation-logloss:0.37215 validation-auc:0.96281 validation-aucpr:0.96886
[26] validation-logloss:0.36671 validation-auc:0.96275 validation-aucpr:0.96879
[27] validation-logloss:0.35998 validation-auc:0.96295 validation-aucpr:0.96896
[28] validation-logloss:0.35288 validation-auc:0.96305 validation-aucpr:0.96911
[29] validation-logloss:0.34753 validation-auc:0.96306 validation-aucpr:0.96914
[30] validation-logloss:0.34088 validation-auc:0.96362 validation-aucpr:0.96962
[31] validation-logloss:0.33598 validation-auc:0.96379 validation-aucpr:0.96975
[32] validation-logloss:0.32998 validation-auc:0.96409 validation-aucpr:0.97001
[33] validation-logloss:0.32595 validation-auc:0.96421 validation-aucpr:0.97004
[34] validation-logloss:0.32041 validation-auc:0.96462 validation-aucpr:0.97039
[35] validation-logloss:0.31555 validation-auc:0.96467 validation-aucpr:0.97046
[36] validation-logloss:0.31219 validation-auc:0.96469 validation-aucpr:0.97041
[37] validation-logloss:0.30742 validation-auc:0.96497 validation-aucpr:0.97066
[38] validation-logloss:0.30273 validation-auc:0.96506 validation-aucpr:0.97074
[39] validation-logloss:0.29897 validation-auc:0.96497 validation-aucpr:0.97070
[40] validation-logloss:0.29483 validation-auc:0.96517 validation-aucpr:0.97084
[41] validation-logloss:0.29102 validation-auc:0.96527 validation-aucpr:0.97093
[42] validation-logloss:0.28748 validation-auc:0.96540 validation-aucpr:0.97104
[43] validation-logloss:0.28405 validation-auc:0.96550 validation-aucpr:0.97111
[44] validation-logloss:0.28180 validation-auc:0.96549 validation-aucpr:0.97117
{'best_iteration': '44', 'best_score': '0.9711655496123021'}
Trial 98, Fold 4: Log loss = 0.2818028283500285, Average precision = 0.9711585620081205, ROC-AUC = 0.965494088452065, Elapsed Time = 1.1227743000017654 seconds
Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 98, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.66844 validation-auc:0.93178 validation-aucpr:0.90280
[1] validation-logloss:0.64632 validation-auc:0.95347 validation-aucpr:0.95278
[2] validation-logloss:0.62748 validation-auc:0.95508 validation-aucpr:0.95885
[3] validation-logloss:0.60708 validation-auc:0.95689 validation-aucpr:0.96282
[4] validation-logloss:0.59037 validation-auc:0.95719 validation-aucpr:0.96288
[5] validation-logloss:0.57161 validation-auc:0.95712 validation-aucpr:0.96285
[6] validation-logloss:0.55689 validation-auc:0.95774 validation-aucpr:0.96310
[7] validation-logloss:0.54047 validation-auc:0.95874 validation-aucpr:0.96421
[8] validation-logloss:0.52453 validation-auc:0.95916 validation-aucpr:0.96486
[9] validation-logloss:0.50992 validation-auc:0.95966 validation-aucpr:0.96553
[10] validation-logloss:0.49817 validation-auc:0.95968 validation-aucpr:0.96561
[11] validation-logloss:0.48470 validation-auc:0.96022 validation-aucpr:0.96627
[12] validation-logloss:0.47239 validation-auc:0.96068 validation-aucpr:0.96655
[13] validation-logloss:0.46029 validation-auc:0.96138 validation-aucpr:0.96708
[14] validation-logloss:0.44871 validation-auc:0.96167 validation-aucpr:0.96739
[15] validation-logloss:0.43954 validation-auc:0.96193 validation-aucpr:0.96747
[16] validation-logloss:0.43048 validation-auc:0.96227 validation-aucpr:0.96770
[17] validation-logloss:0.42048 validation-auc:0.96265 validation-aucpr:0.96802
[18] validation-logloss:0.41102 validation-auc:0.96260 validation-aucpr:0.96802
[19] validation-logloss:0.40273 validation-auc:0.96274 validation-aucpr:0.96820
[20] validation-logloss:0.39417 validation-auc:0.96324 validation-aucpr:0.96867
[21] validation-logloss:0.38585 validation-auc:0.96358 validation-aucpr:0.96886
[22] validation-logloss:0.37959 validation-auc:0.96328 validation-aucpr:0.96858
[23] validation-logloss:0.37353 validation-auc:0.96308 validation-aucpr:0.96838
[24] validation-logloss:0.36786 validation-auc:0.96310 validation-aucpr:0.96837
[25] validation-logloss:0.36124 validation-auc:0.96352 validation-aucpr:0.96737
[26] validation-logloss:0.35562 validation-auc:0.96350 validation-aucpr:0.96738
[27] validation-logloss:0.34951 validation-auc:0.96351 validation-aucpr:0.96743
[28] validation-logloss:0.34351 validation-auc:0.96359 validation-aucpr:0.96754
[29] validation-logloss:0.33814 validation-auc:0.96373 validation-aucpr:0.96761
[30] validation-logloss:0.33281 validation-auc:0.96398 validation-aucpr:0.96787
[31] validation-logloss:0.32892 validation-auc:0.96372 validation-aucpr:0.96787
[32] validation-logloss:0.32353 validation-auc:0.96406 validation-aucpr:0.96812
[33] validation-logloss:0.31918 validation-auc:0.96388 validation-aucpr:0.96792
[34] validation-logloss:0.31568 validation-auc:0.96374 validation-aucpr:0.96781
[35] validation-logloss:0.31241 validation-auc:0.96370 validation-aucpr:0.96773
[36] validation-logloss:0.30865 validation-auc:0.96381 validation-aucpr:0.96780
[37] validation-logloss:0.30537 validation-auc:0.96384 validation-aucpr:0.96775
[38] validation-logloss:0.30100 validation-auc:0.96407 validation-aucpr:0.96791
[39] validation-logloss:0.29796 validation-auc:0.96404 validation-aucpr:0.96785
[40] validation-logloss:0.29454 validation-auc:0.96405 validation-aucpr:0.96922
[41] validation-logloss:0.29101 validation-auc:0.96405 validation-aucpr:0.96922
[42] validation-logloss:0.28869 validation-auc:0.96389 validation-aucpr:0.96905
[43] validation-logloss:0.28629 validation-auc:0.96391 validation-aucpr:0.96906
[44] validation-logloss:0.28312 validation-auc:0.96394 validation-aucpr:0.96925
{'best_iteration': '44', 'best_score': '0.9692529772877284'}
Trial 98, Fold 5: Log loss = 0.2831176401223858, Average precision = 0.9692541584306964, ROC-AUC = 0.9639441131114951, Elapsed Time = 1.0847797999995237 seconds
Optimization Progress: 99%|#########9| 99/100 [4:01:39<00:50, 50.25s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 99, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0] validation-logloss:0.68213 validation-auc:0.92188 validation-aucpr:0.91559
[1] validation-logloss:0.67129 validation-auc:0.94124 validation-aucpr:0.92973
[2] validation-logloss:0.65879 validation-auc:0.95926 validation-aucpr:0.95791
[3] validation-logloss:0.64899 validation-auc:0.96048 validation-aucpr:0.96295
[4] validation-logloss:0.63889 validation-auc:0.96090 validation-aucpr:0.96284
[5] validation-logloss:0.62909 validation-auc:0.96148 validation-aucpr:0.96393
[6] validation-logloss:0.61944 validation-auc:0.96189 validation-aucpr:0.96656
[7] validation-logloss:0.61049 validation-auc:0.96123 validation-aucpr:0.96472
[8] validation-logloss:0.60171 validation-auc:0.96146 validation-aucpr:0.96486
[9] validation-logloss:0.59289 validation-auc:0.96198 validation-aucpr:0.96520
[10] validation-logloss:0.58317 validation-auc:0.96389 validation-aucpr:0.96708
[11] validation-logloss:0.57473 validation-auc:0.96398 validation-aucpr:0.96729
[12] validation-logloss:0.56672 validation-auc:0.96407 validation-aucpr:0.96721
[13] validation-logloss:0.55767 validation-auc:0.96481 validation-aucpr:0.96809
[14] validation-logloss:0.55039 validation-auc:0.96465 validation-aucpr:0.96760
[15] validation-logloss:0.54314 validation-auc:0.96464 validation-aucpr:0.96785
[16] validation-logloss:0.53627 validation-auc:0.96473 validation-aucpr:0.96791
[17] validation-logloss:0.52855 validation-auc:0.96522 validation-aucpr:0.96903
[18] validation-logloss:0.52185 validation-auc:0.96519 validation-aucpr:0.96890
[19] validation-logloss:0.51542 validation-auc:0.96529 validation-aucpr:0.96897
[20] validation-logloss:0.50800 validation-auc:0.96564 validation-aucpr:0.96939
[21] validation-logloss:0.50182 validation-auc:0.96556 validation-aucpr:0.96932
[22] validation-logloss:0.49565 validation-auc:0.96568 validation-aucpr:0.96935
[23] validation-logloss:0.48980 validation-auc:0.96570 validation-aucpr:0.96941
[24] validation-logloss:0.48426 validation-auc:0.96574 validation-aucpr:0.96960
[25] validation-logloss:0.47761 validation-auc:0.96613 validation-aucpr:0.97004
[26] validation-logloss:0.47230 validation-auc:0.96622 validation-aucpr:0.96983
[27] validation-logloss:0.46708 validation-auc:0.96621 validation-aucpr:0.96987
[28] validation-logloss:0.46227 validation-auc:0.96618 validation-aucpr:0.97018
[29] validation-logloss:0.45734 validation-auc:0.96608 validation-aucpr:0.97008
[30] validation-logloss:0.45256 validation-auc:0.96596 validation-aucpr:0.97000
[31] validation-logloss:0.44808 validation-auc:0.96602 validation-aucpr:0.97084
[32] validation-logloss:0.44333 validation-auc:0.96614 validation-aucpr:0.97090
[33] validation-logloss:0.43893 validation-auc:0.96611 validation-aucpr:0.97085
[34] validation-logloss:0.43351 validation-auc:0.96632 validation-aucpr:0.97108
[35] validation-logloss:0.42933 validation-auc:0.96635 validation-aucpr:0.97112
[36] validation-logloss:0.42515 validation-auc:0.96637 validation-aucpr:0.97112
[37] validation-logloss:0.42123 validation-auc:0.96632 validation-aucpr:0.97112
[38] validation-logloss:0.41728 validation-auc:0.96622 validation-aucpr:0.97102
[39] validation-logloss:0.41325 validation-auc:0.96630 validation-aucpr:0.97104
[40] validation-logloss:0.40954 validation-auc:0.96626 validation-aucpr:0.97101
[41] validation-logloss:0.40569 validation-auc:0.96621 validation-aucpr:0.97095
[42] validation-logloss:0.40112 validation-auc:0.96639 validation-aucpr:0.97115
[43] validation-logloss:0.39660 validation-auc:0.96658 validation-aucpr:0.97135
[44] validation-logloss:0.39214 validation-auc:0.96681 validation-aucpr:0.97156
[45] validation-logloss:0.38868 validation-auc:0.96691 validation-aucpr:0.97161
[46] validation-logloss:0.38434 validation-auc:0.96715 validation-aucpr:0.97179
[47] validation-logloss:0.38027 validation-auc:0.96720 validation-aucpr:0.97185
[48] validation-logloss:0.37612 validation-auc:0.96740 validation-aucpr:0.97197
[49] validation-logloss:0.37298 validation-auc:0.96741 validation-aucpr:0.97197
[50] validation-logloss:0.37004 validation-auc:0.96743 validation-aucpr:0.97197
[51] validation-logloss:0.36705 validation-auc:0.96739 validation-aucpr:0.97191
[52] validation-logloss:0.36333 validation-auc:0.96756 validation-aucpr:0.97218
[53] validation-logloss:0.35957 validation-auc:0.96770 validation-aucpr:0.97231
[54] validation-logloss:0.35584 validation-auc:0.96785 validation-aucpr:0.97245
[55] validation-logloss:0.35310 validation-auc:0.96786 validation-aucpr:0.97246
[56] validation-logloss:0.35042 validation-auc:0.96790 validation-aucpr:0.97248
[57] validation-logloss:0.34776 validation-auc:0.96791 validation-aucpr:0.97248
[58] validation-logloss:0.34522 validation-auc:0.96791 validation-aucpr:0.97247
[59] validation-logloss:0.34278 validation-auc:0.96791 validation-aucpr:0.97247
[60] validation-logloss:0.34039 validation-auc:0.96788 validation-aucpr:0.97246
[61] validation-logloss:0.33804 validation-auc:0.96781 validation-aucpr:0.97241
[62] validation-logloss:0.33561 validation-auc:0.96780 validation-aucpr:0.97239
[63] validation-logloss:0.33252 validation-auc:0.96791 validation-aucpr:0.97241
[64] validation-logloss:0.32940 validation-auc:0.96807 validation-aucpr:0.97256
[65] validation-logloss:0.32723 validation-auc:0.96807 validation-aucpr:0.97259
[66] validation-logloss:0.32515 validation-auc:0.96805 validation-aucpr:0.97257
[67] validation-logloss:0.32238 validation-auc:0.96811 validation-aucpr:0.97249
[68] validation-logloss:0.31964 validation-auc:0.96820 validation-aucpr:0.97262
[69] validation-logloss:0.31689 validation-auc:0.96830 validation-aucpr:0.97271
[70] validation-logloss:0.31487 validation-auc:0.96839 validation-aucpr:0.97302
[71] validation-logloss:0.31296 validation-auc:0.96840 validation-aucpr:0.97302
[72] validation-logloss:0.31030 validation-auc:0.96855 validation-aucpr:0.97316
[73] validation-logloss:0.30843 validation-auc:0.96854 validation-aucpr:0.97314
[74] validation-logloss:0.30593 validation-auc:0.96861 validation-aucpr:0.97320
[75] validation-logloss:0.30411 validation-auc:0.96864 validation-aucpr:0.97321
[76] validation-logloss:0.30239 validation-auc:0.96868 validation-aucpr:0.97323
[77] validation-logloss:0.30063 validation-auc:0.96867 validation-aucpr:0.97321
[78] validation-logloss:0.29896 validation-auc:0.96864 validation-aucpr:0.97318
[79] validation-logloss:0.29655 validation-auc:0.96875 validation-aucpr:0.97328
[80] validation-logloss:0.29485 validation-auc:0.96876 validation-aucpr:0.97329
[81] validation-logloss:0.29327 validation-auc:0.96872 validation-aucpr:0.97326
[82] validation-logloss:0.29110 validation-auc:0.96878 validation-aucpr:0.97328
[83] validation-logloss:0.28951 validation-auc:0.96881 validation-aucpr:0.97330
[84] validation-logloss:0.28804 validation-auc:0.96880 validation-aucpr:0.97327
{'best_iteration': '83', 'best_score': '0.9732988522297122'}
Trial 99, Fold 1: Log loss = 0.28804067052732774, Average precision = 0.9732776156643419, ROC-AUC = 0.9688033222924822, Elapsed Time = 3.208025500000076 seconds
Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 99, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0] validation-logloss:0.68171 validation-auc:0.92325 validation-aucpr:0.91102
[1] validation-logloss:0.67056 validation-auc:0.94917 validation-aucpr:0.94054
[2] validation-logloss:0.65989 validation-auc:0.95467 validation-aucpr:0.95663
[3] validation-logloss:0.64813 validation-auc:0.96462 validation-aucpr:0.96619
[4] validation-logloss:0.63836 validation-auc:0.96435 validation-aucpr:0.96735
[5] validation-logloss:0.62837 validation-auc:0.96458 validation-aucpr:0.96779
[6] validation-logloss:0.61945 validation-auc:0.96441 validation-aucpr:0.96749
[7] validation-logloss:0.61007 validation-auc:0.96505 validation-aucpr:0.96790
[8] validation-logloss:0.59963 validation-auc:0.96705 validation-aucpr:0.97018
[9] validation-logloss:0.59118 validation-auc:0.96703 validation-aucpr:0.97009
[10] validation-logloss:0.58289 validation-auc:0.96677 validation-aucpr:0.96978
[11] validation-logloss:0.57504 validation-auc:0.96638 validation-aucpr:0.96943
[12] validation-logloss:0.56735 validation-auc:0.96611 validation-aucpr:0.96917
[13] validation-logloss:0.55821 validation-auc:0.96712 validation-aucpr:0.97030
[14] validation-logloss:0.54947 validation-auc:0.96791 validation-aucpr:0.97118
[15] validation-logloss:0.54096 validation-auc:0.96820 validation-aucpr:0.97155
[16] validation-logloss:0.53258 validation-auc:0.96878 validation-aucpr:0.97213
[17] validation-logloss:0.52463 validation-auc:0.96899 validation-aucpr:0.97233
[18] validation-logloss:0.51808 validation-auc:0.96902 validation-aucpr:0.97239
[19] validation-logloss:0.51173 validation-auc:0.96906 validation-aucpr:0.97224
[20] validation-logloss:0.50420 validation-auc:0.96935 validation-aucpr:0.97251
[21] validation-logloss:0.49710 validation-auc:0.96950 validation-aucpr:0.97265
[22] validation-logloss:0.49001 validation-auc:0.96974 validation-aucpr:0.97290
[23] validation-logloss:0.48414 validation-auc:0.96980 validation-aucpr:0.97295
[24] validation-logloss:0.47756 validation-auc:0.96980 validation-aucpr:0.97297
[25] validation-logloss:0.47107 validation-auc:0.96994 validation-aucpr:0.97313
[26] validation-logloss:0.46472 validation-auc:0.97011 validation-aucpr:0.97327
[27] validation-logloss:0.45945 validation-auc:0.97004 validation-aucpr:0.97320
[28] validation-logloss:0.45363 validation-auc:0.97014 validation-aucpr:0.97334
[29] validation-logloss:0.44760 validation-auc:0.97037 validation-aucpr:0.97353
[30] validation-logloss:0.44282 validation-auc:0.97039 validation-aucpr:0.97354
[31] validation-logloss:0.43710 validation-auc:0.97077 validation-aucpr:0.97385
[32] validation-logloss:0.43273 validation-auc:0.97075 validation-aucpr:0.97366
[33] validation-logloss:0.42819 validation-auc:0.97068 validation-aucpr:0.97356
[34] validation-logloss:0.42400 validation-auc:0.97038 validation-aucpr:0.97332
[35] validation-logloss:0.41897 validation-auc:0.97047 validation-aucpr:0.97339
[36] validation-logloss:0.41379 validation-auc:0.97068 validation-aucpr:0.97358
[37] validation-logloss:0.40893 validation-auc:0.97074 validation-aucpr:0.97363
[38] validation-logloss:0.40486 validation-auc:0.97065 validation-aucpr:0.97355
[39] validation-logloss:0.40112 validation-auc:0.97052 validation-aucpr:0.97272
[40] validation-logloss:0.39723 validation-auc:0.97055 validation-aucpr:0.97273
[41] validation-logloss:0.39266 validation-auc:0.97068 validation-aucpr:0.97284
[42] validation-logloss:0.38888 validation-auc:0.97074 validation-aucpr:0.97287
[43] validation-logloss:0.38516 validation-auc:0.97087 validation-aucpr:0.97297
[44] validation-logloss:0.38149 validation-auc:0.97084 validation-aucpr:0.97292
[45] validation-logloss:0.37732 validation-auc:0.97099 validation-aucpr:0.97304
[46] validation-logloss:0.37394 validation-auc:0.97097 validation-aucpr:0.97304
[47] validation-logloss:0.36985 validation-auc:0.97108 validation-aucpr:0.97317
[48] validation-logloss:0.36623 validation-auc:0.97104 validation-aucpr:0.97306
[49] validation-logloss:0.36233 validation-auc:0.97116 validation-aucpr:0.97315
[50] validation-logloss:0.35866 validation-auc:0.97113 validation-aucpr:0.97314
[51] validation-logloss:0.35509 validation-auc:0.97116 validation-aucpr:0.97308
[52] validation-logloss:0.35230 validation-auc:0.97116 validation-aucpr:0.97309
[53] validation-logloss:0.34939 validation-auc:0.97111 validation-aucpr:0.97302
[54] validation-logloss:0.34649 validation-auc:0.97110 validation-aucpr:0.97302
[55] validation-logloss:0.34371 validation-auc:0.97109 validation-aucpr:0.97301
[56] validation-logloss:0.34105 validation-auc:0.97119 validation-aucpr:0.97303
[57] validation-logloss:0.33838 validation-auc:0.97118 validation-aucpr:0.97299
[58] validation-logloss:0.33589 validation-auc:0.97108 validation-aucpr:0.97282
[59] validation-logloss:0.33275 validation-auc:0.97111 validation-aucpr:0.97291
[60] validation-logloss:0.33036 validation-auc:0.97108 validation-aucpr:0.97290
[61] validation-logloss:0.32800 validation-auc:0.97102 validation-aucpr:0.97287
[62] validation-logloss:0.32505 validation-auc:0.97109 validation-aucpr:0.97293
[63] validation-logloss:0.32214 validation-auc:0.97109 validation-aucpr:0.97293
[64] validation-logloss:0.31992 validation-auc:0.97107 validation-aucpr:0.97291
[65] validation-logloss:0.31758 validation-auc:0.97112 validation-aucpr:0.97292
[66] validation-logloss:0.31523 validation-auc:0.97120 validation-aucpr:0.97297
[67] validation-logloss:0.31257 validation-auc:0.97128 validation-aucpr:0.97305
[68] validation-logloss:0.31066 validation-auc:0.97122 validation-aucpr:0.97293
[69] validation-logloss:0.30854 validation-auc:0.97129 validation-aucpr:0.97299
[70] validation-logloss:0.30650 validation-auc:0.97134 validation-aucpr:0.97306
[71] validation-logloss:0.30447 validation-auc:0.97135 validation-aucpr:0.97304
[72] validation-logloss:0.30254 validation-auc:0.97131 validation-aucpr:0.97300
[73] validation-logloss:0.30018 validation-auc:0.97134 validation-aucpr:0.97323
[74] validation-logloss:0.29839 validation-auc:0.97128 validation-aucpr:0.97318
[75] validation-logloss:0.29585 validation-auc:0.97140 validation-aucpr:0.97328
[76] validation-logloss:0.29353 validation-auc:0.97148 validation-aucpr:0.97333
[77] validation-logloss:0.29112 validation-auc:0.97158 validation-aucpr:0.97344
[78] validation-logloss:0.28901 validation-auc:0.97170 validation-aucpr:0.97346
[79] validation-logloss:0.28739 validation-auc:0.97165 validation-aucpr:0.97342
[80] validation-logloss:0.28532 validation-auc:0.97171 validation-aucpr:0.97347
{'best_iteration': '31', 'best_score': '0.973848252770876'}
Trial 99, Fold 2: Log loss = 0.28372156805296517, Average precision = 0.9737019709783394, ROC-AUC = 0.9716679297140677, Elapsed Time = 3.3942110999996657 seconds
Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 99, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0] validation-logloss:0.68185 validation-auc:0.91807 validation-aucpr:0.89452
[1] validation-logloss:0.66931 validation-auc:0.95799 validation-aucpr:0.95321
[2] validation-logloss:0.65884 validation-auc:0.96007 validation-aucpr:0.95975
[3] validation-logloss:0.64791 validation-auc:0.96367 validation-aucpr:0.96592
[4] validation-logloss:0.63790 validation-auc:0.96450 validation-aucpr:0.96770
[5] validation-logloss:0.62839 validation-auc:0.96385 validation-aucpr:0.96578
[6] validation-logloss:0.61771 validation-auc:0.96550 validation-aucpr:0.96771
[7] validation-logloss:0.60739 validation-auc:0.96663 validation-aucpr:0.96881
[8] validation-logloss:0.59733 validation-auc:0.96691 validation-aucpr:0.96903
[9] validation-logloss:0.58737 validation-auc:0.96775 validation-aucpr:0.96981
[10] validation-logloss:0.57944 validation-auc:0.96742 validation-aucpr:0.96985
[11] validation-logloss:0.56985 validation-auc:0.96831 validation-aucpr:0.97069
[12] validation-logloss:0.56096 validation-auc:0.96882 validation-aucpr:0.97281
[13] validation-logloss:0.55340 validation-auc:0.96866 validation-aucpr:0.97270
[14] validation-logloss:0.54599 validation-auc:0.96860 validation-aucpr:0.97264
[15] validation-logloss:0.53902 validation-auc:0.96858 validation-aucpr:0.97275
[16] validation-logloss:0.53117 validation-auc:0.96854 validation-aucpr:0.97277
[17] validation-logloss:0.52434 validation-auc:0.96840 validation-aucpr:0.97269
[18] validation-logloss:0.51629 validation-auc:0.96894 validation-aucpr:0.97322
[19] validation-logloss:0.50994 validation-auc:0.96886 validation-aucpr:0.97314
[20] validation-logloss:0.50346 validation-auc:0.96897 validation-aucpr:0.97345
[21] validation-logloss:0.49639 validation-auc:0.96908 validation-aucpr:0.97356
[22] validation-logloss:0.49050 validation-auc:0.96890 validation-aucpr:0.97338
[23] validation-logloss:0.48473 validation-auc:0.96894 validation-aucpr:0.97336
[24] validation-logloss:0.47903 validation-auc:0.96883 validation-aucpr:0.97324
[25] validation-logloss:0.47257 validation-auc:0.96892 validation-aucpr:0.97342
[26] validation-logloss:0.46715 validation-auc:0.96891 validation-aucpr:0.97333
[27] validation-logloss:0.46087 validation-auc:0.96921 validation-aucpr:0.97357
[28] validation-logloss:0.45465 validation-auc:0.96947 validation-aucpr:0.97380
[29] validation-logloss:0.44872 validation-auc:0.96965 validation-aucpr:0.97402
[30] validation-logloss:0.44272 validation-auc:0.97002 validation-aucpr:0.97435
[31] validation-logloss:0.43791 validation-auc:0.97003 validation-aucpr:0.97440
[32] validation-logloss:0.43251 validation-auc:0.97017 validation-aucpr:0.97453
[33] validation-logloss:0.42708 validation-auc:0.97022 validation-aucpr:0.97455
[34] validation-logloss:0.42280 validation-auc:0.97003 validation-aucpr:0.97436
[35] validation-logloss:0.41764 validation-auc:0.97012 validation-aucpr:0.97444
[36] validation-logloss:0.41251 validation-auc:0.97029 validation-aucpr:0.97459
[37] validation-logloss:0.40751 validation-auc:0.97042 validation-aucpr:0.97472
[38] validation-logloss:0.40269 validation-auc:0.97052 validation-aucpr:0.97482
[39] validation-logloss:0.39817 validation-auc:0.97045 validation-aucpr:0.97476
[40] validation-logloss:0.39367 validation-auc:0.97047 validation-aucpr:0.97479
[41] validation-logloss:0.38930 validation-auc:0.97060 validation-aucpr:0.97490
[42] validation-logloss:0.38587 validation-auc:0.97061 validation-aucpr:0.97488
[43] validation-logloss:0.38213 validation-auc:0.97063 validation-aucpr:0.97489
[44] validation-logloss:0.37798 validation-auc:0.97071 validation-aucpr:0.97498
[45] validation-logloss:0.37448 validation-auc:0.97071 validation-aucpr:0.97498
[46] validation-logloss:0.37127 validation-auc:0.97066 validation-aucpr:0.97491
[47] validation-logloss:0.36804 validation-auc:0.97064 validation-aucpr:0.97493
[48] validation-logloss:0.36408 validation-auc:0.97077 validation-aucpr:0.97502
[49] validation-logloss:0.36043 validation-auc:0.97081 validation-aucpr:0.97506
[50] validation-logloss:0.35659 validation-auc:0.97091 validation-aucpr:0.97516
[51] validation-logloss:0.35338 validation-auc:0.97096 validation-aucpr:0.97521
[52] validation-logloss:0.34988 validation-auc:0.97107 validation-aucpr:0.97527
[53] validation-logloss:0.34699 validation-auc:0.97109 validation-aucpr:0.97527
[54] validation-logloss:0.34431 validation-auc:0.97103 validation-aucpr:0.97523
[55] validation-logloss:0.34145 validation-auc:0.97113 validation-aucpr:0.97533
[56] validation-logloss:0.33827 validation-auc:0.97117 validation-aucpr:0.97538
[57] validation-logloss:0.33549 validation-auc:0.97125 validation-aucpr:0.97546
[58] validation-logloss:0.33291 validation-auc:0.97126 validation-aucpr:0.97544
[59] validation-logloss:0.33040 validation-auc:0.97123 validation-aucpr:0.97539
[60] validation-logloss:0.32791 validation-auc:0.97123 validation-aucpr:0.97539
[61] validation-logloss:0.32548 validation-auc:0.97122 validation-aucpr:0.97537
[62] validation-logloss:0.32328 validation-auc:0.97119 validation-aucpr:0.97532
[63] validation-logloss:0.32101 validation-auc:0.97120 validation-aucpr:0.97529
[64] validation-logloss:0.31859 validation-auc:0.97128 validation-aucpr:0.97532
[65] validation-logloss:0.31590 validation-auc:0.97127 validation-aucpr:0.97532
[66] validation-logloss:0.31378 validation-auc:0.97125 validation-aucpr:0.97530
[67] validation-logloss:0.31148 validation-auc:0.97125 validation-aucpr:0.97529
[68] validation-logloss:0.30932 validation-auc:0.97127 validation-aucpr:0.97530
[69] validation-logloss:0.30717 validation-auc:0.97126 validation-aucpr:0.97528
[70] validation-logloss:0.30512 validation-auc:0.97129 validation-aucpr:0.97529
[71] validation-logloss:0.30318 validation-auc:0.97125 validation-aucpr:0.97526
[72] validation-logloss:0.30127 validation-auc:0.97121 validation-aucpr:0.97523
[73] validation-logloss:0.29947 validation-auc:0.97116 validation-aucpr:0.97518
[74] validation-logloss:0.29697 validation-auc:0.97129 validation-aucpr:0.97530
[75] validation-logloss:0.29520 validation-auc:0.97135 validation-aucpr:0.97546
[76] validation-logloss:0.29291 validation-auc:0.97133 validation-aucpr:0.97544
[77] validation-logloss:0.29069 validation-auc:0.97137 validation-aucpr:0.97547
[78] validation-logloss:0.28888 validation-auc:0.97147 validation-aucpr:0.97561
[79] validation-logloss:0.28686 validation-auc:0.97145 validation-aucpr:0.97560
[80] validation-logloss:0.28476 validation-auc:0.97149 validation-aucpr:0.97561
[81] validation-logloss:0.28305 validation-auc:0.97151 validation-aucpr:0.97561
[82] validation-logloss:0.28099 validation-auc:0.97156 validation-aucpr:0.97567
[83] validation-logloss:0.27901 validation-auc:0.97167 validation-aucpr:0.97576
[84] validation-logloss:0.27746 validation-auc:0.97167 validation-aucpr:0.97575
{'best_iteration': '83', 'best_score': '0.9757566802452886'}
Trial 99, Fold 3: Log loss = 0.27745504517041203, Average precision = 0.9757531703086391, ROC-AUC = 0.9716715142876405, Elapsed Time = 3.325000699998782 seconds
Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 99, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0] validation-logloss:0.68215 validation-auc:0.90771 validation-aucpr:0.88290
[1] validation-logloss:0.67142 validation-auc:0.93736 validation-aucpr:0.93239
[2] validation-logloss:0.66080 validation-auc:0.94664 validation-aucpr:0.94827
[3] validation-logloss:0.64937 validation-auc:0.95628 validation-aucpr:0.95805
[4] validation-logloss:0.63946 validation-auc:0.95789 validation-aucpr:0.96436
[5] validation-logloss:0.62814 validation-auc:0.96087 validation-aucpr:0.96708
[6] validation-logloss:0.61857 validation-auc:0.96151 validation-aucpr:0.96762
[7] validation-logloss:0.60803 validation-auc:0.96319 validation-aucpr:0.96900
[8] validation-logloss:0.59798 validation-auc:0.96390 validation-aucpr:0.96968
[9] validation-logloss:0.58901 validation-auc:0.96453 validation-aucpr:0.97033
[10] validation-logloss:0.57950 validation-auc:0.96548 validation-aucpr:0.97108
[11] validation-logloss:0.57053 validation-auc:0.96563 validation-aucpr:0.97132
[12] validation-logloss:0.56169 validation-auc:0.96587 validation-aucpr:0.97159
[13] validation-logloss:0.55274 validation-auc:0.96646 validation-aucpr:0.97211
[14] validation-logloss:0.54420 validation-auc:0.96696 validation-aucpr:0.97245
[15] validation-logloss:0.53723 validation-auc:0.96663 validation-aucpr:0.97217
[16] validation-logloss:0.53034 validation-auc:0.96684 validation-aucpr:0.97226
[17] validation-logloss:0.52352 validation-auc:0.96691 validation-aucpr:0.97226
[18] validation-logloss:0.51690 validation-auc:0.96695 validation-aucpr:0.97220
[19] validation-logloss:0.51061 validation-auc:0.96679 validation-aucpr:0.97206
[20] validation-logloss:0.50417 validation-auc:0.96675 validation-aucpr:0.97197
[21] validation-logloss:0.49803 validation-auc:0.96675 validation-aucpr:0.97197
[22] validation-logloss:0.49124 validation-auc:0.96708 validation-aucpr:0.97224
[23] validation-logloss:0.48538 validation-auc:0.96719 validation-aucpr:0.97232
[24] validation-logloss:0.47847 validation-auc:0.96768 validation-aucpr:0.97275
[25] validation-logloss:0.47294 validation-auc:0.96771 validation-aucpr:0.97276
[26] validation-logloss:0.46657 validation-auc:0.96787 validation-aucpr:0.97292
[27] validation-logloss:0.46157 validation-auc:0.96780 validation-aucpr:0.97285
[28] validation-logloss:0.45647 validation-auc:0.96766 validation-aucpr:0.97273
[29] validation-logloss:0.45040 validation-auc:0.96783 validation-aucpr:0.97293
[30] validation-logloss:0.44564 validation-auc:0.96788 validation-aucpr:0.97295
[31] validation-logloss:0.43991 validation-auc:0.96810 validation-aucpr:0.97315
[32] validation-logloss:0.43541 validation-auc:0.96805 validation-aucpr:0.97308
[33] validation-logloss:0.43013 validation-auc:0.96810 validation-aucpr:0.97315
[34] validation-logloss:0.42575 validation-auc:0.96807 validation-aucpr:0.97317
[35] validation-logloss:0.42058 validation-auc:0.96809 validation-aucpr:0.97320
[36] validation-logloss:0.41643 validation-auc:0.96804 validation-aucpr:0.97313
[37] validation-logloss:0.41151 validation-auc:0.96820 validation-aucpr:0.97326
[38] validation-logloss:0.40764 validation-auc:0.96809 validation-aucpr:0.97316
[39] validation-logloss:0.40370 validation-auc:0.96807 validation-aucpr:0.97312
[40] validation-logloss:0.39907 validation-auc:0.96821 validation-aucpr:0.97325
[41] validation-logloss:0.39457 validation-auc:0.96835 validation-aucpr:0.97338
[42] validation-logloss:0.39023 validation-auc:0.96847 validation-aucpr:0.97350
[43] validation-logloss:0.38597 validation-auc:0.96860 validation-aucpr:0.97362
[44] validation-logloss:0.38253 validation-auc:0.96863 validation-aucpr:0.97363
[45] validation-logloss:0.37903 validation-auc:0.96865 validation-aucpr:0.97367
[46] validation-logloss:0.37494 validation-auc:0.96877 validation-aucpr:0.97377
[47] validation-logloss:0.37154 validation-auc:0.96875 validation-aucpr:0.97375
[48] validation-logloss:0.36836 validation-auc:0.96873 validation-aucpr:0.97373
[49] validation-logloss:0.36519 validation-auc:0.96877 validation-aucpr:0.97374
[50] validation-logloss:0.36225 validation-auc:0.96879 validation-aucpr:0.97373
[51] validation-logloss:0.35860 validation-auc:0.96881 validation-aucpr:0.97374
[52] validation-logloss:0.35578 validation-auc:0.96876 validation-aucpr:0.97370
[53] validation-logloss:0.35239 validation-auc:0.96883 validation-aucpr:0.97376
[54] validation-logloss:0.34898 validation-auc:0.96899 validation-aucpr:0.97389
[55] validation-logloss:0.34633 validation-auc:0.96893 validation-aucpr:0.97385
[56] validation-logloss:0.34295 validation-auc:0.96901 validation-aucpr:0.97394
[57] validation-logloss:0.34039 validation-auc:0.96895 validation-aucpr:0.97389
[58] validation-logloss:0.33740 validation-auc:0.96892 validation-aucpr:0.97389
[59] validation-logloss:0.33504 validation-auc:0.96880 validation-aucpr:0.97381
[60] validation-logloss:0.33197 validation-auc:0.96889 validation-aucpr:0.97388
[61] validation-logloss:0.32897 validation-auc:0.96898 validation-aucpr:0.97396
[62] validation-logloss:0.32671 validation-auc:0.96898 validation-aucpr:0.97395
[63] validation-logloss:0.32374 validation-auc:0.96909 validation-aucpr:0.97404
[64] validation-logloss:0.32080 validation-auc:0.96928 validation-aucpr:0.97419
[65] validation-logloss:0.31849 validation-auc:0.96938 validation-aucpr:0.97427
[66] validation-logloss:0.31629 validation-auc:0.96941 validation-aucpr:0.97427
[67] validation-logloss:0.31412 validation-auc:0.96939 validation-aucpr:0.97425
[68] validation-logloss:0.31202 validation-auc:0.96940 validation-aucpr:0.97424
[69] validation-logloss:0.30995 validation-auc:0.96943 validation-aucpr:0.97427
[70] validation-logloss:0.30732 validation-auc:0.96958 validation-aucpr:0.97439
[71] validation-logloss:0.30548 validation-auc:0.96957 validation-aucpr:0.97439
[72] validation-logloss:0.30300 validation-auc:0.96965 validation-aucpr:0.97446
[73] validation-logloss:0.30106 validation-auc:0.96965 validation-aucpr:0.97445
[74] validation-logloss:0.29920 validation-auc:0.96966 validation-aucpr:0.97446
[75] validation-logloss:0.29740 validation-auc:0.96965 validation-aucpr:0.97444
[76] validation-logloss:0.29573 validation-auc:0.96962 validation-aucpr:0.97440
[77] validation-logloss:0.29341 validation-auc:0.96970 validation-aucpr:0.97449
[78] validation-logloss:0.29132 validation-auc:0.96974 validation-aucpr:0.97454
[79] validation-logloss:0.28917 validation-auc:0.96980 validation-aucpr:0.97462
[80] validation-logloss:0.28763 validation-auc:0.96974 validation-aucpr:0.97458
[81] validation-logloss:0.28552 validation-auc:0.96984 validation-aucpr:0.97467
[82] validation-logloss:0.28404 validation-auc:0.96986 validation-aucpr:0.97467
[83] validation-logloss:0.28201 validation-auc:0.96993 validation-aucpr:0.97474
[84] validation-logloss:0.28049 validation-auc:0.96990 validation-aucpr:0.97471
{'best_iteration': '83', 'best_score': '0.9747392883128421'}
Trial 99, Fold 4: Log loss = 0.2804945606615627, Average precision = 0.9747112338846573, ROC-AUC = 0.9699017444341914, Elapsed Time = 3.4602881999999227 seconds
Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 99, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0] validation-logloss:0.68206 validation-auc:0.91551 validation-aucpr:0.89662
[1] validation-logloss:0.66970 validation-auc:0.95149 validation-aucpr:0.94137
[2] validation-logloss:0.65907 validation-auc:0.95651 validation-aucpr:0.95462
[3] validation-logloss:0.64886 validation-auc:0.95838 validation-aucpr:0.96078
[4] validation-logloss:0.63917 validation-auc:0.95898 validation-aucpr:0.96250
[5] validation-logloss:0.62949 validation-auc:0.95902 validation-aucpr:0.96220
[6] validation-logloss:0.61915 validation-auc:0.96088 validation-aucpr:0.96426
[7] validation-logloss:0.61081 validation-auc:0.96048 validation-aucpr:0.96355
[8] validation-logloss:0.60224 validation-auc:0.96072 validation-aucpr:0.96455
[9] validation-logloss:0.59219 validation-auc:0.96196 validation-aucpr:0.96575
[10] validation-logloss:0.58382 validation-auc:0.96249 validation-aucpr:0.96615
[11] validation-logloss:0.57577 validation-auc:0.96232 validation-aucpr:0.96606
[12] validation-logloss:0.56800 validation-auc:0.96246 validation-aucpr:0.96613
[13] validation-logloss:0.56054 validation-auc:0.96255 validation-aucpr:0.96627
[14] validation-logloss:0.55189 validation-auc:0.96330 validation-aucpr:0.96711
[15] validation-logloss:0.54496 validation-auc:0.96316 validation-aucpr:0.96718
[16] validation-logloss:0.53800 validation-auc:0.96303 validation-aucpr:0.96704
[17] validation-logloss:0.53004 validation-auc:0.96373 validation-aucpr:0.96731
[18] validation-logloss:0.52336 validation-auc:0.96385 validation-aucpr:0.96737
[19] validation-logloss:0.51584 validation-auc:0.96399 validation-aucpr:0.96752
[20] validation-logloss:0.50934 validation-auc:0.96426 validation-aucpr:0.96774
[21] validation-logloss:0.50343 validation-auc:0.96446 validation-aucpr:0.96807
[22] validation-logloss:0.49636 validation-auc:0.96461 validation-aucpr:0.96824
[23] validation-logloss:0.49038 validation-auc:0.96467 validation-aucpr:0.96825
[24] validation-logloss:0.48493 validation-auc:0.96469 validation-aucpr:0.96834
[25] validation-logloss:0.47837 validation-auc:0.96496 validation-aucpr:0.96877
[26] validation-logloss:0.47196 validation-auc:0.96552 validation-aucpr:0.96921
[27] validation-logloss:0.46670 validation-auc:0.96545 validation-aucpr:0.96912
[28] validation-logloss:0.46044 validation-auc:0.96583 validation-aucpr:0.96963
[29] validation-logloss:0.45532 validation-auc:0.96595 validation-aucpr:0.96974
[30] validation-logloss:0.45053 validation-auc:0.96587 validation-aucpr:0.96953
[31] validation-logloss:0.44472 validation-auc:0.96606 validation-aucpr:0.96970
[32] validation-logloss:0.44022 validation-auc:0.96602 validation-aucpr:0.96967
[33] validation-logloss:0.43583 validation-auc:0.96593 validation-aucpr:0.96959
[34] validation-logloss:0.43155 validation-auc:0.96600 validation-aucpr:0.96966
[35] validation-logloss:0.42633 validation-auc:0.96622 validation-aucpr:0.96987
[36] validation-logloss:0.42200 validation-auc:0.96631 validation-aucpr:0.96994
[37] validation-logloss:0.41805 validation-auc:0.96632 validation-aucpr:0.96993
[38] validation-logloss:0.41418 validation-auc:0.96630 validation-aucpr:0.96992
[39] validation-logloss:0.40949 validation-auc:0.96625 validation-aucpr:0.96997
[40] validation-logloss:0.40601 validation-auc:0.96609 validation-aucpr:0.96981
[41] validation-logloss:0.40234 validation-auc:0.96609 validation-aucpr:0.96981
[42] validation-logloss:0.39867 validation-auc:0.96611 validation-aucpr:0.96983
[43] validation-logloss:0.39499 validation-auc:0.96624 validation-aucpr:0.96992
[44] validation-logloss:0.39069 validation-auc:0.96636 validation-aucpr:0.97005
[45] validation-logloss:0.38723 validation-auc:0.96638 validation-aucpr:0.97005
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[47] validation-logloss:0.37892 validation-auc:0.96661 validation-aucpr:0.97027
[48] validation-logloss:0.37599 validation-auc:0.96651 validation-aucpr:0.97017
[49] validation-logloss:0.37203 validation-auc:0.96671 validation-aucpr:0.97034
[50] validation-logloss:0.36886 validation-auc:0.96676 validation-aucpr:0.97040
[51] validation-logloss:0.36573 validation-auc:0.96677 validation-aucpr:0.97039
[52] validation-logloss:0.36281 validation-auc:0.96677 validation-aucpr:0.97035
[53] validation-logloss:0.35980 validation-auc:0.96683 validation-aucpr:0.97041
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[55] validation-logloss:0.35350 validation-auc:0.96688 validation-aucpr:0.97049
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[58] validation-logloss:0.34523 validation-auc:0.96701 validation-aucpr:0.97067
[59] validation-logloss:0.34197 validation-auc:0.96711 validation-aucpr:0.97075
[60] validation-logloss:0.33902 validation-auc:0.96710 validation-aucpr:0.97074
[61] validation-logloss:0.33671 validation-auc:0.96713 validation-aucpr:0.97069
[62] validation-logloss:0.33426 validation-auc:0.96721 validation-aucpr:0.97074
[63] validation-logloss:0.33216 validation-auc:0.96723 validation-aucpr:0.97145
[64] validation-logloss:0.32993 validation-auc:0.96726 validation-aucpr:0.97147
[65] validation-logloss:0.32699 validation-auc:0.96733 validation-aucpr:0.97154
[66] validation-logloss:0.32485 validation-auc:0.96736 validation-aucpr:0.97156
[67] validation-logloss:0.32262 validation-auc:0.96738 validation-aucpr:0.97158
[68] validation-logloss:0.32065 validation-auc:0.96732 validation-aucpr:0.97154
[69] validation-logloss:0.31869 validation-auc:0.96733 validation-aucpr:0.97153
[70] validation-logloss:0.31662 validation-auc:0.96731 validation-aucpr:0.97151
[71] validation-logloss:0.31391 validation-auc:0.96743 validation-aucpr:0.97162
[72] validation-logloss:0.31198 validation-auc:0.96741 validation-aucpr:0.97160
[73] validation-logloss:0.31024 validation-auc:0.96735 validation-aucpr:0.97155
[74] validation-logloss:0.30853 validation-auc:0.96733 validation-aucpr:0.97152
[75] validation-logloss:0.30668 validation-auc:0.96735 validation-aucpr:0.97153
[76] validation-logloss:0.30431 validation-auc:0.96742 validation-aucpr:0.97160
[77] validation-logloss:0.30184 validation-auc:0.96755 validation-aucpr:0.97172
[78] validation-logloss:0.30006 validation-auc:0.96752 validation-aucpr:0.97171
[79] validation-logloss:0.29835 validation-auc:0.96753 validation-aucpr:0.97170
[80] validation-logloss:0.29670 validation-auc:0.96756 validation-aucpr:0.97172
[81] validation-logloss:0.29449 validation-auc:0.96763 validation-aucpr:0.97179
[82] validation-logloss:0.29248 validation-auc:0.96771 validation-aucpr:0.97190
[83] validation-logloss:0.29091 validation-auc:0.96777 validation-aucpr:0.97193
[84] validation-logloss:0.28872 validation-auc:0.96794 validation-aucpr:0.97207
{'best_iteration': '84', 'best_score': '0.9720716435454212'}
Trial 99, Fold 5: Log loss = 0.28871626715298626, Average precision = 0.9720776131216773, ROC-AUC = 0.9679444103821786, Elapsed Time = 3.337888300000486 seconds
Optimization Progress: 100%|##########| 100/100 [4:02:03<00:00, 42.55s/it]
Optuna Optimization Elapsed Time: 14523.8524339 seconds
Optimization Progress: 100%|##########| 100/100 [4:02:03<00:00, 145.24s/it]
Training with Best Trial 65
Full_params: {'objective': 'binary:logistic', 'device': 'cpu', 'verbosity': 1, 'validate_parameters': True, 'eval_metric': ['logloss', 'auc', 'aucpr'], 'seed': 42, 'max_depth': 0, 'sampling_method': 'uniform', 'num_parallel_tree': 1, 'booster': 'gbtree', 'eta': 0.08759379181951674, 'gamma': 4.671021255322478e-06, 'min_child_weight': 2.6427694911817074e-05, 'max_delta_step': 65.5415383777442, 'subsample': 0.5614298448675313, 'colsample_bytree': 0.8129876656630327, 'colsample_bylevel': 0.9184442500782993, 'colsample_bynode': 0.6255082544056032, 'lambda': 7.025644820629422, 'alpha': 0.03727945540630586, 'tree_method': 'auto', 'scale_pos_weight': 1, 'grow_policy': 'depthwise', 'max_leaves': 128, 'max_bin': 47, 'num_boost_round': 97}
Training Elapsed Time: 2.7084125000001222 seconds
Log loss: (Train) 0.18729623942177334 vs (Test) 0.1927517325225934
PR-AUC: (Train) 0.976406633670879 vs (Test) 0.975962677739389
ROC-AUC: (Train) 0.9731661086371626 vs (Test) 0.9725283483506415
Training with Best Trial 76
Full_params: {'objective': 'binary:logistic', 'device': 'cpu', 'verbosity': 1, 'validate_parameters': True, 'eval_metric': ['logloss', 'auc', 'aucpr'], 'seed': 42, 'max_depth': 0, 'sampling_method': 'uniform', 'num_parallel_tree': 1, 'booster': 'gbtree', 'eta': 0.06489083823042531, 'gamma': 1.733278388607598, 'min_child_weight': 0.0015768729934482134, 'max_delta_step': 37.67348822307136, 'subsample': 0.8942728317991482, 'colsample_bytree': 0.570634316092556, 'colsample_bylevel': 0.8376499027022023, 'colsample_bynode': 0.9166953679419032, 'lambda': 0.005622055563920644, 'alpha': 4.06229817623886e-08, 'tree_method': 'hist', 'scale_pos_weight': 1, 'grow_policy': 'depthwise', 'max_leaves': 196, 'max_bin': 92, 'num_boost_round': 73}
Training Elapsed Time: 3.324780799997825 seconds
Log loss: (Train) 0.18865200821954037 vs (Test) 0.19412531851812243
PR-AUC: (Train) 0.9765158675564066 vs (Test) 0.9760041164203919
ROC-AUC: (Train) 0.9730942189230347 vs (Test) 0.9721837774893096
In [33]:
save_results(clf_name = "XGBClassifier",
best_trials = best_trials_xgb,
exec_time = exec_time_xgb,
lloss_auc_train = lloss_auc_train_xgb,
lloss_auc_test = lloss_auc_test_xgb,
df_metrics = df_metrics_xgb,
cm_final = cm_final_xgb,
cm_all = cm_xgb_all,
cm_labels = cm_labels_xgb_all)
Optuna with CatBoostClassifier¶
In [34]:
gc.collect();
In [35]:
# Aggregated loan level data
X_df = clean_df.drop(columns = ["target", "anon_ssn"])
# https://catboost.ai/docs/en/concepts/algorithm-missing-values-processing
# Replace <NA> with NaN
X_df = X_df.fillna(np.nan)
# Identify categorical columns
cat_cols = X_df.select_dtypes(include = "category").columns.tolist()
# Convert missing NaN in Dtype categorical to "nan" as Dtype object
for col in cat_cols:
X_df[col] = X_df[col].astype(str)
# Convert missing <NA> in Dtype nullable boolean and nullable Int to NaN as float
X_df = X_df.apply(lambda x: x.map(lambda z: np.nan if pd.isna(z) else z))
y_df = clean_df.target
anon_ssn = clean_df.anon_ssn;
In [36]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring that no anon_ssn (grouped by anon_ssn) appear in both sets
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = 42)
train_idx, test_idx = next(gss.split(X_df, y_df, groups = anon_ssn))
X_train, X_test = X_df.iloc[train_idx], X_df.iloc[test_idx]
y_train, y_test = y_df.iloc[train_idx], y_df.iloc[test_idx]
# Keep track of anon_ssn for cross-validation
anon_ssn_train = anon_ssn[train_idx]
In [37]:
del X_df, y_df, gss, train_idx, test_idx;
In [38]:
# Define the objective function
def objective(trial):
# Error: bayesian bootstrap doesn't support 'subsample' option
bootstrap_type = trial.suggest_categorical("bootstrap_type", ["Bayesian", "Bernoulli",
#"Poisson" # CPU doesn't support
]
)
if bootstrap_type == "Bayesian":
# Do not suggest subsample for Bayesian
subsample = None
else:
subsample = trial.suggest_float("subsample", 1e-1, 1e0)
grow_policy = trial.suggest_categorical("grow_policy", ["SymmetricTree",
"Lossguide", # Only for 'Ordered' and CPU
"Depthwise",
#"Region" # GrowPolicy Region is unimplemented for CPU
]
)
# Conditionally set sampling_frequency based on grow_policy
if grow_policy == "Lossguide":
sampling_frequency = "PerTree"
else:
sampling_frequency = trial.suggest_categorical("sampling_frequency", ["PerTree", "PerTreeLevel"])
posterior_sampling = trial.suggest_categorical("posterior_sampling", [True, False]) # CPU only
if posterior_sampling == True:
model_shrink_mode = "Constant"
else:
model_shrink_mode = trial.suggest_categorical("model_shrink_mode", ["Constant", "Decreasing"]) #CPU only
# https://catboost.ai/docs/en/references/training-parameters/
# https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier
# https://catboost.ai/docs/en/concepts/speed-up-training
params = {"task_type": "CPU",
"objective": "Logloss",
"eval_metric": "Logloss",
"custom_metric": ["AUC", "PRAUC"],
"iterations": trial.suggest_int("iterations", 5, 100),
"learning_rate": trial.suggest_float("learning_rate", 1e-2, 1e-1, log = True),
"random_seed": seed,
"l2_leaf_reg": trial.suggest_float("l2_leaf_reg", 1e-8, 1e1, log = True),
"bootstrap_type": bootstrap_type,
"subsample": subsample,
"sampling_frequency": sampling_frequency,
"random_strength": trial.suggest_float("random_strength", 1e-2, 10.0, log = True),
"depth": trial.suggest_int("depth", 1, 16),
"grow_policy": grow_policy,
"min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 5, 200), # Minimum data in one leaf
"has_time": trial.suggest_categorical("has_time", [True, False]),
"rsm": trial.suggest_float("rsm", 1e-1, 1e0),
"leaf_estimation_method": trial.suggest_categorical("leaf_estimation_method", ["Newton", "Gradient"]),
"leaf_estimation_backtracking": trial.suggest_categorical("leaf_estimation_backtracking", ["No", "AnyImprovement"]),
"fold_len_multiplier": trial.suggest_float("fold_len_multiplier", 1e0, 1e1),
"approx_on_full_history": False, # Can't use approx-on-full-history with Plain boosting-type
"auto_class_weights": trial.suggest_categorical("auto_class_weights", ["None", "Balanced", "SqrtBalanced"]),
"boosting_type": "Plain", # for CPU
"boost_from_average": trial.suggest_categorical("boost_from_average", [True, False]),
"posterior_sampling": posterior_sampling,
"allow_const_label": trial.suggest_categorical("allow_const_label", [True, False]),
"score_function": trial.suggest_categorical("score_function", ["Cosine", "L2", # CPU only
]
),
"model_shrink_mode": model_shrink_mode,
"thread_count": -1,
"border_count": trial.suggest_int("border_count", 40, 255),
"verbose": True, # Keeps live per-iteration training logs visible in the output cell
"allow_writing_files": False # Avoid CatBoost "Error 32" on Windows (file-lock in catboost_info); disables writing into catboost_info/ (so no file lock issues)
}
# Ensure max_leaves (# Number of leaves in one tree) is only used when grow_policy is Lossguide
if params["grow_policy"] == "Lossguide":
params["max_leaves"] = trial.suggest_int("max_leaves", 2, 256)
sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):
# Split data into training and validation sets
X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]
y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]
# Summarize the composition of classes in the train and validation sets
train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])
print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where 0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')
# Create Pool objects for training and validation
train_pool_fold = Pool(X_train_fold, y_train_fold, cat_features = cat_cols)
valid_pool_fold = Pool(X_valid_fold, y_valid_fold, cat_features = cat_cols)
start_fold = time.perf_counter()
clf = CatBoostClassifier(**params).fit(train_pool_fold, eval_set = valid_pool_fold)
end_fold = time.perf_counter()
# Get probabilities and for the positive class
y_prob_fold = clf.predict_proba(X_valid_fold)[:, 1]
y_pred_fold = clf.predict(X_valid_fold) # Class prediction
print(f'Trial {trial.number}, Fold {fold_idx}: '
f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
f'Elapsed Time = {end_fold - start_fold} seconds')
lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))
del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, train_pool_fold, valid_pool_fold, clf, start_fold, end_fold
gc.collect()
mean_lloss = np.mean(lloss_scores)
mean_pr_auc = np.mean(pr_auc_scores)
mean_roc_auc = np.mean(roc_auc_scores)
del lloss_scores, pr_auc_scores, roc_auc_scores
gc.collect()
return mean_lloss, mean_pr_auc, mean_roc_auc
In [39]:
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
ascii = True, # Plain text mode
dynamic_ncols = True # Auto-fit width
)
def update_progress(study_cbc, trial):
trial_progress.update(1)
# Disable Optuna's stdout handler so notebook isn’t spammed
optuna.logging.disable_default_handler()
# Enable propagation to Python’s logging
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)
# Configure Python logging
logging.basicConfig(filename = "optuna_debug_CatBoostClassifier.log", filemode = "w", level = logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")
study_cbc = optuna.create_study(study_name = "Optuna for CatBoostClassifier",
directions = ["minimize", "maximize", "maximize"],
sampler = module.AutoSampler(seed = seed)
)
start_optuna = time.perf_counter()
study_cbc.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')
gc.collect()
fig = plot_pareto_front(study_cbc, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)
fig.show()
trial_progress.close()
# Plot optimization history for each objective
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
optuna.visualization.plot_optimization_history(study_cbc,
target = lambda t: t.values[i], # Correctly target each objective
target_name = obj).show()
best_trials = study_cbc.best_trials
best_trials_cbc = {}
exec_time_cbc, lloss_auc_train_cbc, lloss_auc_test_cbc, all_metrics = [],[], [], []
cm_cbc_all, cm_labels_cbc_all = [],[]
# Prepare the data - 80% training set and 20% test set
train_pool_all = Pool(X_train, y_train, cat_features = cat_cols)
test_pool_all = Pool(X_test, y_test, cat_features = cat_cols)
for i, trial in enumerate(best_trials):
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Training with Best Trial {trial.number} </span>"))
best_params = trial.params
display(HTML(best_params.__repr__()))
# Non-optimized and best Optuna optimized parameters
full_params = {"objective": "Logloss",
"eval_metric": "Logloss",
"custom_metric": ["AUC", "PRAUC"],
"random_seed": seed,
"thread_count": -1,
"verbose": True,
"allow_writing_files": False,
**best_params
}
print("Full_params:", full_params)
best_trials_cbc[trial.number] = full_params
# https://catboost.ai/docs/en/references/training-parameters/common#auto_class_weights
# https://catboost.ai/docs/en/references/training-parameters/performance
final_cbc = CatBoostClassifier(**full_params)
start_train = time.perf_counter()
final_cbc.fit(train_pool_all)
end_train = time.perf_counter()
print(f'Training Elapsed Time: {end_train - start_train} seconds')
y_prob_all = final_cbc.predict_proba(X_test)[:, 1]
y_pred_all = final_cbc.predict(X_test)
print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
print(f'PR-AUC: (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
print(f'ROC-AUC: (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')
exec_time_cbc.append({"Classifier": "CatBoostClassifier",
"Best Trial": trial.number,
"Optimization Elapsed Time (s)": end_optuna - start_optuna,
"Training Elapsed Time (s)": end_train - start_train})
lloss_auc_train_cbc.append({"Classifier": "CatBoostClassifier",
"Best Trial": trial.number,
"Set": "Training",
"Log loss": trial.values[0],
"PR-AUC": trial.values[1],
"ROC-AUC": trial.values[2]})
lloss_auc_test_cbc.append({"Classifier": "CatBoostClassifier",
"Best Trial": trial.number,
"Set": "Test",
"Log loss": log_loss(y_test, y_prob_all),
"PR-AUC": average_precision_score(y_test, y_prob_all),
"ROC-AUC": roc_auc_score(y_test, y_prob_all)})
report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
all_metrics.append({"Classifier": "CatBoostClassifier",
"Trial": trial.number,
"Accuracy": accuracy_score(y_test, y_pred_all),
"Precision (Safe)": report["Safe"]["precision"],
"Recall (Safe)": report["Safe"]["recall"],
"F1-score (Safe)": report["Safe"]["f1-score"],
"Precision (Risky)": report["Risky"]["precision"],
"Recall (Risky)": report["Risky"]["recall"],
"F1-score (Risky)": report["Risky"]["f1-score"],
"Precision (Macro avg)": report["macro avg"]["precision"],
"Recall (Macro avg)": report["macro avg"]["recall"],
"F1-score (Macro avg)": report["macro avg"]["f1-score"],
"Precision (Weighted avg)": report["weighted avg"]["precision"],
"Recall (Weighted avg)": report["weighted avg"]["recall"],
"F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})
# Store confusion matrix
cm_final_cbc = confusion_matrix(y_test, y_pred_all)
cm_cbc_all.append(cm_final_cbc)
cm_labels_cbc_all.append(f'CatBoostClassifier Confusion Matrix for Best Trial {trial.number}') # Store label for subplots
df_metrics_cbc = pd.DataFrame(all_metrics)
gc.collect();
Optimization Progress: 0%| | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 0, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6749843 test: 0.6749868 best: 0.6749868 (0) total: 427ms remaining: 8.54s 1: learn: 0.6566303 test: 0.6566865 best: 0.6566865 (1) total: 639ms remaining: 6.07s 2: learn: 0.6411094 test: 0.6411880 best: 0.6411880 (2) total: 906ms remaining: 5.43s 3: learn: 0.6222098 test: 0.6219594 best: 0.6219594 (3) total: 1.16s remaining: 4.92s 4: learn: 0.6095943 test: 0.6094700 best: 0.6094700 (4) total: 1.36s remaining: 4.37s 5: learn: 0.5962167 test: 0.5963761 best: 0.5963761 (5) total: 1.61s remaining: 4.03s 6: learn: 0.5836222 test: 0.5838488 best: 0.5838488 (6) total: 1.86s remaining: 3.72s 7: learn: 0.5704152 test: 0.5706220 best: 0.5706220 (7) total: 2.12s remaining: 3.44s 8: learn: 0.5562966 test: 0.5564756 best: 0.5564756 (8) total: 2.31s remaining: 3.08s 9: learn: 0.5448487 test: 0.5450990 best: 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Optimization Progress: 2%|2 | 2/100 [00:49<37:38, 23.05s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 2, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6804296 test: 0.6806120 best: 0.6806120 (0) total: 153ms remaining: 11.6s 1: learn: 0.6677884 test: 0.6680673 best: 0.6680673 (1) total: 296ms remaining: 11.1s 2: learn: 0.6554878 test: 0.6559308 best: 0.6559308 (2) total: 449ms remaining: 11.1s 3: learn: 0.6434826 test: 0.6440523 best: 0.6440523 (3) total: 596ms remaining: 10.9s 4: learn: 0.6323349 test: 0.6331974 best: 0.6331974 (4) total: 752ms remaining: 10.8s 5: learn: 0.6210927 test: 0.6221362 best: 0.6221362 (5) total: 900ms remaining: 10.6s 6: learn: 0.6110129 test: 0.6125733 best: 0.6125733 (6) total: 1.06s remaining: 10.7s 7: learn: 0.6000466 test: 0.6016630 best: 0.6016630 (7) total: 1.21s remaining: 10.4s 8: learn: 0.5894133 test: 0.5913387 best: 0.5913387 (8) total: 1.4s remaining: 10.6s 9: learn: 0.5790718 test: 0.5811954 best: 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Optimization Progress: 3%|3 | 3/100 [02:00<1:12:45, 45.00s/it]
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Optimization Progress: 4%|4 | 4/100 [11:14<6:33:50, 246.15s/it]
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Optimization Progress: 5%|5 | 5/100 [12:07<4:39:15, 176.38s/it]
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Optimization Progress: 6%|6 | 6/100 [12:54<3:27:22, 132.37s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 6, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6802252 test: 0.6804421 best: 0.6804421 (0) total: 21.6ms remaining: 260ms 1: learn: 0.6682112 test: 0.6683883 best: 0.6683883 (1) total: 43.2ms remaining: 237ms 2: learn: 0.6589036 test: 0.6589366 best: 0.6589366 (2) total: 64.3ms remaining: 214ms 3: learn: 0.6448929 test: 0.6448146 best: 0.6448146 (3) total: 85ms remaining: 191ms 4: learn: 0.6338868 test: 0.6337594 best: 0.6337594 (4) total: 106ms remaining: 169ms 5: learn: 0.6256641 test: 0.6255702 best: 0.6255702 (5) total: 125ms remaining: 146ms 6: learn: 0.6150270 test: 0.6149716 best: 0.6149716 (6) total: 145ms remaining: 124ms 7: learn: 0.6067636 test: 0.6065486 best: 0.6065486 (7) total: 165ms remaining: 103ms 8: learn: 0.5986717 test: 0.5983345 best: 0.5983345 (8) total: 185ms remaining: 82.1ms 9: learn: 0.5880264 test: 0.5876876 best: 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Optimization Progress: 7%|7 | 7/100 [13:03<2:22:45, 92.10s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 7, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5989916 test: 0.5987729 best: 0.5987729 (0) total: 48.7ms remaining: 682ms 1: learn: 0.5212612 test: 0.5209932 best: 0.5209932 (1) total: 97ms remaining: 631ms 2: learn: 0.4632245 test: 0.4633237 best: 0.4633237 (2) total: 142ms remaining: 568ms 3: learn: 0.4174577 test: 0.4177841 best: 0.4177841 (3) total: 189ms remaining: 519ms 4: learn: 0.3814671 test: 0.3829626 best: 0.3829626 (4) total: 233ms remaining: 465ms 5: learn: 0.3555651 test: 0.3578947 best: 0.3578947 (5) total: 281ms remaining: 422ms 6: learn: 0.3310748 test: 0.3339130 best: 0.3339130 (6) total: 325ms remaining: 372ms 7: learn: 0.3140789 test: 0.3184745 best: 0.3184745 (7) total: 372ms remaining: 325ms 8: learn: 0.2981974 test: 0.3031023 best: 0.3031023 (8) total: 418ms remaining: 278ms 9: learn: 0.2870217 test: 0.2923589 best: 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Optimization Progress: 8%|8 | 8/100 [13:15<1:41:53, 66.45s/it]
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Optimization Progress: 9%|9 | 9/100 [15:21<2:09:17, 85.25s/it]
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Optimization Progress: 10%|# | 10/100 [16:33<2:01:52, 81.25s/it]
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Optimization Progress: 11%|#1 | 11/100 [17:05<1:37:52, 65.99s/it]
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Optimization Progress: 12%|#2 | 12/100 [18:29<1:44:46, 71.44s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 12, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5384561 test: 0.5403447 best: 0.5403447 (0) total: 491ms remaining: 48.6s 1: learn: 0.4310432 test: 0.4363623 best: 0.4363623 (1) total: 1.12s remaining: 55s 2: learn: 0.3619269 test: 0.3696297 best: 0.3696297 (2) total: 1.76s remaining: 56.9s 3: learn: 0.3111326 test: 0.3220037 best: 0.3220037 (3) total: 2.42s remaining: 58.1s 4: learn: 0.2729093 test: 0.2877730 best: 0.2877730 (4) total: 3.1s remaining: 58.9s 5: learn: 0.2457871 test: 0.2640025 best: 0.2640025 (5) total: 3.77s remaining: 59.1s 6: learn: 0.2231111 test: 0.2444385 best: 0.2444385 (6) total: 4.47s remaining: 59.4s 7: learn: 2.4701636 test: 0.2325594 best: 0.2325594 (7) total: 5.11s remaining: 58.8s 8: learn: 10.6390693 test: 12.9046381 best: 0.2325594 (7) total: 5.78s remaining: 58.5s 9: learn: 13.3196306 test: 12.8510000 best: 0.2325594 (7) total: 6.38s remaining: 57.4s 10: learn: 579.0603453 test: 561.8532997 best: 0.2325594 (7) total: 6.97s remaining: 56.4s 11: learn: 2295.0834727 test: 2260.0907995 best: 0.2325594 (7) total: 7.55s remaining: 55.4s 12: learn: 379934.8232905 test: 383171.6665990 best: 0.2325594 (7) total: 8.02s remaining: 53.6s 13: learn: 315249.5239398 test: 347726.9128801 best: 0.2325594 (7) total: 8.63s remaining: 53s
Training has stopped (degenerate solution on iteration 14, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 21, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 15, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 16, probably too small l2-regularization, try to increase it)
bestTest = 0.2189336578 bestIteration = 11 Shrink model to first 12 iterations. Trial 12, Fold 5: Log loss = 0.21893365778022436, Average precision = 0.972602290968769, ROC-AUC = 0.9685415528677331, Elapsed Time = 10.713243800000782 seconds
Optimization Progress: 13%|#3 | 13/100 [19:58<1:51:13, 76.70s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 13, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6362268 test: 0.6369981 best: 0.6369981 (0) total: 172ms remaining: 6s 1: learn: 0.5884682 test: 0.5887326 best: 0.5887326 (1) total: 355ms remaining: 6.04s 2: learn: 0.5494788 test: 0.5503714 best: 0.5503714 (2) total: 541ms remaining: 5.95s 3: learn: 0.5141643 test: 0.5154301 best: 0.5154301 (3) total: 747ms remaining: 5.98s 4: learn: 0.4837059 test: 0.4848689 best: 0.4848689 (4) total: 931ms remaining: 5.77s 5: learn: 0.4534961 test: 0.4544274 best: 0.4544274 (5) total: 1.12s remaining: 5.6s 6: learn: 0.4310285 test: 0.4323185 best: 0.4323185 (6) total: 1.3s remaining: 5.37s 7: learn: 0.4071864 test: 0.4086668 best: 0.4086668 (7) total: 1.5s remaining: 5.24s 8: learn: 0.3908716 test: 0.3925285 best: 0.3925285 (8) total: 1.66s remaining: 4.99s 9: learn: 0.3696076 test: 0.3716048 best: 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Optimization Progress: 14%|#4 | 14/100 [20:40<1:35:04, 66.33s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 14, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5801609 test: 0.5909252 best: 0.5909252 (0) total: 3.17s remaining: 2m 19s 1: learn: 0.4936147 test: 0.5096646 best: 0.5096646 (1) total: 6.14s remaining: 2m 11s 2: learn: 0.4258390 test: 0.4477475 best: 0.4477475 (2) total: 9.18s remaining: 2m 8s 3: learn: 0.3703814 test: 0.4004829 best: 0.4004829 (3) total: 12.4s remaining: 2m 6s 4: learn: 0.3300916 test: 0.3629523 best: 0.3629523 (4) total: 15.4s remaining: 2m 3s 5: learn: 0.2984747 test: 0.3345477 best: 0.3345477 (5) total: 18.5s remaining: 2m 6: learn: 0.2702072 test: 0.3118904 best: 0.3118904 (6) total: 21.4s remaining: 1m 56s 7: learn: 0.2462469 test: 0.2949055 best: 0.2949055 (7) total: 24.1s remaining: 1m 51s 8: learn: 0.2281451 test: 0.2814214 best: 0.2814214 (8) total: 27.4s remaining: 1m 49s 9: learn: 0.2154538 test: 0.2711527 best: 0.2711527 (9) total: 30.3s remaining: 1m 46s 10: learn: 0.2035282 test: 0.2635893 best: 0.2635893 (10) total: 33.1s remaining: 1m 42s 11: learn: 0.1930792 test: 0.2553380 best: 0.2553380 (11) total: 35.9s remaining: 1m 38s 12: learn: 0.1837754 test: 0.2489476 best: 0.2489476 (12) total: 38.8s remaining: 1m 35s 13: learn: 0.1749195 test: 0.2436962 best: 0.2436962 (13) total: 42s remaining: 1m 32s 14: learn: 0.1683841 test: 0.2403143 best: 0.2403143 (14) total: 45s remaining: 1m 30s 15: learn: 0.1604667 test: 0.2369559 best: 0.2369559 (15) total: 48s remaining: 1m 26s 16: learn: 0.1534018 test: 0.2340586 best: 0.2340586 (16) total: 51s remaining: 1m 24s 17: learn: 0.1469744 test: 0.2321920 best: 0.2321920 (17) total: 53.8s remaining: 1m 20s 18: learn: 0.1397973 test: 0.2299127 best: 0.2299127 (18) total: 56.4s remaining: 1m 17s 19: learn: 0.1357238 test: 0.2274836 best: 0.2274836 (19) total: 59.6s remaining: 1m 14s 20: learn: 0.1315739 test: 0.2258505 best: 0.2258505 (20) total: 1m 2s remaining: 1m 11s 21: learn: 0.1270202 test: 0.2239872 best: 0.2239872 (21) total: 1m 5s remaining: 1m 8s 22: learn: 0.1236635 test: 0.2228864 best: 0.2228864 (22) total: 1m 8s remaining: 1m 5s 23: learn: 0.1204728 test: 0.2218333 best: 0.2218333 (23) total: 1m 12s remaining: 1m 3s 24: learn: 0.1170833 test: 0.2209510 best: 0.2209510 (24) total: 1m 15s remaining: 1m 25: learn: 0.1131268 test: 0.2209637 best: 0.2209510 (24) total: 1m 18s remaining: 57.4s 26: learn: 0.1095461 test: 0.2205932 best: 0.2205932 (26) total: 1m 21s remaining: 54.1s 27: learn: 0.1066295 test: 0.2201905 best: 0.2201905 (27) total: 1m 24s remaining: 51.1s 28: learn: 0.1042506 test: 0.2196624 best: 0.2196624 (28) total: 1m 27s remaining: 48.1s 29: learn: 0.1016227 test: 0.2192604 best: 0.2192604 (29) total: 1m 29s remaining: 44.9s 30: learn: 0.0977455 test: 0.2188646 best: 0.2188646 (30) total: 1m 32s remaining: 42s 31: learn: 0.0945755 test: 0.2185130 best: 0.2185130 (31) total: 1m 35s remaining: 39s 32: learn: 0.0926957 test: 0.2182133 best: 0.2182133 (32) total: 1m 38s remaining: 35.8s 33: learn: 0.0904558 test: 0.2179245 best: 0.2179245 (33) total: 1m 41s remaining: 32.8s 34: learn: 0.0878753 test: 0.2179111 best: 0.2179111 (34) total: 1m 44s remaining: 29.8s 35: learn: 0.0830881 test: 0.2187615 best: 0.2179111 (34) total: 1m 47s remaining: 26.8s 36: learn: 0.0808470 test: 0.2185576 best: 0.2179111 (34) total: 1m 49s remaining: 23.8s 37: learn: 0.0788830 test: 0.2185941 best: 0.2179111 (34) total: 1m 53s remaining: 20.8s 38: learn: 0.0758702 test: 0.2189666 best: 0.2179111 (34) total: 1m 56s remaining: 17.9s 39: learn: 0.0737398 test: 0.2189955 best: 0.2179111 (34) total: 1m 58s remaining: 14.8s 40: learn: 0.0723090 test: 0.2189501 best: 0.2179111 (34) total: 2m 1s remaining: 11.8s 41: learn: 0.0706000 test: 0.2191205 best: 0.2179111 (34) total: 2m 4s remaining: 8.88s 42: learn: 0.0681112 test: 0.2193127 best: 0.2179111 (34) total: 2m 7s remaining: 5.91s 43: learn: 0.0656441 test: 0.2193762 best: 0.2179111 (34) total: 2m 9s remaining: 2.95s 44: learn: 0.0646914 test: 0.2192511 best: 0.2179111 (34) total: 2m 12s remaining: 0us bestTest = 0.2179111493 bestIteration = 34 Shrink model to first 35 iterations. 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Trial 14, Fold 4: Log loss = 0.2116095543792527, Average precision = 0.9721683375773902, ROC-AUC = 0.9668279421828319, Elapsed Time = 137.0592063999975 seconds Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 14, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5803209 test: 0.5914360 best: 0.5914360 (0) total: 3.27s remaining: 2m 24s 1: learn: 0.4917459 test: 0.5130217 best: 0.5130217 (1) total: 6.38s remaining: 2m 17s 2: learn: 0.4236202 test: 0.4527485 best: 0.4527485 (2) total: 9.35s remaining: 2m 10s 3: learn: 0.3717660 test: 0.4063369 best: 0.4063369 (3) total: 12.7s remaining: 2m 9s 4: learn: 0.3281444 test: 0.3701779 best: 0.3701779 (4) total: 15.9s remaining: 2m 7s 5: learn: 0.2959626 test: 0.3413374 best: 0.3413374 (5) total: 19.1s remaining: 2m 4s 6: learn: 0.2708576 test: 0.3191538 best: 0.3191538 (6) total: 22s remaining: 1m 59s 7: learn: 0.2494503 test: 0.3021471 best: 0.3021471 (7) total: 25.3s remaining: 1m 57s 8: learn: 0.2302248 test: 0.2890283 best: 0.2890283 (8) total: 28.4s remaining: 1m 53s 9: learn: 0.2162939 test: 0.2777274 best: 0.2777274 (9) total: 31.4s remaining: 1m 49s 10: learn: 0.2041671 test: 0.2684269 best: 0.2684269 (10) total: 34.6s remaining: 1m 46s 11: learn: 0.1942773 test: 0.2612122 best: 0.2612122 (11) total: 37.9s remaining: 1m 44s 12: learn: 0.1838066 test: 0.2548227 best: 0.2548227 (12) total: 40.8s remaining: 1m 40s 13: learn: 0.1753989 test: 0.2500792 best: 0.2500792 (13) total: 43.9s remaining: 1m 37s 14: learn: 0.1665754 test: 0.2459458 best: 0.2459458 (14) total: 47.1s remaining: 1m 34s 15: learn: 0.1577527 test: 0.2426342 best: 0.2426342 (15) total: 50.1s remaining: 1m 30s 16: learn: 0.1517693 test: 0.2404095 best: 0.2404095 (16) total: 53.4s remaining: 1m 27s 17: learn: 0.1461579 test: 0.2375820 best: 0.2375820 (17) total: 56.5s remaining: 1m 24s 18: learn: 0.1409462 test: 0.2358171 best: 0.2358171 (18) total: 1m remaining: 1m 22s 19: learn: 0.1367763 test: 0.2334854 best: 0.2334854 (19) total: 1m 3s remaining: 1m 19s 20: learn: 0.1317757 test: 0.2322473 best: 0.2322473 (20) total: 1m 7s remaining: 1m 16s 21: learn: 0.1286373 test: 0.2311968 best: 0.2311968 (21) total: 1m 10s remaining: 1m 14s 22: learn: 0.1226746 test: 0.2296791 best: 0.2296791 (22) total: 1m 14s remaining: 1m 11s 23: learn: 0.1182230 test: 0.2284024 best: 0.2284024 (23) total: 1m 17s remaining: 1m 7s 24: learn: 0.1143845 test: 0.2273456 best: 0.2273456 (24) total: 1m 20s remaining: 1m 4s 25: learn: 0.1077534 test: 0.2268715 best: 0.2268715 (25) total: 1m 23s remaining: 1m 1s 26: learn: 0.1041038 test: 0.2266512 best: 0.2266512 (26) total: 1m 26s remaining: 57.8s 27: learn: 0.1013187 test: 0.2255078 best: 0.2255078 (27) total: 1m 29s remaining: 54.5s 28: learn: 0.0986119 test: 0.2250147 best: 0.2250147 (28) total: 1m 32s remaining: 50.9s 29: learn: 0.0956607 test: 0.2242976 best: 0.2242976 (29) total: 1m 35s remaining: 47.8s 30: learn: 0.0918244 test: 0.2235352 best: 0.2235352 (30) total: 1m 38s remaining: 44.5s 31: learn: 0.0893024 test: 0.2229120 best: 0.2229120 (31) total: 1m 41s remaining: 41.2s 32: learn: 0.0848291 test: 0.2233149 best: 0.2229120 (31) total: 1m 44s remaining: 38.1s 33: learn: 0.0831844 test: 0.2229017 best: 0.2229017 (33) total: 1m 47s remaining: 34.7s 34: learn: 0.0815398 test: 0.2226362 best: 0.2226362 (34) total: 1m 50s remaining: 31.5s 35: learn: 0.0790131 test: 0.2228296 best: 0.2226362 (34) total: 1m 53s remaining: 28.3s 36: learn: 0.0753729 test: 0.2231285 best: 0.2226362 (34) total: 1m 56s remaining: 25.2s 37: learn: 0.0734253 test: 0.2230945 best: 0.2226362 (34) total: 1m 59s remaining: 22s 38: learn: 0.0702069 test: 0.2231708 best: 0.2226362 (34) total: 2m 2s remaining: 18.8s 39: learn: 0.0689018 test: 0.2229141 best: 0.2226362 (34) total: 2m 5s remaining: 15.7s 40: learn: 0.0677969 test: 0.2225275 best: 0.2225275 (40) total: 2m 8s remaining: 12.6s 41: learn: 0.0663907 test: 0.2220228 best: 0.2220228 (41) total: 2m 12s remaining: 9.44s 42: learn: 0.0649653 test: 0.2216970 best: 0.2216970 (42) total: 2m 15s remaining: 6.28s 43: learn: 0.0634324 test: 0.2212573 best: 0.2212573 (43) total: 2m 18s remaining: 3.15s 44: learn: 0.0621476 test: 0.2211322 best: 0.2211322 (44) total: 2m 21s remaining: 0us bestTest = 0.2211322192 bestIteration = 44 Trial 14, Fold 5: Log loss = 0.22007899087043226, Average precision = 0.9700105951317912, ROC-AUC = 0.9646967281645393, Elapsed Time = 141.9878531999966 seconds
Optimization Progress: 15%|#5 | 15/100 [32:12<6:01:13, 254.98s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 15, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6728683 test: 0.6732393 best: 0.6732393 (0) total: 141ms remaining: 7.04s 1: learn: 0.6541275 test: 0.6545015 best: 0.6545015 (1) total: 287ms remaining: 7.02s 2: learn: 0.6368697 test: 0.6372832 best: 0.6372832 (2) total: 424ms remaining: 6.79s 3: learn: 0.6211260 test: 0.6216329 best: 0.6216329 (3) total: 553ms remaining: 6.49s 4: learn: 0.6054688 test: 0.6059944 best: 0.6059944 (4) total: 694ms remaining: 6.39s 5: learn: 0.5895789 test: 0.5901938 best: 0.5901938 (5) total: 824ms remaining: 6.18s 6: learn: 0.5751578 test: 0.5758150 best: 0.5758150 (6) total: 957ms remaining: 6.02s 7: learn: 0.5613624 test: 0.5621416 best: 0.5621416 (7) total: 1.1s remaining: 5.91s 8: learn: 0.5480040 test: 0.5487776 best: 0.5487776 (8) total: 1.25s remaining: 5.85s 9: learn: 0.5354522 test: 0.5363188 best: 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Optimization Progress: 16%|#6 | 16/100 [32:56<4:27:57, 191.40s/it]
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Optimization Progress: 17%|#7 | 17/100 [34:51<3:52:58, 168.42s/it]
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Optimization Progress: 18%|#8 | 18/100 [35:15<2:50:59, 125.12s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 18, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6674286 test: 0.6677229 best: 0.6677229 (0) total: 181ms remaining: 12.1s 1: learn: 0.6432510 test: 0.6438432 best: 0.6438432 (1) total: 350ms remaining: 11.6s 2: learn: 0.6203010 test: 0.6212118 best: 0.6212118 (2) total: 534ms remaining: 11.6s 3: learn: 0.5984492 test: 0.5997089 best: 0.5997089 (3) total: 750ms remaining: 12s 4: learn: 0.5778435 test: 0.5795152 best: 0.5795152 (4) total: 937ms remaining: 11.8s 5: learn: 0.5583987 test: 0.5604094 best: 0.5604094 (5) total: 1.13s remaining: 11.6s 6: learn: 0.5401218 test: 0.5423817 best: 0.5423817 (6) total: 1.3s remaining: 11.3s 7: learn: 0.5226471 test: 0.5252553 best: 0.5252553 (7) total: 1.47s remaining: 11s 8: learn: 0.5064308 test: 0.5093129 best: 0.5093129 (8) total: 1.64s remaining: 10.8s 9: learn: 0.4910992 test: 0.4942924 best: 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Optimization Progress: 19%|#9 | 19/100 [36:32<2:29:23, 110.66s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 19, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6493495 test: 0.6493227 best: 0.6493227 (0) total: 58.3ms remaining: 233ms 1: learn: 0.6100900 test: 0.6100392 best: 0.6100392 (1) total: 121ms remaining: 181ms 2: learn: 0.5817833 test: 0.5819830 best: 0.5819830 (2) total: 183ms remaining: 122ms 3: learn: 0.5564804 test: 0.5570457 best: 0.5570457 (3) total: 240ms remaining: 59.9ms 4: learn: 0.5284393 test: 0.5291573 best: 0.5291573 (4) total: 304ms remaining: 0us bestTest = 0.5291573093 bestIteration = 4 Trial 19, Fold 1: Log loss = 0.5294335510119113, Average precision = 0.9694654657745588, ROC-AUC = 0.9645724896402402, Elapsed Time = 0.4126544999999169 seconds Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 19, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.6500462 test: 0.6501067 best: 0.6501067 (0) total: 68.8ms remaining: 275ms 1: learn: 0.6176881 test: 0.6180616 best: 0.6180616 (1) total: 131ms remaining: 196ms 2: learn: 0.5876388 test: 0.5884129 best: 0.5884129 (2) total: 195ms remaining: 130ms 3: learn: 0.5575685 test: 0.5586698 best: 0.5586698 (3) total: 260ms remaining: 64.9ms 4: learn: 0.5335288 test: 0.5349131 best: 0.5349131 (4) total: 318ms remaining: 0us bestTest = 0.5349131433 bestIteration = 4 Trial 19, Fold 2: Log loss = 0.5350452831126273, Average precision = 0.9684881298278613, ROC-AUC = 0.9657373227623803, Elapsed Time = 0.4333470999990823 seconds Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 19, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.6551776 test: 0.6549643 best: 0.6549643 (0) total: 66.8ms remaining: 267ms 1: learn: 0.6220921 test: 0.6218435 best: 0.6218435 (1) total: 123ms remaining: 185ms 2: learn: 0.5891958 test: 0.5887209 best: 0.5887209 (2) total: 186ms remaining: 124ms 3: learn: 0.5634596 test: 0.5628272 best: 0.5628272 (3) total: 249ms remaining: 62.3ms 4: learn: 0.5362578 test: 0.5355707 best: 0.5355707 (4) total: 325ms remaining: 0us bestTest = 0.5355706597 bestIteration = 4 Trial 19, Fold 3: Log loss = 0.5358263510296953, Average precision = 0.9717330150833315, ROC-AUC = 0.968760759671967, Elapsed Time = 0.4338562000011734 seconds Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 19, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.6574934 test: 0.6576269 best: 0.6576269 (0) total: 63.8ms remaining: 255ms 1: learn: 0.6302521 test: 0.6307208 best: 0.6307208 (1) total: 128ms remaining: 192ms 2: learn: 0.5980759 test: 0.5988533 best: 0.5988533 (2) total: 200ms remaining: 133ms 3: learn: 0.5657710 test: 0.5667482 best: 0.5667482 (3) total: 269ms remaining: 67.2ms 4: learn: 0.5407174 test: 0.5421242 best: 0.5421242 (4) total: 341ms remaining: 0us bestTest = 0.5421241734 bestIteration = 4 Trial 19, Fold 4: Log loss = 0.5423984690305971, Average precision = 0.9679680371166938, ROC-AUC = 0.962191829939126, Elapsed Time = 0.45545209999909275 seconds Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 19, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.6552983 test: 0.6563531 best: 0.6563531 (0) total: 59.9ms remaining: 240ms 1: learn: 0.6182081 test: 0.6199267 best: 0.6199267 (1) total: 123ms remaining: 184ms 2: learn: 0.5863516 test: 0.5885258 best: 0.5885258 (2) total: 188ms remaining: 125ms 3: learn: 0.5575569 test: 0.5600240 best: 0.5600240 (3) total: 247ms remaining: 61.8ms 4: learn: 0.5298845 test: 0.5328504 best: 0.5328504 (4) total: 316ms remaining: 0us bestTest = 0.5328503903 bestIteration = 4 Trial 19, Fold 5: Log loss = 0.5331276533217285, Average precision = 0.9677386078861334, ROC-AUC = 0.9630175947085818, Elapsed Time = 0.42010640000080457 seconds
Optimization Progress: 20%|## | 20/100 [36:43<1:47:31, 80.65s/it]
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Optimization Progress: 21%|##1 | 21/100 [36:58<1:20:15, 60.95s/it]
Trial 20, Fold 5: Log loss = 0.26414094643409086, Average precision = 0.9619330790696504, ROC-AUC = 0.9570425282871634, Elapsed Time = 1.4874039000023913 seconds Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 21, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6052402 test: 0.6056813 best: 0.6056813 (0) total: 298ms remaining: 4.17s 1: learn: 0.5345421 test: 0.5357213 best: 0.5357213 (1) total: 680ms remaining: 4.42s 2: learn: 0.4777000 test: 0.4796167 best: 0.4796167 (2) total: 1.08s remaining: 4.32s 3: learn: 0.4332827 test: 0.4362740 best: 0.4362740 (3) total: 1.4s remaining: 3.85s 4: learn: 0.3946632 test: 0.3981686 best: 0.3981686 (4) total: 1.77s remaining: 3.54s 5: learn: 0.3648220 test: 0.3692311 best: 0.3692311 (5) total: 2.15s remaining: 3.22s 6: learn: 0.3402481 test: 0.3454375 best: 0.3454375 (6) total: 2.5s remaining: 2.86s 7: learn: 0.3204276 test: 0.3265295 best: 0.3265295 (7) 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Optimization Progress: 22%|##2 | 22/100 [37:31<1:08:23, 52.61s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 22, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6484288 test: 0.6485089 best: 0.6485089 (0) total: 125ms remaining: 873ms 1: learn: 0.6092857 test: 0.6096999 best: 0.6096999 (1) total: 254ms remaining: 762ms 2: learn: 0.5698356 test: 0.5699160 best: 0.5699160 (2) total: 398ms remaining: 663ms 3: learn: 0.5376881 test: 0.5379766 best: 0.5379766 (3) total: 537ms remaining: 537ms 4: learn: 0.5088473 test: 0.5095523 best: 0.5095523 (4) total: 665ms remaining: 399ms 5: learn: 0.4821093 test: 0.4829636 best: 0.4829636 (5) total: 810ms remaining: 270ms 6: learn: 0.4580630 test: 0.4592787 best: 0.4592787 (6) total: 947ms remaining: 135ms 7: learn: 0.4363021 test: 0.4376143 best: 0.4376143 (7) total: 1.1s remaining: 0us bestTest = 0.4376143381 bestIteration = 7 Trial 22, Fold 1: Log loss = 0.43821208600053174, Average precision = 0.9714780359988808, 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Optimization Progress: 23%|##3 | 23/100 [37:46<52:52, 41.20s/it]
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Optimization Progress: 24%|##4 | 24/100 [38:32<54:20, 42.90s/it]
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Optimization Progress: 25%|##5 | 25/100 [40:28<1:20:43, 64.58s/it]
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Optimization Progress: 26%|##6 | 26/100 [43:10<1:55:58, 94.04s/it]
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Elapsed Time = 7.5332304000003205 seconds
Optimization Progress: 27%|##7 | 27/100 [43:57<1:36:54, 79.65s/it]
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Trial 27, Fold 5: Log loss = 0.2094846198788979, Average precision = 0.9713292401149299, ROC-AUC = 0.9662394143767534, Elapsed Time = 153.3656974000005 seconds
Optimization Progress: 28%|##8 | 28/100 [57:08<5:51:48, 293.17s/it]
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Fold 5: Log loss = 0.34193552435926494, Average precision = 0.9387729437504191, ROC-AUC = 0.94084387715289, Elapsed Time = 1.1335534999998345 seconds
Optimization Progress: 29%|##9 | 29/100 [57:21<4:07:29, 209.14s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 29, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5234054 test: 0.5269370 best: 0.5269370 (0) total: 107ms remaining: 748ms 1: learn: 0.4077799 test: 0.4151052 best: 0.4151052 (1) total: 232ms remaining: 697ms 2: learn: 0.3443801 test: 0.3568744 best: 0.3568744 (2) total: 339ms remaining: 565ms 3: learn: 0.3011532 test: 0.3167597 best: 0.3167597 (3) total: 440ms remaining: 440ms 4: learn: 0.2689592 test: 0.2888302 best: 0.2888302 (4) total: 547ms remaining: 328ms 5: learn: 0.2463223 test: 0.2703737 best: 0.2703737 (5) total: 658ms remaining: 219ms 6: learn: 0.2249648 test: 0.2525461 best: 0.2525461 (6) total: 769ms remaining: 110ms 7: learn: 0.2132401 test: 0.2425724 best: 0.2425724 (7) total: 860ms remaining: 0us bestTest = 0.2425723814 bestIteration = 7 Trial 29, Fold 1: Log loss = 0.24257238140409293, Average precision = 0.9737298682606091, 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Optimization Progress: 30%|### | 30/100 [57:34<2:55:18, 150.26s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 30, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6297320 test: 0.6292425 best: 0.6292425 (0) total: 19.4ms remaining: 194ms 1: learn: 0.5775852 test: 0.5768352 best: 0.5768352 (1) total: 41.6ms remaining: 187ms 2: learn: 0.5356362 test: 0.5347636 best: 0.5347636 (2) total: 62.2ms remaining: 166ms 3: learn: 0.4875776 test: 0.4860527 best: 0.4860527 (3) total: 82ms remaining: 144ms 4: learn: 0.4577605 test: 0.4563919 best: 0.4563919 (4) total: 101ms remaining: 122ms 5: learn: 0.4222557 test: 0.4204229 best: 0.4204229 (5) total: 121ms remaining: 101ms 6: learn: 0.3943860 test: 0.3924508 best: 0.3924508 (6) total: 141ms remaining: 80.4ms 7: learn: 0.3704038 test: 0.3681564 best: 0.3681564 (7) total: 160ms remaining: 60.1ms 8: learn: 0.3524545 test: 0.3499191 best: 0.3499191 (8) total: 180ms remaining: 40ms 9: learn: 0.3451964 test: 0.3431423 best: 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Optimization Progress: 31%|###1 | 31/100 [57:43<2:04:04, 107.89s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 31, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6533743 test: 0.6532918 best: 0.6532918 (0) total: 65.6ms remaining: 328ms 1: learn: 0.6194632 test: 0.6194319 best: 0.6194319 (1) total: 141ms remaining: 281ms 2: learn: 0.5793730 test: 0.5792039 best: 0.5792039 (2) total: 218ms remaining: 218ms 3: learn: 0.5449760 test: 0.5449069 best: 0.5449069 (3) total: 287ms remaining: 143ms 4: learn: 0.5066023 test: 0.5071254 best: 0.5071254 (4) total: 376ms remaining: 75.3ms 5: learn: 0.4808199 test: 0.4816899 best: 0.4816899 (5) total: 458ms remaining: 0us bestTest = 0.4816899151 bestIteration = 5 Trial 31, Fold 1: Log loss = 0.48227684860666004, Average precision = 0.9616832003120429, ROC-AUC = 0.9561859535041558, Elapsed Time = 0.5688093000026129 seconds Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 31, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.6578868 test: 0.6581268 best: 0.6581268 (0) total: 52.7ms remaining: 264ms 1: learn: 0.6156270 test: 0.6157125 best: 0.6157125 (1) total: 129ms remaining: 259ms 2: learn: 0.5762515 test: 0.5768996 best: 0.5768996 (2) total: 210ms remaining: 210ms 3: learn: 0.5399041 test: 0.5419145 best: 0.5419145 (3) total: 293ms remaining: 146ms 4: learn: 0.5057528 test: 0.5078313 best: 0.5078313 (4) total: 378ms remaining: 75.6ms 5: learn: 0.4797256 test: 0.4821003 best: 0.4821003 (5) total: 451ms remaining: 0us bestTest = 0.4821002784 bestIteration = 5 Trial 31, Fold 2: Log loss = 0.4822819670587704, Average precision = 0.9604624298469059, ROC-AUC = 0.9572299625395416, Elapsed Time = 0.5691735000000335 seconds Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 31, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.6476212 test: 0.6477717 best: 0.6477717 (0) total: 69.2ms remaining: 346ms 1: learn: 0.6049135 test: 0.6047355 best: 0.6047355 (1) total: 148ms remaining: 297ms 2: learn: 0.5698312 test: 0.5693648 best: 0.5693648 (2) total: 218ms remaining: 218ms 3: learn: 0.5357541 test: 0.5349822 best: 0.5349822 (3) total: 308ms remaining: 154ms 4: learn: 0.5062721 test: 0.5053796 best: 0.5053796 (4) total: 380ms remaining: 76.1ms 5: learn: 0.4794568 test: 0.4780800 best: 0.4780800 (5) total: 473ms remaining: 0us bestTest = 0.4780800369 bestIteration = 5 Trial 31, Fold 3: Log loss = 0.47858746848210454, Average precision = 0.9611131940341558, ROC-AUC = 0.9581101140314551, Elapsed Time = 0.5772212000010768 seconds Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 31, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.6543290 test: 0.6545023 best: 0.6545023 (0) total: 52.1ms remaining: 261ms 1: learn: 0.6142547 test: 0.6148548 best: 0.6148548 (1) total: 124ms remaining: 249ms 2: learn: 0.5759854 test: 0.5767401 best: 0.5767401 (2) total: 198ms remaining: 198ms 3: learn: 0.5500581 test: 0.5508322 best: 0.5508322 (3) total: 277ms remaining: 139ms 4: learn: 0.5153094 test: 0.5161562 best: 0.5161562 (4) total: 361ms remaining: 72.3ms 5: learn: 0.4905766 test: 0.4915648 best: 0.4915648 (5) total: 443ms remaining: 0us bestTest = 0.4915648485 bestIteration = 5 Trial 31, Fold 4: Log loss = 0.49187389418534044, Average precision = 0.9610626872962896, ROC-AUC = 0.9565157424694378, Elapsed Time = 0.5466653000003134 seconds Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 31, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.6484769 test: 0.6489480 best: 0.6489480 (0) total: 69.6ms remaining: 348ms 1: learn: 0.6056154 test: 0.6070429 best: 0.6070429 (1) total: 151ms remaining: 303ms 2: learn: 0.5687554 test: 0.5708132 best: 0.5708132 (2) total: 220ms remaining: 220ms 3: learn: 0.5356215 test: 0.5382773 best: 0.5382773 (3) total: 311ms remaining: 155ms 4: learn: 0.5068299 test: 0.5097400 best: 0.5097400 (4) total: 384ms remaining: 76.8ms 5: learn: 0.4784862 test: 0.4822374 best: 0.4822374 (5) total: 477ms remaining: 0us bestTest = 0.4822374051 bestIteration = 5 Trial 31, Fold 5: Log loss = 0.4826240433436574, Average precision = 0.9592506933182632, ROC-AUC = 0.9545434479683407, Elapsed Time = 0.5817145000000892 seconds
Optimization Progress: 32%|###2 | 32/100 [57:53<1:29:08, 78.65s/it]
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Optimization Progress: 33%|###3 | 33/100 [58:09<1:06:41, 59.73s/it]
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Optimization Progress: 34%|###4 | 34/100 [1:00:07<1:24:54, 77.19s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 34, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5943476 test: 0.5990645 best: 0.5990645 (0) total: 190ms remaining: 4.38s 1: learn: 0.5212467 test: 0.5274094 best: 0.5274094 (1) total: 372ms remaining: 4.09s 2: learn: 0.4649574 test: 0.4731094 best: 0.4731094 (2) total: 549ms remaining: 3.84s 3: learn: 0.4104977 test: 0.4204379 best: 0.4204379 (3) total: 731ms remaining: 3.65s 4: learn: 0.3753823 test: 0.3892092 best: 0.3892092 (4) total: 919ms remaining: 3.49s 5: learn: 0.3437354 test: 0.3602125 best: 0.3602125 (5) total: 1.14s remaining: 3.41s 6: learn: 0.3154581 test: 0.3346817 best: 0.3346817 (6) total: 1.36s remaining: 3.3s 7: learn: 0.2891738 test: 0.3112655 best: 0.3112655 (7) total: 1.56s remaining: 3.12s 8: learn: 0.2699985 test: 0.2956367 best: 0.2956367 (8) total: 1.78s remaining: 2.97s 9: learn: 0.2515779 test: 0.2790994 best: 0.2790994 (9) total: 2.02s remaining: 2.83s 10: learn: 0.2377430 test: 0.2673821 best: 0.2673821 (10) total: 2.23s remaining: 2.64s 11: learn: 0.2264094 test: 0.2583536 best: 0.2583536 (11) total: 2.46s remaining: 2.46s 12: learn: 0.2164267 test: 0.2504224 best: 0.2504224 (12) total: 2.67s remaining: 2.26s 13: learn: 0.2069969 test: 0.2422374 best: 0.2422374 (13) total: 2.89s remaining: 2.06s 14: learn: 0.2290826 test: 0.2352201 best: 0.2352201 (14) total: 3.15s remaining: 1.89s 15: learn: 0.2556292 test: 0.2293058 best: 0.2293058 (15) total: 3.38s remaining: 1.69s
Training has stopped (degenerate solution on iteration 16, probably too small l2-regularization, try to increase it)
bestTest = 0.2293057997 bestIteration = 15 Shrink model to first 16 iterations. 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Training has stopped (degenerate solution on iteration 23, probably too small l2-regularization, try to increase it)
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Optimization Progress: 35%|###5 | 35/100 [1:00:41<1:09:45, 64.39s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 35, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5937483 test: 0.5966649 best: 0.5966649 (0) total: 68.9ms remaining: 2.48s 1: learn: 0.5094030 test: 0.5140879 best: 0.5140879 (1) total: 159ms remaining: 2.79s 2: learn: 0.4231109 test: 0.4316569 best: 0.4316569 (2) total: 246ms remaining: 2.79s 3: learn: 0.3738528 test: 0.3845591 best: 0.3845591 (3) total: 338ms remaining: 2.79s 4: learn: 0.3259108 test: 0.3385297 best: 0.3385297 (4) total: 429ms remaining: 2.75s 5: learn: 0.2980231 test: 0.3122958 best: 0.3122958 (5) total: 525ms remaining: 2.71s 6: learn: 0.2767767 test: 0.2931342 best: 0.2931342 (6) total: 617ms remaining: 2.65s 7: learn: 0.2615518 test: 0.2782832 best: 0.2782832 (7) total: 708ms remaining: 2.57s 8: learn: 0.2455048 test: 0.2639997 best: 0.2639997 (8) total: 803ms remaining: 2.5s 9: learn: 0.2335749 test: 0.2541992 best: 0.2541992 (9) total: 893ms remaining: 2.41s 10: learn: 0.2224435 test: 0.2445282 best: 0.2445282 (10) total: 986ms remaining: 2.33s 11: learn: 0.2106635 test: 0.2357523 best: 0.2357523 (11) total: 1.08s remaining: 2.25s 12: learn: 0.2048203 test: 0.2323197 best: 0.2323197 (12) total: 1.17s remaining: 2.16s 13: learn: 0.2001978 test: 0.2293196 best: 0.2293196 (13) total: 1.26s remaining: 2.06s 14: learn: 0.1952103 test: 0.2261645 best: 0.2261645 (14) total: 1.35s remaining: 1.99s 15: learn: 0.1919123 test: 0.2241957 best: 0.2241957 (15) total: 1.45s remaining: 1.9s 16: learn: 0.1864241 test: 0.2207171 best: 0.2207171 (16) total: 1.55s remaining: 1.82s 17: learn: 0.1832486 test: 0.2189516 best: 0.2189516 (17) total: 1.63s remaining: 1.72s 18: learn: 0.1799431 test: 0.2188193 best: 0.2188193 (18) total: 1.73s remaining: 1.64s 19: learn: 0.1784047 test: 0.2179312 best: 0.2179312 (19) total: 1.82s remaining: 1.54s 20: learn: 0.1746889 test: 0.2170770 best: 0.2170770 (20) total: 1.91s remaining: 1.46s 21: learn: 0.1727769 test: 0.2161512 best: 0.2161512 (21) total: 2s remaining: 1.36s 22: learn: 0.1700697 test: 0.2146060 best: 0.2146060 (22) total: 2.09s remaining: 1.27s 23: learn: 0.1679370 test: 0.2156218 best: 0.2146060 (22) total: 2.18s remaining: 1.18s 24: learn: 0.1653224 test: 0.2138320 best: 0.2138320 (24) total: 2.27s remaining: 1.09s 25: learn: 0.1634993 test: 0.2136834 best: 0.2136834 (25) total: 2.36s remaining: 997ms 26: learn: 0.1613185 test: 0.2129319 best: 0.2129319 (26) total: 2.44s remaining: 906ms 27: learn: 0.1599076 test: 0.2121355 best: 0.2121355 (27) total: 2.54s remaining: 816ms 28: learn: 0.1579754 test: 0.2122690 best: 0.2121355 (27) total: 2.63s remaining: 724ms 29: learn: 0.1566730 test: 0.2118358 best: 0.2118358 (29) total: 2.73s remaining: 636ms 30: learn: 0.1548380 test: 0.2115831 best: 0.2115831 (30) total: 2.84s remaining: 549ms 31: learn: 0.1541379 test: 0.2110852 best: 0.2110852 (31) total: 2.89s remaining: 451ms 32: learn: 0.1530239 test: 0.2109044 best: 0.2109044 (32) total: 2.99s remaining: 362ms 33: learn: 0.1521723 test: 0.2104875 best: 0.2104875 (33) total: 3.08s remaining: 272ms 34: learn: 0.1505012 test: 0.2108399 best: 0.2104875 (33) total: 3.17s remaining: 181ms 35: learn: 0.1484048 test: 0.2110630 best: 0.2104875 (33) total: 3.26s remaining: 90.6ms 36: learn: 0.1475744 test: 0.2110590 best: 0.2104875 (33) total: 3.35s remaining: 0us bestTest = 0.210487509 bestIteration = 33 Shrink model to first 34 iterations. 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Trial 35, Fold 5: Log loss = 0.2133493554362293, Average precision = 0.9701531810737392, ROC-AUC = 0.9649087564795719, Elapsed Time = 3.324585100002878 seconds
Optimization Progress: 36%|###6 | 36/100 [1:01:06<55:57, 52.46s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 36, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6335414 test: 0.6332409 best: 0.6332409 (0) total: 32.2ms remaining: 967ms 1: learn: 0.5829026 test: 0.5825127 best: 0.5825127 (1) total: 63.4ms remaining: 919ms 2: learn: 0.5379638 test: 0.5374102 best: 0.5374102 (2) total: 96.1ms remaining: 897ms 3: learn: 0.4991890 test: 0.4987643 best: 0.4987643 (3) total: 128ms remaining: 867ms 4: learn: 0.4664064 test: 0.4658071 best: 0.4658071 (4) total: 161ms remaining: 835ms 5: learn: 0.4379235 test: 0.4373468 best: 0.4373468 (5) total: 192ms remaining: 801ms 6: learn: 0.4127562 test: 0.4121545 best: 0.4121545 (6) total: 225ms remaining: 771ms 7: learn: 0.3913927 test: 0.3907187 best: 0.3907187 (7) total: 256ms remaining: 736ms 8: learn: 0.3734834 test: 0.3729463 best: 0.3729463 (8) total: 292ms remaining: 715ms 9: learn: 0.3591529 test: 0.3586467 best: 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Optimization Progress: 37%|###7 | 37/100 [1:01:19<42:49, 40.79s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 37, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6695437 test: 0.6695171 best: 0.6695171 (0) total: 588ms remaining: 53.5s 1: learn: 0.6472349 test: 0.6472905 best: 0.6472905 (1) total: 1.17s remaining: 52.6s 2: learn: 0.6259377 test: 0.6260614 best: 0.6260614 (2) total: 1.68s remaining: 49.8s 3: learn: 0.6075096 test: 0.6077917 best: 0.6077917 (3) total: 2.25s remaining: 49.5s 4: learn: 0.5904155 test: 0.5908382 best: 0.5908382 (4) total: 2.74s remaining: 47.7s 5: learn: 0.5738991 test: 0.5744462 best: 0.5744462 (5) total: 3.17s remaining: 45.4s 6: learn: 0.5569791 test: 0.5577504 best: 0.5577504 (6) total: 3.69s remaining: 44.8s 7: learn: 0.5423353 test: 0.5433830 best: 0.5433830 (7) total: 4.25s remaining: 44.7s 8: learn: 0.5262154 test: 0.5275270 best: 0.5275270 (8) total: 4.81s remaining: 44.3s 9: learn: 0.5117036 test: 0.5131838 best: 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Optimization Progress: 38%|###8 | 38/100 [1:05:16<1:42:49, 99.52s/it]
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Optimization Progress: 39%|###9 | 39/100 [1:05:48<1:20:41, 79.37s/it]
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Trial 39, Fold 5: Log loss = 0.20273699574091272, Average precision = 0.9736236803525101, ROC-AUC = 0.9700538431525556, Elapsed Time = 18.862699500001327 seconds
Optimization Progress: 40%|#### | 40/100 [1:07:32<1:26:43, 86.72s/it]
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Optimization Progress: 41%|####1 | 41/100 [1:08:12<1:11:28, 72.69s/it]
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Optimization Progress: 42%|####2 | 42/100 [1:08:28<53:51, 55.72s/it]
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Optimization Progress: 43%|####3 | 43/100 [1:10:42<1:15:10, 79.14s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 43, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5460660 test: 0.5450925 best: 0.5450925 (0) total: 39.8ms remaining: 358ms 1: learn: 0.4601231 test: 0.4600283 best: 0.4600283 (1) total: 76.4ms remaining: 306ms 2: learn: 0.3965213 test: 0.3962833 best: 0.3962833 (2) total: 113ms remaining: 263ms 3: learn: 0.3598688 test: 0.3600733 best: 0.3600733 (3) total: 148ms remaining: 222ms 4: learn: 0.3338137 test: 0.3344572 best: 0.3344572 (4) total: 183ms remaining: 183ms 5: learn: 0.3068850 test: 0.3084256 best: 0.3084256 (5) total: 220ms remaining: 147ms 6: learn: 0.2888460 test: 0.2912281 best: 0.2912281 (6) total: 258ms remaining: 111ms 7: learn: 0.2724850 test: 0.2757664 best: 0.2757664 (7) total: 295ms remaining: 73.7ms 8: learn: 0.2610050 test: 0.2656276 best: 0.2656276 (8) total: 332ms remaining: 36.9ms 9: learn: 0.2543256 test: 0.2595717 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Optimization Progress: 44%|####4 | 44/100 [1:10:52<54:36, 58.51s/it]
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Trial 44, Fold 5: Log loss = 0.19910171185297648, Average precision = 0.9742595684718001, ROC-AUC = 0.9715143340207718, Elapsed Time = 9.025208800001565 seconds
Optimization Progress: 45%|####5 | 45/100 [1:11:47<52:33, 57.34s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 45, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6197849 test: 0.6218831 best: 0.6218831 (0) total: 217ms remaining: 2.38s 1: learn: 0.5579534 test: 0.5607020 best: 0.5607020 (1) total: 405ms remaining: 2.02s 2: learn: 0.5075304 test: 0.5106510 best: 0.5106510 (2) total: 612ms remaining: 1.83s 3: learn: 0.4645591 test: 0.4732353 best: 0.4732353 (3) total: 876ms remaining: 1.75s 4: learn: 0.4320801 test: 0.4419082 best: 0.4419082 (4) total: 1.11s remaining: 1.56s 5: learn: 0.4042108 test: 0.4144016 best: 0.4144016 (5) total: 1.38s remaining: 1.38s 6: learn: 0.3821669 test: 0.3935248 best: 0.3935248 (6) total: 1.6s remaining: 1.14s 7: learn: 0.3602152 test: 0.3720033 best: 0.3720033 (7) total: 1.83s remaining: 916ms 8: learn: 0.3420685 test: 0.3547263 best: 0.3547263 (8) total: 2.06s remaining: 686ms 9: learn: 0.3256564 test: 0.3396126 best: 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Optimization Progress: 46%|####6 | 46/100 [1:12:08<41:52, 46.54s/it]
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Optimization Progress: 47%|####6 | 47/100 [1:12:24<32:52, 37.22s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 47, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5445765 test: 0.5485984 best: 0.5485984 (0) total: 178ms remaining: 15.8s 1: learn: 0.4299264 test: 0.4366798 best: 0.4366798 (1) total: 349ms remaining: 15.3s 2: learn: 0.3603643 test: 0.3724235 best: 0.3724235 (2) total: 546ms remaining: 15.8s 3: learn: 0.3140387 test: 0.3300967 best: 0.3300967 (3) total: 730ms remaining: 15.7s 4: learn: 0.2775221 test: 0.2979354 best: 0.2979354 (4) total: 930ms remaining: 15.8s 5: learn: 0.2458362 test: 0.2704039 best: 0.2704039 (5) total: 1.14s remaining: 16s 6: learn: 0.2238562 test: 0.2528266 best: 0.2528266 (6) total: 1.36s remaining: 16.2s 7: learn: 0.2070770 test: 0.2402302 best: 0.2402302 (7) total: 1.56s remaining: 16s 8: learn: 0.1927852 test: 0.2290207 best: 0.2290207 (8) total: 1.74s remaining: 15.7s 9: learn: 0.1828432 test: 0.2224468 best: 0.2224468 (9) total: 1.92s remaining: 15.3s 10: learn: 0.1755071 test: 0.2187629 best: 0.2187629 (10) total: 2.09s remaining: 15s 11: learn: 2121.9126702 test: 2464.0379721 best: 0.2187629 (10) total: 2.3s remaining: 14.9s 12: learn: 99990.2005741 test: 73489.1326533 best: 0.2187629 (10) total: 2.48s remaining: 14.7s 13: learn: 223380.4554914 test: 168834.0762833 best: 0.2187629 (10) total: 2.68s remaining: 14.5s 14: learn: 260895.6906733 test: 193329.0233095 best: 0.2187629 (10) total: 2.86s remaining: 14.3s 15: learn: 387161.3639864 test: 281032.0530646 best: 0.2187629 (10) total: 3.02s remaining: 13.9s 16: learn: 407549.0252662 test: 293471.4635057 best: 0.2187629 (10) total: 3.2s remaining: 13.8s 17: learn: 508876.8258082 test: 363575.5100022 best: 0.2187629 (10) total: 3.34s remaining: 13.4s 18: learn: 615532.8003254 test: 438996.4287909 best: 0.2187629 (10) total: 3.5s remaining: 13.1s
Training has stopped (degenerate solution on iteration 19, probably too small l2-regularization, try to increase it)
bestTest = 0.2187628502 bestIteration = 10 Shrink model to first 11 iterations. Trial 47, Fold 1: Log loss = 0.2188074000879337, Average precision = 0.9724157484850913, ROC-AUC = 0.9689162400883268, Elapsed Time = 3.7517687999970804 seconds Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 47, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.5411664 test: 0.5493164 best: 0.5493164 (0) total: 194ms remaining: 17.2s 1: learn: 0.4199534 test: 0.4324356 best: 0.4324356 (1) total: 384ms remaining: 16.9s 2: learn: 0.3558354 test: 0.3695494 best: 0.3695494 (2) total: 562ms remaining: 16.3s 3: learn: 0.3040169 test: 0.3213347 best: 0.3213347 (3) total: 763ms remaining: 16.4s 4: learn: 0.2637208 test: 0.2824708 best: 0.2824708 (4) total: 973ms remaining: 16.5s 5: learn: 0.2346057 test: 0.2591154 best: 0.2591154 (5) total: 1.2s remaining: 16.8s 6: learn: 0.2126006 test: 0.2400408 best: 0.2400408 (6) total: 1.42s remaining: 16.9s 7: learn: 0.1914403 test: 0.2231867 best: 0.2231867 (7) total: 1.65s remaining: 16.9s 8: learn: 383.5799923 test: 0.2150845 best: 0.2150845 (8) total: 1.86s remaining: 16.7s 9: learn: 317.9185685 test: 0.2096493 best: 0.2096493 (9) total: 2.05s remaining: 16.4s 10: learn: 317.9114271 test: 0.2045151 best: 0.2045151 (10) total: 2.23s remaining: 16s 11: learn: 317.9049357 test: 0.2000909 best: 0.2000909 (11) total: 2.41s remaining: 15.6s 12: learn: 317.8985681 test: 0.1972696 best: 0.1972696 (12) total: 2.58s remaining: 15.3s 13: learn: 1856.2323625 test: 0.1941526 best: 0.1941526 (13) total: 2.77s remaining: 15.1s 14: learn: 1856.2265767 test: 0.1928957 best: 0.1928957 (14) total: 2.92s remaining: 14.6s 15: learn: 1790.5560957 test: 0.1909503 best: 0.1909503 (15) total: 3.09s remaining: 14.3s 16: learn: 1790.5490639 test: 0.1900652 best: 0.1900652 (16) total: 3.27s remaining: 14s 17: learn: 1790.5424898 test: 0.1897081 best: 0.1897081 (17) total: 3.43s remaining: 13.7s 18: learn: 3659.0168157 test: 3543.8937189 best: 0.1897081 (17) total: 3.61s remaining: 13.5s 19: learn: 3659.0079558 test: 3543.8862557 best: 0.1897081 (17) total: 3.73s remaining: 13.1s 20: learn: 4172.1539862 test: 3543.8780847 best: 0.1897081 (17) total: 3.88s remaining: 12.8s 21: learn: 27389.8183839 test: 32912.1852086 best: 0.1897081 (17) total: 4.07s remaining: 12.6s 22: learn: 210606.1368468 test: 314248.7028504 best: 0.1897081 (17) total: 4.2s remaining: 12.2s 23: learn: 444161.2545133 test: 625601.7477575 best: 0.1897081 (17) total: 4.33s remaining: 11.9s 24: learn: 655769.7163478 test: 860040.9578908 best: 0.1897081 (17) total: 4.48s remaining: 11.6s 25: learn: 962552.0428314 test: 1202318.1205499 best: 0.1897081 (17) total: 4.62s remaining: 11.4s 26: learn: 1189393.9362534 test: 1523284.3560800 best: 0.1897081 (17) total: 4.74s remaining: 11.1s bestTest = 0.189708114 bestIteration = 17 Shrink model to first 18 iterations.
Training has stopped (degenerate solution on iteration 27, probably too small l2-regularization, try to increase it)
Trial 47, Fold 2: Log loss = 0.18946390033828614, Average precision = 0.9756765924089011, ROC-AUC = 0.9731557408766955, Elapsed Time = 5.012145599997893 seconds Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 47, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.5266539 test: 0.5306859 best: 0.5306859 (0) total: 197ms remaining: 17.6s 1: learn: 0.4245679 test: 0.4309326 best: 0.4309326 (1) total: 387ms remaining: 17s 2: learn: 0.3616663 test: 0.3715064 best: 0.3715064 (2) total: 590ms remaining: 17.1s 3: learn: 0.3050630 test: 0.3182559 best: 0.3182559 (3) total: 772ms remaining: 16.6s 4: learn: 0.2679481 test: 0.2867087 best: 0.2867087 (4) total: 991ms remaining: 16.8s 5: learn: 0.2376870 test: 0.2601645 best: 0.2601645 (5) total: 1.18s remaining: 16.6s 6: learn: 0.2151076 test: 0.2417223 best: 0.2417223 (6) total: 1.43s remaining: 17s 7: learn: 0.1978812 test: 0.2286708 best: 0.2286708 (7) total: 1.66s remaining: 17s 8: learn: 383.2880893 test: 0.2192734 best: 0.2192734 (8) total: 1.86s remaining: 16.7s 9: learn: 3047.3132981 test: 699.8858606 best: 0.2192734 (8) total: 2.09s remaining: 16.7s 10: learn: 3372.1361871 test: 700.7840286 best: 0.2192734 (8) total: 2.31s remaining: 16.6s 11: learn: 3883.6656152 test: 700.7783552 best: 0.2192734 (8) total: 2.54s remaining: 16.5s 12: learn: 5300.3400875 test: 700.7733421 best: 0.2192734 (8) total: 2.79s remaining: 16.5s 13: learn: 6194.9683125 test: 700.7706197 best: 0.2192734 (8) total: 3.04s remaining: 16.5s 14: learn: 6706.4957220 test: 700.7685803 best: 0.2192734 (8) total: 3.23s remaining: 16.2s 15: learn: 7218.0215380 test: 700.7659895 best: 0.2192734 (8) total: 3.46s remaining: 16s 16: learn: 8209.9517439 test: 700.7635380 best: 0.2192734 (8) total: 3.65s remaining: 15.7s 17: learn: 8701.4602152 test: 700.7614157 best: 0.2192734 (8) total: 3.86s remaining: 15.4s 18: learn: 9212.9823914 test: 700.7592509 best: 0.2192734 (8) total: 4.07s remaining: 15.2s 19: learn: 10560.3659693 test: 6105.0388158 best: 0.2192734 (8) total: 4.26s remaining: 14.9s 20: learn: 21333.8865498 test: 6105.0258843 best: 0.2192734 (8) total: 4.48s remaining: 14.7s 21: learn: 28055.2963502 test: 9160.3869512 best: 0.2192734 (8) total: 4.77s remaining: 14.7s 22: learn: 42248.2567659 test: 9160.3676051 best: 0.2192734 (8) total: 4.92s remaining: 14.3s 23: learn: 46712.3498100 test: 9160.3482430 best: 0.2192734 (8) total: 5.12s remaining: 14.1s 24: learn: 53538.5796675 test: 9160.3289761 best: 0.2192734 (8) total: 5.24s remaining: 13.6s 25: learn: 53206.2414700 test: 9160.3093842 best: 0.2192734 (8) total: 5.41s remaining: 13.3s 26: learn: 72000.6733033 test: 12755.4488043 best: 0.2192734 (8) total: 5.63s remaining: 13.1s 27: learn: 326051.8507325 test: 183113.8079437 best: 0.2192734 (8) total: 5.83s remaining: 12.9s 28: learn: 593451.4072636 test: 366527.7465668 best: 0.2192734 (8) total: 5.98s remaining: 12.6s 29: learn: 817086.5939836 test: 470468.9057101 best: 0.2192734 (8) total: 6.12s remaining: 12.2s 30: learn: 1094828.7866171 test: 653624.9451635 best: 0.2192734 (8) total: 6.28s remaining: 11.9s 31: learn: 1255571.4823463 test: 776938.1392473 best: 0.2192734 (8) total: 6.43s remaining: 11.6s 32: learn: 1248765.6190287 test: 779089.1767183 best: 0.2192734 (8) total: 6.55s remaining: 11.3s 33: learn: 1255771.6931545 test: 779087.5365427 best: 0.2192734 (8) total: 6.69s remaining: 11s bestTest = 0.2192733625 bestIteration = 8 Shrink model to first 9 iterations.
Training has stopped (degenerate solution on iteration 34, probably too small l2-regularization, try to increase it)
Trial 47, Fold 3: Log loss = 0.2196358003113448, Average precision = 0.9733511790412094, ROC-AUC = 0.9703974336903237, Elapsed Time = 6.92220449999877 seconds Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 47, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.5446024 test: 0.5477560 best: 0.5477560 (0) total: 188ms remaining: 16.7s 1: learn: 0.4438218 test: 0.4490892 best: 0.4490892 (1) total: 364ms remaining: 16s 2: learn: 0.3765651 test: 0.3852653 best: 0.3852653 (2) total: 577ms remaining: 16.7s 3: learn: 0.3167976 test: 0.3304497 best: 0.3304497 (3) total: 789ms remaining: 17s 4: learn: 0.2814704 test: 0.2979695 best: 0.2979695 (4) total: 989ms remaining: 16.8s 5: learn: 0.2550445 test: 0.2745692 best: 0.2745692 (5) total: 1.2s remaining: 16.9s 6: learn: 0.2331345 test: 0.2565067 best: 0.2565067 (6) total: 1.42s remaining: 16.8s 7: learn: 0.2157695 test: 0.2446878 best: 0.2446878 (7) total: 1.64s remaining: 16.8s 8: learn: 0.1981667 test: 0.2314778 best: 0.2314778 (8) total: 1.88s remaining: 16.9s 9: learn: 514.4968286 test: 4100.8092724 best: 0.2314778 (8) total: 2.12s remaining: 16.9s 10: learn: 514.4847062 test: 4100.7917005 best: 0.2314778 (8) total: 2.34s remaining: 16.8s 11: learn: 514.4754040 test: 4100.7778090 best: 0.2314778 (8) total: 2.54s remaining: 16.5s 12: learn: 514.4679927 test: 4100.7655998 best: 0.2314778 (8) total: 2.73s remaining: 16.2s 13: learn: 3691.1423801 test: 5367.1329394 best: 0.2314778 (8) total: 2.94s remaining: 15.9s 14: learn: 70433.9179269 test: 57337.6110917 best: 0.2314778 (8) total: 3.14s remaining: 15.7s 15: learn: 125913.4071238 test: 98626.1461615 best: 0.2314778 (8) total: 3.33s remaining: 15.4s 16: learn: 717217.9178473 test: 399966.5941299 best: 0.2314778 (8) total: 3.5s remaining: 15s 17: learn: 2834768.3175421 test: 1842874.8236286 best: 0.2314778 (8) total: 3.67s remaining: 14.7s 18: learn: 3235163.9187140 test: 2481513.7720534 best: 0.2314778 (8) total: 3.83s remaining: 14.3s bestTest = 0.2314777689 bestIteration = 8 Shrink model to first 9 iterations.
Training has stopped (degenerate solution on iteration 19, probably too small l2-regularization, try to increase it)
Trial 47, Fold 4: Log loss = 0.23151441709257464, Average precision = 0.9722849244760045, ROC-AUC = 0.9682106018011831, Elapsed Time = 4.109465400000772 seconds Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 47, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5321272 test: 0.5373220 best: 0.5373220 (0) total: 213ms remaining: 19s 1: learn: 0.4259160 test: 0.4411418 best: 0.4411418 (1) total: 422ms remaining: 18.6s 2: learn: 0.3580407 test: 0.3762831 best: 0.3762831 (2) total: 615ms remaining: 17.8s 3: learn: 0.3082562 test: 0.3344205 best: 0.3344205 (3) total: 834ms remaining: 17.9s 4: learn: 0.2684827 test: 0.2994503 best: 0.2994503 (4) total: 1.04s remaining: 17.7s 5: learn: 0.2382337 test: 0.2723594 best: 0.2723594 (5) total: 1.29s remaining: 18.1s 6: learn: 0.2164230 test: 0.2542840 best: 0.2542840 (6) total: 1.55s remaining: 18.4s 7: learn: 0.2022145 test: 0.2425345 best: 0.2425345 (7) total: 1.79s remaining: 18.3s 8: learn: 383.8920718 test: 1527.3730288 best: 0.2425345 (7) total: 2.05s remaining: 18.5s 9: learn: 317.3408870 test: 1527.3610993 best: 0.2425345 (7) total: 2.31s remaining: 18.5s 10: learn: 683.2890259 test: 1527.3528918 best: 0.2425345 (7) total: 2.55s remaining: 18.3s 11: learn: 6643.6088200 test: 4593.9107728 best: 0.2425345 (7) total: 2.77s remaining: 18s 12: learn: 12553.5765119 test: 14665.9662048 best: 0.2425345 (7) total: 2.98s remaining: 17.7s 13: learn: 24481.3886227 test: 35842.3177287 best: 0.2425345 (7) total: 3.21s remaining: 17.4s 14: learn: 1842301.5905066 test: 1648623.6895771 best: 0.2425345 (7) total: 3.41s remaining: 17s 15: learn: 3639567.5663221 test: 3064742.6491588 best: 0.2425345 (7) total: 3.63s remaining: 16.8s 16: learn: 5441478.6428118 test: 4930257.0877915 best: 0.2425345 (7) total: 3.85s remaining: 16.5s
Training has stopped (degenerate solution on iteration 17, probably too small l2-regularization, try to increase it)
bestTest = 0.2425344631 bestIteration = 7 Shrink model to first 8 iterations. Trial 47, Fold 5: Log loss = 0.242535836249544, Average precision = 0.9710069158723872, ROC-AUC = 0.9665398807201383, Elapsed Time = 4.201541700000234 seconds
Optimization Progress: 48%|####8 | 48/100 [1:12:56<30:53, 35.65s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 48, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6792664 test: 0.6794444 best: 0.6794444 (0) total: 204ms remaining: 14.7s 1: learn: 0.6658175 test: 0.6661715 best: 0.6661715 (1) total: 410ms remaining: 14.5s 2: learn: 0.6526446 test: 0.6531355 best: 0.6531355 (2) total: 637ms remaining: 14.9s 3: learn: 0.6399495 test: 0.6406199 best: 0.6406199 (3) total: 832ms remaining: 14.3s 4: learn: 0.6276409 test: 0.6284870 best: 0.6284870 (4) total: 1.03s remaining: 14s 5: learn: 0.6157320 test: 0.6167990 best: 0.6167990 (5) total: 1.24s remaining: 13.9s 6: learn: 0.6039515 test: 0.6052771 best: 0.6052771 (6) total: 1.48s remaining: 14s 7: learn: 0.5926401 test: 0.5941191 best: 0.5941191 (7) total: 1.7s remaining: 13.8s 8: learn: 0.5815731 test: 0.5832748 best: 0.5832748 (8) total: 1.9s remaining: 13.5s 9: learn: 0.5709932 test: 0.5728782 best: 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Optimization Progress: 49%|####9 | 49/100 [1:14:29<44:50, 52.75s/it]
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Optimization Progress: 50%|##### | 50/100 [1:15:30<46:09, 55.38s/it]
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Optimization Progress: 51%|#####1 | 51/100 [1:16:43<49:39, 60.80s/it]
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Optimization Progress: 52%|#####2 | 52/100 [1:17:16<41:50, 52.30s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 52, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6007363 test: 0.6117303 best: 0.6117303 (0) total: 214ms remaining: 16.7s 1: learn: 0.5330657 test: 0.5516216 best: 0.5516216 (1) total: 419ms remaining: 16.1s 2: learn: 0.4784990 test: 0.5018970 best: 0.5018970 (2) total: 632ms remaining: 16s 3: learn: 0.4336885 test: 0.4603398 best: 0.4603398 (3) total: 870ms remaining: 16.3s 4: learn: 0.3999728 test: 0.4251496 best: 0.4251496 (4) total: 1.09s remaining: 16.2s 5: learn: 0.3708225 test: 0.3945938 best: 0.3945938 (5) total: 1.31s remaining: 15.9s 6: learn: 0.3449674 test: 0.3676794 best: 0.3676794 (6) total: 1.55s remaining: 16s 7: learn: 0.3259138 test: 0.3471731 best: 0.3471731 (7) total: 1.84s remaining: 16.3s 8: learn: 0.3107728 test: 0.3327771 best: 0.3327771 (8) total: 2.1s remaining: 16.4s 9: learn: 0.2937127 test: 0.3161216 best: 0.3161216 (9) total: 2.39s remaining: 16.5s 10: learn: 0.2803343 test: 0.3027993 best: 0.3027993 (10) total: 2.68s remaining: 16.6s 11: learn: 0.2659738 test: 0.2892195 best: 0.2892195 (11) total: 2.95s remaining: 16.5s 12: learn: 0.2544560 test: 0.2788092 best: 0.2788092 (12) total: 3.24s remaining: 16.4s 13: learn: 0.2452722 test: 0.2700145 best: 0.2700145 (13) total: 3.55s remaining: 16.5s 14: learn: 0.2367518 test: 0.2621928 best: 0.2621928 (14) total: 3.86s remaining: 16.5s 15: learn: 0.2301217 test: 0.2564757 best: 0.2564757 (15) total: 4.11s remaining: 16.2s 16: learn: 0.2237138 test: 0.2506024 best: 0.2506024 (16) total: 4.36s remaining: 15.9s 17: learn: 0.2144302 test: 0.2424323 best: 0.2424323 (17) total: 4.66s remaining: 15.8s 18: learn: 0.2064025 test: 0.2355595 best: 0.2355595 (18) total: 4.98s remaining: 15.7s 19: learn: 0.2020122 test: 0.2320539 best: 0.2320539 (19) total: 5.24s remaining: 15.5s 20: learn: 0.1975701 test: 0.2286192 best: 0.2286192 (20) total: 5.51s remaining: 15.2s 21: learn: 0.1939508 test: 0.2260034 best: 0.2260034 (21) total: 5.8s remaining: 15s 22: learn: 15.2768573 test: 0.2219228 best: 0.2219228 (22) total: 6.06s remaining: 14.8s 23: learn: 12.1099148 test: 0.2191927 best: 0.2191927 (23) total: 6.36s remaining: 14.6s 24: learn: 12.1067310 test: 0.2173214 best: 0.2173214 (24) total: 6.67s remaining: 14.4s 25: learn: 12.1032913 test: 0.2151956 best: 0.2151956 (25) total: 6.95s remaining: 14.2s 26: learn: 12.1000974 test: 0.2130473 best: 0.2130473 (26) total: 7.22s remaining: 13.9s 27: learn: 33.4793361 test: 0.2108273 best: 0.2108273 (27) total: 7.47s remaining: 13.6s 28: learn: 30.3142507 test: 12.3503368 best: 0.2108273 (27) total: 7.75s remaining: 13.4s 29: learn: 30.3121085 test: 12.3491773 best: 0.2108273 (27) total: 8.01s remaining: 13.1s 30: learn: 30.3099687 test: 12.3483104 best: 0.2108273 (27) total: 8.27s remaining: 12.8s 31: learn: 54.2351964 test: 298.8604287 best: 0.2108273 (27) total: 8.54s remaining: 12.5s 32: learn: 706.8792712 test: 1529.0069553 best: 0.2108273 (27) total: 8.8s remaining: 12.3s
Training has stopped (degenerate solution on iteration 33, probably too small l2-regularization, try to increase it)
bestTest = 0.2108272576 bestIteration = 27 Shrink model to first 28 iterations. Trial 52, Fold 1: Log loss = 0.21069644814043154, Average precision = 0.9717256097227264, ROC-AUC = 0.9694559602672747, Elapsed Time = 9.182990499997686 seconds Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 52, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.6111835 test: 0.6226610 best: 0.6226610 (0) total: 218ms remaining: 17s 1: learn: 0.5394007 test: 0.5589982 best: 0.5589982 (1) total: 442ms remaining: 17s 2: learn: 0.4878466 test: 0.5128214 best: 0.5128214 (2) total: 690ms remaining: 17.5s 3: learn: 0.4439685 test: 0.4699998 best: 0.4699998 (3) total: 959ms remaining: 18s 4: learn: 0.4043180 test: 0.4323217 best: 0.4323217 (4) total: 1.23s remaining: 18.1s 5: learn: 0.3712810 test: 0.3995021 best: 0.3995021 (5) total: 1.48s remaining: 18s 6: learn: 0.3457900 test: 0.3719430 best: 0.3719430 (6) total: 1.73s remaining: 17.8s 7: learn: 0.3252993 test: 0.3516343 best: 0.3516343 (7) total: 2.03s remaining: 18s 8: learn: 0.3061276 test: 0.3321531 best: 0.3321531 (8) total: 2.3s remaining: 17.9s 9: learn: 0.2908269 test: 0.3159480 best: 0.3159480 (9) total: 2.56s remaining: 17.7s 10: learn: 0.2763067 test: 0.3005335 best: 0.3005335 (10) total: 2.85s remaining: 17.6s 11: learn: 0.2641114 test: 0.2892182 best: 0.2892182 (11) total: 3.14s remaining: 17.5s 12: learn: 0.2537885 test: 0.2786214 best: 0.2786214 (12) total: 3.39s remaining: 17.2s 13: learn: 0.2433435 test: 0.2680905 best: 0.2680905 (13) total: 3.67s remaining: 17s 14: learn: 0.2352450 test: 0.2601812 best: 0.2601812 (14) total: 3.96s remaining: 16.9s 15: learn: 0.2266276 test: 0.2520925 best: 0.2520925 (15) total: 4.26s remaining: 16.8s 16: learn: 0.2201787 test: 0.2459260 best: 0.2459260 (16) total: 4.53s remaining: 16.5s 17: learn: 0.2145855 test: 0.2408975 best: 0.2408975 (17) total: 4.8s remaining: 16.3s 18: learn: 0.2090800 test: 0.2352024 best: 0.2352024 (18) total: 5.06s remaining: 16s 19: learn: 0.2042282 test: 0.2307075 best: 0.2307075 (19) total: 5.31s remaining: 15.7s 20: learn: 0.1994770 test: 0.2263185 best: 0.2263185 (20) total: 5.58s remaining: 15.4s 21: learn: 0.1953308 test: 0.2231280 best: 0.2231280 (21) total: 5.87s remaining: 15.2s 22: learn: 0.1916367 test: 0.2201898 best: 0.2201898 (22) total: 6.13s remaining: 14.9s 23: learn: 0.1887684 test: 0.2178409 best: 0.2178409 (23) total: 6.4s remaining: 14.7s 24: learn: 0.1855452 test: 0.2149730 best: 0.2149730 (24) total: 6.66s remaining: 14.4s
Training has stopped (degenerate solution on iteration 25, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 20, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 26, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 38, probably too small l2-regularization, try to increase it)
bestTest = 0.2081745998 bestIteration = 36 Shrink model to first 37 iterations. Trial 52, Fold 5: Log loss = 0.20776672363436344, Average precision = 0.9726535945074268, ROC-AUC = 0.9700186908942274, Elapsed Time = 9.64951579999979 seconds
Optimization Progress: 53%|#####3 | 53/100 [1:18:03<39:49, 50.84s/it]
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Optimization Progress: 54%|#####4 | 54/100 [1:28:49<2:55:41, 229.16s/it]
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Optimization Progress: 55%|#####5 | 55/100 [1:29:22<2:07:48, 170.40s/it]
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Optimization Progress: 56%|#####6 | 56/100 [1:32:26<2:07:52, 174.37s/it]
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Optimization Progress: 57%|#####6 | 57/100 [1:33:28<1:40:56, 140.84s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 57, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5890787 test: 0.5936873 best: 0.5936873 (0) total: 522ms remaining: 48.5s 1: learn: 0.5084948 test: 0.5145016 best: 0.5145016 (1) total: 989ms remaining: 45.5s 2: learn: 0.4453946 test: 0.4528188 best: 0.4528188 (2) total: 1.48s remaining: 44.9s 3: learn: 0.3962743 test: 0.4047435 best: 0.4047435 (3) total: 1.88s remaining: 42.4s 4: learn: 0.3565412 test: 0.3671223 best: 0.3671223 (4) total: 2.36s remaining: 42s 5: learn: 0.3261005 test: 0.3381460 best: 0.3381460 (5) total: 2.74s remaining: 40.2s 6: learn: 0.3030712 test: 0.3171905 best: 0.3171905 (6) total: 3.17s remaining: 39.5s 7: learn: 0.2826533 test: 0.2983120 best: 0.2983120 (7) total: 3.59s remaining: 38.6s 8: learn: 0.2658466 test: 0.2832187 best: 0.2832187 (8) total: 4.05s remaining: 38.2s 9: learn: 0.2527713 test: 0.2711335 best: 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Trial 57, Fold 1: Log loss = 0.1975462884624243, Average precision = 0.9758262951181361, ROC-AUC = 0.9721045248371354, Elapsed Time = 37.68159860000014 seconds Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 57, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.5899010 test: 0.5927589 best: 0.5927589 (0) total: 507ms remaining: 47.1s 1: learn: 0.5093047 test: 0.5142643 best: 0.5142643 (1) total: 1.01s remaining: 46.6s 2: learn: 0.4465705 test: 0.4534929 best: 0.4534929 (2) total: 1.51s remaining: 45.9s 3: learn: 0.3972341 test: 0.4059481 best: 0.4059481 (3) total: 2.01s remaining: 45.3s 4: learn: 0.3598375 test: 0.3693817 best: 0.3693817 (4) total: 2.43s remaining: 43.3s 5: learn: 0.3294544 test: 0.3394370 best: 0.3394370 (5) total: 2.9s remaining: 42.5s 6: learn: 0.3056175 test: 0.3165340 best: 0.3165340 (6) total: 3.32s remaining: 41.2s 7: learn: 0.2846520 test: 0.2969479 best: 0.2969479 (7) 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Trial 57, Fold 3: Log loss = 0.18739240902453494, Average precision = 0.9752750355044638, ROC-AUC = 0.9735378282979072, Elapsed Time = 37.481389799999306 seconds Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 57, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.5889277 test: 0.5908707 best: 0.5908707 (0) total: 523ms remaining: 48.6s 1: learn: 0.5085563 test: 0.5136694 best: 0.5136694 (1) total: 1.03s remaining: 47.2s 2: learn: 0.4449576 test: 0.4522369 best: 0.4522369 (2) total: 1.52s remaining: 46.1s 3: learn: 0.3952368 test: 0.4035509 best: 0.4035509 (3) total: 2s remaining: 45.1s 4: learn: 0.3566469 test: 0.3660009 best: 0.3660009 (4) total: 2.4s remaining: 42.8s 5: learn: 0.3262410 test: 0.3363588 best: 0.3363588 (5) total: 2.84s remaining: 41.7s 6: learn: 0.3019965 test: 0.3134657 best: 0.3134657 (6) total: 3.43s remaining: 42.7s 7: learn: 0.2834502 test: 0.2960993 best: 0.2960993 (7) 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Trial 57, Fold 5: Log loss = 0.19910033921792109, Average precision = 0.9734823221915858, ROC-AUC = 0.9713457815432064, Elapsed Time = 36.97576919999847 seconds
Optimization Progress: 58%|#####8 | 58/100 [1:36:47<1:50:41, 158.12s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 58, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6563950 test: 0.6565181 best: 0.6565181 (0) total: 111ms remaining: 3.88s 1: learn: 0.6032791 test: 0.6052388 best: 0.6052388 (1) total: 1.93s remaining: 32.8s 2: learn: 0.5609840 test: 0.5650305 best: 0.5650305 (2) total: 4s remaining: 44s 3: learn: 0.5161422 test: 0.5234598 best: 0.5234598 (3) total: 6.08s remaining: 48.7s 4: learn: 0.4861434 test: 0.4943470 best: 0.4943470 (4) total: 8.18s remaining: 50.7s 5: learn: 0.4502123 test: 0.4592075 best: 0.4592075 (5) total: 10.2s remaining: 51s 6: learn: 0.4186427 test: 0.4292636 best: 0.4292636 (6) total: 12.3s remaining: 50.9s 7: learn: 0.3969615 test: 0.4083434 best: 0.4083434 (7) total: 14.3s remaining: 50.1s 8: learn: 0.3683639 test: 0.3825851 best: 0.3825851 (8) total: 16.4s remaining: 49.1s 9: learn: 0.3473279 test: 0.3626512 best: 0.3626512 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Optimization Progress: 59%|#####8 | 59/100 [1:42:51<2:30:23, 220.09s/it]
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Optimization Progress: 60%|###### | 60/100 [1:44:56<2:07:40, 191.52s/it]
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Optimization Progress: 61%|######1 | 61/100 [1:46:35<1:46:27, 163.78s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 61, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5311544 test: 0.5345780 best: 0.5345780 (0) total: 114ms remaining: 2.27s 1: learn: 0.4264863 test: 0.4314730 best: 0.4314730 (1) total: 222ms remaining: 2.11s 2: learn: 0.3606027 test: 0.3680394 best: 0.3680394 (2) total: 339ms remaining: 2.03s 3: learn: 0.3150024 test: 0.3260176 best: 0.3260176 (3) total: 458ms remaining: 1.95s 4: learn: 0.2793900 test: 0.2935812 best: 0.2935812 (4) total: 577ms remaining: 1.85s 5: learn: 0.2583997 test: 0.2745268 best: 0.2745268 (5) total: 691ms remaining: 1.73s 6: learn: 0.2392554 test: 0.2576366 best: 0.2576366 (6) total: 807ms remaining: 1.61s 7: learn: 0.2234033 test: 0.2437379 best: 0.2437379 (7) total: 923ms remaining: 1.5s 8: learn: 0.2127209 test: 0.2355047 best: 0.2355047 (8) total: 1.06s remaining: 1.41s 9: learn: 0.2019645 test: 0.2275382 best: 0.2275382 (9) total: 1.19s remaining: 1.31s 10: learn: 0.1934348 test: 0.2205094 best: 0.2205094 (10) total: 1.31s remaining: 1.19s 11: learn: 0.1875968 test: 0.2160301 best: 0.2160301 (11) total: 1.42s remaining: 1.07s 12: learn: 0.1827931 test: 0.2132209 best: 0.2132209 (12) total: 1.54s remaining: 946ms 13: learn: 0.1786750 test: 0.2109046 best: 0.2109046 (13) total: 1.65s remaining: 823ms 14: learn: 0.1975012 test: 0.2928730 best: 0.2109046 (13) total: 1.76s remaining: 706ms 15: learn: 0.1939075 test: 0.2912446 best: 0.2109046 (13) total: 1.88s remaining: 588ms 16: learn: 0.2499129 test: 0.3784955 best: 0.2109046 (13) total: 1.99s remaining: 469ms 17: learn: 0.2476881 test: 0.3770666 best: 0.2109046 (13) total: 2.11s remaining: 351ms 18: learn: 0.2812899 test: 0.4908095 best: 0.2109046 (13) total: 2.23s remaining: 234ms 19: learn: 0.2784393 test: 0.4891935 best: 0.2109046 (13) total: 2.35s remaining: 117ms 20: learn: 0.2784471 test: 0.4883762 best: 0.2109046 (13) total: 2.46s remaining: 0us bestTest = 0.2109046388 bestIteration = 13 Shrink model to first 14 iterations. 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Trial 61, Fold 5: Log loss = 0.2073408952745976, Average precision = 0.9740838197006582, ROC-AUC = 0.9712975493748025, Elapsed Time = 2.611271099998703 seconds
Optimization Progress: 62%|######2 | 62/100 [1:46:56<1:16:38, 121.02s/it]
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Optimization Progress: 63%|######3 | 63/100 [1:47:14<55:23, 89.83s/it]
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Trial 63, Fold 4: Log loss = 0.20050170080730612, Average precision = 0.9738741510547041, ROC-AUC = 0.9689064023786872, Elapsed Time = 8.791355500001373 seconds Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 63, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5979033 test: 0.6005158 best: 0.6005158 (0) total: 91.6ms remaining: 7.79s 1: learn: 0.5626567 test: 0.5667608 best: 0.5667608 (1) total: 134ms remaining: 5.64s 2: learn: 0.4921501 test: 0.4965468 best: 0.4965468 (2) total: 194ms remaining: 5.37s 3: learn: 0.4213797 test: 0.4276038 best: 0.4276038 (3) total: 294ms remaining: 6.04s 4: learn: 0.3698140 test: 0.3771198 best: 0.3771198 (4) total: 397ms remaining: 6.43s 5: learn: 0.3417551 test: 0.3494085 best: 0.3494085 (5) total: 498ms remaining: 6.64s 6: learn: 0.3127740 test: 0.3205417 best: 0.3205417 (6) total: 603ms remaining: 6.81s 7: learn: 0.2979547 test: 0.3063482 best: 0.3063482 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precision = 0.971973721771268, ROC-AUC = 0.9688050833286885, Elapsed Time = 8.45616439999867 seconds
Optimization Progress: 64%|######4 | 64/100 [1:48:07<47:23, 78.97s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 64, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6270816 test: 0.6272108 best: 0.6272108 (0) total: 63.6ms remaining: 3.31s 1: learn: 0.5709433 test: 0.5711774 best: 0.5711774 (1) total: 128ms remaining: 3.25s 2: learn: 0.5232252 test: 0.5237121 best: 0.5237121 (2) total: 191ms remaining: 3.19s 3: learn: 0.4823789 test: 0.4829348 best: 0.4829348 (3) total: 254ms remaining: 3.12s 4: learn: 0.4476997 test: 0.4485957 best: 0.4485957 (4) total: 321ms remaining: 3.08s 5: learn: 0.4176515 test: 0.4190183 best: 0.4190183 (5) total: 384ms remaining: 3.01s 6: learn: 0.3923052 test: 0.3938203 best: 0.3938203 (6) total: 445ms remaining: 2.93s 7: learn: 0.3694487 test: 0.3711216 best: 0.3711216 (7) total: 507ms remaining: 2.85s 8: learn: 0.3500923 test: 0.3521287 best: 0.3521287 (8) total: 571ms remaining: 2.79s 9: learn: 0.3336712 test: 0.3360098 best: 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Trial 64, Fold 5: Log loss = 0.21765149735541112, Average precision = 0.970327625197403, ROC-AUC = 0.9678532969176746, Elapsed Time = 3.6505628999984765 seconds
Optimization Progress: 65%|######5 | 65/100 [1:48:34<36:53, 63.23s/it]
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Trial 65, Fold 5: Log loss = 0.23116782291661211, Average precision = 0.9720569451347603, ROC-AUC = 0.9682291213793359, Elapsed Time = 25.182768200000282 seconds
Optimization Progress: 66%|######6 | 66/100 [1:50:52<48:36, 85.79s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 66, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6038481 test: 0.6125825 best: 0.6125825 (0) total: 6.31s remaining: 44.2s 1: learn: 0.5188425 test: 0.5425638 best: 0.5425638 (1) total: 14.1s remaining: 42.2s 2: learn: 0.4562161 test: 0.4901837 best: 0.4901837 (2) total: 20.4s remaining: 34s 3: learn: 0.4043148 test: 0.4462300 best: 0.4462300 (3) total: 25.8s remaining: 25.8s 4: learn: 0.3525741 test: 0.4048929 best: 0.4048929 (4) total: 31.7s remaining: 19s 5: learn: 0.3168511 test: 0.3752779 best: 0.3752779 (5) total: 37s remaining: 12.3s 6: learn: 0.2783105 test: 0.3446947 best: 0.3446947 (6) total: 42.6s remaining: 6.09s 7: learn: 0.2486222 test: 0.3212030 best: 0.3212030 (7) total: 48.1s remaining: 0us bestTest = 0.3212030184 bestIteration = 7 Trial 66, Fold 1: Log loss = 0.3212030184293957, Average precision = 0.9676886982497143, ROC-AUC 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Optimization Progress: 67%|######7 | 67/100 [1:54:54<1:12:56, 132.61s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 67, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5308367 test: 0.5334931 best: 0.5334931 (0) total: 61.5ms remaining: 1.04s 1: learn: 0.4119444 test: 0.4171130 best: 0.4171130 (1) total: 125ms remaining: 999ms 2: learn: 0.3379642 test: 0.3449680 best: 0.3449680 (2) total: 188ms remaining: 942ms 3: learn: 0.3011151 test: 0.3083902 best: 0.3083902 (3) total: 247ms remaining: 864ms 4: learn: 0.2696773 test: 0.2773132 best: 0.2773132 (4) total: 308ms remaining: 802ms 5: learn: 0.2498357 test: 0.2592433 best: 0.2592433 (5) total: 372ms remaining: 743ms 6: learn: 0.2347770 test: 0.2458484 best: 0.2458484 (6) total: 431ms remaining: 677ms 7: learn: 0.2255207 test: 0.2379487 best: 0.2379487 (7) total: 492ms remaining: 615ms 8: learn: 0.2177878 test: 0.2318626 best: 0.2318626 (8) total: 554ms remaining: 554ms 9: learn: 0.2123682 test: 0.2282185 best: 0.2282185 (9) total: 617ms remaining: 494ms 10: learn: 0.2073813 test: 0.2236595 best: 0.2236595 (10) total: 677ms remaining: 431ms 11: learn: 0.2024308 test: 0.2201602 best: 0.2201602 (11) total: 740ms remaining: 370ms 12: learn: 0.1981273 test: 0.2180637 best: 0.2180637 (12) total: 805ms remaining: 309ms 13: learn: 0.1947860 test: 0.2167048 best: 0.2167048 (13) total: 868ms remaining: 248ms 14: learn: 0.1929895 test: 0.2152865 best: 0.2152865 (14) total: 929ms remaining: 186ms 15: learn: 0.1890878 test: 0.2125549 best: 0.2125549 (15) total: 991ms remaining: 124ms 16: learn: 0.1873403 test: 0.2117731 best: 0.2117731 (16) total: 1.05s remaining: 62.1ms 17: learn: 0.1852215 test: 0.2109326 best: 0.2109326 (17) total: 1.12s remaining: 0us bestTest = 0.2109326431 bestIteration = 17 Trial 67, Fold 1: Log loss = 0.2109326430998132, Average precision = 0.9693742843982487, ROC-AUC = 0.9667552683022421, Elapsed Time = 1.2243921000008413 seconds Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 67, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.5116023 test: 0.5141740 best: 0.5141740 (0) total: 63ms remaining: 1.07s 1: learn: 0.4257167 test: 0.4316891 best: 0.4316891 (1) total: 125ms remaining: 1s 2: learn: 0.3543765 test: 0.3612416 best: 0.3612416 (2) total: 188ms remaining: 939ms 3: learn: 0.3109329 test: 0.3187222 best: 0.3187222 (3) total: 250ms remaining: 875ms 4: learn: 0.2777554 test: 0.2878122 best: 0.2878122 (4) total: 312ms remaining: 812ms 5: learn: 0.2500602 test: 0.2604702 best: 0.2604702 (5) total: 375ms remaining: 750ms 6: learn: 0.2360304 test: 0.2476421 best: 0.2476421 (6) total: 441ms remaining: 693ms 7: learn: 0.2246791 test: 0.2380085 best: 0.2380085 (7) total: 498ms remaining: 622ms 8: learn: 0.2178107 test: 0.2314467 best: 0.2314467 (8) total: 560ms remaining: 560ms 9: learn: 0.2129245 test: 0.2271637 best: 0.2271637 (9) total: 622ms remaining: 497ms 10: learn: 0.2076415 test: 0.2229373 best: 0.2229373 (10) total: 684ms remaining: 435ms 11: learn: 0.2017919 test: 0.2188999 best: 0.2188999 (11) total: 751ms remaining: 376ms 12: learn: 0.1961952 test: 0.2138627 best: 0.2138627 (12) total: 813ms remaining: 313ms 13: learn: 0.1942325 test: 0.2129220 best: 0.2129220 (13) total: 876ms remaining: 250ms 14: learn: 0.1920418 test: 0.2108790 best: 0.2108790 (14) total: 937ms remaining: 187ms 15: learn: 0.1895377 test: 0.2089532 best: 0.2089532 (15) total: 1s remaining: 125ms 16: learn: 0.2047257 test: 0.2422178 best: 0.2089532 (15) total: 1.06s remaining: 62.6ms 17: learn: 0.2034148 test: 0.2417271 best: 0.2089532 (15) total: 1.13s remaining: 0us bestTest = 0.2089532283 bestIteration = 15 Shrink model to first 16 iterations. Trial 67, Fold 2: Log loss = 0.208953228258031, Average precision = 0.9717722839040971, ROC-AUC = 0.9683297060554348, Elapsed Time = 1.232151399999566 seconds Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 67, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.5269672 test: 0.5302358 best: 0.5302358 (0) total: 65.5ms remaining: 1.11s 1: learn: 0.4347368 test: 0.4376431 best: 0.4376431 (1) total: 129ms remaining: 1.03s 2: learn: 0.3579215 test: 0.3620770 best: 0.3620770 (2) total: 192ms remaining: 960ms 3: learn: 0.3063125 test: 0.3113898 best: 0.3113898 (3) total: 257ms remaining: 900ms 4: learn: 0.2754623 test: 0.2818396 best: 0.2818396 (4) total: 321ms remaining: 835ms 5: learn: 0.2533066 test: 0.2618419 best: 0.2618419 (5) total: 386ms remaining: 771ms 6: learn: 0.2379659 test: 0.2474018 best: 0.2474018 (6) total: 448ms remaining: 704ms 7: learn: 0.2280851 test: 0.2383508 best: 0.2383508 (7) total: 510ms remaining: 638ms 8: learn: 0.2178364 test: 0.2294978 best: 0.2294978 (8) total: 574ms remaining: 574ms 9: learn: 0.2332119 test: 0.2543952 best: 0.2294978 (8) total: 638ms remaining: 511ms 10: learn: 0.2292176 test: 0.2520130 best: 0.2294978 (8) total: 700ms remaining: 445ms 11: learn: 0.2203169 test: 0.2459908 best: 0.2294978 (8) total: 762ms remaining: 381ms 12: learn: 0.2174781 test: 0.2450912 best: 0.2294978 (8) total: 826ms remaining: 318ms 13: learn: 0.2140852 test: 0.2421165 best: 0.2294978 (8) total: 888ms remaining: 254ms 14: learn: 0.2113313 test: 0.2398027 best: 0.2294978 (8) total: 953ms remaining: 191ms 15: learn: 0.2104487 test: 0.2380092 best: 0.2294978 (8) total: 1.02s remaining: 127ms 16: learn: 0.2082114 test: 0.2379327 best: 0.2294978 (8) total: 1.08s remaining: 63.5ms 17: learn: 0.2064527 test: 0.2373509 best: 0.2294978 (8) total: 1.14s remaining: 0us bestTest = 0.2294977727 bestIteration = 8 Shrink model to first 9 iterations. Trial 67, Fold 3: Log loss = 0.22949777268239238, Average precision = 0.9704896064872417, ROC-AUC = 0.9673667425913264, Elapsed Time = 1.2557278999993287 seconds Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 67, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.5399962 test: 0.5420976 best: 0.5420976 (0) total: 59ms remaining: 1s 1: learn: 0.4366313 test: 0.4396874 best: 0.4396874 (1) total: 122ms remaining: 976ms 2: learn: 0.3659843 test: 0.3710791 best: 0.3710791 (2) total: 186ms remaining: 929ms 3: learn: 0.3138812 test: 0.3212191 best: 0.3212191 (3) total: 247ms remaining: 865ms 4: learn: 0.2790154 test: 0.2882059 best: 0.2882059 (4) total: 304ms remaining: 789ms 5: learn: 0.2567535 test: 0.2676397 best: 0.2676397 (5) total: 367ms remaining: 733ms 6: learn: 0.2411789 test: 0.2527669 best: 0.2527669 (6) total: 430ms remaining: 676ms 7: learn: 0.2295620 test: 0.2410108 best: 0.2410108 (7) total: 490ms remaining: 613ms 8: learn: 0.2211462 test: 0.2335786 best: 0.2335786 (8) total: 555ms remaining: 555ms 9: learn: 0.2110079 test: 0.2249983 best: 0.2249983 (9) total: 621ms remaining: 497ms 10: learn: 0.2058577 test: 0.2209926 best: 0.2209926 (10) total: 686ms remaining: 436ms 11: learn: 0.2022723 test: 0.2184358 best: 0.2184358 (11) total: 752ms remaining: 376ms 12: learn: 0.1994626 test: 0.2176441 best: 0.2176441 (12) total: 816ms remaining: 314ms 13: learn: 0.2040053 test: 0.2308095 best: 0.2176441 (12) total: 883ms remaining: 252ms 14: learn: 0.2005297 test: 0.2304924 best: 0.2176441 (12) total: 949ms remaining: 190ms 15: learn: 0.1983725 test: 0.2300883 best: 0.2176441 (12) total: 1.01s remaining: 127ms 16: learn: 0.1965882 test: 0.2300897 best: 0.2176441 (12) total: 1.08s remaining: 63.6ms 17: learn: 0.1911552 test: 0.2295691 best: 0.2176441 (12) total: 1.15s remaining: 0us bestTest = 0.2176441442 bestIteration = 12 Shrink model to first 13 iterations. 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Trial 67, Fold 5: Log loss = 0.21787832346472952, Average precision = 0.9695044188372833, ROC-AUC = 0.9658037641900303, Elapsed Time = 1.2686074999983248 seconds
Optimization Progress: 68%|######8 | 68/100 [1:55:08<51:47, 97.12s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 68, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6715229 test: 0.6713295 best: 0.6713295 (0) total: 38.4ms remaining: 1.65s 1: learn: 0.6507826 test: 0.6513380 best: 0.6513380 (1) total: 77ms remaining: 1.62s 2: learn: 0.6307988 test: 0.6319539 best: 0.6319539 (2) total: 116ms remaining: 1.59s 3: learn: 0.6125123 test: 0.6135329 best: 0.6135329 (3) total: 155ms remaining: 1.54s 4: learn: 0.5954194 test: 0.5963300 best: 0.5963300 (4) total: 196ms remaining: 1.53s 5: learn: 0.5802584 test: 0.5811088 best: 0.5811088 (5) total: 234ms remaining: 1.48s 6: learn: 0.5642124 test: 0.5650419 best: 0.5650419 (6) total: 280ms remaining: 1.48s 7: learn: 0.5488591 test: 0.5496760 best: 0.5496760 (7) total: 321ms remaining: 1.44s 8: learn: 0.5345535 test: 0.5353299 best: 0.5353299 (8) total: 364ms remaining: 1.42s 9: learn: 0.5214069 test: 0.5221706 best: 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Optimization Progress: 69%|######9 | 69/100 [1:55:26<37:49, 73.22s/it]
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Optimization Progress: 70%|####### | 70/100 [1:56:52<38:36, 77.23s/it]
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Trial 70, Fold 5: Log loss = 0.1964205843343422, Average precision = 0.9743607278685225, ROC-AUC = 0.972080931943593, Elapsed Time = 44.91034370000125 seconds
Optimization Progress: 71%|#######1 | 71/100 [2:00:51<1:00:42, 125.62s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 71, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5937818 test: 0.5937905 best: 0.5937905 (0) total: 88.5ms remaining: 5.4s 1: learn: 0.5161468 test: 0.5171198 best: 0.5171198 (1) total: 182ms remaining: 5.46s 2: learn: 0.4561093 test: 0.4571813 best: 0.4571813 (2) total: 275ms remaining: 5.41s 3: learn: 0.4088388 test: 0.4102799 best: 0.4102799 (3) total: 369ms remaining: 5.35s 4: learn: 0.3708212 test: 0.3728695 best: 0.3728695 (4) total: 467ms remaining: 5.32s 5: learn: 0.3413325 test: 0.3439232 best: 0.3439232 (5) total: 566ms remaining: 5.28s 6: learn: 0.3169946 test: 0.3204268 best: 0.3204268 (6) total: 665ms remaining: 5.23s 7: learn: 0.2982813 test: 0.3025617 best: 0.3025617 (7) total: 768ms remaining: 5.18s 8: learn: 0.2828963 test: 0.2874215 best: 0.2874215 (8) total: 871ms remaining: 5.13s 9: learn: 0.2698101 test: 0.2752712 best: 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Optimization Progress: 72%|#######2 | 72/100 [2:01:34<47:01, 100.78s/it]
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Optimization Progress: 73%|#######3 | 73/100 [2:04:32<55:48, 124.02s/it]
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Optimization Progress: 74%|#######4 | 74/100 [2:05:45<47:07, 108.74s/it]
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Trial 74, Fold 5: Log loss = 0.20718779155537703, Average precision = 0.9731871268430515, ROC-AUC = 0.9684625531835833, Elapsed Time = 168.26519910000206 seconds
Optimization Progress: 75%|#######5 | 75/100 [2:19:59<2:18:31, 332.46s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 75, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5097813 test: 0.5126082 best: 0.5126082 (0) total: 201ms remaining: 1.41s 1: learn: 0.4078349 test: 0.4143247 best: 0.4143247 (1) total: 402ms remaining: 1.21s 2: learn: 0.3377477 test: 0.3489414 best: 0.3489414 (2) total: 628ms remaining: 1.05s 3: learn: 0.2877183 test: 0.3029563 best: 0.3029563 (3) total: 848ms remaining: 848ms 4: learn: 0.2565791 test: 0.2753284 best: 0.2753284 (4) total: 1.07s remaining: 643ms 5: learn: 0.2344505 test: 0.2559626 best: 0.2559626 (5) total: 1.27s remaining: 424ms 6: learn: 0.2171929 test: 0.2410710 best: 0.2410710 (6) total: 1.46s remaining: 209ms 7: learn: 0.2030235 test: 0.2301766 best: 0.2301766 (7) total: 1.69s remaining: 0us bestTest = 0.2301766315 bestIteration = 7 Trial 75, Fold 1: Log loss = 0.23017663145427283, Average precision = 0.9720650521748062, 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Optimization Progress: 76%|#######6 | 76/100 [2:20:17<1:35:14, 238.09s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 76, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6531104 test: 0.6532013 best: 0.6532013 (0) total: 83.6ms remaining: 4.59s 1: learn: 0.6171570 test: 0.6171406 best: 0.6171406 (1) total: 163ms remaining: 4.4s 2: learn: 0.5840155 test: 0.5838715 best: 0.5838715 (2) total: 244ms remaining: 4.3s 3: learn: 0.5586446 test: 0.5587452 best: 0.5587452 (3) total: 324ms remaining: 4.21s 4: learn: 0.5307507 test: 0.5306415 best: 0.5306415 (4) total: 401ms remaining: 4.09s 5: learn: 0.5056393 test: 0.5054170 best: 0.5054170 (5) total: 481ms remaining: 4.01s 6: learn: 0.4830397 test: 0.4828917 best: 0.4828917 (6) total: 571ms remaining: 4s 7: learn: 0.4621968 test: 0.4619300 best: 0.4619300 (7) total: 651ms remaining: 3.9s 8: learn: 0.4451438 test: 0.4448856 best: 0.4448856 (8) total: 732ms remaining: 3.82s 9: learn: 0.4298765 test: 0.4297603 best: 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Optimization Progress: 77%|#######7 | 77/100 [2:20:52<1:07:53, 177.11s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 77, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5565804 test: 0.5536771 best: 0.5536771 (0) total: 145ms remaining: 12.9s 1: learn: 0.4730340 test: 0.4714051 best: 0.4714051 (1) total: 300ms remaining: 13.2s 2: learn: 0.4116212 test: 0.4102433 best: 0.4102433 (2) total: 447ms remaining: 13s 3: learn: 0.3711878 test: 0.3712388 best: 0.3712388 (3) total: 599ms remaining: 12.9s 4: learn: 0.3340063 test: 0.3352479 best: 0.3352479 (4) total: 734ms remaining: 12.5s 5: learn: 0.3072189 test: 0.3097578 best: 0.3097578 (5) total: 862ms remaining: 12.1s 6: learn: 0.2810098 test: 0.2850808 best: 0.2850808 (6) total: 1.01s remaining: 12s 7: learn: 0.2646874 test: 0.2700704 best: 0.2700704 (7) total: 1.15s remaining: 11.8s 8: learn: 0.2516047 test: 0.2584804 best: 0.2584804 (8) total: 1.3s remaining: 11.7s 9: learn: 0.2403658 test: 0.2482175 best: 0.2482175 (9) total: 1.45s remaining: 11.6s 10: learn: 0.2300272 test: 0.2398940 best: 0.2398940 (10) total: 1.6s remaining: 11.5s 11: learn: 513.8837067 test: 2051.2052998 best: 0.2398940 (10) total: 1.77s remaining: 11.5s 12: learn: 513.8748288 test: 2051.1948931 best: 0.2398940 (10) total: 1.95s remaining: 11.6s 13: learn: 513.8684589 test: 2051.1868951 best: 0.2398940 (10) total: 2.1s remaining: 11.4s 14: learn: 513.8629134 test: 2051.1797293 best: 0.2398940 (10) total: 2.25s remaining: 11.2s 15: learn: 513.8574007 test: 2051.1721080 best: 0.2398940 (10) total: 2.41s remaining: 11.1s 16: learn: 513.8530273 test: 2051.1649878 best: 0.2398940 (10) total: 2.55s remaining: 10.9s 17: learn: 513.8498087 test: 2051.1589768 best: 0.2398940 (10) total: 2.67s remaining: 10.7s 18: learn: 3211.4289789 test: 6145.8456147 best: 0.2398940 (10) total: 2.83s remaining: 10.6s 19: learn: 3211.4200116 test: 6145.8317203 best: 0.2398940 (10) total: 2.98s remaining: 10.4s 20: learn: 4239.3923879 test: 6145.8158475 best: 0.2398940 (10) total: 3.13s remaining: 10.3s 21: learn: 4239.3811615 test: 6145.8015831 best: 0.2398940 (10) total: 3.27s remaining: 10.1s 22: learn: 4239.3710281 test: 6145.7883034 best: 0.2398940 (10) total: 3.4s remaining: 9.9s 23: learn: 4239.3608002 test: 6145.7747947 best: 0.2398940 (10) total: 3.54s remaining: 9.75s 24: learn: 4753.2584320 test: 6145.7617401 best: 0.2398940 (10) total: 3.65s remaining: 9.5s 25: learn: 4753.2465066 test: 6145.7476546 best: 0.2398940 (10) total: 3.78s remaining: 9.31s 26: learn: 4753.2351888 test: 6145.7339449 best: 0.2398940 (10) total: 3.93s remaining: 9.16s 27: learn: 5524.3584532 test: 6145.7207384 best: 0.2398940 (10) total: 4.05s remaining: 8.98s 28: learn: 5524.3459304 test: 6145.7070924 best: 0.2398940 (10) total: 4.19s remaining: 8.81s 29: learn: 5524.3326851 test: 6145.6935299 best: 0.2398940 (10) total: 4.34s remaining: 8.68s 30: learn: 5524.3205160 test: 6145.6809816 best: 0.2398940 (10) total: 4.45s remaining: 8.46s 31: learn: 5524.3083146 test: 6145.6678264 best: 0.2398940 (10) total: 4.56s remaining: 8.27s 32: learn: 5524.2955064 test: 6145.6546587 best: 0.2398940 (10) total: 4.71s remaining: 8.13s 33: learn: 5524.2828630 test: 6145.6416827 best: 0.2398940 (10) total: 4.84s remaining: 7.97s 34: learn: 5524.2706434 test: 6145.6284658 best: 0.2398940 (10) total: 4.96s remaining: 7.79s 35: learn: 5524.2581176 test: 6145.6153826 best: 0.2398940 (10) total: 5.09s remaining: 7.63s 36: learn: 5524.2455906 test: 6145.6018261 best: 0.2398940 (10) total: 5.22s remaining: 7.47s 37: learn: 5524.2328993 test: 6145.5886028 best: 0.2398940 (10) total: 5.35s remaining: 7.32s 38: learn: 5524.2203781 test: 6145.5756523 best: 0.2398940 (10) total: 5.47s remaining: 7.16s 39: learn: 5524.2081102 test: 6145.5625054 best: 0.2398940 (10) total: 5.59s remaining: 6.99s 40: learn: 5524.1957784 test: 6145.5492304 best: 0.2398940 (10) total: 5.71s remaining: 6.82s 41: learn: 5524.1836174 test: 6145.5366175 best: 0.2398940 (10) total: 5.82s remaining: 6.66s 42: learn: 5524.1713694 test: 6145.5235618 best: 0.2398940 (10) total: 5.94s remaining: 6.5s 43: learn: 5524.1590325 test: 6145.5105554 best: 0.2398940 (10) total: 6.07s remaining: 6.35s 44: learn: 5459.8840857 test: 6145.4971554 best: 0.2398940 (10) total: 6.22s remaining: 6.22s 45: learn: 5459.8721463 test: 6145.4842713 best: 0.2398940 (10) total: 6.33s remaining: 6.06s 46: learn: 5459.8601257 test: 6145.4710566 best: 0.2398940 (10) total: 6.46s remaining: 5.91s 47: learn: 5459.8480935 test: 6145.4579951 best: 0.2398940 (10) total: 6.58s remaining: 5.76s 48: learn: 5459.8357493 test: 6145.4448119 best: 0.2398940 (10) total: 6.72s remaining: 5.63s 49: learn: 6423.7749901 test: 9224.5651931 best: 0.2398940 (10) total: 6.88s remaining: 5.5s 50: learn: 6809.9319855 test: 10766.4688222 best: 0.2398940 (10) total: 7.01s remaining: 5.36s bestTest = 0.2398939532 bestIteration = 10 Shrink model to first 11 iterations.
Training has stopped (degenerate solution on iteration 51, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 36, probably too small l2-regularization, try to increase it)
bestTest = 0.2429889915 bestIteration = 10 Shrink model to first 11 iterations. 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Training has stopped (degenerate solution on iteration 42, probably too small l2-regularization, try to increase it)
bestTest = 0.234211359 bestIteration = 10 Shrink model to first 11 iterations. Trial 77, Fold 4: Log loss = 0.23421135897576462, Average precision = 0.9724300880487378, ROC-AUC = 0.9673531829486088, Elapsed Time = 6.118050200002472 seconds Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 77, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5648622 test: 0.5668813 best: 0.5668813 (0) total: 190ms remaining: 16.9s 1: learn: 0.4894681 test: 0.4923432 best: 0.4923432 (1) total: 338ms remaining: 14.9s 2: learn: 0.4195867 test: 0.4214040 best: 0.4214040 (2) total: 493ms remaining: 14.3s 3: learn: 0.3703088 test: 0.3742635 best: 0.3742635 (3) total: 651ms remaining: 14s 4: learn: 0.3336377 test: 0.3396583 best: 0.3396583 (4) total: 832ms remaining: 14.1s 5: learn: 0.3068980 test: 0.3141934 best: 0.3141934 (5) total: 984ms remaining: 13.8s 6: learn: 0.2841855 test: 0.2926142 best: 0.2926142 (6) total: 1.13s remaining: 13.4s 7: learn: 0.2634750 test: 0.2739564 best: 0.2739564 (7) total: 1.29s remaining: 13.2s 8: learn: 0.2502256 test: 0.2625385 best: 0.2625385 (8) total: 1.44s remaining: 13s 9: learn: 0.2389818 test: 0.2526448 best: 0.2526448 (9) total: 1.6s remaining: 12.8s 10: learn: 0.2268744 test: 0.2416729 best: 0.2416729 (10) total: 1.77s remaining: 12.7s 11: learn: 648.5487299 test: 0.2351372 best: 0.2351372 (11) total: 1.91s remaining: 12.4s 12: learn: 648.5404621 test: 0.2294992 best: 0.2294992 (12) total: 2.09s remaining: 12.4s 13: learn: 648.5347687 test: 0.2264725 best: 0.2264725 (13) total: 2.22s remaining: 12.1s 14: learn: 648.5297327 test: 0.2237511 best: 0.2237511 (14) total: 2.37s remaining: 11.8s 15: learn: 648.5247757 test: 0.2218021 best: 0.2218021 (15) total: 2.51s remaining: 11.6s 16: learn: 648.5208653 test: 0.2202242 best: 0.2202242 (16) total: 2.65s remaining: 11.4s 17: learn: 3220.5868651 test: 5119.0751186 best: 0.2202242 (16) total: 2.78s remaining: 11.1s 18: learn: 3220.5775656 test: 5119.0630231 best: 0.2202242 (16) total: 2.93s remaining: 10.9s 19: learn: 3220.5675183 test: 5119.0493745 best: 0.2202242 (16) total: 3.06s remaining: 10.7s 20: learn: 3482.5481471 test: 5119.0375151 best: 0.2202242 (16) total: 3.21s remaining: 10.6s 21: learn: 3932.6627594 test: 6910.6720447 best: 0.2202242 (16) total: 3.34s remaining: 10.3s 22: learn: 3932.6530127 test: 6910.6564174 best: 0.2202242 (16) total: 3.48s remaining: 10.1s 23: learn: 4961.4081175 test: 6910.6404190 best: 0.2202242 (16) total: 3.63s remaining: 9.98s 24: learn: 4961.3955300 test: 6910.6245863 best: 0.2202242 (16) total: 3.79s remaining: 9.86s 25: learn: 7738.9280674 test: 13359.6560405 best: 0.2202242 (16) total: 3.98s remaining: 9.79s 26: learn: 7738.9113574 test: 13359.6277380 best: 0.2202242 (16) total: 4.09s remaining: 9.54s 27: learn: 7738.8932273 test: 13359.5988155 best: 0.2202242 (16) total: 4.26s remaining: 9.42s 28: learn: 7738.8760008 test: 13359.5700871 best: 0.2202242 (16) total: 4.41s remaining: 9.28s bestTest = 0.2202242128 bestIteration = 16 Shrink model to first 17 iterations.
Training has stopped (degenerate solution on iteration 29, probably too small l2-regularization, try to increase it)
Trial 77, Fold 5: Log loss = 0.22022421278444518, Average precision = 0.970420295649105, ROC-AUC = 0.9660487895509355, Elapsed Time = 4.705272299997887 seconds
Optimization Progress: 78%|#######8 | 78/100 [2:21:36<50:16, 137.12s/it]
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Optimization Progress: 79%|#######9 | 79/100 [2:24:00<48:46, 139.34s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 79, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6825476 test: 0.6828433 best: 0.6828433 (0) total: 35.4ms remaining: 815ms 1: learn: 0.6725666 test: 0.6728272 best: 0.6728272 (1) total: 71.3ms remaining: 784ms 2: learn: 0.6616335 test: 0.6619511 best: 0.6619511 (2) total: 108ms remaining: 758ms 3: learn: 0.6508057 test: 0.6511323 best: 0.6511323 (3) total: 146ms remaining: 728ms 4: learn: 0.6397463 test: 0.6399790 best: 0.6399790 (4) total: 181ms remaining: 688ms 5: learn: 0.6290711 test: 0.6292841 best: 0.6292841 (5) total: 216ms remaining: 649ms 6: learn: 0.6186491 test: 0.6188141 best: 0.6188141 (6) total: 253ms remaining: 613ms 7: learn: 0.6085253 test: 0.6086273 best: 0.6086273 (7) total: 289ms remaining: 578ms 8: learn: 0.5987580 test: 0.5988602 best: 0.5988602 (8) total: 327ms remaining: 544ms 9: learn: 0.5893275 test: 0.5894758 best: 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Optimization Progress: 80%|######## | 80/100 [2:24:14<33:49, 101.49s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 80, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5529500 test: 0.5528939 best: 0.5528939 (0) total: 101ms remaining: 8.97s 1: learn: 0.4472210 test: 0.4489717 best: 0.4489717 (1) total: 206ms remaining: 9.07s 2: learn: 0.3876643 test: 0.3908444 best: 0.3908444 (2) total: 309ms remaining: 8.96s 3: learn: 0.3348323 test: 0.3406929 best: 0.3406929 (3) total: 410ms remaining: 8.81s 4: learn: 0.3059041 test: 0.3125768 best: 0.3125768 (4) total: 500ms remaining: 8.5s 5: learn: 0.2787688 test: 0.2871093 best: 0.2871093 (5) total: 602ms remaining: 8.42s 6: learn: 0.2621827 test: 0.2721093 best: 0.2721093 (6) total: 700ms remaining: 8.3s 7: learn: 0.2448374 test: 0.2557445 best: 0.2557445 (7) total: 804ms remaining: 8.24s 8: learn: 4.7553184 test: 0.2443686 best: 0.2443686 (8) total: 908ms remaining: 8.17s 9: learn: 4.7454163 test: 0.2357062 best: 0.2357062 (9) total: 1.01s remaining: 8.05s 10: learn: 4.7375817 test: 0.2295680 best: 0.2295680 (10) total: 1.11s remaining: 7.96s 11: learn: 4.7302209 test: 0.2236569 best: 0.2236569 (11) total: 1.21s remaining: 7.88s 12: learn: 4.7242175 test: 0.2196267 best: 0.2196267 (12) total: 1.31s remaining: 7.78s 13: learn: 6.0765476 test: 2.0228969 best: 0.2196267 (12) total: 1.41s remaining: 7.67s 14: learn: 6.0712761 test: 2.0186821 best: 0.2196267 (12) total: 1.52s remaining: 7.61s 15: learn: 6.0681860 test: 2.0164181 best: 0.2196267 (12) total: 1.61s remaining: 7.46s 16: learn: 8.4483231 test: 5.8221667 best: 0.2196267 (12) total: 1.71s remaining: 7.35s
Training has stopped (degenerate solution on iteration 17, probably too small l2-regularization, try to increase it)
bestTest = 0.2196266537 bestIteration = 12 Shrink model to first 13 iterations. Trial 80, Fold 1: Log loss = 0.21962665373815726, Average precision = 0.9736935458727383, ROC-AUC = 0.969040882814031, Elapsed Time = 1.926980200005346 seconds Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 80, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.5363507 test: 0.5383151 best: 0.5383151 (0) total: 104ms remaining: 9.26s 1: learn: 0.4394560 test: 0.4437305 best: 0.4437305 (1) total: 208ms remaining: 9.17s 2: learn: 0.3768750 test: 0.3826198 best: 0.3826198 (2) total: 319ms remaining: 9.25s 3: learn: 0.3276336 test: 0.3341281 best: 0.3341281 (3) total: 422ms remaining: 9.07s 4: learn: 0.2946404 test: 0.3024942 best: 0.3024942 (4) total: 527ms remaining: 8.95s 5: learn: 0.2700044 test: 0.2786865 best: 0.2786865 (5) total: 635ms remaining: 8.89s 6: learn: 0.2475294 test: 0.2572929 best: 0.2572929 (6) total: 744ms remaining: 8.82s 7: learn: 0.2348978 test: 0.2453161 best: 0.2453161 (7) total: 843ms remaining: 8.64s 8: learn: 0.2217459 test: 0.2332528 best: 0.2332528 (8) total: 943ms remaining: 8.49s 9: learn: 0.2123231 test: 0.2244717 best: 0.2244717 (9) total: 1.06s remaining: 8.47s 10: learn: 0.2043987 test: 0.2172099 best: 0.2172099 (10) total: 1.16s remaining: 8.32s 11: learn: 0.1990532 test: 0.2125029 best: 0.2125029 (11) total: 1.26s remaining: 8.2s
Training has stopped (degenerate solution on iteration 12, probably too small l2-regularization, try to increase it)
bestTest = 0.2125029044 bestIteration = 11 Shrink model to first 12 iterations. Trial 80, Fold 2: Log loss = 0.2125029043547971, Average precision = 0.9746115179581751, ROC-AUC = 0.9721019283805082, Elapsed Time = 1.4735628999987966 seconds Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 80, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.5230123 test: 0.5244451 best: 0.5244451 (0) total: 120ms remaining: 10.7s 1: learn: 0.4369878 test: 0.4387668 best: 0.4387668 (1) total: 216ms remaining: 9.52s 2: learn: 0.3722028 test: 0.3757026 best: 0.3757026 (2) total: 318ms remaining: 9.24s 3: learn: 0.3290222 test: 0.3326867 best: 0.3326867 (3) total: 418ms remaining: 8.99s 4: learn: 0.2902896 test: 0.2951478 best: 0.2951478 (4) total: 529ms remaining: 8.99s 5: learn: 0.2654312 test: 0.2716437 best: 0.2716437 (5) total: 635ms remaining: 8.89s 6: learn: 0.2476247 test: 0.2560263 best: 0.2560263 (6) total: 741ms remaining: 8.79s 7: learn: 0.2335366 test: 0.2435394 best: 0.2435394 (7) 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Trial 80, Fold 3: Log loss = 0.18781194846724483, Average precision = 0.9755745016444857, ROC-AUC = 0.9733321154086929, Elapsed Time = 8.1260306999975 seconds Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 80, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.5538821 test: 0.5551782 best: 0.5551782 (0) total: 109ms remaining: 9.73s 1: learn: 0.4523582 test: 0.4559568 best: 0.4559568 (1) total: 214ms remaining: 9.41s 2: learn: 0.3811483 test: 0.3857977 best: 0.3857977 (2) total: 319ms remaining: 9.25s 3: learn: 0.3317547 test: 0.3376816 best: 0.3376816 (3) total: 420ms remaining: 9.03s 4: learn: 0.2919761 test: 0.2983985 best: 0.2983985 (4) total: 523ms remaining: 8.88s 5: learn: 0.2702518 test: 0.2784221 best: 0.2784221 (5) total: 637ms remaining: 8.92s 6: learn: 0.2520465 test: 0.2612995 best: 0.2612995 (6) total: 745ms remaining: 8.83s 7: learn: 0.2364687 test: 0.2466696 best: 0.2466696 (7) total: 849ms remaining: 8.7s 8: learn: 0.2263920 test: 0.2373249 best: 0.2373249 (8) total: 946ms remaining: 8.51s 9: learn: 0.2144470 test: 0.2267600 best: 0.2267600 (9) total: 1.05s remaining: 8.39s 10: learn: 0.2065679 test: 0.2206621 best: 0.2206621 (10) total: 1.16s remaining: 8.32s 11: learn: 0.1992731 test: 0.2138082 best: 0.2138082 (11) total: 1.26s remaining: 8.18s 12: learn: 0.1942910 test: 0.2098400 best: 0.2098400 (12) total: 1.36s remaining: 8.08s 13: learn: 11.0677119 test: 0.2062640 best: 0.2062640 (13) total: 1.46s remaining: 7.95s
Training has stopped (degenerate solution on iteration 14, probably too small l2-regularization, try to increase it)
bestTest = 0.2062639631 bestIteration = 13 Shrink model to first 14 iterations. 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Trial 80, Fold 5: Log loss = 0.20690184187035557, Average precision = 0.9726087518814819, ROC-AUC = 0.9694474852758115, Elapsed Time = 9.387293200001295 seconds
Optimization Progress: 81%|########1 | 81/100 [2:24:44<25:24, 80.22s/it]
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 81, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6482903 test: 0.6509808 best: 0.6509808 (0) total: 1.78s remaining: 24.9s 1: learn: 0.6083457 test: 0.6129483 best: 0.6129483 (1) total: 3.53s remaining: 23s 2: learn: 0.5715556 test: 0.5785374 best: 0.5785374 (2) total: 5.4s remaining: 21.6s 3: learn: 0.5381297 test: 0.5479297 best: 0.5479297 (3) total: 7.29s remaining: 20s 4: learn: 0.5084421 test: 0.5194222 best: 0.5194222 (4) total: 9.05s remaining: 18.1s 5: learn: 0.4810553 test: 0.4942125 best: 0.4942125 (5) total: 11s remaining: 16.6s 6: learn: 0.4549130 test: 0.4712900 best: 0.4712900 (6) total: 13.2s remaining: 15.1s 7: learn: 0.4323980 test: 0.4504097 best: 0.4504097 (7) total: 15.1s remaining: 13.2s 8: learn: 0.4120358 test: 0.4308878 best: 0.4308878 (8) total: 17s remaining: 11.3s 9: learn: 0.3927969 test: 0.4136924 best: 0.4136924 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Optimization Progress: 82%|########2 | 82/100 [2:27:05<29:33, 98.51s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 82, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6792135 test: 0.6794098 best: 0.6794098 (0) total: 146ms remaining: 10.5s 1: learn: 0.6655851 test: 0.6660033 best: 0.6660033 (1) total: 299ms remaining: 10.6s 2: learn: 0.6524160 test: 0.6530500 best: 0.6530500 (2) total: 451ms remaining: 10.5s 3: learn: 0.6395385 test: 0.6404013 best: 0.6404013 (3) total: 608ms remaining: 10.5s 4: learn: 0.6271336 test: 0.6282315 best: 0.6282315 (4) total: 771ms remaining: 10.5s 5: learn: 0.6150301 test: 0.6163224 best: 0.6163224 (5) total: 938ms remaining: 10.5s 6: learn: 0.6033852 test: 0.6048642 best: 0.6048642 (6) total: 1.09s remaining: 10.3s 7: learn: 0.5920285 test: 0.5937406 best: 0.5937406 (7) total: 1.25s remaining: 10.1s 8: learn: 0.5813017 test: 0.5834310 best: 0.5834310 (8) total: 1.41s remaining: 10s 9: learn: 0.5705995 test: 0.5729515 best: 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Optimization Progress: 83%|########2 | 83/100 [2:28:18<25:44, 90.87s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 83, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6756482 test: 0.6756304 best: 0.6756304 (0) total: 149ms remaining: 597ms 1: learn: 0.6571632 test: 0.6571571 best: 0.6571571 (1) total: 282ms remaining: 423ms 2: learn: 0.6390481 test: 0.6391530 best: 0.6391530 (2) total: 436ms remaining: 291ms 3: learn: 0.6225629 test: 0.6227631 best: 0.6227631 (3) total: 590ms remaining: 148ms 4: learn: 0.6080865 test: 0.6083350 best: 0.6083350 (4) total: 743ms remaining: 0us bestTest = 0.6083349893 bestIteration = 4 Trial 83, Fold 1: Log loss = 0.6085548097717014, Average precision = 0.9677374077073312, ROC-AUC = 0.9623888148052555, Elapsed Time = 0.855243199999677 seconds Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 83, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.6737654 test: 0.6739189 best: 0.6739189 (0) total: 162ms remaining: 646ms 1: learn: 0.6559453 test: 0.6562379 best: 0.6562379 (1) total: 290ms remaining: 435ms 2: learn: 0.6381826 test: 0.6387133 best: 0.6387133 (2) total: 409ms remaining: 272ms 3: learn: 0.6211427 test: 0.6217961 best: 0.6217961 (3) total: 534ms remaining: 134ms 4: learn: 0.6060927 test: 0.6068532 best: 0.6068532 (4) total: 639ms remaining: 0us bestTest = 0.6068531554 bestIteration = 4 Trial 83, Fold 2: Log loss = 0.6069581930502699, Average precision = 0.9667052353253812, ROC-AUC = 0.9635686951602471, Elapsed Time = 0.7514337999964482 seconds Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 83, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.6743651 test: 0.6742674 best: 0.6742674 (0) total: 132ms remaining: 527ms 1: learn: 0.6564276 test: 0.6563068 best: 0.6563068 (1) total: 258ms remaining: 387ms 2: learn: 0.6401335 test: 0.6398945 best: 0.6398945 (2) total: 404ms remaining: 270ms 3: learn: 0.6248566 test: 0.6245883 best: 0.6245883 (3) total: 545ms remaining: 136ms 4: learn: 0.6089148 test: 0.6084059 best: 0.6084059 (4) total: 684ms remaining: 0us bestTest = 0.6084059102 bestIteration = 4 Trial 83, Fold 3: Log loss = 0.6085853531793376, Average precision = 0.9679774228780744, ROC-AUC = 0.9647212928942221, Elapsed Time = 0.795822699998098 seconds Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 83, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.6741266 test: 0.6741613 best: 0.6741613 (0) total: 148ms remaining: 590ms 1: learn: 0.6558635 test: 0.6558631 best: 0.6558631 (1) total: 279ms remaining: 419ms 2: learn: 0.6355681 test: 0.6357133 best: 0.6357133 (2) total: 447ms remaining: 298ms 3: learn: 0.6192175 test: 0.6193365 best: 0.6193365 (3) total: 589ms remaining: 147ms 4: learn: 0.6042852 test: 0.6046300 best: 0.6046300 (4) total: 744ms remaining: 0us bestTest = 0.6046299578 bestIteration = 4 Trial 83, Fold 4: Log loss = 0.6048031026637479, Average precision = 0.9675249858750178, ROC-AUC = 0.9630636297631566, Elapsed Time = 0.8545415999979014 seconds Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 83, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.6781933 test: 0.6784902 best: 0.6784902 (0) total: 142ms remaining: 567ms 1: learn: 0.6594324 test: 0.6600237 best: 0.6600237 (1) total: 254ms remaining: 380ms 2: learn: 0.6444042 test: 0.6452517 best: 0.6452517 (2) total: 391ms remaining: 261ms 3: learn: 0.6280163 test: 0.6291749 best: 0.6291749 (3) total: 521ms remaining: 130ms 4: learn: 0.6107263 test: 0.6120034 best: 0.6120034 (4) total: 692ms remaining: 0us bestTest = 0.6120033708 bestIteration = 4 Trial 83, Fold 5: Log loss = 0.6121398935774273, Average precision = 0.9638235104347864, ROC-AUC = 0.9585350500715307, Elapsed Time = 0.8031102000022656 seconds
Optimization Progress: 84%|########4 | 84/100 [2:28:30<17:55, 67.19s/it]
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Trial 84, Fold 2: Log loss = 0.19977313163179264, Average precision = 0.9717598719261711, ROC-AUC = 0.9678683169137171, Elapsed Time = 162.48672610000358 seconds Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876 Trial 84, Fold 3: Validation size = 5156 where 0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379 0: learn: 0.6411888 test: 0.6435786 best: 0.6435786 (0) total: 1.87s remaining: 2m 25s 1: learn: 0.5862412 test: 0.5921817 best: 0.5921817 (1) total: 3.84s remaining: 2m 27s 2: learn: 0.5412774 test: 0.5499072 best: 0.5499072 (2) total: 5.81s remaining: 2m 27s 3: learn: 0.4991514 test: 0.5096556 best: 0.5096556 (3) total: 7.86s remaining: 2m 27s 4: learn: 0.4614866 test: 0.4741943 best: 0.4741943 (4) total: 9.98s remaining: 2m 27s 5: learn: 0.4284875 test: 0.4425605 best: 0.4425605 (5) total: 12s remaining: 2m 26s 6: learn: 0.3990706 test: 0.4159774 best: 0.4159774 (6) total: 14.1s remaining: 2m 24s 7: learn: 0.3747137 test: 0.3933945 best: 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Trial 84, Fold 3: Log loss = 0.2021955531654966, Average precision = 0.9718344579736193, ROC-AUC = 0.9678893767275669, Elapsed Time = 160.59512720000203 seconds Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 84, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.6368613 test: 0.6388689 best: 0.6388689 (0) total: 1.85s remaining: 2m 24s 1: learn: 0.5813839 test: 0.5881084 best: 0.5881084 (1) total: 3.88s remaining: 2m 29s 2: learn: 0.5379309 test: 0.5464319 best: 0.5464319 (2) total: 5.88s remaining: 2m 28s 3: learn: 0.4972070 test: 0.5075352 best: 0.5075352 (3) total: 7.88s remaining: 2m 27s 4: learn: 0.4598051 test: 0.4736560 best: 0.4736560 (4) total: 9.88s remaining: 2m 26s 5: learn: 0.4276172 test: 0.4435499 best: 0.4435499 (5) total: 11.9s remaining: 2m 24s 6: learn: 0.4004253 test: 0.4178298 best: 0.4178298 (6) total: 13.9s remaining: 2m 23s 7: learn: 0.3781427 test: 0.3973535 best: 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Trial 84, Fold 5: Log loss = 0.21263086407727635, Average precision = 0.9705225371020497, ROC-AUC = 0.9647914088772458, Elapsed Time = 162.46784879999905 seconds
Optimization Progress: 85%|########5 | 85/100 [2:42:09<1:13:10, 292.69s/it]
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Trial 85, Fold 4: Log loss = 0.18958672972794569, Average precision = 0.974982758563082, ROC-AUC = 0.9725237010331647, Elapsed Time = 19.01251290000073 seconds Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 85, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5518133 test: 0.5576849 best: 0.5576849 (0) total: 417ms remaining: 18.8s 1: learn: 0.4551307 test: 0.4642812 best: 0.4642812 (1) total: 795ms remaining: 17.5s 2: learn: 0.3921763 test: 0.4041626 best: 0.4041626 (2) total: 1.22s remaining: 17.4s 3: learn: 0.3478186 test: 0.3624957 best: 0.3624957 (3) total: 1.66s remaining: 17.4s 4: learn: 0.3120564 test: 0.3308596 best: 0.3308596 (4) total: 2.13s remaining: 17.5s 5: learn: 0.2858774 test: 0.3078343 best: 0.3078343 (5) total: 2.6s remaining: 17.3s 6: learn: 0.2627277 test: 0.2873952 best: 0.2873952 (6) total: 3.12s remaining: 17.4s 7: learn: 0.2439212 test: 0.2708964 best: 0.2708964 (7) total: 3.65s remaining: 17.4s 8: learn: 0.2288328 test: 0.2588669 best: 0.2588669 (8) total: 4.17s remaining: 17.2s 9: learn: 0.2155693 test: 0.2479589 best: 0.2479589 (9) total: 4.7s remaining: 16.9s 10: learn: 0.2041771 test: 0.2388870 best: 0.2388870 (10) total: 5.16s remaining: 16.4s 11: learn: 0.1963735 test: 0.2331124 best: 0.2331124 (11) total: 5.57s remaining: 15.8s 12: learn: 0.1878242 test: 0.2266824 best: 0.2266824 (12) total: 6.07s remaining: 15.4s 13: learn: 0.1825588 test: 0.2231828 best: 0.2231828 (13) total: 6.53s remaining: 14.9s 14: learn: 0.1776641 test: 0.2197130 best: 0.2197130 (14) total: 6.95s remaining: 14.4s 15: learn: 0.1725178 test: 0.2160360 best: 0.2160360 (15) total: 7.4s remaining: 13.9s 16: learn: 0.1672796 test: 0.2129330 best: 0.2129330 (16) total: 7.87s remaining: 13.4s 17: learn: 0.1639880 test: 0.2110082 best: 0.2110082 (17) total: 8.28s remaining: 12.9s 18: learn: 0.1604465 test: 0.2091596 best: 0.2091596 (18) total: 8.71s remaining: 12.4s 19: learn: 0.1570678 test: 0.2077197 best: 0.2077197 (19) total: 9.22s remaining: 12s 20: learn: 0.1540402 test: 0.2059518 best: 0.2059518 (20) total: 9.66s remaining: 11.5s 21: learn: 0.1516019 test: 0.2048061 best: 0.2048061 (21) total: 10.1s remaining: 11s 22: learn: 0.1492291 test: 0.2041865 best: 0.2041865 (22) total: 10.5s remaining: 10.5s 23: learn: 0.1476021 test: 0.2038457 best: 0.2038457 (23) total: 10.9s remaining: 9.99s 24: learn: 0.1459095 test: 0.2031258 best: 0.2031258 (24) total: 11.3s remaining: 9.48s 25: learn: 0.1439671 test: 0.2027061 best: 0.2027061 (25) total: 11.7s remaining: 8.99s 26: learn: 0.1419628 test: 0.2019717 best: 0.2019717 (26) total: 12.1s remaining: 8.52s 27: learn: 0.1403767 test: 0.2013008 best: 0.2013008 (27) total: 12.5s remaining: 8.02s 28: learn: 0.1394026 test: 0.2008895 best: 0.2008895 (28) total: 12.8s remaining: 7.5s 29: learn: 0.1377052 test: 0.2002541 best: 0.2002541 (29) total: 13.2s remaining: 7.04s 30: learn: 0.1364902 test: 0.2000528 best: 0.2000528 (30) total: 13.6s remaining: 6.58s 31: learn: 0.1359664 test: 0.1999880 best: 0.1999880 (31) total: 13.9s remaining: 6.06s 32: learn: 0.1346481 test: 0.1999981 best: 0.1999880 (31) total: 14.2s remaining: 5.6s 33: learn: 0.1334661 test: 0.1997061 best: 0.1997061 (33) total: 14.6s remaining: 5.15s 34: learn: 0.1323059 test: 0.1994286 best: 0.1994286 (34) total: 14.9s remaining: 4.7s 35: learn: 0.1316215 test: 0.1994192 best: 0.1994192 (35) total: 15.2s remaining: 4.22s 36: learn: 0.1307832 test: 0.1993241 best: 0.1993241 (36) total: 15.5s remaining: 3.77s 37: learn: 0.1299739 test: 0.1991084 best: 0.1991084 (37) total: 15.8s remaining: 3.32s 38: learn: 0.1290823 test: 0.1989465 best: 0.1989465 (38) total: 16.1s remaining: 2.89s 39: learn: 0.1281790 test: 0.1990339 best: 0.1989465 (38) total: 16.4s remaining: 2.46s 40: learn: 0.1273732 test: 0.1990427 best: 0.1989465 (38) total: 16.7s remaining: 2.04s 41: learn: 0.1268070 test: 0.1991239 best: 0.1989465 (38) total: 17s remaining: 1.62s 42: learn: 0.1261087 test: 0.1990270 best: 0.1989465 (38) total: 17.3s remaining: 1.21s 43: learn: 0.1249252 test: 0.1986211 best: 0.1986211 (43) total: 17.6s remaining: 802ms 44: learn: 0.1243504 test: 0.1987310 best: 0.1986211 (43) total: 18s remaining: 399ms 45: learn: 0.1235734 test: 0.1987615 best: 0.1986211 (43) total: 18.3s remaining: 0us bestTest = 0.1986211047 bestIteration = 43 Shrink model to first 44 iterations. Trial 85, Fold 5: Log loss = 0.19862110415844503, Average precision = 0.9733036329049334, ROC-AUC = 0.970154915184958, Elapsed Time = 18.505486000001838 seconds
Optimization Progress: 86%|########6 | 86/100 [2:43:51<54:56, 235.45s/it]
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Optimization Progress: 87%|########7 | 87/100 [2:45:12<40:57, 189.07s/it]
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Optimization Progress: 88%|########8 | 88/100 [2:46:37<31:34, 157.84s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 88, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6224519 test: 0.6231479 best: 0.6231479 (0) total: 130ms remaining: 1.17s 1: learn: 0.5622996 test: 0.5634602 best: 0.5634602 (1) total: 242ms remaining: 969ms 2: learn: 0.5123254 test: 0.5138418 best: 0.5138418 (2) total: 353ms remaining: 824ms 3: learn: 0.4704919 test: 0.4728426 best: 0.4728426 (3) total: 472ms remaining: 708ms 4: learn: 0.4351923 test: 0.4385608 best: 0.4385608 (4) total: 616ms remaining: 616ms 5: learn: 0.4034795 test: 0.4079117 best: 0.4079117 (5) total: 752ms remaining: 501ms 6: learn: 0.3764601 test: 0.3815135 best: 0.3815135 (6) total: 871ms remaining: 373ms 7: learn: 0.3539116 test: 0.3596568 best: 0.3596568 (7) total: 992ms remaining: 248ms 8: learn: 0.3355914 test: 0.3419586 best: 0.3419586 (8) total: 1.11s remaining: 124ms 9: learn: 0.3192551 test: 0.3265294 best: 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Optimization Progress: 89%|########9 | 89/100 [2:46:52<21:05, 115.01s/it]
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Training has stopped (degenerate solution on iteration 23, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 41, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 52, probably too small l2-regularization, try to increase it)
bestTest = 0.1989359254 bestIteration = 35 Shrink model to first 36 iterations. 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Trial 89, Fold 4: Log loss = 0.195184653196275, Average precision = 0.975645862695531, ROC-AUC = 0.9715126367757057, Elapsed Time = 20.797833399999945 seconds Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 89, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5997204 test: 0.6015978 best: 0.6015978 (0) total: 296ms remaining: 29s 1: learn: 0.5331859 test: 0.5368385 best: 0.5368385 (1) total: 607ms remaining: 29.4s 2: learn: 0.4750935 test: 0.4799975 best: 0.4799975 (2) total: 926ms remaining: 29.6s 3: learn: 0.4279704 test: 0.4343782 best: 0.4343782 (3) total: 1.21s remaining: 28.6s 4: learn: 0.3876051 test: 0.3946569 best: 0.3946569 (4) total: 1.47s remaining: 27.6s 5: learn: 0.3605710 test: 0.3688405 best: 0.3688405 (5) total: 1.77s remaining: 27.5s 6: learn: 0.3362796 test: 0.3453312 best: 0.3453312 (6) total: 2.06s remaining: 27.1s 7: learn: 0.3180857 test: 0.3278492 best: 0.3278492 (7) 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Optimization Progress: 90%|######### | 90/100 [2:48:14<17:31, 105.14s/it]
Trial 89, Fold 5: Log loss = 0.20299273275865473, Average precision = 0.9739603927737867, ROC-AUC = 0.9704795347713803, Elapsed Time = 21.790719999997236 seconds Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 90, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6247489 test: 0.6244435 best: 0.6244435 (0) total: 17.3ms remaining: 1.54s 1: learn: 0.5811202 test: 0.5813624 best: 0.5813624 (1) total: 35.9ms remaining: 1.58s 2: learn: 0.5408880 test: 0.5404131 best: 0.5404131 (2) total: 56ms remaining: 1.62s 3: learn: 0.5149893 test: 0.5148705 best: 0.5148705 (3) total: 74.7ms remaining: 1.61s 4: learn: 0.4664361 test: 0.4662511 best: 0.4662511 (4) total: 94.6ms remaining: 1.61s 5: learn: 0.4316747 test: 0.4313231 best: 0.4313231 (5) total: 113ms remaining: 1.58s 6: learn: 0.4064880 test: 0.4062864 best: 0.4062864 (6) total: 133ms remaining: 1.58s 7: learn: 0.3832688 test: 0.3836813 best: 0.3836813 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Optimization Progress: 91%|#########1| 91/100 [2:48:33<11:52, 79.17s/it]
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Trial 91, Fold 3: Log loss = 0.18561336490114735, Average precision = 0.9770555590377952, ROC-AUC = 0.9741588043310765, Elapsed Time = 24.98324109999521 seconds Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592 Trial 91, Fold 4: Validation size = 5182 where 0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665 0: learn: 0.5890804 test: 0.5904647 best: 0.5904647 (0) total: 300ms remaining: 26.4s 1: learn: 0.5080049 test: 0.5104645 best: 0.5104645 (1) total: 616ms remaining: 26.8s 2: learn: 0.4445021 test: 0.4477090 best: 0.4477090 (2) total: 917ms remaining: 26.3s 3: learn: 0.3952452 test: 0.3998256 best: 0.3998256 (3) total: 1.23s remaining: 26.2s 4: learn: 0.3562410 test: 0.3620580 best: 0.3620580 (4) total: 1.54s remaining: 25.8s 5: learn: 0.3263296 test: 0.3333527 best: 0.3333527 (5) total: 1.85s remaining: 25.7s 6: learn: 0.3028677 test: 0.3109525 best: 0.3109525 (6) total: 2.15s remaining: 25.2s 7: learn: 0.2831838 test: 0.2924570 best: 0.2924570 (7) 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Trial 91, Fold 4: Log loss = 0.18991550218277667, Average precision = 0.976480947163987, ROC-AUC = 0.9723979979303323, Elapsed Time = 25.658579900002223 seconds Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897 Trial 91, Fold 5: Validation size = 5188 where 0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054 0: learn: 0.5882392 test: 0.5906055 best: 0.5906055 (0) total: 301ms remaining: 26.5s 1: learn: 0.5071399 test: 0.5115621 best: 0.5115621 (1) total: 592ms remaining: 25.8s 2: learn: 0.4434272 test: 0.4497720 best: 0.4497720 (2) total: 905ms remaining: 25.9s 3: learn: 0.3937875 test: 0.4019268 best: 0.4019268 (3) total: 1.19s remaining: 25.4s 4: learn: 0.3567716 test: 0.3665522 best: 0.3665522 (4) total: 1.49s remaining: 25.1s 5: learn: 0.3260631 test: 0.3375056 best: 0.3375056 (5) total: 1.79s remaining: 24.8s 6: learn: 0.3016418 test: 0.3145295 best: 0.3145295 (6) total: 2.1s remaining: 24.6s 7: learn: 0.2813397 test: 0.2957537 best: 0.2957537 (7) 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Optimization Progress: 92%|#########2| 92/100 [2:50:46<12:43, 95.48s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 92, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5956332 test: 0.5955421 best: 0.5955421 (0) total: 92.5ms remaining: 5.08s 1: learn: 0.5193324 test: 0.5193298 best: 0.5193298 (1) total: 192ms remaining: 5.17s 2: learn: 0.4594002 test: 0.4595830 best: 0.4595830 (2) total: 295ms remaining: 5.21s 3: learn: 0.4118118 test: 0.4123675 best: 0.4123675 (3) total: 401ms remaining: 5.21s 4: learn: 0.3743687 test: 0.3748782 best: 0.3748782 (4) total: 508ms remaining: 5.18s 5: learn: 0.3443778 test: 0.3454412 best: 0.3454412 (5) total: 618ms remaining: 5.15s 6: learn: 0.3213494 test: 0.3229271 best: 0.3229271 (6) total: 731ms remaining: 5.11s 7: learn: 0.3022276 test: 0.3040114 best: 0.3040114 (7) total: 847ms remaining: 5.08s 8: learn: 0.2861403 test: 0.2881999 best: 0.2881999 (8) total: 954ms remaining: 4.98s 9: learn: 0.2740314 test: 0.2767556 best: 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Optimization Progress: 93%|#########3| 93/100 [2:51:28<09:16, 79.48s/it]
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Optimization Progress: 94%|#########3| 94/100 [2:52:08<06:44, 67.39s/it]
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Optimization Progress: 96%|#########6| 96/100 [2:53:48<03:45, 56.39s/it]
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Optimization Progress: 97%|#########7| 97/100 [2:54:29<02:35, 51.70s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 97, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.6242062 test: 0.6253154 best: 0.6253154 (0) total: 217ms remaining: 19.1s 1: learn: 0.5671448 test: 0.5694278 best: 0.5694278 (1) total: 429ms remaining: 18.6s 2: learn: 0.5163842 test: 0.5199639 best: 0.5199639 (2) total: 659ms remaining: 18.9s 3: learn: 0.4740568 test: 0.4779965 best: 0.4779965 (3) total: 888ms remaining: 18.9s 4: learn: 0.4378443 test: 0.4425196 best: 0.4425196 (4) total: 1.12s remaining: 18.8s 5: learn: 0.4060623 test: 0.4114563 best: 0.4114563 (5) total: 1.35s remaining: 18.7s 6: learn: 0.3805406 test: 0.3864833 best: 0.3864833 (6) total: 1.58s remaining: 18.5s 7: learn: 0.3584780 test: 0.3648486 best: 0.3648486 (7) total: 1.81s remaining: 18.3s 8: learn: 0.3384992 test: 0.3455346 best: 0.3455346 (8) total: 2.04s remaining: 18.1s 9: learn: 0.3220730 test: 0.3297077 best: 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Trial 97, Fold 1: Log loss = 0.21082753340035829, Average precision = 0.9728964449793732, ROC-AUC = 0.9681904893343465, Elapsed Time = 20.86236479999934 seconds Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 97, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.6236358 test: 0.6249362 best: 0.6249362 (0) total: 238ms remaining: 21s 1: learn: 0.5647823 test: 0.5672655 best: 0.5672655 (1) total: 463ms remaining: 20.1s 2: learn: 0.5158725 test: 0.5192086 best: 0.5192086 (2) total: 690ms remaining: 19.8s 3: learn: 0.4734664 test: 0.4772955 best: 0.4772955 (3) total: 924ms remaining: 19.6s 4: learn: 0.4370236 test: 0.4412982 best: 0.4412982 (4) total: 1.16s remaining: 19.5s 5: learn: 0.4062735 test: 0.4113503 best: 0.4113503 (5) total: 1.43s remaining: 19.8s 6: learn: 0.3800769 test: 0.3859503 best: 0.3859503 (6) total: 1.7s remaining: 19.9s 7: learn: 0.3579309 test: 0.3642609 best: 0.3642609 (7) 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Optimization Progress: 98%|#########8| 98/100 [2:56:27<02:23, 71.62s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371 Trial 98, Fold 1: Validation size = 5175 where 0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913 0: learn: 0.5968750 test: 0.5991725 best: 0.5991725 (0) total: 138ms remaining: 10.5s 1: learn: 0.5167462 test: 0.5208641 best: 0.5208641 (1) total: 281ms remaining: 10.5s 2: learn: 0.4547995 test: 0.4584184 best: 0.4584184 (2) total: 410ms remaining: 10.1s 3: learn: 0.4095910 test: 0.4147788 best: 0.4147788 (3) total: 557ms remaining: 10.2s 4: learn: 0.3766586 test: 0.3820032 best: 0.3820032 (4) total: 697ms remaining: 10s 5: learn: 0.3478912 test: 0.3546664 best: 0.3546664 (5) total: 831ms remaining: 9.83s 6: learn: 0.3195902 test: 0.3280797 best: 0.3280797 (6) total: 978ms remaining: 9.78s 7: learn: 0.3019855 test: 0.3116274 best: 0.3116274 (7) total: 1.11s remaining: 9.58s 8: learn: 0.2838560 test: 0.2944675 best: 0.2944675 (8) total: 1.26s remaining: 9.53s 9: learn: 0.2688377 test: 0.2817223 best: 0.2817223 (9) total: 1.42s remaining: 9.5s 10: learn: 0.2563287 test: 0.2701333 best: 0.2701333 (10) total: 1.56s remaining: 9.36s 11: learn: 0.2436324 test: 0.2586319 best: 0.2586319 (11) total: 1.69s remaining: 9.14s 12: learn: 1.4499444 test: 0.2474971 best: 0.2474971 (12) total: 1.85s remaining: 9.09s 13: learn: 1.4402849 test: 0.2388634 best: 0.2388634 (13) total: 1.99s remaining: 8.95s 14: learn: 1.4323262 test: 0.2329519 best: 0.2329519 (14) total: 2.15s remaining: 8.88s 15: learn: 2.6433769 test: 0.2269289 best: 0.2269289 (15) total: 2.29s remaining: 8.75s 16: learn: 94.7961396 test: 108.9798878 best: 0.2269289 (15) total: 2.43s remaining: 8.59s bestTest = 0.2269289229 bestIteration = 15 Shrink model to first 16 iterations.
Training has stopped (degenerate solution on iteration 17, probably too small l2-regularization, try to increase it)
Trial 98, Fold 1: Log loss = 0.22696060308168534, Average precision = 0.9719228885972324, ROC-AUC = 0.9674070847851337, Elapsed Time = 2.6826937000005273 seconds Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396 Trial 98, Fold 2: Validation size = 5137 where 0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986 0: learn: 0.6016898 test: 0.6029375 best: 0.6029375 (0) total: 144ms remaining: 11s 1: learn: 0.5279998 test: 0.5304055 best: 0.5304055 (1) total: 282ms remaining: 10.6s 2: learn: 0.4590162 test: 0.4637861 best: 0.4637861 (2) total: 444ms remaining: 11s 3: learn: 0.4147109 test: 0.4213086 best: 0.4213086 (3) total: 579ms remaining: 10.6s 4: learn: 0.3727901 test: 0.3797932 best: 0.3797932 (4) total: 730ms remaining: 10.5s 5: learn: 0.3403563 test: 0.3487030 best: 0.3487030 (5) total: 864ms remaining: 10.2s 6: learn: 0.3153129 test: 0.3238323 best: 0.3238323 (6) total: 1.01s remaining: 10.1s 7: learn: 0.2940122 test: 0.3036071 best: 0.3036071 (7) total: 1.16s remaining: 9.97s 8: learn: 0.2760001 test: 0.2871191 best: 0.2871191 (8) total: 1.29s remaining: 9.79s 9: learn: 0.2583909 test: 0.2719383 best: 0.2719383 (9) total: 1.46s remaining: 9.76s 10: learn: 0.2440667 test: 0.2580860 best: 0.2580860 (10) total: 1.62s remaining: 9.7s 11: learn: 0.2336067 test: 0.2478549 best: 0.2478549 (11) total: 1.76s remaining: 9.55s 12: learn: 0.2243852 test: 0.2395097 best: 0.2395097 (12) total: 1.93s remaining: 9.52s 13: learn: 0.2157405 test: 0.2322450 best: 0.2322450 (13) total: 2.09s remaining: 9.4s 14: learn: 1.4266033 test: 5.1391837 best: 0.2322450 (13) total: 2.25s remaining: 9.3s 15: learn: 1.4203719 test: 5.1336475 best: 0.2322450 (13) total: 2.4s remaining: 9.15s 16: learn: 1.4161080 test: 5.1299051 best: 0.2322450 (13) total: 2.54s remaining: 8.97s 17: learn: 7.9096676 test: 44.4203935 best: 0.2322450 (13) total: 2.69s remaining: 8.82s 18: learn: 7.9056335 test: 44.4169357 best: 0.2322450 (13) total: 2.84s remaining: 8.66s 19: learn: 7.9001740 test: 44.4115922 best: 0.2322450 (13) total: 2.99s remaining: 8.53s 20: learn: 7.8612052 test: 44.4059114 best: 0.2322450 (13) total: 3.13s remaining: 8.36s 21: learn: 7.8582231 test: 44.4032758 best: 0.2322450 (13) total: 3.29s remaining: 8.22s 22: learn: 7.8562915 test: 44.4016636 best: 0.2322450 (13) total: 3.42s remaining: 8.03s 23: learn: 7.6473516 test: 44.3998431 best: 0.2322450 (13) total: 3.57s remaining: 7.89s 24: learn: 7.6446830 test: 44.3986193 best: 0.2322450 (13) total: 3.73s remaining: 7.75s 25: learn: 9.4715406 test: 49.9575426 best: 0.2322450 (13) total: 3.88s remaining: 7.62s 26: learn: 69.9872737 test: 96.3613415 best: 0.2322450 (13) total: 4.01s remaining: 7.42s 27: learn: 817.0419979 test: 936.4704886 best: 0.2322450 (13) total: 4.14s remaining: 7.25s 28: learn: 629.5373794 test: 925.6394512 best: 0.2322450 (13) total: 4.34s remaining: 7.19s 29: learn: 2047.5605626 test: 2621.8375430 best: 0.2322450 (13) total: 4.49s remaining: 7.03s 30: learn: 3056.8632855 test: 3784.3916738 best: 0.2322450 (13) total: 4.64s remaining: 6.89s
Training has stopped (degenerate solution on iteration 31, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 30, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 34, probably too small l2-regularization, try to increase it)
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Training has stopped (degenerate solution on iteration 32, probably too small l2-regularization, try to increase it)
bestTest = 0.2158848539 bestIteration = 18 Shrink model to first 19 iterations. Trial 98, Fold 5: Log loss = 0.21565013467815503, Average precision = 0.9731286815912488, ROC-AUC = 0.9690805046169852, Elapsed Time = 4.74505119999958 seconds
Optimization Progress: 99%|#########9| 99/100 [2:56:59<00:59, 59.68s/it]
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Optimization Progress: 100%|##########| 100/100 [2:59:34<00:00, 88.22s/it]
Optuna Optimization Elapsed Time: 10774.411937399997 seconds
Optimization Progress: 100%|##########| 100/100 [2:59:34<00:00, 107.75s/it]
Training with Best Trial 91
{'bootstrap_type': 'Bernoulli', 'subsample': 0.8903206126906871, 'grow_policy': 'Lossguide', 'posterior_sampling': False, 'model_shrink_mode': 'Constant', 'iterations': 89, 'learning_rate': 0.08799821484684099, 'l2_leaf_reg': 0.26486908504555773, 'random_strength': 0.8218257282282394, 'depth': 11, 'min_data_in_leaf': 46, 'has_time': False, 'rsm': 0.8824980018648074, 'leaf_estimation_method': 'Gradient', 'leaf_estimation_backtracking': 'No', 'fold_len_multiplier': 3.6584072693303096, 'auto_class_weights': 'SqrtBalanced', 'boost_from_average': False, 'allow_const_label': True, 'score_function': 'L2', 'border_count': 165, 'max_leaves': 48}
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Training Elapsed Time: 27.940681599997333 seconds
Log loss: (Train) 0.18900408697386856 vs (Test) 0.19319317338089453
PR-AUC: (Train) 0.9765517530153833 vs (Test) 0.9757429181240718
ROC-AUC: (Train) 0.9732920829918623 vs (Test) 0.9722518803451436
In [40]:
save_results(clf_name = "CatBoostClassifier",
best_trials = best_trials_cbc,
exec_time = exec_time_cbc,
lloss_auc_train = lloss_auc_train_cbc,
lloss_auc_test = lloss_auc_test_cbc,
df_metrics = df_metrics_cbc,
cm_final = cm_final_cbc,
cm_all = cm_cbc_all,
cm_labels = cm_labels_cbc_all)
Comparison (best trials)¶
- Parameters
- Mutli-objectives
- Classification report
- Confusion matrices
- Elapsed times
In [41]:
# List of classifiers for downstream processing
clfs = ["LGBM", "HistGradientBoostingClassifier", "CatBoostClassifier", "XGBClassifier"]
Parameters¶
In [42]:
# List of JSON files and classifier names
json_files = [{"file": "best_trials_LGBM.json", "clf": "LGBMClassifier"},
{"file": "best_trials_HistGradientBoostingClassifier.json", "clf": "HistGradientBoostingClassifier"},
{"file": "best_trials_CatBoostClassifier.json", "clf": "CatBoostClassifier"},
{"file": "best_trials_XGBClassifier.json", "clf": "XGBClassifier"}
]
# Dictionary to store the combined data
all_records = {}
# Read and process JSON files
for item in json_files:
file_path = os.path.join(temp_dir, item["file"])
clf_name = item["clf"]
with open(file_path, "r") as f:
data = json.load(f)
# Store parameters for each model and trial in the dictionary
for trial_num, params in data.items():
all_records[(clf_name, trial_num)] = params
# Show all rows without truncation
pd.set_option("display.max_rows", None)
display(pd.DataFrame(all_records))
# Default setting
pd.reset_option("display.max_rows")
| LGBMClassifier | HistGradientBoostingClassifier | CatBoostClassifier | XGBClassifier | ||
|---|---|---|---|---|---|
| 68 | 69 | 91 | 65 | 76 | |
| objective | binary | NaN | Logloss | binary:logistic | binary:logistic |
| metric | [binary_logloss, auc, average_precision] | NaN | NaN | NaN | NaN |
| device_type | cpu | NaN | NaN | NaN | NaN |
| verbosity | 2 | NaN | NaN | 1 | 1 |
| random_state | 42 | 42 | NaN | NaN | NaN |
| deterministic | True | NaN | NaN | NaN | NaN |
| bagging_seed | 42 | NaN | NaN | NaN | NaN |
| feature_fraction_seed | 42 | NaN | NaN | NaN | NaN |
| drop_seed | 42 | NaN | NaN | NaN | NaN |
| force_col_wise | True | NaN | NaN | NaN | NaN |
| num_threads | -1 | NaN | NaN | NaN | NaN |
| max_depth | -1 | None | NaN | 0 | 0 |
| boosting_type | gbdt | NaN | NaN | NaN | NaN |
| lambda_l1 | 0.001799 | NaN | NaN | NaN | NaN |
| lambda_l2 | 0.0 | NaN | NaN | NaN | NaN |
| num_leaves | 81 | NaN | NaN | NaN | NaN |
| feature_fraction | 0.852907 | NaN | NaN | NaN | NaN |
| bagging_fraction | 0.723172 | NaN | NaN | NaN | NaN |
| bagging_freq | 6 | NaN | NaN | NaN | NaN |
| min_child_samples | 28 | NaN | NaN | NaN | NaN |
| learning_rate | 0.074913 | 0.096154 | 0.087998 | NaN | NaN |
| is_unbalance | True | NaN | NaN | NaN | NaN |
| max_bin | 184 | NaN | NaN | 47 | 92 |
| min_sum_hessian_in_leaf | 0.0052 | NaN | NaN | NaN | NaN |
| max_delta_step | 40.700039 | NaN | NaN | 65.541538 | 37.673488 |
| feature_fraction_bynode | 0.952448 | NaN | NaN | NaN | NaN |
| num_boost_round | 88 | NaN | NaN | NaN | NaN |
| loss | NaN | log_loss | NaN | NaN | NaN |
| verbose | NaN | 2 | True | NaN | NaN |
| categorical_features | NaN | from_dtype | NaN | NaN | NaN |
| max_iter | NaN | 64 | NaN | NaN | NaN |
| max_leaf_nodes | NaN | 81 | NaN | NaN | NaN |
| min_samples_leaf | NaN | 47 | NaN | NaN | NaN |
| l2_regularization | NaN | 0.000012 | NaN | NaN | NaN |
| class_weight | NaN | None | NaN | NaN | NaN |
| max_features | NaN | 0.77363 | NaN | NaN | NaN |
| max_bins | NaN | 217 | NaN | NaN | NaN |
| interaction_cst | NaN | pairwise | NaN | NaN | NaN |
| warm_start | NaN | False | NaN | NaN | NaN |
| eval_metric | NaN | NaN | Logloss | [logloss, auc, aucpr] | [logloss, auc, aucpr] |
| custom_metric | NaN | NaN | [AUC, PRAUC] | NaN | NaN |
| random_seed | NaN | NaN | 42 | NaN | NaN |
| thread_count | NaN | NaN | -1 | NaN | NaN |
| allow_writing_files | NaN | NaN | False | NaN | NaN |
| bootstrap_type | NaN | NaN | Bernoulli | NaN | NaN |
| subsample | NaN | NaN | 0.890321 | 0.56143 | 0.894273 |
| grow_policy | NaN | NaN | Lossguide | depthwise | depthwise |
| posterior_sampling | NaN | NaN | False | NaN | NaN |
| model_shrink_mode | NaN | NaN | Constant | NaN | NaN |
| iterations | NaN | NaN | 89 | NaN | NaN |
| l2_leaf_reg | NaN | NaN | 0.264869 | NaN | NaN |
| random_strength | NaN | NaN | 0.821826 | NaN | NaN |
| depth | NaN | NaN | 11 | NaN | NaN |
| min_data_in_leaf | NaN | NaN | 46 | NaN | NaN |
| has_time | NaN | NaN | False | NaN | NaN |
| rsm | NaN | NaN | 0.882498 | NaN | NaN |
| leaf_estimation_method | NaN | NaN | Gradient | NaN | NaN |
| leaf_estimation_backtracking | NaN | NaN | No | NaN | NaN |
| fold_len_multiplier | NaN | NaN | 3.658407 | NaN | NaN |
| auto_class_weights | NaN | NaN | SqrtBalanced | NaN | NaN |
| boost_from_average | NaN | NaN | False | NaN | NaN |
| allow_const_label | NaN | NaN | True | NaN | NaN |
| score_function | NaN | NaN | L2 | NaN | NaN |
| border_count | NaN | NaN | 165 | NaN | NaN |
| max_leaves | NaN | NaN | 48 | 128 | 196 |
| device | NaN | NaN | NaN | cpu | cpu |
| validate_parameters | NaN | NaN | NaN | True | True |
| seed | NaN | NaN | NaN | 42 | 42 |
| sampling_method | NaN | NaN | NaN | uniform | uniform |
| num_parallel_tree | NaN | NaN | NaN | 1 | 1 |
| booster | NaN | NaN | NaN | gbtree | gbtree |
| eta | NaN | NaN | NaN | 0.087594 | 0.064891 |
| gamma | NaN | NaN | NaN | 0.000005 | 1.733278 |
| min_child_weight | NaN | NaN | NaN | 0.000026 | 0.001577 |
| colsample_bytree | NaN | NaN | NaN | 0.812988 | 0.570634 |
| colsample_bylevel | NaN | NaN | NaN | 0.918444 | 0.83765 |
| colsample_bynode | NaN | NaN | NaN | 0.625508 | 0.916695 |
| lambda | NaN | NaN | NaN | 7.025645 | 0.005622 |
| alpha | NaN | NaN | NaN | 0.037279 | 0.0 |
| tree_method | NaN | NaN | NaN | auto | hist |
| scale_pos_weight | NaN | NaN | NaN | 1 | 1 |
Multi-objectives¶
- log-loss, PR-AUC, ROC-AUC
In [43]:
# Combine training/test performance metrics CSVs
lloss_auc_train_df = pd.concat([pd.read_csv(f'{temp_dir}/lloss_auc_train_{clf}.csv') for clf in clfs], ignore_index = True)
lloss_auc_test_df = pd.concat([pd.read_csv(f'{temp_dir}/lloss_auc_test_{clf}.csv') for clf in clfs], ignore_index = True)
lloss_auc_df = pd.concat([lloss_auc_train_df, lloss_auc_test_df], ignore_index = True).sort_values(by = ["Classifier", "Best Trial", "Set"],
ascending=[False, True, False]).reset_index(drop = True)
# Reshape to long format
df_long = lloss_auc_df.melt(id_vars = ["Classifier", "Best Trial", "Set"],
value_vars = ["Log loss", "PR-AUC", "ROC-AUC"],
var_name = "Metric",
value_name = "Score")
# Combine Classifier+Trial label for x-axis
df_long["Classifier_Trial"] = df_long["Classifier"] + " (Trial " + df_long["Best Trial"].astype(str) + ")"
fig = px.bar(df_long, x = "Classifier_Trial", y = "Score",
color = "Set", barmode = "group", facet_col = "Metric",
text = "Score",
color_discrete_map = {"Training": "#56B4E9", "Test": "#009E73"},
facet_col_spacing = 0.04 # Increase facet spacing
)
# Format numbers on bars
fig.update_traces(texttemplate = "%{text:.3f}", textposition = "outside")
# Independent y-axes for each facet
fig.update_yaxes(matches = None, showticklabels = True, showgrid = True)
# Remove "Classifier_Trial" from x-aixs title
fig.update_xaxes(title_text = None)
# Remove all y-axis titles, then add only for the first facet
fig.update_yaxes(title_text = None)
fig.layout["yaxis"].title.text = "Score"
# Title and layout tweaks
fig.update_layout(height = 600, width = 1600, title = dict(text = "Classifier Performance (Train vs Test Across Metrics and Best Trials)",
x = 0.5, xanchor = "center", yanchor = "top",
font = dict(size = 20, family = "Arial", color = "black", weight = "bold")),
legend_title = dict(text = "Set", side = "top"),
legend = dict(orientation = "v", x = 1.04, xanchor = "center", y = 0.9, yanchor = "bottom"),
margin = dict(t = 80, b = 40)
)
# Remove "Metric=" from facet titles
fig.for_each_annotation(lambda a: a.update(text = a.text.split("=")[-1]))
# Custom intervals for y-axis
fig.layout["yaxis1"].range = [0.18, 0.23] # PR-AUC facet
fig.layout["yaxis2"].range = [0.96, 0.98] # PR-AUC facet
fig.layout["yaxis3"].range = [0.96, 0.98] # ROC-AUC facet
fig.show();
display(lloss_auc_df)
del lloss_auc_train_df, lloss_auc_test_df, lloss_auc_df, df_long, fig;
| Classifier | Best Trial | Set | Log loss | PR-AUC | ROC-AUC | |
|---|---|---|---|---|---|---|
| 0 | XGBClassifier | 65 | Training | 0.187296 | 0.976407 | 0.973166 |
| 1 | XGBClassifier | 65 | Test | 0.192752 | 0.975963 | 0.972528 |
| 2 | XGBClassifier | 76 | Training | 0.188652 | 0.976516 | 0.973094 |
| 3 | XGBClassifier | 76 | Test | 0.194125 | 0.976004 | 0.972184 |
| 4 | LGBM | 68 | Training | 0.186913 | 0.976984 | 0.973562 |
| 5 | LGBM | 68 | Test | 0.193374 | 0.975140 | 0.971766 |
| 6 | HistGradientBoostingClassifier | 69 | Training | 0.211290 | 0.971433 | 0.966883 |
| 7 | HistGradientBoostingClassifier | 69 | Test | 0.221352 | 0.967821 | 0.962714 |
| 8 | CatBoostClassifier | 91 | Training | 0.189004 | 0.976552 | 0.973292 |
| 9 | CatBoostClassifier | 91 | Test | 0.193193 | 0.975743 | 0.972252 |
The grouped bar chart above compares the training and test performance of 4 tree-based classifiers (two XGBoost variants, LightGBM, HistGradientBoostingClassifier and CatBoostClassifier) based on the best trial selected from the multi-objective 5-fold, 100-trial Optuna optimization. The metrics reported include log-loss as a calibration measure, and PR-AUC and ROC-AUC as discriminative measures.
A lower log-loss indicates that the model’s predicted probabilities align more closely with the actual outcomes, reflecting both well-calibrated probability estimates and improved predictive accuracy. In contrast, PR-AUC and ROC-AUC assess the model’s ability to distinguish between safe and risky loans across decision thresholds, with higher values indicating stronger discriminative power. A smaller train–test gap across these metrics suggests better generalization to unseen data and reduced risk of overfitting.
Looking first at the two XGBoost models i.e. Trial 65 and Trial 76, both perform at nearly identical levels, with very strong discriminative power (PR-AUC $\approx 0.976$, ROC-AUC $\approx 0.972$) and competitive calibration (test log-loss $\approx 0.193 - 0.194$). Their train–test gaps are exceptionally small i.e. $<0.001$ for both PR-AUC and ROC-AUC, and $\approx 0.005 - 0.006$ in log-loss. Between the two, XGBoost Trial 65 has a slight edge, with marginally lower test log-loss (0.1928 vs. 0.1941) and higher ROC-AUC (0.9725 vs. 0.9722), making it the more robust choice.
CatBoostClassifier and LightGBM also achieve strong results, clearly outperforming HistGradientBoostingClassifier. CatBoostClassifier, in particular, records one of the lowest test log-loss values ($\approx 0.1932$) and maintains excellent AUCs (PR-AUC $\approx 0.9757$, ROC-AUC $\approx 0.9723$), though with slightly larger train–test gaps than XGBoost Trial 65. LightGBM is similarly competitive, with strong calibration (test log-loss $\approx 0.1934$) and high discriminative power (PR-AUC $\approx 0.9751$, ROC-AUC $\approx 0.9718$), though its train–test gaps are larger still.
In contrast, the HistGradientBoostingClassifier performs noticeably worse. Its test log-loss ($\approx 0.2214$) is substantially higher, and both PR-AUC ($\approx 0.9678$) and ROC-AUC ($\approx 0.9627$) are lower than the other models. This highlights its weaker calibration and discrimination.
Taken together, XGBoost Trial 65 demonstrates the best overall balance of calibration, discriminative performance and generalization, with CatBoostClassifier and LightGBM as strong alternatives that also clearly outperform HistGradientBoostingClassifier.
So when these numbers are put in context for XGBoost Trial 65, the test log-loss of $\approx 0.193$ means that when the model assigns a loan a $\approx 19.3\%$ probability of being risky, the actual proportion of loans that the model scores at $\approx 19.3\%$ tends to be close to 19.3% in reality. The test PR-AUC of $\approx 0.976$ shows the model is performing at $\approx 97.6\%$ of the best possible performance. In other words, it’s almost perfect at finding risky loans while making very few mistakes in wrongly flagging safe loans as risky, and it balances this well across different probability thresholds. Finally, the test ROC-AUC of $\approx 0.973$ means that if we randomly select one safe and one risky loan, the model will correctly give the risky loan a higher risk score $\approx 97.3\%$ of the time, regardless of where the decision threshold is set.
Classification reports¶
In [44]:
# Combine classification report CSVs
metrics_df = pd.concat([pd.read_csv(f'{temp_dir}/df_metrics_{clf}.csv') for clf in clfs], ignore_index = True)
# Custom order of metrics
ordered_metrics = ["Accuracy",
"Precision (Safe)", "Precision (Risky)", "Precision (Macro avg)", "Precision (Weighted avg)",
"Recall (Safe)", "Recall (Risky)", "Recall (Macro avg)", "Recall (Weighted avg)",
"F1-score (Safe)", "F1-score (Risky)", "F1-score (Macro avg)", "F1-score (Weighted avg)"]
metrics_df["Classifier_Trial"] = metrics_df["Classifier"] + " (Trial " + metrics_df["Trial"].astype(str) + ")"
df_long = metrics_df.melt(id_vars=["Classifier_Trial"], value_vars = ordered_metrics,
var_name = "Metric", value_name = "Score")
fig = px.bar(df_long, x = "Metric", y = "Score",
color = "Classifier_Trial", barmode = "group",
category_orders = {"Metric": ordered_metrics},
)
fig.update_layout(height = 600, width = 1600,
title = dict(text = "Classifier Comparison Across Metrics (Best Trials)",
x = 0.5, xanchor = "center", yanchor = "top",
font = dict(size = 20, family = "Arial", color = "black", weight = "bold")),
legend_title = None,
xaxis_tickangle = -45, template = "plotly_white",
yaxis = dict(range=[0.84, 0.98])
)
fig.show();
display(metrics_df.drop(columns = "Classifier_Trial"))
del metrics_df, ordered_metrics, df_long, fig;
| Classifier | Trial | Accuracy | Precision (Safe) | Recall (Safe) | F1-score (Safe) | Precision (Risky) | Recall (Risky) | F1-score (Risky) | Precision (Macro avg) | Recall (Macro avg) | F1-score (Macro avg) | Precision (Weighted avg) | Recall (Weighted avg) | F1-score (Weighted avg) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LGBM | 68 | 0.919215 | 0.879867 | 0.973122 | 0.924148 | 0.969155 | 0.864062 | 0.913597 | 0.924511 | 0.918592 | 0.918872 | 0.924001 | 0.919215 | 0.918933 |
| 1 | HistGradientBoostingClassifier | 69 | 0.913346 | 0.869317 | 0.975260 | 0.919246 | 0.971082 | 0.850000 | 0.906516 | 0.920199 | 0.912630 | 0.912881 | 0.919618 | 0.913346 | 0.912953 |
| 2 | CatBoostClassifier | 91 | 0.921841 | 0.880848 | 0.977703 | 0.926752 | 0.974296 | 0.864688 | 0.916225 | 0.927572 | 0.921195 | 0.921488 | 0.927038 | 0.921841 | 0.921549 |
| 3 | XGBClassifier | 65 | 0.919370 | 0.878855 | 0.974954 | 0.924414 | 0.971147 | 0.862500 | 0.913605 | 0.925001 | 0.918727 | 0.919009 | 0.924473 | 0.919370 | 0.919071 |
| 4 | XGBClassifier | 76 | 0.918134 | 0.878378 | 0.972816 | 0.923188 | 0.968750 | 0.862187 | 0.912368 | 0.923564 | 0.917502 | 0.917778 | 0.923048 | 0.918134 | 0.917840 |
At first glance, CatBoostClassifier stands out on the grouped bar chart because it shows the strongest classification report metrics overall i.e. accuracy, precision, recall, F1-score and both macro and weighted averages.
However, the most important question here is how well a model can catch risky loans, because missing them means real financial loss. On this point, CatBoostClassifier reports a recall on risky loans of $\approx 0.8647$, which is very slightly lower than LightGBM's $\approx 0.8641$, but the difference is only in the fourth decimal place, essentially negligible in practice. In other words, both models correctly flag about 86 out of every 100 risky loans when using the standard 0.5 decision threshold.
It's worth noting that both CatBoostClassifier and LightGBM used class reweighting. LightGBM was tuned with is_unbalance = True, which applies a relatively strong weighting scheme by directly up-weighting risky loans. This often improves recall on the minority class but can also slightly reduce precision because the model becomes more aggressive in labeling loans as risky. CatBoostClassifier, in contrast, was tuned with auto_class_weights = SqrtBalanced, which applies a softer adjustment (inverse square-root of class frequencies). This's less aggressive than LightGBM’s scheme and tends to yield a more balanced trade-off between precision and recall.
Importantly, the choice of these weighting parameters reflects Optuna’s multi-objective optimization rather than true dataset imbalance. Even though the folds were nearly 1:1 balanced, Optuna favoured configurations with weighting because they slightly improved log-loss through better probability calibration and PR-AUC by enhancing sensitivity to risky loans. In this setting, class-weighting acted less as an imbalance correction and more as a fine-tuning mechanism that interacted with other hyperparameters to achieve marginal but meaningful gains in the combined objectives.
Despite this, CatBoostClassifier still edges out LightGBM overall, nearly matching it on risky recall ($\approx 0.8647$ vs $\approx 0.8641$) while clearly dominating on other key metrics like accuracy, precision, F1-score, both macro and weighted averages. This suggests that CatBoostClassifier benefits from weighting but does so in a way that preserves stability and avoids overcompensating. Its macro and weighted recall scores are also strong at $\approx 0.9212$ and $\approx 0.9218$, showing that it treats both safe and risky loans fairly well. Moreover, the model also shows the same level of consistency between training and test sets on the optimization metrics in log-loss, PR-AUC and ROC-AUC, suggesting its predictions are better calibrated and more likely to generalize well in practice.
Breaking down the numbers:
- Accuracy ($\approx 92.2\%$): Out of every 100 loans, CatBoostClassifier correctly flags $\approx 92$ as risky or safe.
- Safe loans:
- Precision ($\approx 88.1\%$): When CatBoostClassifier says a loan is safe, it’s right ($\approx 88$) times out of 100.
- Recall ($\approx 97.8\%$): Out of 100 genuinely safe loans, CatBoostClassifier correctly classifies ($\approx 98$) and misclassifies ($\approx 2$).
- F1-score ($\approx 92.7\%$): Strong balance, showing it captures the majority of safe loans while avoiding unnecessary misclassifications.
- Risky loans:
- Precision ($\approx 97.4\%$): When the model classifies a loan as risky, ($\approx 97$) in 100 are truly risky. Very few safe loans are mislabeled as risky.
- Recall ($\approx 86.5\%$): Out of 100 truly risky loans, it catches about ($\approx 87$), with ($\approx 13$) slipping through incorrectly as safe.
- F1-score ($\approx 91.6\%$): Shows a solid balance between catching risky loans and maintaining precision.
Looking at the averages:
- Macro precision ($\approx 92.8\%$) | Macro recall ($\approx 92.1\%$): Strong fairness, treating safe and risky loans equally well.
- Weighted precision ($\approx 92.7\%$) | Weighted recall ($\approx 92.2\%$): In real-world distributions, where datasets usually have far more safe loans, CatBoostClassifier still achieves the same levels of performance.
Summary:
- Both CatBoostClassifier and LightGBM use class weighting, but CatBoostClassifier applies a softer scheme (
SqrtBalanced) that promotes balance rather than aggressiveness. - CatBoostClassifier correctly classifies most safe loans and most risky loans, with particularly strong reliability when labeling risky loans (precision $\approx 97.4\%$).
- Its recall for risky loans ($\approx 86.5\%$) means it can identify the majority of high-risk loans, reducing the chance that risky loans are misclassified as safe.
- Its macro and weighted averages ($\approx 92\% - 93\%$) highlight that it treats both loan classes fairly.
- Its optimization consistency (log-loss, PR-AUC, ROC-AUC) suggests well-calibrated predictions and better generalization in practice.
- Optuna’s selection of weighting parameters, despite near-balanced folds, shows that subtle reweighting can still improve calibration and discrimination, reinforcing why CatBoostClassifier is the strongest overall choice.
Confusion matrices¶
In [45]:
# Load confusion matrices and their labels
grouped_matrices = []
grouped_labels = []
for clf in clfs:
with open(f'{temp_dir}/cm_{clf}_all.pkl', "rb") as f:
cm_all = pickle.load(f)
with open(f'{temp_dir}/cm_labels_{clf}_all.pkl', "rb") as f:
cm_labels = pickle.load(f)
grouped_matrices.append(cm_all)
grouped_labels.append(cm_labels)
# Plot confusion matrices
# Determine grid size: one row per classifier, columns = maximum number of matrices among classifiers
num_classifiers = len(grouped_matrices)
max_cols = max(len(cm_list) for cm_list in grouped_matrices)
# Subplots grid (rows = classifiers, cols = matrices) with size scaled for readability
fig, axes = plt.subplots(num_classifiers, max_cols, figsize = (4 * max_cols, 2 * num_classifiers))
# Ensure axes is a 2D array
if num_classifiers == 1:
axes = [axes]
if max_cols == 1:
axes = [[ax] for ax in axes]
# For each classifier group, plot all its confusion matrices along one row
for row_idx, (cm_list, label_list, row_axes) in enumerate(zip(grouped_matrices, grouped_labels, axes)):
for col_idx in range(max_cols):
if col_idx < len(label_list) and col_idx < len(cm_list):
ax = row_axes[col_idx]
sns.heatmap(cm_list[col_idx], annot = True, fmt = "d", cmap = "Blues",
ax = ax, cbar = False, annot_kws = {"size": 10})
# Update title and axis labels (introducing newline for clarity)
ax.set_title(label_list[col_idx].replace("for Best Trial", "\nfor Best Trial"), fontsize = 10)
ax.set_xlabel("Predicted Labels", fontsize = 10)
ax.set_ylabel("True Labels", fontsize = 10)
ax.set_xticklabels(["Safe", "Risky"], fontsize = 10)
ax.set_yticklabels(["Safe", "Risky"], fontsize = 10)
else:
row_axes[col_idx].axis('off') # Hide empty subplot
plt.tight_layout()
plt.show()
del grouped_matrices, grouped_labels, num_classifiers, max_cols, fig, axes;
When looking at the confusion matrices of the best trials, it’s clear that in loan risk models, false negatives are especially costly. They represent risky loans wrongly classified as safe, which can directly translate into financial losses. Using the standard decision threshold of 0.5, CatBoostClassifier reports the lowest number of false negatives (433), slightly better than LightGBM (435) and ahead of both XGBoost (440–441) and HistGradientBoostingClassifier (480).
At the same time, CatBoostClassifier also produces the fewest false positives (73), meaning it avoids unnecessarily misclassifying safe loans as risky. By contrast, LightGBM makes 88 false positives, XGBoost 82–89 and HistGradientBoostingClassifier 81. This combination of strong recall on risky loans with minimal false alarms gives CatBoostClassifier the clearest edge among the models.
HistGradientBoostingClassifier performs notably worse at this threshold, allowing nearly 480 risky loans through incorrectly. XGBoost is stronger, but it still lags behind CatBoostClassifier by permitting more risky loans to slip through and by flagging more safe loans incorrectly.
Overall, CatBoostClassifier provides the best trade-off at the conventional 0.5 threshold. It captures the majority of risky loans while keeping false positives very low, which both protects the bank from defaults and preserves customer relationships by not unfairly labeling safe loans as risky. Importantly, Optuna’s optimization tuned CatBoostClassifier with auto_class_weights = SqrtBalanced and LightGBM with is_unbalance = True. While both involve class reweighting, CatBoostClassifier’s softer square-root scheme achieves a more balanced outcome, suggesting its results are not only competitive but also more stable and better calibrated in practice.
Elapsed times¶
In [46]:
# Combine execution time CSVs
exec_time_df = pd.concat([pd.read_csv(f'{temp_dir}/exec_time_{clf}.csv') for clf in clfs], ignore_index = True)
display(exec_time_df);
del exec_time_df;
| Classifier | Best Trial | Optimization Elapsed Time (s) | Training Elapsed Time (s) | |
|---|---|---|---|---|
| 0 | LGBM | 68 | 1416.186557 | 3.886080 |
| 1 | HistGradientBoostingClassifier | 69 | 1254.363137 | 3.126119 |
| 2 | CatBoostClassifier | 91 | 10774.411937 | 27.940682 |
| 3 | XGBClassifier | 65 | 14523.852434 | 2.708413 |
| 4 | XGBClassifier | 76 | 14523.852434 | 3.324781 |
In my setup, Optuna optimization and model training were run only on the CPU of my personal laptop. With these limited resources, models such as LightGBM and HistGradientBoosting were very efficient, completing training in just a few seconds and finishing optimization in $\approx 20$ minutes ($\approx 1,250 – 1,400$ seconds). CatBoostClassifier, on the other hand, required noticeably more time per training run, close to half a minute, and its full optimization stretched into several hours ($> 10,000$ seconds). XGBoost’s individual training runs were as fast as LightGBM and HistGradientBoostingClassifier, but the overall optimization process was by far the slowest, taking $> 4$ hours ($>14,000$ seconds). This slowdown was mainly due to Optuna exploring a larger and more complex hyperparameter space, which caused the optimization process to balloon far beyond the actual training time. On this kind of hardware, LightGBM appeared to be the most practical choice for speed.
These results, however, reflect the limits of personal hardware more than the true capabilities of the algorithms. Large organizations, especially banks, rely on advanced IT infrastructure with powerful servers, far more memory, and distributed frameworks that handle demanding machine learning workloads efficiently. In such environments, runtime differences between LightGBM, CatBoostClassifier and XGBoost become much smaller because the infrastructure is built for scale and speed.
That’s why I believe the focus should be on predictive performance rather than raw training time. In financial risk modeling, accuracy is what prevents losses. CatBoostClassifier has consistently shown stronger and more balanced predictive performance without the need for artificial weighting adjustments. While it ran more slowly on my laptop, in an enterprise IT setting the runtime disadvantage would no longer be a major concern. What ultimately matters is CatBoostClassifier’s ability to classify safe and risky loans accurately, which makes it the better choice.
Overall Review¶
Context and Model Choice¶
When I tested models on my personal laptop, some models such as LightGBM and HistGradientBoostingClassifier ran very fast while others like CatBoostClassifier and XGBoost felt slower. This difference was really more about the limits of my laptop than the models themselves.
In a bank or corporation setting, models are normally trained on much more powerful machines with far greater processing power. On that type of setup, the slower models also run fast enough to be practical.
During optimization, LightGBM produced repeated warnings such as "No further splits with positive gain" until the later trials. This's normal behavior when the algorithm cannot find additional useful splits. The warnings diminished toward the end and did not affect overall performance as shown by the small train–test gap in log-loss, PR-AUC and ROC-AUC.
For the modelling phase, the binary target is the loan’s current status, derived from a nominal feature and encoded so one class represents safe loans and the other represents risky loans. The important point here is that in this dataset the ratio of safe to risky loans is roughly equal, about one to one. Because of this balance, accuracy is a meaningful metric when comparing models. At the same time recall for risky loans is still especially important from a business perspective because missing those loans has greater financial consequences.
So for me, the real question is not about speed but about which model provides the most reliable results while also recognizing that banks and regulators may require other measures for compliance and specific best practices, which go beyond my current knowledge. A good model should catch risky loans, treat safe loans fairly, produce risk scores that can be trusted, and be easy to explain when someone asks why a particular decision was made. Across these points, CatBoostClassifier stands out as the best overall choice.
Strengths and Limitations of CatBoostClassifier¶
Strengths
- Finds risky loans effectively without being overly harsh on safe ones
- Handles different kinds of data naturally such as numbers, categories, and yes or no answers
- Produces probabilities that are usually well-aligned with real outcomes
- Includes tools such as feature importance and SHAP to help explain what drives predictions
- Runs efficiently once used on enterprise‑level systems
Limitations
- Appears slower when run only on a small personal machine
- Requires subject matter expert (SME) input to make sure the data used makes sense in a business and compliance setting
- Needs periodic updates to stay reliable, such as tuning thresholds or retraining
- Can become too complex if too many engineered features are added without proper discipline
- Like any model, accuracy will decline over time if data drift occurs, for example when borrower behavior, inputs or their relationships change
Data and Feature Engineering¶
Adding richer and more varied data can improve the model’s ability to detect risk. Examples include loan usage patterns, broader economic conditions, ratios such as debt to income or balance to limit, and inputs from other trusted external sources.
It's important to work with SMEs so that chosen features are not only predictive but also reasonable, explainable and compliant with business rules and regulatory expectations. Proper tracking and clear documentation of why each feature and data source included, make the model easier to explain and ensure traceability for audits.
Adjusting the Decision Threshold¶
By default models apply a 50% cutoff where anything above is labeled risky and anything below is labeled safe. In lending though, missing a risky loan which is a false negative is generally much more costly than incorrectly flagging a safe loan as risky which is a false positive.
So keeping the threshold fixed at 50% may not be the best option. For example lowering it to 40% would capture more risky loans but would incorrectly flag more safe loans. Raising it would cut down on false positives but would cause more risky loans to slip through.
The best threshold is about finding the right balance for the business and aligning it with its appetite for financial risk. This should be reviewed regularly.
Monitoring and Data Drift¶
Over time borrower behavior, financial status, loan types and market conditions change. Because of this, models do not stay accurate forever. This is known as data drift.
To handle this we should track changes in input data and monitor how predictions behave. Alerts should be set up if recall or accuracy drops below an agreed level. The model should be retrained with recent data every few months, or faster if the market shifts quickly, such as during a downturn. Documentation on how drift is tracked and addressed should always be maintained.
Fairness and Bias¶
The model must treat different borrower groups fairly, across loan types, incomes or regions. This can be checked by comparing performance across these groups and ensuring no group is consistently disadvantaged.
Borrowers should also receive a clear, plain-language explanation when their loans are no longer in good standing. Fairness is therefore not just a technical matter, but also about how decisions are communicated back to customers.
Interpretability and Business Narratives¶
It's not enough to simply say the model makes a prediction. The reasoning needs to be explained in plain words.
For example, instead of giving a math‑heavy explanation, one could say "Debt to income ratio was a key factor because it reflects how comfortably someone can repay".
This kind of everyday explanation makes the model easier to defend with both internal teams and external reviewers.
Operational Integration¶
The model should work smoothly in the loan lifecycle. Sometimes a quick decision is required, while in other cases checks can be run overnight in batches. What matters most is that the system works reliably, runs efficiently and always uses correct data so that decisions are not delayed.
Before going live, the model should also be reviewed by an independent validation team to ensure checks are not only done by the same people who built.
Data Quality and Governance¶
Even the most advanced model will fail if the inputs are messy. Data quality checks for missing values, odd values, incorrect fields or format changes are necessary. Changes should be logged and a record kept so the data can be reviewed later if questions arise. This provides a clear audit trail for transparency and regulatory trust.
Stress Testing and Segment Stability¶
The model should also be tested under tough conditions such as higher unemployment or interest rate hikes. Stress testing shows if the model still provides useful guidance when the economy is unstable.
It's also important to check how well the model performs across different loan products such as mortgages and credit cards, and across borrower groups. A reliable model should perform consistently and not favour one group over another.
Human in the Loop¶
The model shouldn’t decide everything on its own. Loan officers need to review predictions and override them when needed. If overrides happen often, it may signal weaknesses in the model.
Retraining Policy¶
A decision should be made in advance about how often the model will be retrained. One option is retraining every quarter which is simpler to manage. Another option is continuous updates with new data which is more responsive but harder to govern. Whatever the approach, it should be clearly documented and approved.
Privacy and Security¶
When using new external data sources, it's important to ensure the data is handled safely and kept private. Data should only be used in ways that customers and regulators would view as fair and responsible.
Recommendations¶
- Adopt CatBoostClassifier as the main model
- Use LightGBM as a backup or in an ensemble
- Benchmark CatBoostClassifier against other algorithms to validate that it remains the best option
- Work with SMEs to confirm features are meaningful and compliant
- Adjust decision thresholds regularly to balance business needs and risk appetite
- Put in place monitoring for drift and fairness
- Carry out stress testing and stability checks across products and groups
- Define override rules for credit officers
- Create a clear retraining policy and data handling guidelines
- Keep documentation transparent for both internal and external review
Next Steps¶
- Work with SMEs to fine-tune which data the model uses
- Test how well the model’s thresholds work and adjust if needed
- Set up a monitoring system to track fairness, data drift and how the model performs in tough situations
- Decide how often the model should be updated, every few months or more often if needed
- Agree on whether the model will make predictions instantly (real-time) or in batches (like overnight runs)
- Pilot the model on a smaller group before deploying it widely
- Explore and benchmark alternative algorithms to confirm CatBoostClassifier remains the best choice
- Use benchmarking results to validate CatBoostClassifier’s performance and meet regulatory expectations
Closing Message¶
In this case, CatBoostClassifier is a strong choice for telling apart safe loans from risky ones. But the algorithm by itself isn’t enough. The real value comes from managing the model carefully throughout its entire life.
That means working with SMEs to choose the right data, fine-tuning thresholds, keeping an eye on changes over time, checking that the model is fair, testing how it performs under pressure, retraining it when needed, making sure it fits smoothly into everyday processess and letting people step in to review tricky cases.
Handled this way, the bank ends up with a model that is not only accurate but also fair, easy to explain, reliable and built to last. This protects both the business and its customers over the long run.
Neverthelss, the finer details on regulations and governance would need SME and legal guidance to ensure full compliance with regulatory, authorities, business needs and maintain customer trust.
Session Information¶
Log the full session environment including OS, CPU, Python version and loaded modules to support reproducibility and assist in debugging environment-specific issues.
In [47]:
import importlib
importlib.metadata.version("markupsafe")
Out[47]:
'3.0.2'
In [48]:
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Session Information </span>"))
# https://pypi.org/project/session-info/
session_info.show(na = True, os = True, cpu = True, jupyter = True, dependencies = True,
std_lib = True, private = True, write_req_file = False, req_file_name = None, html = None
)
Session Information
C:\Users\grace\AppData\Local\Programs\Python\Python311\Lib\site-packages\session_info\main.py:213: UserWarning:
The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead.
Out[48]:
Click to view session information
----- __main__ NA catboost 1.2.7 collections NA gc NA importlib NA io NA json 2.0.9 lightgbm 4.6.0 logging 0.5.1.2 matplotlib 3.10.0 numpy 1.26.4 optuna 4.1.0 optunahub 0.2.0 optunahub_registry NA os NA pandas 2.2.3 pickle NA platform 1.0.8 plotly 5.24.1 seaborn 0.13.2 session_info v1.0.1 sklearn 1.6.1 time NA tqdm 4.67.1 xgboost 2.0.3 -----
Click to view modules imported as dependencies
PIL 10.4.0 __future__ NA __mp_main__ NA _abc NA _ast NA _asyncio NA _bisect NA _blake2 NA _bz2 NA _catboost NA _cffi_backend 1.17.1 _codecs NA _collections NA _collections_abc NA _compat_pickle NA _compression NA _contextvars NA _csparsetools NA _csv 1.0 _ctypes 1.1.0 _cython_0_29_37 NA _cython_3_0_10 NA _cython_3_0_11 NA _cython_3_0_8 NA _cython_3_1_0a0 NA _datetime NA _decimal 1.70 _distutils_hack NA _elementtree NA _frozen_importlib NA _frozen_importlib_external NA _functools NA _hashlib NA _heapq NA _imp NA _io NA _json NA _locale NA _loss NA _lsprof NA _lzma NA _moduleTNC NA _multibytecodec NA _multiprocessing NA _ni_label NA _opcode NA _openssl NA _operator NA _overlapped NA _pickle NA _plotly_utils NA _pydev_bundle NA _pydev_runfiles NA _pydevd_bundle NA _pydevd_frame_eval NA _pydevd_sys_monitoring NA _queue NA _random NA _sha512 NA _signal NA _sitebuiltins NA _socket NA _sodium NA _sqlite3 2.6.0 _sre NA _ssl NA _stat NA _statistics NA _string NA _strptime NA _struct NA _thread NA _typing NA _uuid NA _warnings NA _weakref NA _weakrefset NA _win32sysloader NA _winapi NA _zoneinfo NA abc NA anyio NA argparse 1.1 array NA arrow 1.3.0 ast NA asttokens NA astunparse 1.6.3 asyncio NA atexit NA attr 24.3.0 attrs 24.3.0 babel 2.16.0 base64 NA bdb NA binascii NA bisect NA bz2 NA cProfile NA calendar NA certifi 2024.12.14 cffi 1.17.1 charset_normalizer 3.4.1 cloudpickle 3.1.1 cmaes 0.11.1 cmath NA cmd NA code NA codecs NA codeop NA colorama 0.4.6 colorlog NA colorsys NA comm 0.2.2 concurrent NA contextlib NA contextvars NA copy NA copyreg NA cryptography 44.0.0 csv 1.0 ctypes 1.1.0 cycler 0.12.1 cython_runtime NA dataclasses NA datetime NA dateutil 2.9.0.post0 debugpy 1.8.11 decimal 1.70 decorator 5.1.1 defusedxml 0.7.1 deprecated 1.2.18 difflib NA dis NA email NA encodings NA enum NA errno NA executing 2.1.0 fastjsonschema NA faulthandler NA filecmp NA fnmatch NA fqdn NA fractions NA functools NA ga4mp NA genericpath NA getopt NA getpass NA gettext NA github NA glob NA google NA graphviz 0.20.3 gzip NA hashlib NA heapq NA hmac NA html NA http NA idna 3.10 inspect NA ipaddress 1.0 ipykernel 6.29.5 ipywidgets 8.1.5 isoduration NA itertools NA jedi 0.19.2 jinja2 3.1.5 joblib 1.4.2 json5 0.10.0 jsonpointer 3.0.0 jsonschema 4.23.0 jsonschema_specifications NA jupyter_events 0.11.0 jupyter_server 2.15.0 jupyterlab_server 2.27.3 jwt 2.10.1 kaleido 0.2.1 keyword NA kiwisolver 1.4.7 linecache NA locale NA lzma NA markupsafe 3.0.2 marshal 4 math NA matplotlib_inline 0.1.7 mimetypes NA mmap NA mpl_toolkits NA msvcrt NA multiprocessing NA nacl 1.5.0 nbformat 5.10.4 nt NA ntpath NA nturl2path NA numbers NA numexpr 2.10.2 opcode NA operator NA overrides NA packaging 24.2 parso 0.8.4 pathlib NA patsy 1.0.1 pdb NA pickletools NA pkgutil NA platformdirs 4.3.6 plistlib NA posixpath NA pprint NA profile NA prometheus_client NA prompt_toolkit 3.0.48 pstats NA psutil 6.1.1 pure_eval 0.2.3 pyarrow 18.1.0 pycparser 2.22 pydev_ipython NA pydevconsole NA pydevd 3.2.3 pydevd_file_utils NA pydevd_plugins NA pydevd_tracing NA pydoc NA pydoc_data NA pyexpat NA pygments 2.19.1 pyparsing 3.2.0 pythoncom NA pythonjsonlogger NA pytz 2024.2 pywin32_bootstrap NA pywin32_system32 NA pywintypes NA queue NA quopri NA random NA re 2.2.1 referencing NA reprlib NA requests 2.32.3 rfc3339_validator 0.1.4 rfc3986_validator 0.1.1 rpds NA runpy NA scipy 1.13.1 secrets NA select NA selectors NA send2trash NA shlex NA shutil NA signal NA site NA six 1.17.0 sniffio 1.3.1 socket NA socketserver 0.4 sqlite3 2.6.0 ssl NA stack_data 0.6.3 stat NA statistics NA statsmodels 0.14.4 string NA stringprep NA struct NA subprocess NA sys 3.11.2 (tags/v3.11.2:878ead1, Feb 7 2023, 16:38:35) [MSC v.1934 64 bit (AMD64)] sysconfig NA tarfile 0.9.0 tempfile NA tenacity NA textwrap NA threading NA threadpoolctl 3.5.0 timeit NA token NA tokenize NA torch 2.5.1+cu118 torchgen NA tornado 6.4.2 traceback NA traitlets 5.14.3 types NA typing NA typing_extensions NA unicodedata NA unittest NA uri_template NA urllib NA urllib3 2.3.0 uuid NA warnings NA wcwidth 0.2.13 weakref NA webbrowser NA webcolors NA websocket 1.8.0 win32api NA win32com NA win32con NA win32trace NA winerror NA winreg NA wrapt 1.17.2 wsgiref NA xarray 2025.1.2 xml NA xmlrpc NA yaml 6.0.2 zipfile NA zipimport NA zlib 1.0 zmq 26.2.0 zoneinfo NA
----- IPython 8.31.0 jupyter_client 8.6.3 jupyter_core 5.7.2 jupyterlab 4.3.4 notebook 7.3.2 ----- Python 3.11.2 (tags/v3.11.2:878ead1, Feb 7 2023, 16:38:35) [MSC v.1934 64 bit (AMD64)] Windows-10-10.0.22631-SP0 8 logical CPU cores, Intel64 Family 6 Model 140 Stepping 1, GenuineIntel ----- Session information updated at 2025-09-24 01:01